Compression and reconstruction of interference distribution

Abstract

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit compressed interference information to a network entity. The UE may measure interference at the UE across a set of interference measurement resources. The UE may determine an interference distribution across the set of resources. The UE may encode the interference distribution using a compression scheme that may reduce a payload or size of the interference distribution. The compression scheme may include compressing the distribution of interference. Encoding the interference information may include generating a mean vector and a covariance matrix that represent the distribution of measured interference across a given set of interference measurement resources. The UE may transmit the encoded interference information, and a network entity may decode the encoded interference information. The network entity may schedule communication with the UE based on the interference information.

Description

[Technical Field]

[0001] cross reference

[0001] This patent application claims the interests of U.S. Provisional Patent Application No. 63 / 305,174, entitled “INTERFERENCE DISTRIBUTION COMPRESSION AND RECONSTRUCTION,” filed on 31 January 2022, and U.S. Patent Application No. 17 / 876,397, entitled “INTERFERENCE DISTRIBUTION COMPRESSION AND RECONSTRUCTION,” filed on 28 July 2022, each of which has been assigned to the assignee of this application and is expressly incorporated herein by reference.

[0002] introduction

[0002] The following generally pertains to wireless communications, and more specifically to interference compression and reporting.

[0003]

[0003] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, messaging, and broadcast. These systems may be able to support communication with multiple users by sharing available system resources (e.g., time, frequency, and power). Examples of such multiple access systems include fourth-generation (4G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth-generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations or one or more network access nodes, each simultaneously supporting communication for multiple communication devices, which may also be known as user equipment (UE). [Overview of the Initiative]

[0004]

[0004] Methods for wireless communication in user equipment (UE) are described. In some examples, the method may include measuring interference in the UE across a set of interference measurement resources. In some examples, the method may include encoding interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, according to a compression scheme. In some examples, the method may further include transmitting the interference information encoded according to the compression scheme to a first network entity.

[0005]

[0005] The following describes an apparatus for wireless communication in a UE. The apparatus may include a processor and memory coupled to the processor. In some examples, the processor may be configured to cause the apparatus to measure interference in the UE across a set of interference measurement resources. In some examples, the processor may be configured to encode interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, according to a compression scheme. In some examples, the processor may be configured to transmit the interference information encoded according to the compression scheme to a first network entity.

[0006]

[0006] Another apparatus for wireless communication in a UE is described. In some examples, the apparatus may include means for measuring interference in the UE across a set of interference measurement resources. In some examples, the apparatus may include means for encoding interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, according to a compression scheme. In some examples, the apparatus may include means for transmitting the interference information encoded according to the compression scheme to a first network entity.

[0007]

[0007] A non-temporary computer-readable medium for storing code for wireless communication in a UE is described. In some examples, the code may include instructions that can be executed by a processor to measure interference in the UE across a set of interference measurement resources. In some examples, the code may include instructions that can be executed by a processor to encode interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, according to a compression scheme. In some examples, the code may include instructions that can be executed by a processor to transmit the encoded interference information according to a compression scheme to a first network entity.

[0008]

[0008] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, interference in the UE may be interference plus noise, and the distribution of interference may be the distribution of interference plus noise across a set of interference measurement resources.

[0009]

[0009] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the distribution of interference in the UE includes a probability mass function with respect to a set of resources in time, frequency, and / or space.

[0010]

[0010] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the set of resources in time, frequency, and / or space includes a set of interference measurement resources.

[0011]

[0011] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference in the UE is based on measured interference across the set of interference measurement resources.

[0012]

[0012] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the set of resources in time, frequency, and / or space includes resources that occur later than the set of interference measurement resources, and the distribution of interference in the UE is predicted based at least in part on the measured interference across the set of interference measurement resources.

[0013]

[0013] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for generating compressed, estimated, or predicted interference distributions across a set of interference measurement resources.

[0014]

[0014] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the compression scheme includes a codeword-based compression scheme or an artificial neural network-based compression scheme.

[0015]

[0015] Some examples of the methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for generating mean vectors and covariance matrices of latent random variables representing the distribution of interference in a UE across a set of interference measurement resources, the set of interference measurement resources including two or more sets of time resources, frequency resources, or spatial resources.

[0016]

[0016] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for receiving instructions for an encoding configuration for encoding interference information from a first network entity or one or more second network entities associated with the first network entity.

[0017]

[0017] Some examples of the methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for transmitting instructions of the UE's ability to encode interference information to a first network entity or one or more second network entities associated with the first network entity, and receiving instructions of an encoding configuration for encoding interference information includes receiving instructions of an index associated with the encoding configuration in response to transmitting instructions of the UE's ability to encode interference information from the first network entity or one or more second network entities associated with the first network entity, and a set of encoding configurations including an encoding configuration may be associated with a set of indices including an index.

[0018]

[0018] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network.

[0019]

[0019] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for selecting one coding configuration from a set of coding configurations, each coding configuration in the set of coding configurations may be associated with each index in a set of indices, and for transmitting instructions to a first network entity or one or more second network entities associated with the first network entity for one index in the set of indices associated with the selected coding configuration.

[0020]

[0020] Some examples of the methods, apparatuses, and non-transitory computer-readable media described in the present specification may further include operations, features, means, or instructions for receiving an indication of one or more parameters associated with a compression scheme from a first network entity or from one or more second network entities associated with the first network entity.

[0021]

[0021] In some examples of the methods, apparatuses, and non-transitory computer-readable media described in the present specification, the one or more parameters associated with the compression scheme include a code size, a number of layers, a number of nodes per layer, a loss function, or a combination thereof.

[0022]

[0022] Some examples of the methods, apparatuses, and non-transitory computer-readable media described in the present specification may further include operations, features, means, or instructions for receiving an indication of a set of interference measurement resources from a first network entity or from one or more second network entities associated with the first network entity.

[0023]

[0023] Some examples of the methods, apparatuses, and non-transitory computer-readable media described in the present specification may further include operations, features, means, or instructions for receiving an indication of one or more parameters associated with measuring interference at a UE across a set of interference measurement resources from a first network entity or from one or more second network entities associated with the first network entity.

[0024]

[0024] In some examples of the methods, apparatuses, and non-transitory computer-readable media described in the present specification, the one or more parameters associated with measuring interference at a UE across a set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0025]

[0025] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for receiving instructions for an input format for encoding interference information representing the distribution of interference in the UE from a first network entity or one or more second network entities associated with the first network entity.

[0026]

[0026] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for transmitting channel state feedback reports containing interference information encoded according to a compression scheme.

[0027]

[0027] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for using an artificial neural network associated with a compression scheme to determine one or more model parameters associated with a compression scheme, and for transmitting one or more model parameters to a first network entity or one or more second network entities associated with the first network entity.

[0028]

[0028] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for receiving one or more parameters associated with a compression scheme based on transmitting one or more model parameters from a first network entity or one or more second network entities associated with the first network entity.

[0029]

[0029] A method for wireless communication in a network entity is described. In some examples, the method may include obtaining encoded interference information representing the distribution of interference. In some examples, the method may include decoding the encoded interference information according to a compression scheme in order to output the decoded interference information.

[0030]

[0030] An apparatus for wireless communication in a network entity is described. The apparatus may include a processor and memory coupled to the processor. In some examples, the processor may be configured to cause the apparatus to acquire encoded interference information representing the distribution of interference. In some examples, the processor may be configured to cause the apparatus to decode the encoded interference information according to a compression scheme in order to output the decoded interference information.

[0031]

[0031] Another device for wireless communication in a network entity is described. In some examples, the device may include means for obtaining encoded interference information representing the distribution of interference. In some examples, the device may include means for decoding the encoded interference information according to a compression scheme in order to output the decoded interference information.

[0032]

[0032] A non-temporary computer-readable medium for storing code for wireless communication in a network entity is described. In some examples, the code may include instructions that can be executed by a processor to obtain encoded interference information representing the distribution of interference. In some examples, the code may include instructions that can be executed by a processor to decode the encoded interference information according to a compression scheme in order to output the decoded interference information.

[0033]

[0033] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the interference distribution may be an interference-plus-noise distribution across a set of interference measurement resources.

[0034]

[0034] In some examples of the methods, apparatus, and non-temporal computer-readable media described herein, the encoded interference information includes a mean vector and a covariance matrix of latent random variables representing the distribution of interference in the UE across a set of interference measurement resources. The methods, apparatus, and non-temporal computer-readable media described herein may further include operations, features, means, or instructions for generating samples based on the mean vector and the covariance matrix described herein, and for decoding the encoded interference information based at least in part on the samples.

[0035]

[0035] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for outputting scheduling information for communications in a UE based on decoded interference information.

[0036]

[0036] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, functions, means, or instructions for outputting instructions for an encoding configuration for encoding interference information in a UE.

[0037]

[0037] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, functions, means, or instructions for obtaining instructions of the UE's ability to encode interference information and, based on instructions of the UE's ability to encode interference information, for outputting instructions of indices associated with encoding configurations, and a set of encoding configurations including encoding configurations may be associated with a set of indices including indices.

[0038]

[0038] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network.

[0039]

[0039] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for obtaining an instruction for one index from a set of indices associated with a selected coding configuration, where each coding configuration from the set of coding configurations may be associated with each index from the set of indices.

[0040]

[0040] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for outputting instructions for one or more parameters associated with a compression scheme.

[0041]

[0041] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, one or more parameters associated with the compression scheme include the code size, the number of layers, the number of nodes per layer, the loss function, or a combination thereof.

[0042]

[0042] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for outputting instructions for one or more parameters associated with measuring interference across a set of interference measurement resources.

[0043]

[0043] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, one or more parameters associated with measuring interference across a set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof.

[0044]

[0044] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for outputting instructions for an input format for encoding interference information representing the distribution of interference.

[0045]

[0045] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for obtaining a channel state feedback report containing encoded interference information encoded according to a compression scheme.

[0046]

[0046] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for using an artificial neural network associated with a compression scheme to determine one or more model parameters associated with a compression scheme, and for outputting one or more parameters associated with a compression scheme based on one or more model parameters.

[0047]

[0047] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for obtaining one or more model parameters associated with a compression scheme and for outputting one or more parameters associated with a compression scheme based on one or more model parameters. [Brief explanation of the drawing]

[0048] [Figure 1]

[0048] An example of a wireless communication system that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure is illustrated. [Figure 2]

[0049] An example of a wireless communication system that supports interference distribution compression and reconstruction according to one or more aspects of this disclosure is illustrated below. [Figure 3a]

[0050] Examples of encoding and decoding schemes that support interference distribution compression and reconstruction according to one or more aspects of the present disclosure are illustrated. [Figure 3b] Examples of encoding and decoding schemes that support interference distribution compression and reconstruction according to one or more aspects of the present disclosure are illustrated. [Figure 4]

[0051] An example of an autoencoder that supports interference distribution compression and reconstruction according to one or more aspects of this disclosure is illustrated. [Figure 5]

[0052] An example of a machine learning process that supports the compression and reconstruction of interference distributions according to one or more aspects of this disclosure is illustrated. [Figure 6]

[0053] An example of a process flow supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is illustrated. [Figure 7]

[0054] A block diagram of a device supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is shown. [Figure 8] A block diagram of a device supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is shown. [Figure 9]

[0055] A block diagram of a communications manager supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is shown. [Figure 10]

[0056] The diagram shows a system including a device that supports compression and reconstruction of interference distributions, according to one or more aspects of this disclosure. [Figure 11]

[0057] A block diagram of a device supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is shown. [Figure 12] A block diagram of a device supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is shown. [Figure 13]

[0058] A block diagram of a communications manager supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is shown. [Figure 14]

[0059] The diagram shows a system including a device that supports compression and reconstruction of interference distributions, according to one or more aspects of this disclosure. [Figure 15]

[0060] A flowchart illustrating a method for supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is provided. [Figure 16] A flowchart illustrating a method for supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is provided. [Figure 17] A flowchart illustrating a method for supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is provided. [Figure 18] A flowchart illustrating a method for supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is provided. [Figure 19] A flowchart illustrating a method for supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is provided. [Figure 20] A flowchart illustrating a method for supporting interference distribution compression and reconstruction according to one or more aspects of this disclosure is provided. [Figure 21]

[0061] An example of a network architecture that supports interference distribution compression and reconstruction according to one or more aspects of this disclosure is illustrated. [Modes for carrying out the invention]

[0049]

[0062] Interference in a UE can be caused, for example, by communications at neighboring base stations or by sidelink communications between other UEs. Interference (or interference plus noise) experienced in a UE can vary in the time domain, frequency domain, and spatial domain. Interference plus noise experienced in a UE can refer to the interference power plus noise power observed in the UE. Interference (or interference plus noise) experienced in a UE can also be correlated in the time domain, frequency domain, and spatial domain. For example, a change in interference (or interference plus noise) in the time domain of a UE can affect interference (or interference plus noise) in the frequency domain or spatial domain. A UE can predict future interference (or interference plus noise) on communication resources based on interference (or interference plus noise) measured in the past across communication resources. For example, a UE can determine the correlation of past interference (or interference plus noise) measurements in the time domain, frequency domain, and spatial domain, and predict future interference (or interference plus noise) based on the determined correlation.

[0050]

[0063] In a wireless communication system, a UE may measure interference (or interference plus noise) at the UE using interference measurement resources such as Channel State Information (CSI) Reference Signal (CSI-RS), CSI Interference Measurement (CSI-IM), or generally through Interference Measurement Resources (IMR). CSI-RS refers to a reference signal transmitted by a serving base station or network node that the UE may use to estimate the channel and report channel quality information to the serving base station or network node. CSI-IM refers to a set of resource elements reserved for interference measurement, which may be configurable, for example, via radio resource control. IMR may be a time-frequency resource allocated to the UE by the network for the UE to measure interference at the UE. Interference (or interference plus noise) at the UE may be reported to the serving base station or network node via Channel State Feedback (CSF) reporting. CSF reporting may not report information regarding the time correlation, frequency correlation, or spatial correlation characteristics of the interference (or interference plus noise). Therefore, a serving base station or network node receiving a CSF report is not informed of the time correlation, frequency correlation, or spatial correlation characteristics of interference (or interference plus noise) at the UE. Consequently, it may be impossible for the serving base station or network node to use these interference (or interference plus noise) correlation characteristics when scheduling communications for the UE based on the CSF report. Thus, the serving base station or network node may schedule communications for the UE using resources with relatively high interference (or interference plus noise), which may result in signal loss or inefficient communications. For example, communications may have relatively high interference if the interference results in data signal loss or inefficiency of data communications. Furthermore, since interference (or interference plus noise) at the UE can have large variations in the time domain, frequency domain, or spatial domain, explicit reporting of interference at the UE to the serving base station or network node may be associated with large resource overhead.

[0051]

[0064] To enable a base station or network node to consider the time correlation, frequency correlation, or spatial correlation characteristics of interference (or interference plus noise) in the UE, the UE may report interference (or interference plus noise) information to the base station or network node. The reported interference (or interference plus noise) information may represent the distribution of interference (or interference plus noise) in the UE. The interference (or interference plus noise) distribution or interference (or interference plus noise) distribution may be a sequence of estimated and / or predicted interference (or interference plus noise) values ​​based on multiple instances of measured interference (or interference plus noise) measurements across multiple sets of interference measurement resources in the time domain, frequency domain, and / or spatial domain in the UE. The estimated interference (or interference plus noise) values ​​may refer to past interference (or interference plus noise) estimated based on multiple instances of measured interference (or interference plus noise) measurements across multiple sets of interference measurement resources (for example, since the measurement itself may be an estimate). The predicted interference (or interference plus noise) value may refer to a predicted future interference (or interference plus noise) value based on multiple instances of measured interference (or interference plus noise) measurements across a set of multiple interference measurement resources. For example, in one embodiment, the UE may transmit encoded (e.g., compressed) interference (or interference plus noise) information to a base station, while allowing the base station to consider interference (or interference plus noise) correlations when scheduling communications at the UE, thereby reducing the resource overhead associated with explicit interference (or interference plus noise) distribution reporting. The UE may measure multiple instances of interference (or interference plus noise) at the UE across a set of multiple interference measurement resources in the time domain, frequency domain, and / or spatial domain. The set of interference measurement resources may include, for example, CSI-RS, CSI-IM, or IMR. The UE may determine the interference (or interference plus noise) distribution across the set of resources. The UE may encode the interference (or interference plus noise) distribution using a compression scheme that can reduce the payload or size of the interference (or interference plus noise) distribution.The compression method may include compressing the measured distribution of interference (or interference plus noise) across a given set of interference measurement resources.

[0052]

[0065] In one or more examples, the interference (or interference plus noise) distribution can be a probability density function or probability mass function with respect to time, frequency, and spatial variables. A probability density function shows the probability of a continuous random variable having a value within a given range. The probability that a random variable has a value within the range x and x+dx is given by f(x)dx. A probability mass function is a function that gives the probability that a discrete random variable is exactly equal to a given value. For example, if X is within the range R X If the discrete random variables are {x1, x2, x3...}, then the function P X (x k ) = P(X=x k )(where k=1,2,3,...) is the probability mass function of X.

[0053]

[0066] In some examples, encoding interference (or interference plus noise) information according to a compression scheme may involve generating a mean vector and covariance matrix of latent random variables representing the distribution of measured interference (or interference plus noise) across a given set of interference measurement resources. The term “latent” in latent values, latent variables, or latent vectors refers to values, variables, or vectors that are derived or inferred through a mathematical model, and not values, variables, or vectors that are directly measured or observed. The mean vector and covariance matrix may represent the distribution of a data matrix or sequence (e.g., a stochastic latent random vector) associated with the measured interference (or interference plus noise). For example, if the data matrix consists of a set of measurements of the time, frequency, and spatial variables of the interference (or interference plus noise), the mean vector may be a vector of the mean of the set of measurements for each time, frequency, and spatial variable of the interference (or interference plus noise). The covariance matrix may include the variances of the time, frequency, and spatial variables along the main diagonal of the covariance matrix, and the covariances between each pair of variables at other covariance matrix positions. A UE may transmit encoded interference (or interference plus noise) information, and a base station may receive and decode encoded interference (or interference plus noise) information. A base station or network node may schedule communication with the UE based on the interference (or interference plus noise) information.

[0054]

[0067] In some examples, a UE may receive control signaling that constitutes interference measurement resources and coding configurations. For example, a base station may configure interference measurement resources for the UE, compression schemes, compression scheme parameters, an encoder (e.g., coding configuration for the encoder), an input format to the encoder, and / or parameters associated with measuring interference (or interference plus noise) in the UE. Exemplary coding configurations may include, for example, coding configurations for autoencoders or artificial neural networks. For example, a base station may configure the granularity of interference (or interference plus noise) estimation. Granularity refers to the scale size or measurement step size. For example, a base station may configure, with respect to frequency granularity, whether the interference (or interference plus noise) estimation is based on the full band or subbands (and different granularities for different subbands). As another example, a base station may configure, with respect to time granularity, whether the interference (or interference plus noise) estimation is based on symbol-level interference (or interference plus noise), slot-level interference (or interference plus noise), or multi-slot-level interference (or interference plus noise). As another example, with respect to spatial granularity, a base station may constitute an interference (or interference plus noise) estimation for a particular beam. As yet another example, the input format to the encoder may include the estimated interference (or interference plus noise) distribution, zero-power or non-zero-power CSI-RS for interference (or interference plus noise) measurement, or the estimated interference (or interference plus noise) on the previous resource. The CSI-RS used to measure the channel may be referred to as non-zero-power CSI-RS. Zero-power CSI-RS refers to a CSI-RS that occupies the configured resource element, but in which the base station does not transmit any energy on the resource element.

[0055]

[0068] An UE may include an artificial neural network (NN) that can update model parameters associated with the compression scheme based on past interference (or interference plus noise) measurements in the UE. Artificial neural networks that can support such machine learning techniques for channel compression include fully connected NNs, batch normalized NNs, dropout NNs, convolutional NNs, residual NNs, rectified linear unit (ReLU) NNs, and other types of NNs. A fully connected NN includes a series of fully connected layers that connect all neurons in one layer to all neurons in other layers. A batch normalized NN includes a normalization step that fixes the mean and variance of each layer of input to the neural network. A dropout NN may ignore randomly selected nodes during training of the neural network. For example, at each training stage, individual nodes are either dropped from the dropout network with probability 1-p or retained with probability p. A convolutional NN is also called a shift-invariant or spatially invariant artificial neural network (SIANN). A convolutional neural network includes an input layer, a hidden layer, and an output layer. In a feedforward neural network, hidden layers can be called hidden because their inputs and outputs are masked by the activation function and the final convolution. In a convolutional neural network, hidden layers include layers that perform convolutions. Hidden layers may include layers that perform the dot product of the convolution kernel and the layer's input matrix. This product can be a Frobenius inner product, and its activation function is generally ReLU. As the convolution kernel slides along the layer's input matrix, the convolution operation generates a feature map, which contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers. A ReLU NN has an argument f(x) = x +The ReLu activation function is defined as the positive part of max(0,x), where x is the input to the neuron. The residual NN can jump between layers of the NN using skip connections and can be implemented with two or three-layer skipping, including nonlinearity (e.g., ReLus) and inter-layer batch normalization. The UE can report learned or updated model parameters to the base station. The base station can receive learned or updated model parameters from multiple UEs. Based on the learned parameters received from multiple UEs, the base station can update the parameters associated with encoding or decoding.

[0056]

[0069] In some examples, the compression scheme may include an autoencoder. An autoencoder can refer to a neural network (NN) that reconstructs the output from the input using a feedforward technique. The input is compressed in the encoder and then sent to the decoder for decompression. The autoencoder can be trained to minimize the loss in the output.

[0057]

[0070] Encoding interference (or interference plus noise) information according to a compression scheme allows UEs to report measured and / or predicted interference (or interference plus noise) at the UE without transmitting the entire measured and / or predicted interference (or interference plus noise) distribution, thereby reducing resource overhead and enabling serving base stations or network nodes to receive and determine the correlation characteristics of interference (or interference plus noise) at the UE, which the base station can consider when scheduling communications at the UE. For example, a base station can avoid scheduling communications for the UE on communication resources with high estimated or predicted interference (or interference plus noise). The base station can schedule communications for the UE on communication resources with low estimated or predicted interference. Furthermore, using machine learning techniques at the UE and / or base station can enable the UE and base station to minimize errors in the compression and reconstruction of interference (or interference plus noise) information, while also reducing the resource overhead associated with reporting interference (or interference plus noise) information.

[0058]

[0071] The aspects of this disclosure will first be described in the context of wireless communication systems. These aspects will be further illustrated and described with reference to coding and decoding schemes, machine learning processes, and process flows. These aspects will also be further illustrated and described with reference to diagrams of devices, systems, and flowcharts related to interference distribution compression and reconstruction.

[0059]

[0072] Figure 1 illustrates an example of a wireless communication system 100 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The wireless communication system 100 may include one or more base stations 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a Long-Term Evolution (LTE) network, an LTE Advanced (LTE-A) network, an LTE-A Pro network, or a New Radio (NR) network. In some examples, the wireless communication system 100 may support extended broadband communication, ultra-high reliability communication, low latency communication, communication with low-cost and low-complexity devices, or any combination thereof.

[0060]

[0073] Base stations 105 may be distributed across a geographical area to form a wireless communication system 100 and may be devices of different forms or with different capabilities. Base stations 105 and UEs 115 may communicate wirelessly via one or more communication links 125. Each base station 105 may provide a coverage area 110 from which UEs 115 and base stations 105 can establish one or more communication links 125. A coverage area 110 may be an example of a geographical area from which base stations 105 and UEs 115 can support the communication of signals according to one or more radio access technologies.

[0061]

[0074] The UE115 may be distributed across the entire coverage area 110 of the wireless communication system 100, and each UE115 may be fixed, mobile, or both at different times. The UE115 may be different forms of devices or devices with different capabilities. Several exemplary UE115 are shown in Figure 1. The UE115 described herein may be capable of communicating with various types of devices, such as other UE115, base stations 105, or network equipment (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network equipment), as shown in Figure 1.

[0062]

[0075] In some examples, one or more components of the wireless communication system 100 may act as or be referred to as network nodes. As used herein, a network node may refer to any UE 115, base station 105, entity, apparatus, device, or computing system of the core network 130 configured to implement any techniques described herein. For example, a network node may be a UE 115. In another example, a network node may be a base station 105. In yet another example, a first network node may be configured to communicate with a second or third network node. In one aspect of this example, the first network node may be a UE 115, the second network node may be a base station 105, and the third network node may be a UE 115. In yet another aspect of this example, the first network node may be a UE 115, the second network node may be a base station 105, and the third network node may be a base station 105. In yet another aspect of this example, the first, second, and third network nodes may be different. Similarly, references to UE115, base station 105, equipment, devices, or computing systems may include disclosures of UE115, base station 105, equipment, devices, or computing systems that are network nodes. For example, a disclosure that UE115 is configured to receive information from base station 105 also discloses that a first network node is configured to receive information from a second network node. In this example, consistent with the present disclosure, a first network node may refer to a first UE115, first base station 105, first equipment, first device, or first computing system configured to receive information, and a second network node may refer to a second UE115, second base station 105, second equipment, second device, or second computing system.

[0063]

[0076] Base station 105 can communicate with core network 130, or can communicate with each other, or can do both. For example, base station 105 can interface with core network 130 through one or more backhaul links 120 (e.g., via S1, N2, N3, or other interfaces). Base station 105 can communicate with each other via backhaul links 120 (e.g., via X2, Xn, or other interfaces) either directly (e.g., directly between base station 105) or indirectly (e.g., via core network 130), or both. In some examples, backhaul links 120 may be one or more wireless links, or may include one or more wireless links. UE 115 can communicate with core network 130 via communication link 155.

[0064]

[0077] One or more of the base stations 105 described herein may include, or may be referred to as, a base station transceiver station, a radio base station, an access point, a radio transceiver, a NodeB, an eNodeB (eNB), a next-generation NodeB or giga-NodeB (any of which may be referred to as a gNB), a Home NodeB, a Home eNodeB, or other preferred terms.

[0065]

[0078] UE115 may include, or be referred to as, a mobile device, wireless device, remote device, handheld device, or subscriber device, or any other preferred term; “device” may also be referred to as, in other examples, a unit, station, terminal, or client. UE115 may also include, or be referred to as, personal electronic devices such as cellular phones, personal digital assistants (PDAs), tablet computers, laptop computers, or personal computers. In some examples, UE115 may include, or be referred to as, a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine-type communications (MTC) device, in other examples, it may be implemented in electrical appliances or various items such as vehicles, meters, etc.

[0066]

[0079] The UE115 described herein may be capable of communicating with other UE115s that may function as relays, as shown in Figure 1, and with various types of devices, including, among other examples, base stations 105 and network equipment, such as macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations.

[0067]

[0080] UE115 and base station 105 may communicate wirelessly with each other via one or more communication links 125 over one or more carriers. The term “carrier” may refer to a set of radio frequency spectrum resources having a defined physical layer structure for supporting communication links 125. For example, a carrier used for communication link 125 may include a portion of the radio frequency spectrum band (e.g., a bandwidth part (BWP)) operating according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry collected signaling (e.g., synchronization signals, system information), control signaling to coordinate operations with the carrier, user data, or other signaling. The wireless communication system 100 may support communication with UE115 using carrier aggregation or multi-carrier operation. UE115 may consist of multiple downlink component carriers and one or more uplink component carriers according to a carrier aggregation configuration. Carrier aggregation can be used with both frequency division duplexing (FDD) component carriers and time division duplexing (TDD) component carriers.

[0068]

[0081] In some examples (e.g., in carrier aggregation configurations), a carrier may also have collection or control signaling to coordinate its operation with other carriers. A carrier may be associated with a frequency channel (e.g., an evolved universal mobile telecommunication system terrestrial radio access (E-UTRA) absolute radio frequency channel number (EARFCN)) and may be arranged according to a channel raster for discovery by the UE115. A carrier may operate in standalone mode, where initial collection and connection may be performed via the carrier by the UE115, or it may operate in non-standalone mode, where connection is anchored using different carriers (e.g., of the same or different radio access technologies).

[0069]

[0082] A communication link 125 shown in the wireless communication system 100 may include uplink transmissions from the UE 115 to the base station 105, or downlink transmissions from the base station 105 to the UE 115. The carrier may carry downlink communications or uplink communications (for example, in FDD mode), or may be configured to carry downlink communications and uplink communications (for example, in TDD mode).

[0070]

[0083] A carrier may be associated with a specific bandwidth of the radio frequency spectrum, and in some examples, the carrier bandwidth may be referred to as the carrier or the “system bandwidth” of the wireless communication system 100. For example, the carrier bandwidth may be one of several determined bandwidths for the carrier of a particular radio access technology (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 megahertz (MHz)). Devices of the wireless communication system 100 (e.g., base station 105, UE 115, or both) may have a hardware configuration that supports communication over a specific carrier bandwidth, or may be configurable to support communication over one of a set of carrier bandwidths. In some examples, the wireless communication system 100 may include a base station 105 or UE 115 that supports simultaneous communication over carriers associated with multiple carrier bandwidths. In some examples, each UE 115 being served may be configured to operate across a portion (e.g., subband, BWP) or all of the carrier bandwidth.

[0071]

[0084] The signal waveform transmitted on the carrier may consist of multiple subcarriers (using multi-carrier modulation (MCM) techniques such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In systems employing MCM techniques, a resource element may consist of one symbol period (e.g., the duration of one modulation symbol) and one subcarrier, where the symbol period and subcarrier spacing are inversely related. The number of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both). Therefore, the more resource elements the UE115 receives and the higher the order of the modulation scheme, the higher the data rate for the UE115 can become. Wireless communication resources may refer to a combination of radio frequency spectrum resources, temporal resources, and spatial resources (e.g., spatial layers or beams), and the use of multiple spatial layers may further increase the data rate or data integrity for communication with the UE115.

[0072]

[0085] One or more numerologies may be supported for a carrier, and the numerology may include a subcarrier spacing (Δf) and a cyclic prefix. A carrier may be split into one or more BWPs having the same or different numerologies. In some examples, UE115 may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time, and communication for UE115 may be limited to one or more active BWPs.

[0073]

[0086] The time interval for base station 105 or UE115 is, for example, T s = 1 / (Δf max ·N f ) can be expressed as a multiple of the basic time unit, which can refer to a sampling period of seconds, where Δfmax may represent a maximum supported subcarrier spacing, and N f may represent a maximum supported Discrete Fourier Transform (DFT) size. Time intervals of communication resources may be organized according to radio frames each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) ranging, for example, from 0 to 1023.

[0074]

[0087] Each frame may include a plurality of consecutively numbered subframes or slots, and each subframe or slot may have the same duration. In some examples, a frame may be divided into subframes (e.g., in the time domain), and each subframe may be further divided into a number of slots. Alternatively, each frame may include a variable number of slots, and the number of slots may depend on the subcarrier spacing. Each slot may include a number of symbol periods depending, for example, on the length of the cyclic prefix prepended to each symbol period. In some wireless communication systems 100, a slot may be further divided into a plurality of minislots including one or more symbols. Excluding the cyclic prefix, each symbol period may include one or more (e.g., N f ) sampling periods. The duration of a symbol period may depend on the subcarrier spacing or the operating frequency band.

[0075]

[0088] A subframe, slot, minislot, or symbol may be the minimum scheduling unit (e.g., in the time domain) of the wireless communication system 100, and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally or alternatively, the minimum scheduling unit of the wireless communication system 100 may be dynamically selected (e.g., in a burst of shortened TTIs (sTTIs)).

[0076]

[0089] Physical channels can be multiplexed on a carrier according to various techniques. Physical control channels and physical data channels can be multiplexed on a downlink carrier using, for example, one or more of the following techniques: time division multiplexing (TDM), frequency division multiplexing (FDM), or hybrid TDM-FDM. A control region for a physical control channel (e.g., a control resource set, or CORESET) may be defined by several symbolic periods and may extend across the carrier's system bandwidth or a subset of its system bandwidth. One or more control regions (e.g., CORESETs) may be configured for a set of UE115s. For example, one or more UE115s may monitor or search for control regions for control information according to one or more search space sets, each of which may contain one or more control channel candidates at one or more aggregation levels configured in a cascaded manner. The aggregation level for a candidate control channel may refer to several control channel resources (e.g., control channel elements, CCEs) associated with encoded information for a control information format having a given payload size. The search space set may include a common search space set configured to send control information to multiple UE115s, and a UE-specific search space set for sending control information to a specific UE115.

[0077]

[0090] Each base station 105 may provide communication coverage through one or more cells, e.g., macrocells, small cells, hotspots, or other types of cells, or any combination thereof. The term “cell” may refer to a logical communication entity used for communication with base station 105 (e.g., over a carrier) and may be associated with an identifier for distinguishing neighboring cells (e.g., a physical cell identifier (PCID), a virtual cell identifier (VCID), or other). In some examples, a cell may also refer to a geographical coverage area 110 or a portion of a geographical coverage area 110 (e.g., a sector) on which the logical communication entity operates. Such cells may range from smaller areas (e.g., structures, subsets of structures) to larger areas, depending on various factors such as the capabilities of base station 105. For example, a cell may, among other things, be a building, a subset of a building, or external space between or overlapping with a geographical coverage area 110.

[0078]

[0091] Macrocells generally cover relatively large geographical areas (e.g., a radius of several kilometers) and can enable unrestricted access by UE115s subscribed to the services of a network provider that supports macrocells. Small cells may be associated with lower-power base stations 105 compared to macrocells, and small cells may operate in the same or different (e.g., licensed, unlicensed) frequency bands as macrocells. Small cells may provide unrestricted access to UE115s subscribed to the services of a network provider, or they may provide restricted access to UE115s associated with small cells (e.g., UE115s in a closed subscriber group (CSG), UE115s associated with users in a home or office). Base station 105 may support one or more cells and may support communication on one or more cells using one or more component carriers.

[0079]

[0092] In some cases, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)) that can provide access to different types of devices.

[0080]

[0093] In some examples, base station 105 may be mobile and therefore can provide communication coverage to a mobile geographic coverage area 110. In some examples, different geographic coverage areas 110 associated with different technologies may overlap, but different geographic coverage areas 110 may be supported by the same base station 105. In other examples, overlapping geographic coverage areas 110 associated with different technologies may be supported by different base stations 105. The wireless communication system 100 may include, for example, heterogeneous networks in which different types of base stations 105 provide coverage to various geographic coverage areas 110 using the same or different radio access technologies.

[0081]

[0094] The wireless communication system 100 may support synchronous or asynchronous operation. In synchronous operation, base stations 105 may have similar frame timings, and transmissions from different base stations 105 may be approximately synchronized in time. In asynchronous operation, base stations 105 may have different frame timings, and transmissions from different base stations 105 may, in some cases, not be synchronized in time. The techniques described herein may be used for either synchronous or asynchronous operation.

[0082]

[0095] Some UE115s, such as MTC devices or IoT devices, may be low-cost or low-complexity devices that can provide automated communication between machines (e.g., via machine-to-machine (M2M) communication). M2M communication or MTC may refer to data communication technology that enables devices to communicate with each other or with base stations 105 without human intervention. In some examples, M2M communication or MTC may include communication from devices that incorporate sensors or meters to measure or capture information and relay that information to a central server or application program that utilizes such information or presents it to a human interacting with the application program. Some UE115s may be designed to collect information or enable automated behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security detection, physical access control, and transaction-based business billing.

[0083]

[0096] Some UE115s may be configured to employ power-saving operating modes, such as half-duplex communication (e.g., modes that support one-way communication via transmit or receive, but not simultaneous transmit and receive). In some examples, half-duplex communication may be performed at a reduced peak rate. Other power-saving techniques for the UE115 include entering a power-saving deep sleep mode when not engaged in active communication, operating over a limited bandwidth (e.g., according to narrowband communication), or a combination of these techniques. For example, some UE115s may be configured for operation using narrowband protocol types related to a defined portion or range within the carrier, within the carrier's guard band, or outside the carrier (e.g., a set of subcarriers or resource blocks (RBs)).

[0084]

[0097] The wireless communication system 100 may be configured to support ultra-reliable low-latency communication, or various combinations thereof. For example, the wireless communication system 100 may be configured to support ultra-reliable low-latency communication (URLLC). The UE 115 may be designed to support ultra-reliable, low-latency, or critical functions. Ultra-reliable communication may include private or group communication and may be supported by one or more services such as push-to-talk, video, and data. Support for ultra-reliable, low-latency functions may include service prioritization, and such services may be used for public safety or general commercial purposes. The terms ultra-reliable, low-latency, and ultra-reliable low-latency may be used interchangeably herein.

[0085]

[0098] In some examples, UE115 may also be able to communicate directly with other UE115 via a device-to-device (D2D) communication link 135 (for example, using a peer-to-peer (P2P) protocol or a D2D protocol). One or more UE115s utilizing D2D communication may be within the geographical coverage area 110 of base station 105. Other UE115s in such a group may be outside the geographical coverage area 110 of base station 105, or otherwise be unable to receive transmissions from base station 105. In some examples, a group of UE115s communicating via D2D communication may utilize a one-to-many (1:M) system, where each UE115 transmits to all other UE115s in the group. In some examples, base station 105 facilitates the scheduling of resources for D2D communication. In other cases, D2D communication is performed between UE115s without the involvement of base station 105.

[0086]

[0099] In some systems, the D2D communication link 135 may be an example of a communication channel between vehicles (e.g., UE 115), such as a side-link communication channel. In some examples, vehicles may communicate using vehicle-to-everything (V2X) communication, vehicle-to-vehicle (V2V) communication, or any combination thereof. Vehicles may signal information related to traffic conditions, signal scheduling, weather, safety, emergencies, or any other information related to the V2X system. In some examples, vehicles in a V2X system may communicate with roadside infrastructure such as roadside units, or with the network via one or more network nodes (e.g., base station 105) using vehicle-to-network (V2N) communication, or both.

[0087]

[0100] The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or a 5G core (5GC), which may include at least one control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management function (AMF)), and at least one user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)). The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management for UE 115 serviced by base station 105 associated with the core network 130. User IP packets may be forwarded through user plane entities that may provide IP address assignment and other functions. The user plane entity may be connected to IP services 150 for one or more network operators. IP services 150 may include access to the Internet, one or more intranets, IP Multimedia Subsystems (IMS), or packet-switched streaming services.

[0088]

[0101] Some of the network devices, such as the base station 105, may include subcomponents such as an access network entity 140, which may be an example of an access node controller (ANC). Each access network entity 140 may communicate with the UE 115 through one or more other access network transmission entities 145, which may be referred to as radio heads, smart radio heads, or transmission / reception points (TRPs). Each access network transmission entity 145 may include one or more antenna panels. In some configurations, the various functions of each access network entity 140 or base station 105 may be distributed across various network devices (e.g., radio heads and ANCs) or integrated into a single network device (e.g., base station 105).

[0089]

[0102] Accordingly, as described herein, a base station 105 or network entity may include one or more components located at a single physical location, or one or more components located at various physical locations. In embodiments in which the base station 105 includes components located at various physical locations, each of the various components may perform various functions so that the various components collectively achieve similar functionality to that of a base station 105 located at a single physical location. Accordingly, the base station 105 described herein may be equivalent to a standalone base station 105 (also known as a monolithic base station) or a base station 105 including components located at various physical or virtualized locations (also known as a disaggregated base station). In some implementations, such a base station 105 including components located at various physical locations may be referred to as, or associated with, a disaggregated radio access network (RAN) architecture, such as an open RAN (O-RAN) or virtualized RAN (VRAN) architecture. In some implementations, such components of base station 105 may include or refer to one or more of a central unit (or centralized unit, CU), a distributed unit (DU), or a radio unit (RU). A network entity may directly acquire or output information or signals with UE 115 (e.g., an RU may directly transmit and receive signals with UE 115), or a network entity may indirectly acquire or output information or signals with UE 115 through an intermediate device (e.g., a DU may receive information or signals from UE 115 via an RU).

[0090]

[0103] In some examples, a base station 105 or network entity may be implemented in a non-aggregated architecture (e.g., non-aggregated base station architecture, non-aggregated RAN architecture) configured to utilize a protocol stack that is physically or logically distributed among two or more network entities, such as an IAB network, O-RAN (e.g., a network configuration provided by the O-RAN Alliance), or VRAN (e.g., Cloud RAN (C-RAN)). For example, a network entity may include one or more of the following: CU, DU, RU, RAN intelligent controller (RIC) (e.g., quasi-real-time RIC (quasi-RT RIC), non-real-time RIC (non-RT RIC)), service management and orchestration (SMO) system, or any combination thereof. An RU may also be referred to as a radio head, smart radio head, remote radio head (RRH), remote radio unit (RRU), or transmit / receive point (TRP). One or more components of a network entity in a non-aggregated RAN architecture may be juxtaposed, or one or more components of a network entity may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities in a non-aggregated RAN architecture may be implemented as virtual units (e.g., virtual CUs (VCUs), virtual DUs (VDUs), virtual RUs (VRUs)).

[0091]

[0104] The functional division between CUs, DUs, and RUs is flexible, and different functions may be supported depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, radio frequency (RF) functions, and any combination thereof) are performed in the CU, DU, or RU. For example, functional division of the protocol stack may be employed between CUs and DUs such that a CU may support one or more layers of the protocol stack, and a DU may support one or more different layers of the protocol stack. In some examples, a CU may host higher protocol layer functions (e.g., Layer 3 (L3), Layer 2 (L2)) and signaling functions (e.g., Radio Resource Control (RRC), Service Data Adaptive Protocol (SDAP), Packet Data Convergence Protocol (PDCP)). A CU may be connected to one or more DUs or RUs, and one or more DUs or RUs may host Layer 1 (L1) (e.g., Physical (PHY) layer) or L2 (e.g., Radio Link Control (RLC) layer, Medium Access Control layer) functions and lower protocol layers such as signaling, each of which may be at least partially controlled by the CU. Additionally or alternatively, functional partitioning of the protocol stack may be employed between DUs and RUs such that a DU may support one or more layers of the protocol stack, and an RU may support one or more different layers of the protocol stack. A DU may support one or more different cells (e.g., via one or more RUs). In some cases, functional partitioning between a CU and a DU, or between a DU and an RU, may be within the protocol layer (e.g., some functions for the protocol layer may be performed by one of the CU, DU, or RU, while other functions of the protocol layer may be performed by one of the different CU, DU, or RU). A CU can be further functionally divided into CU control plane (CU-CP) functions and CU user plane (CU-UP) functions. A CU may be connected to one or more DUs via midhaul communication links (e.g., F1, F1-c, F1-u), and a DU may be connected to one or more RUs via fronthaul communication links (e.g., open fronthaul (FH) interfaces).In some examples, a midhaul or fronthaul communication link may be implemented according to the inter-layer interfaces (e.g., channels) of the protocol stacks supported by each network entity communicating over such a link.

[0092]

[0105] The wireless communication system 100 may typically operate using one or more frequency bands in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally, the region from 300 MHz to 3 GHz is known as the ultra-high frequency (UHF) region or decimeter band, as wavelengths range from approximately 1 decimeter to 1 meter. While UHF waves may be blocked or redirected by building and environmental characteristics, they can penetrate structures sufficiently to allow a macrocell to service an indoor UE 115. Transmitting UHF waves can be associated with smaller antennas and shorter distances (e.g., less than 100 kilometers) compared to transmitting using lower frequencies and longer waves in the short frequency (HF) or very high frequency (VHF) portion of the spectrum below 300 MHz.

[0093]

[0106] The wireless communication system 100 may also operate in the super high frequency (SHF) region, using a frequency band from 3 GHz to 30 GHz, also known as the centimeter band, or in the extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz), also known as the millimeter band. In some examples, the wireless communication system 100 may support millimeter wave (mmW) communication between a UE 115 and a base station 105, where the EHF antennas of each device may be smaller and more densely spaced than UHF antennas. In some examples, this may facilitate the use of antenna arrays within the device. However, the propagation of EHF transmissions may be subject to greater atmospheric attenuation than SHF or UHF transmissions, and may be over shorter distances. The techniques disclosed herein may be employed across transmissions using one or more different frequency domains, and the specified use of bands across these frequency domains may vary by country or regulatory body.

[0094]

[0107] The electromagnetic spectrum is often subdivided into various classes, bands, and channels based on frequency / wavelength. In 5G NR, two initial operating bands are identified as frequency range designations FR1 (410 MHz to 7.125 GHz) and FR2 (24.25 GHz to 52.6 GHz). It should be understood that while a portion of FR1 is above 6 GHz, FR1 is often referred to (interchangeably) as the "sub-6 GHz" band in various documents and papers. A similar nomenclature issue may arise with FR2, which is often referred to (interchangeably) as the "millimeter wave" band in documents and papers, even though it is different from the extremely high frequency (EHF) band (30 GHz to 300 GHz) identified by the International Telecommunication Union (ITU) as the "millimeter wave" band.

[0095]

[0108] The frequencies between FR1 and FR2 are often referred to as intermediate band frequencies. In recent 5G NR research, the operating band for these intermediate band frequencies is identified as frequency range designation FR3 (7.125 GHz to 24.25 GHz). The frequency bands falling within FR3 may inherit the FR1 and / or FR2 characteristics, and thus, in effect, the features of FR1 and / or FR2 can be extended to the intermediate band frequencies. Furthermore, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0096]

[0109] With the above aspects in mind, unless otherwise specifically stated, terms such as "sub-6GHz" can broadly refer to frequencies that may be below 6GHz, within FR1, or include intermediate band frequencies, as used herein. Furthermore, unless otherwise specifically stated, terms such as "millimeter wave" can broadly refer to frequencies that may include intermediate band frequencies, frequencies that may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or frequencies that may be within the EHF band.

[0097]

[0110] The wireless communication system 100 can utilize both licensed and unlicensed radio frequency spectrum bands. For example, the wireless communication system 100 can utilize License Assisted Access (LAA), LTE-Unlicensed (LTE-U) radio access technology, or NR technology in unlicensed bands such as the 5 GHz industrial, scientific, and medical (ISM) band. When operating in unlicensed radio frequency spectrum bands, devices such as base station 105 and UE 115 can utilize carrier detection for collision detection and avoidance. In some examples, operation in unlicensed bands may be based on a carrier aggregation configuration in conjunction with component carriers operating in licensed bands (e.g., LAA). Operation in unlicensed spectrums may include, among other examples, downlink transmission, uplink transmission, P2P transmission, or D2D transmission.

[0098]

[0111] Base station 105 or UE115 may be equipped with multiple antennas that can be used to utilize techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of base station 105 or UE115 may be located in one or more antenna arrays or antenna panels that can support MIMO operation or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be juxtaposed in an antenna assembly such as an antenna tower. In some examples, the antennas or antenna arrays associated with base station 105 may be located in diverse geographical locations. Base station 105 may have an antenna array having several rows and columns of antenna ports that base station 105 can use to support beamforming for communication with UE115. Similarly, UE115 may have one or more antenna arrays that can support various MIMO or beamforming operations. Additionally or alternatively, antenna panels may support radio frequency beamforming for signals transmitted through antenna ports.

[0099]

[0112] A base station 105 or UE115 may use MIMO communication to increase spectral efficiency by leveraging multipath signal propagation by transmitting or receiving multiple signals through different spatial layers. Such a technique may be referred to as spatial multiplexing. Multiple signals may be transmitted by a transmitting device through, for example, different antennas or different combinations of antennas. Similarly, multiple signals may be received by a receiving device through different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream and may carry bits associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers may be associated with different antenna ports used for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO), in which multiple spatial layers are transmitted to the same receiving device, and multiple-user MIMO (MU-MIMO), in which multiple spatial layers are transmitted to multiple devices.

[0100]

[0113] Beamforming, which may be called spatial filtering, directional transmission, or directional reception, is a signal processing technique that can be used in a transmitting or receiving device (e.g., base station 105, UE115) to shape or steer an antenna beam (e.g., transmit beam, receive beam) along a spatial path between the transmitting and receiving devices. Beamforming can be achieved by combining signals communicated through the antenna elements of an antenna array such that several signals propagating in a particular orientation relative to the antenna array are subjected to constructive interference, while other signals are subjected to destructive interference. Coordination of signals communicated through antenna elements may include the transmitting or receiving device applying amplitude offset, phase offset, or both to the signals carried through the antenna elements associated with the device. Coordination associated with each antenna element may be defined by a beamforming weight set associated with a particular orientation (e.g., relative to the antenna array of the transmitting or receiving device, or to some other orientation).

[0101]

[0114] The base station 105 or UE 115 may use beam sweeping techniques as part of its beamforming operation. For example, the base station 105 may use multiple antennas or antenna arrays (e.g., antenna panels) to perform beamforming operations for directional communication with the UE 115. Several signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be transmitted multiple times by the base station 105 in different directions. For example, the base station 105 may transmit signals according to different beamforming weight sets associated with different transmission directions. Transmissions in different beam directions may be used (e.g., by a transmitting device such as the base station 105, or by a receiving device such as the UE 115) to identify beam directions for later transmission or reception by the base station 105.

[0102]

[0115] Some signals, such as data signals associated with a specific receiving device, may be transmitted by the base station 105 in a single beam direction (e.g., a direction associated with a receiving device such as UE115). In some examples, the beam direction associated with transmission along a single beam direction may be determined based on signals transmitted in one or more beam directions. For example, UE115 may receive one or more signals transmitted by the base station 105 in different directions and report to the base station 105 an indication of the signals received with the highest signal quality, or otherwise acceptable signal quality.

[0103]

[0116] In some examples, transmission by a device (e.g., by base station 105 or UE115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or radio frequency beamforming to generate a combined beam for transmission (e.g., from base station 105 to UE115). UE115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to a configured number of beams across the system bandwidth or one or more subbands. Base station 105 may transmit a reference signal (e.g., cell-specific reference signal, CRS, CSI-RS) that can be precoded or amplified. UE115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., multi-panel type codebook, linear combination type codebook, port selection type codebook). These techniques will be described with reference to signals transmitted by base station 105 in one or more directions, but UE 115 may employ similar techniques to transmit signals multiple times in different directions (for example, to identify beam directions for subsequent transmission or reception by UE 115) or to transmit signals in a single direction (for example, to transmit data to a receiving device).

[0104]

[0117] When a receiving device (e.g., UE115) receives various signals from a base station 105, such as synchronization signals, reference signals, beam selection signals, or other control signals, it may attempt multiple receiving configurations (e.g., directional listening). For example, the receiving device may attempt multiple receiving directions by receiving through different antenna subarrays, by processing the received signal according to different antenna subarrays, by receiving according to different sets of receive beamforming weights (e.g., different directional listening weights) applied to the received signal at multiple antenna elements of an antenna array, or by processing the received signal according to different sets of receive beamforming weights applied to the received signal at multiple antenna elements of an antenna array, any of which may be referred to as "listening" by different receiving configurations or receiving directions. In some examples, the receiving device may use a single receiving configuration to receive along a single beam direction (e.g., when receiving a data signal). The single receiving configuration may be matched to a beam direction determined based on listening by different receiving configuration directions (e.g., a beam direction determined to have the highest signal strength, the highest signal-to-noise ratio (SNR), or otherwise acceptable signal quality based on listening by multiple beam directions).

[0105]

[0118] The wireless communication system 100 may be a packet-based network operating according to a layered protocol stack. In the user plane, communication at the bearer or Packet Data Convergence Protocol (PDCP) layer may be IP-based. The Radio Link Control (RLC) layer may perform packet segmentation and reassembly for communication on logical channels. The Medium Access Control (MAC) layer may perform priority processing and multiplexing logical channels to transport channels. The MAC layer may also use error detection techniques, error correction techniques, or both to improve link efficiency by supporting retransmission at the MAC layer. In the control plane, the RRC protocol layer may establish, configure, and maintain RRC connections between the UE 115 and the base station 105 or core network 130, supporting radio bearers for user plane data. At the physical layer, transport channels may be mapped to physical channels.

[0106]

[0119] UE115 and base station 105 may support data retransmission to increase the likelihood of successful data reception. Hybrid automatic repeat request (HARQ) feedback is one technique to increase the likelihood of data being correctly received on communication link 125. HARQ may include a combination of error detection (e.g., using cyclic redundancy check, CRC), forward error correction (FEC), and retransmission (e.g., automatic repeat request, ARQ). HARQ may improve throughput at the MAC layer under poor radio conditions (e.g., low signal-to-noise conditions). In some examples, devices may support same-slot HARQ feedback, where the device provides HARQ feedback within a slot for data received in a previous symbol within a particular slot. In other cases, the device may provide HARQ feedback in a subsequent slot or according to some other time interval.

[0107]

[0120] The techniques described herein may be implemented via additional or alternative radio devices, including IAB nodes 104, DU165, CU160, RU170, etc., in addition to being performed between UE115 and base station 105, or as an alternative thereto. For example, in some implementations, the embodiments described herein may be implemented in the context of a non-aggregated RAN architecture (e.g., an open RAN architecture). In a non-aggregated architecture, the RAN may be divided into three areas of function corresponding to CU160, DU165, and RU170. The division of function between CU160, DU165, and RU175 is flexible and therefore results in a number of different function substitutions depending on which functions (e.g., MAC functions, baseband functions, radio frequency functions, and any combination thereof) are performed in CU160, DU165, and RU175. For example, a functional partitioning of the protocol stack may be employed between the DU165 and RU170 such that the DU165 can support one or more layers of the protocol stack, and the RU170 can support one or more different layers of the protocol stack.

[0108]

[0121] Several wireless communication systems (e.g., wireless communication system 100), infrastructure for NR access, and spectral resources may complement wired backhaul connections to additionally support wireless backhaul link capabilities and provide an IAB network architecture. One or more base stations 105 may include CU160, DU165, and RU170 and may be referred to as a donor base station 105 or IAB donor. One or more DU165 (e.g., and / or RU170) associated with a donor base station 105 may be partially controlled by a CU160 associated with the donor base station 105. One or more donor base stations 105 (e.g., IAB donors) may communicate with one or more additional base stations 105 (e.g., IAB nodes 104) via supported access links and backhaul links. An IAB node 104 may support mobile terminal (MT) functionality controlled and / or scheduled by the DU165 of the coupled IAB donor. In addition, the IAB node 104 may include a DU 165 that supports communication links with additional entities (e.g., IAB node 104, UE 115, etc.) in the access network's (e.g., downstream) relay chain or configuration. In such cases, one or more components of the non-aggregated RAN architecture (e.g., one or more IAB nodes 104 or components of IAB node 104) may be configured to operate according to the techniques described herein.

[0109]

[0122] In some embodiments, the wireless communication system 100 may include a core network 130 (e.g., a next-generation core network, NGC), one or more IAB donors, IAB nodes 104, and UEs 115, where the IAB nodes 104 may be partially controlled by each other and / or by the IAB donors. The IAB donors and IAB nodes 104 may be examples of base station 105. The IAB donors and one or more IAB nodes 104 may be configured as some kind of relay chain (e.g., or communicating accordingly).

[0110]

[0123] For example, the access network (AN) or RAN may refer to communication between an access node (e.g., an IAB donor), an IAB node 104, and one or more UEs 115. The IAB donor may facilitate the connection between the core network 130 and the AN (e.g., via a wired or wireless connection to the core network 130). That is, the IAB donor may refer to a RAN node having a wired or wireless connection to the core network 130. The IAB donor may include a CU 160 and at least one DU 165 (e.g., and RU 170), where the CU 160 may communicate with the core network 130 via an NG interface (e.g., some backhaul link). The CU 160 may host layer 3 (L3) (e.g., RRC, service data adaptation protocol (SDAP), PDCP, etc.) functions and signaling. At least one DU165 and / or RU170 may host lower layers such as Layer 1 (L1) and Layer 2 (L2) (e.g., RLC, MAC, physical (PHY)) functions and signaling, each of which may be at least partially controlled by a CU160. A DU165 may support one or more different cells. IAB donors and IAB nodes 104 may communicate via the F1 interface according to some protocol (e.g., the F1 AP protocol) that defines signaling messages. Additionally, a CU160 may communicate with the core network via the NG interface (which may be an example of a backhaul link) and with other CU160s (e.g., CU160s associated with alternate IAB donors) via the Xn-C interface (which may be an example of a backhaul link).

[0111]

[0124] IAB node 104 may refer to a RAN node that provides IAB functionality (e.g., access for UE 115, wireless self-backhaul capability, etc.). IAB node 104 may include DU 165 and MT. DU 165 may act as a distributed scheduling node for child nodes associated with IAB node 104, and MT may act as a scheduled node for a parent node associated with IAB node 104. That is, an IAB donor may be referred to as a parent node communicating with one or more child nodes (e.g., an IAB donor may relay transmissions for UEs through one or more other IAB nodes 104). Additionally, depending on the relay chain or configuration of the AN, IAB node 104 may be referred to as a parent or child node for other IAB nodes 104. Therefore, the MT entity of IAB node 104 (e.g., MT) may provide a Uu interface for child nodes to receive signaling from parent IAB node 104, and the DU interface (e.g., DU165) may provide a Uu interface for parent nodes to signal to child IAB node 104 or UE115.

[0112]

[0125] For example, IAB node 104 may be referred to as a parent node associated with an IAB node, and a child node associated with an IAB donor. An IAB donor may include a CU 160 having a wired (e.g., optical fiber) or wireless connection to the core network and may act as a parent node to IAB node 104. For example, the DU 165 of the IAB donor may relay transmissions to UE 115 via IAB node 104, or may directly signal transmissions to UE 115. The CU 160 of the IAB donor may signal the establishment of a communication link to IAB node 104 via the F1 interface, and IAB node 104 may schedule transmissions (e.g., transmissions to UE 115 relayed from the IAB donor) via the DU 165. That is, data may be relayed to and from IAB node 104 via signaling through the NR Uu interface to MT of IAB node 104. Communication with IAB node 104 may be scheduled by DU165 of the IAB donor, and communication with IAB node 104 may be scheduled by DU165 of IAB node 104.

[0113]

[0126] In the case of the techniques described herein applied in the context of a non-aggregated RAN architecture, one or more components of the non-aggregated RAN architecture (e.g., one or more IAB nodes 104 or components of IAB nodes 104) may be configured to support techniques for large round-trip times in the random access channel procedures described herein. For example, some operations described as being performed by UE 115 or base station 105 may be additionally or alternatively performed by components of the non-aggregated RAN architecture (e.g., IAB nodes, DUs, CUs, etc.).

[0114]

[0127] UE115 can measure interference at UE115 using interference measurement resources such as CSI-RS, CSI-IM, or through IMR. Interference at UE115 can be caused, for example, by communications at a neighboring base station 105, or by sidelink communications between other UE115s. In NR, the slot structure can be more flexible compared to the slot structure in LTE. For example, NR may include minislots and URLLC slots. In some examples, short bursts of transmission within a normal extended mobile broadband (eMBB) slot may start at any symbol position. In NR, unscheduled uplink transmissions from UEs without permission from the base station may occur. NR may also include highly adaptive reference signal patterns (e.g., demodulated reference signal (DMRS) and CSI-RS patterns may depend on the number of available antenna ports, delay tolerance, or Doppler spread). Therefore, interference may differ significantly between one UE115 and another.

[0115]

[0128] Interference at UE115 can vary in the time, frequency, and spatial domains, and interference at UE115 can also be correlated in the time, frequency, and spatial domains. For example, the correlation characteristics of interference observed at a particular UE115 may vary depending on scheduling decisions at a neighboring base station 105. UE115 can predict interference on future communication resources based on interference correlations learned from previous communication resources. Interference measured at UE115 may be reported to the serving base station 105 via a CSF report. For example, a CSF report may include a rank index (RI), channel quality index (CQI), and PMI. The RI, CQI, and PMI may take into account interference level and channel estimations at UE115. A CSF report (e.g., the included RI, CQI, and PMI) may not report information regarding the time, frequency, or spatial correlation characteristics of the interference. Therefore, when the serving base station 105 schedules communication with the UE 115 based on the CSF report, it may not utilize the time correlation, frequency correlation, or spatial correlation characteristics of interference at the UE 115. Because interference variability at the UE 115 can be large (e.g., larger than the channel variability reported in the CSF report), explicitly reporting the interference distribution to the serving base station may be associated with significant resource overhead and may be difficult to parameterize across different UEs.

[0116]

[0129] In some examples, UE115-a may include a communications manager 102 configured to support one or more embodiments of the techniques for compressing and reconstructing interference distributions described herein. For example, UE115 may transmit compressed interference information to base station 105 via communications manager 102 to reduce the resource overhead associated with explicit interference distribution reporting, while allowing base station 105 to consider interference correlations when scheduling communication with UE115. In some examples, base station 105 may include a communications manager 101 configured to support one or more embodiments of the techniques for compressing and reconstructing interference distributions described herein. For example, base station 105 may receive compressed interference information via communications manager 101.

[0117]

[0130] UE115 can measure interference in UE115 across a set of interference measurement resources. UE115 can then determine the interference distribution across the set of resources. In some examples, the interference distribution may be either a probability density function or a probability mass function. UE115 can encode the interference distribution using a compression scheme that can reduce the payload or size of the interference distribution. The compression scheme may involve compressing the measured interference distribution across a given set of interference measurement resources. In some examples, encoding interference information according to the compression scheme may involve generating a mean vector and covariance matrix of latent random variables representing the measured interference distribution across a given set of interference measurement resources. The mean vector and covariance matrix of latent random variables may show the distribution of stochastic latent random vectors associated with the measured interference. UE115 can transmit the encoded interference information via communication manager 102, and base station 105 can receive and decode the encoded interference information via communication manager 101. Base station 105 can schedule communication with UE115 based on the interference information via communication manager 101.

[0118]

[0131] In some examples, the UE115 or base station 105 may predict future interference distributions based on past interference measurements at the UE115 (e.g., predicted across a time / frequency grid for future symbols / slots). For example, if high interference is predicted for certain resources, base station 105 may avoid allocating those specific resources to the UE115. For available resources (e.g., resources with low predicted interference), the UE115 or base station 105 may predict the time / frequency correlation of interference that could be used for demodulating communications involving the UE115.

[0119]

[0132] In some examples, UE115 may receive control signaling via communication manager 102 that constitutes interference measurement resources and coding configurations. For example, base station 105 may transmit control signaling via communication manager 101 that constitutes interference measurement resources, compression scheme, compression scheme parameters, coding configuration for the encoder, and / or input format to the encoder for UE115. In some examples, base station 105 may adjust the interference measurement resources, compression scheme, compression scheme parameters, encoder, and / or input format to the encoder for UE115 based on the location of UE115 (for example, UE115 at the edge of cell 110 may experience more interference than UE115 at the center of the cell). In some examples, base station 105 may adjust the interference measurement resources, compression scheme, compression scheme parameters, encoder, and / or input format to the encoder for UE115 based on knowledge of interference patterns. For example, the interference pattern may depend on external factors such as the number of active UE115s, transmission configuration indications (TCIs), the beam, and / or the load in neighboring cells 110.

[0120]

[0133] UE115 may include an artificial neural network (NN) that can update model parameters associated with the compression scheme based on past interference measurements at the UE. Artificial NNs that can support such machine learning techniques for channel compression include fully connected NNs, batch normalized NNs, dropout NNs, convolutional NNs, residual NNs, ReLU NNs, and other types of NNs. UE115 may report the learned or updated model parameters to the base station. Base station 105 may receive learned or updated model parameters from multiple UE115s. Base station 105 may update the parameters associated with encoding or decoding based on the learned parameters received from multiple UE115s.

[0121]

[0134] Figure 2 illustrates an example of a wireless communication system 200 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The wireless communication system 200 illustrates an example of communication between a network entity 205-a associated with a first coverage area 110-a, a network entity 205-b associated with a second coverage area 110-b, and UE 115-a, UE 115-b, and UE 115-c, which include referring to Figure 1 and may be examples of corresponding devices described herein. For example, network entities 205-a and / or network entities 205-b may include all or some components of a base station 105 described herein.

[0122]

[0135] In the example of the wireless communication system 200, UE115-a, one or more components of network entity 205-a, or both, may perform estimation of signal propagation conditions between network entity 205-a and UE115-a, which may be referred to as channel estimation. Signal propagation conditions may refer to path loss for a signal. For example, one or more components of network entity 205-a may transmit downlink signaling 210-a which may include a reference signal 215 (e.g., CSI-RS, or CRS, or another reference signal, or a combination of reference signals). UE115-a may monitor such a reference signal and perform calculations based on the measured or predicted characteristics of the reference signal 215 of the downlink signaling 210-a (e.g., signal-to-noise ratio, signal-to-interference-noise ratio, reference signal received power) to support various techniques for channel estimation. Based on monitoring or receiving the reference signal 215, UE 115-a may transmit uplink signaling 220-a that can be received by network entity 205-a (e.g., a response uplink transmit). The uplink signaling 220-a may include, among other information or combinations of channel information, a CSF report 225 which may be part of an uplink control information (UCI) transmit by UE 115-a (e.g., of a physical uplink control channel (PUCCH)), a report of channel conditions which is at least partially based on channel estimation performed by UE 115-a, or a measurement of the reference signal 215 or an instruction for measurement performed by UE 115-a (e.g., to support channel estimation calculations by network entity 205-a).

[0123]

[0136] As described herein, UE115-a may experience interference. For example, interference may be caused by communication in neighboring network entity 205-b (e.g., downlink signaling 210-b or uplink signaling 220-b between neighboring network entity 205-b and UE115-b) or by sidelink communication between neighboring UE115s (e.g., sidelink signaling between UE115-b and UE115-c via sidelink communication link 135-a). UE115-a may measure interference in UE115-a using interference measurement manager 245. For example, interference measurement manager 245 may measure interference using interference measurement resources 280 allocated to measure interference in UE115-a (e.g., through CSI-RS, CSI-IM, or IMR). Based on the measured interference, UE115-a may determine the interference distribution 255.

[0124]

[0137] UE115-a may send a message 235 containing interference information representing an interference distribution 255 to network entity 205-a, where the interference distribution is encoded according to a compression scheme. UE115-a may include an encoder 240 for encoding the interference information according to a compression scheme. Encoder 240 may refer to software, firmware, or hardware, or any combination thereof, capable of encoding the interference information according to a compression scheme. Encoding the interference information according to a compression scheme may reduce the resource overhead associated with explicit interference distribution reporting while allowing network entity 205-a to consider interference correlations when scheduling communication with UE115-a. UE115-a may measure interference in UE115-a across a set of interference measurement resources. UE115-a may use an interference measurement manager 245 to determine the interference distribution 255 across a set of interference measurement resources 280. UE115-a may encode the interference information representing the interference distribution 255 using a compression scheme that can reduce the payload or size of the interference information. The compression scheme may involve compressing the measured interference distribution across a given set of interference measurement resources. In some examples, the interference distribution may be one of a probability density function or a probability mass function. In some examples, encoding interference information according to the compression scheme may involve generating a mean vector and covariance matrix of latent random variables representing the measured interference distribution across a given set of interference measurement resources. UE115-a may transmit the encoded interference information via message 235, and network entity 205-a may receive message 235 and decode the encoded interference information contained therein. Based on the interference information received in message 235, network entity 205-a may transmit scheduling information 275 for communication with UE115-a.

[0125]

[0138] In some examples, the encoder 240 may be configured according to various machine learning techniques (for neural network-based interferometric information compression schemes, when operating, for example, as an autoencoder or otherwise according to an autoencoder), where such techniques may be performed by one or both of the network entities 205-a or UE115-a.

[0126]

[0139] The compression scheme may also involve a decoder 230 in the network entity 205-a, which may refer to software, firmware, or hardware, or any combination thereof, capable of decompressing encoded interference information in a message 235 received by the network entity 205-a at the uplink signaling 220-a. In some examples, the decoder 230 may be configured according to various machine learning techniques (for neural network-based interference information compression schemes, when operating, for example, as an autoencoder or otherwise according to an autoencoder), where such techniques may be performed by either or both of the network entity 205-a or UE115-a.

[0127]

[0140] In some cases, UE115-a or network entity 205-a may predict future interference distributions based on past interference measurements in UE115-a (e.g., predicted across a time / frequency grid for future symbols / slots). For example, if network entity 205-a predicts high interference in certain resources, it may avoid allocating those specific resources to UE115-a. For available resources (e.g., resources with low predicted interference), UE115-a or network entity 205-a may predict the time / frequency correlation of interference that could be used for demodulating communications involving UE115-a. For example, in the time domain, the 120th symbol of interference distribution 255 may be associated with low interference.

[0128]

[0141] In some examples, UE115-a may receive control signaling 260 that constitutes interference measurement resources and coding configurations. The control signaling 260 may be transmitted dynamically through radio resource control messages, MAC control element (MAC-CE) messages, or downlink control information (DCI) messages. For example, network entity 205-a may configure interference measurement resources 280, compression scheme parameters 281, coding configuration 282 for the encoder, input format 283 to the encoder, and / or parameters 284 associated with measuring interference for UE115-a. In some examples, network entity 205-a may adjust the interference measurement resources 280, compression scheme parameters 281, coding configuration, input format 283 to the encoder, and / or parameters 284 associated with measuring interference for UE115-a based on the location of UE115-a (for example, UE115-a at the edge of a cell associated with network entity 205-a may experience higher interference than UE115-a at the center of the cell). In some examples, network entity 205-a may adjust the interference measurement resources 280 for UE115-a, the compression scheme parameters 281, the coding configuration 282, the input format to the encoder 283, and / or the parameters 284 associated with measuring interference, based on knowledge of the interference pattern. For example, the interference pattern may depend on external factors such as the number of active UE115s, transmit configuration instructions (TCIs), beams, and / or loads associated with neighboring network entity 205-b.

[0129]

[0142] For example, network entity 205-a may configure an encoding configuration 282 (for example, for encoder 240) in UE115-a via control signaling 260. Network entity 205-a may use the corresponding decoder to reconstruct interference information representing the interference distribution received from UE115-a as encoded interference information. In some examples, the configured encoding configuration 282 (which constitutes encoder 240) and the corresponding decoder 230 may be or include an artificial neural network or an autoencoder. An autoencoder may be used to reduce the size of the input to a smaller representation, and then the original data is reconstructed using the compressed version and code.

[0130]

[0143] In some examples, the network entity 205-a may configure the compression scheme parameters 281 in UE115-a via control signaling 260. For example, in the case of machine learning-based encoders and decoders, the compression scheme parameters 281 may be the code size, the number of layers, the number of nodes per layer, and / or the loss function used in UE115-a to compress interference information. The code size may represent the trade-off between the overhead size associated with interference reporting and the interference distribution reconstruction error.

[0131]

[0144] In some examples, network entity 205-a may configure parameters 284 associated with measuring interference in UE115-a via control signaling 260. For example, network entity 205-a may configure time, frequency, and / or space, e.g., beam, resource, to be used for measuring, estimating, and / or predicting interference. In some examples, network entity 205-a may configure the granularity of interference estimation and / or prediction. For example, network entity 205-a may configure, with respect to frequency granularity, whether the interference estimation is based on the full band or subbands (and different granularities for different subbands). In another example, network entity 205-a may configure, with respect to time granularity, whether the interference estimation is based on symbol-level interference, slot-level interference, or multi-slot-level interference. In yet another example, with respect to spatial granularity, network entity 205-a may configure interference estimation for a particular beam.

[0132]

[0145] In some examples, network entity 205-a may configure an input format 283 for encoder 240 via control signaling 260. For example, the input format 283 may include an estimated interference distribution, zero-power or non-zero-power CSI-RS for interference measurement, and / or measured estimated interference on previous interference measurement resources.

[0133]

[0146] In some examples, UE115-a may transmit a capability message 265 indicating UE115-a's ability to encode interference information. For example, UE115-a may indicate one or more types of encoders and / or encoding parameters that UE115-a can support. In some examples, capability message 265 may include a recommended encoding configuration 285. For example, UE115-a may indicate an index associated with the recommended encoder (e.g., from a set of encoders each associated with a respective index in table 290). For example, UE115-a and network entity 205-a may each be configured with a table or index that stores encoder types and / or encoding configurations. In response, in some examples, network entity 205-a may transmit control signaling 260 that constitutes an encoding configuration (e.g., encoding configuration 282, compression scheme parameters 281, or input format 283) based on the indicated capability of UE115-a.

[0134]

[0147] In some examples, the output of encoder 240 may be a compressed estimated or predicted interference distribution for a set of resources in time, frequency, and space. In some examples, UE115-a may report the compressed interference distribution as part of the CSF report. For example, the compressed interference distribution may be included as a quantity in the CSI report (e.g., in NR, the layer 1 interference report may be added to the CSF report).

[0135]

[0148] In some examples, the output of the encoder 240 may be a mean vector and a covariance matrix representing the distribution of latent random vectors associated with the measured interference, as described herein with reference to Figure 3b.

[0136]

[0149] Machine learning techniques may be used by the wireless communication system 200 to support interference information compression schemes and may include training an encoder (e.g., training an autoencoder, evaluating or configuring parameters to be used in encoder 240), encoding information such as encoding interference information, training a decoder (e.g., training an autoencoder, evaluating or configuring parameters to be used in decoder 230), decoding information such as decoding interference information, or any combination thereof. Such machine learning techniques may include one or more artificial neural networks that can be implemented by one or both of the transmitting device (e.g., UE 115-a) or the receiving device (e.g., network entity 205-a). Artificial neural networks that can support such machine learning techniques for channel compression include fully connected neural networks, batch normalized neural networks, dropout neural networks, convolutional neural networks, residual neural networks, ReLU neural networks, and other types of neural networks.

[0137]

[0150] Machine learning techniques may be implemented by the wireless communication system 200 to train interference information compression schemes, but in some examples, there may be a mismatch between the interference information used for training and the interference information used for inference. For example, a machine learning technique may be trained according to a known interference distribution (e.g., laboratory conditions, known or predicted parameters, known or predicted hardware characteristics, specific modeling techniques) that may not match the device for inference (e.g., network entity 205-a, UE115-a, or both hardware characteristics or configuration) or interference distribution statistics (e.g., interference signals from other UE115 or network entity 205 affecting downlink signaling 210-a or uplink signaling 220-a, information associated with a given interference distribution, or payload), which may be more complex or present risks or uncertainties regarding some machine learning techniques. In some examples, one compression scheme may be unsuitable or otherwise less desirable for carrying interference information compared to another compression scheme.

[0138]

[0151] UE115-a may include an artificial neural network (NN) that can update model parameters associated with the compression scheme based on past interference measurements in the UE. Artificial NNs that can support such machine learning techniques for channel compression include fully connected NNs, batch normalization NNs, dropout NNs, convolutional NNs, residual NNs, ReLU NNs, and other types of NNs. UE115-a may report the learned or updated model parameters to network entity 205-a in update message 270. Network entity 205-a may receive learned or updated model parameters from multiple UE115s. Network entity 205-a may update the parameters associated with encoding or decoding based on the learned parameters received from multiple UE115s.

[0139]

[0152] In some examples, UE115-a may report learned interference compression model parameters (e.g., neural network weights) that can assist network entity 205-a in augmenting a global model for compressing interference distributions across UE115 served by network entity 205-a. For example, network entity 205-a may apply a federative learning method for compressing interference distributions.

[0140]

[0153] For example, according to a federated learning method, UE115-a may observe interference measurement resources (e.g., CSI-RS, CSI-IM, or IMR) and estimate interference from these resources. Network entity 205-a may define and configure architectures for the encoder 240 and decoder 230. For example, UE115-a may store a table 290-b of encoder models in memory, and network entity 205-a may signal the index of a selected coding configuration 282 to UE115 via control signaling 260. In some examples, network entity 205-a may store a table 290-a of encoder models in memory, which may correspond to table 290-b stored in the memory of UE115-a. In some examples, UE115-a may signal the index of a selected or recommended encoder model (e.g., coding configuration 286) (e.g., via uplink control information).

[0141]

[0154] UE115-a may encode interference information representing the interference distribution across interference measurement resources and transmit the encoded interference information to network entity 205-a. UE115-a may update machine learning-based model parameters (e.g., model coefficients) to compress or decompress the interference distribution (based on sufficient interference measurements). However, in some examples, UE115-a continues to encode the interference distribution using the configured weights.

[0142]

[0155] Network entity 205-a may send a signaling to UE115-a requesting it to report the learned model parameters. In response, UE115-a may report the learned model parameters to network entity 205-a. Network entity 205-a may update the global model for interference prediction. In some examples, network entity 205-a may send a control signaling 260 indicating the updated parameters for the interference distribution encoder (e.g., updated coefficients for an existing encoder model defined in a table in UE115-a's memory).

[0143]

[0156] To support the transmission of interference information reports 235, UE115-a may include a compression scheme manager 250 that can operate to select between compression schemes (e.g., indicated by control signaling 260) based on various criteria. In some examples, one of the first or second compression schemes may be configured (e.g., in the compression scheme manager 250) as the default or target interference distribution compression scheme (e.g., a neural network-based interference distribution compression scheme). In some examples, UE115-a (e.g., the compression scheme manager 250) may switch to a different interference distribution compression scheme (e.g., a codebook-based interference distribution compression scheme, a regular codebook, a legacy codebook, a fallback interference distribution compression scheme) or select an interference distribution compression scheme otherwise, depending on whether certain conditions are met or not.

[0144]

[0157] In some examples, the conditions for evaluating or selecting between a first interference distribution compression scheme and a second interference distribution compression scheme include calculation or comparison of differences or errors associated with different interference distribution compression schemes (e.g., thresholding or comparison of mean squared error (MSE) for one interference distribution compression scheme versus another, such as deciding to switch to a conventional codebook-based interference distribution compression scheme when a neural network-based interference distribution compression scheme has a higher MSE). In some examples, operating conditions of UE 115-a may be considered when evaluating or selecting an interference distribution compression scheme (e.g., at compression scheme manager 250), such as evaluating or selecting an interference distribution compression scheme based on power availability (e.g., battery status), power consumption (e.g., associated with one interference distribution compression scheme or another), processor availability (e.g., available processing cycles), processor load (e.g., associated with one interference distribution compression scheme or another), or any combination thereof.

[0145]

[0158] In one example for evaluating interference distribution compression schemes with respect to power consumption (e.g., associated with performing encoding in accordance with a particular interference distribution compression scheme), a codebook-based interference distribution compression scheme may be associated with power P1, and a neural network-based interference distribution compression scheme may be associated with power P2. The evaluation between the codebook-based interference distribution compression scheme and the neural network-based compression scheme by UE 115-a may be associated with a parameter α that may be communicated using control signaling 260. P1 * If the condition α < P2 is satisfied, UE 115-a may select encoding or decoding in accordance with the codebook-based interference distribution compression scheme (e.g., configuring encoder 240 in accordance with a codebook-based interference information compression scheme, encoding interference information in accordance with the codebook-based interference distribution compression scheme, and indicating that decoder 230 should be configured in accordance with the codebook-based interference distribution compression scheme). P1 *If the condition α<P2 is not satisfied, UE 115-a may select encoding or decoding according to a neural network-based interference information compression scheme (e.g., configuring encoder 240 according to an autoencoder, encoding an interference distribution according to the neural network-based compression scheme, indicating that decoder 230 should be configured according to a neural network-based interference distribution compression scheme, and indicating that decoder 230 should be configured according to an autoencoder).

[0146]

[0159] Additionally or alternatively, in an example for evaluating an interference distribution compression scheme against a processing load (e.g., associated with a processing load for performing encoding according to a particular interference distribution compression scheme), a codebook-based interference distribution compression scheme may be associated with processing load L1, and a neural network-based interference distribution compression scheme may be associated with processing load L2. Evaluation between the codebook-based interference distribution compression scheme and the neural network-based compression scheme by UE 115-a may be associated with parameter β, which may be communicated using control signaling 260. L1 * If the condition β<L2 is satisfied, UE 115-a may select to perform encoding according to a codebook-based interference distribution compression scheme (e.g., configuring encoder 240 according to the codebook-based interference distribution compression scheme, encoding an interference distribution according to the codebook-based interference distribution compression scheme, and indicating that decoder 230 should be configured according to the codebook-based interference distribution compression scheme). L1 * If the condition β<L2 is not satisfied, UE 115-a may select encoding or decoding according to a neural network-based interference distribution compression scheme (e.g., configuring encoder 240 according to an autoencoder, encoding an interference distribution according to the neural network-based compression scheme, indicating that decoder 230 should be configured according to a neural network-based interference distribution compression scheme, and indicating that decoder 230 should be configured according to an autoencoder).

[0147]

[0160] In some examples, the criteria for selecting one or another interference distribution compression scheme may be supported by an artificial neural network involved in the interference distribution compression scheme itself or a corresponding neural network configuration (e.g., an artificial neural network associated with the encoder 240, an artificial neural network associated with the decoder 230, or an artificial neural network associated with those configurations). For example, an artificial neural network used in evaluating an interference distribution compression scheme (e.g., in the compression scheme manager 250) for sending an interference information report 235 may take in the input of an autoencoder, the estimated channels (e.g., information regarding the estimation of signal propagation between network entities 205-a and UE115-a), and the output of a normal or default interference distribution compression scheme (e.g., the output of a neural network-based interference distribution compression scheme). In some examples, an artificial neural network supporting such evaluation may output a Boolean value indicating whether to fall back to a normal or default interference distribution compression scheme (e.g., whether to fall back to a codebook-based interference distribution compression scheme).

[0148]

[0161] In some examples, network entity 205-a may configure parameters for interference prediction at a particular UE115 based on the location of the UE115 or based on known interference patterns. For example, network entity 205-a may send control signaling 260 to the UE115-a indicating an index of an interference distribution encoder from a defined table (for example, stored in the memory of the UE115-a).

[0149]

[0162] Figure 3a illustrates an example of an encoding and decoding scheme 300 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. In some examples, the encoding and decoding scheme 300 may be implemented by or be implemented by an aspect of the wireless communication system 100 or wireless communication system 200. The encoding and decoding scheme 300 may include a UE115-d, which may be an example of the UE115 described herein. The encoding and decoding scheme 300 may also include a network entity 205-c, which may include all or some components of the base station 105 described herein.

[0150]

[0163] UE115-d may encode a past interference sequence 310-a representing interference measured in UE115-d via encoder 240-a. The output 320-a of encoder 240-a may be a compressed interference distribution. UE115-d may transmit the compressed interference distribution to network entity 205-c. Network entity 205-c may include decoder 230-a, which receives the compressed interference distribution as input and decodes the compressed interference distribution according to the compression scheme. The output 330-a of decoder 230-a may be the interference distribution.

[0151]

[0164] In some examples, input 310-a could be a predicted interference sequence (for example, UE115 could predict future interference based on interference measurements), and output 330-a could correspondingly be a reconstructed predicted interference distribution.

[0152]

[0165] The use of encoder 240-a and decoder 230-a may enable UE 115-d to report measured and / or predicted interference at UE 115-d without transmitting the entire measured or predicted interference distribution, and may enable network entity 205-c to receive and determine the correlation characteristics of interference at UE 115-d, which network entity 205-c may consider when scheduling communications at UE 115-d.

[0153]

[0166] Figure 3b illustrates an example of an encoding and decoding scheme 305 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. In some examples, the encoding and decoding scheme 305 may be implemented by or be implemented by an aspect of the wireless communication system 100 or wireless communication system 200. The encoding and decoding scheme 305 may include a UE115-e, which may be an example of the UE115 described herein. The encoding and decoding scheme 305 may also include a network entity 205-d, which may include all or some components of the base station 105 described herein.

[0154]

[0167] UE115-e can encode a past interference sequence 310-b representing the interference measured in UE115 using a generative model via encoder 240-b. In some examples, encoder 240-b can take the past interference sequence 310-b and generate a mean vector (m) and a covariance matrix (V) that represent the distribution z~N(m,V) of latent random vectors, where m and V are the encoder output (m,V)=q ψ (x) Here, q ψψ represents an encoder 240-b parameterized by ψ, where N refers to a Gaussian (e.g., normal) distribution. UE115-e may transmit the encoder output 320-b, along with the mean vector (m) and covariance matrix (V). Network entity 205-d may include decoder 230-b. Network entity 205-d may generate a random sample according to the distribution ~(,) and input z to decoder 230-b. The output 330-b of decoder 230-b is the predicted interference sample x ~ q θ (x│z) is possible. Here q θ This shows decoder 230-b parameterized by θ. Decoder 230-b receives a compressed interference distribution as input and decodes the compressed interference distribution according to the compression scheme. The output 330-b of decoder 230-b may be an interference sample sequence. The probability density function of the predicted interference sample x can be obtained as follows:

[0155]

number

[0156] This is achieved by sampling z multiple times.

[0157]

number

[0158] It can be approximated as follows.

[0159]

[0168] Figure 4 illustrates an example of an autoencoder 400 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The autoencoder 400 may be implemented in or in network entities 205-e and / or UE115, as described with reference to Figures 1 to 3.

[0160]

[0169] The autoencoder 400 includes an encoder 240-c and a decoder 230-c, each of which may contain multiple layers (440 and 430, respectively). Encoder 240-c may be implemented in UE115-f. Encoder 240-c may receive input 405 in the first layer 440-a, and encoder 240-c may compress the received input in each consecutive layer 440-b and 440-c. Input 405 may be an interference distribution. Each layer 440 of encoder 240-c may contain multiple nodes 445. Encoder 240-c may reduce the size of the interference distribution to a smaller representation in each layer 440 of the encoder. For example, each consecutive layer 440 of encoder 240-c may contain fewer nodes 445. Code 415 may be the output of encoder 240-c. Code 415 may be transmitted to decoder 230-c. Decoder 230-c may be implemented in network entity 205-d. For example, code 415 may be transmitted from UE 115, which may include encoder 240-c, to network entity 205-e, which may include decoder 230-c.

[0161]

[0170] Decoder 230-c may recover the original data or an estimate of the original data by receiving the code 415 in the first layer 430-c and passing the data through successive layers 430-c, 430-b, and 430-a. Each layer 430 of decoder 230-c may contain multiple nodes 435. Each successive layer 430 of decoder 230-c may contain more nodes. The output 410 of decoder 230-c may be the recovered data (e.g., interference distribution).

[0162]

[0171] The encoder 240-c and decoder 230-c can be trained to minimize errors and resource overhead in the compression and reconstruction of the interference distribution. For example, the code size may represent a trade-off between the overhead size and the interference distribution reconstruction error. The encoder 240-c and decoder 230-c can adjust the code size, the number of layers, the number of nodes per layer, and / or the loss function based on comparing the input 405 with the output 410. For example, the encoder 240-c and decoder 230-c can execute control routines periodically or aperiodicly so that the encoder 240-c and decoder 230-c can determine the error in reconstruction and adjust the parameters of the encoder 240-c and decoder 230-c (for example, the encoder 240-c may compress and send a known dataset to the decoder 230-c). In some examples, UE115 may periodically or aperiodically transmit uncompressed interference distribution data to network entity 205-e so that encoder 240-c and decoder 230-c can determine errors in reconstructing the corresponding compressed interference distribution data and adjust the parameters of encoder 240-c and decoder 230-c.

[0163]

[0172] Figure 5 illustrates an example of a machine learning process 500 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The machine learning process 500 may be implemented at the base station 105 (e.g., in a network entity), or at the UE 115, or both, as described with reference to Figures 1 to 3.

[0164]

[0173] The machine learning process 500 may include a machine learning algorithm 510. As illustrated, the machine learning algorithm 510 may be an example of an artificial neural network, such as a feedforward (FF) or deep feedforward (DFF) neural network, a recurrent neural network (RNN), a long-term / short-term memory (LSTM) neural network, or any other type of artificial neural network. However, any other machine learning algorithm may be supported. For example, the machine learning algorithm 510 may implement the nearest neighbor algorithm, the linear regression algorithm, the naive Bayes algorithm, the random forest algorithm, or any other machine learning algorithm. Furthermore, the machine learning process 500 may involve supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or any combination thereof.

[0165]

[0174] The machine learning algorithm 510 may include an input layer 515, one or more hidden layers 520, and an output layer 525. In a neural network fully connected to one hidden layer 520, each hidden layer node 535 may receive values ​​as input from each input layer node 530, where each input may be weighted. These neural network weights may be based on a cost function that is revised during training of the machine learning algorithm 510. Similarly, each output layer node 540 may receive values ​​as input from each hidden layer node 535, where the inputs are weighted. If post-deployment training (e.g., online training) is supported, memory may be allocated to store errors and / or gradients for reversing matrix multiplications. These errors and / or gradients may support updating the machine learning algorithm 510 based on the outputted feedback. Training the machine learning algorithm 510 can support the calculation of weights for mapping input patterns to desired output results (for example, connecting input layer nodes 530 to hidden layer nodes 535 and hidden layer nodes 535 to output layer nodes 540). This training may result in a device-specific machine learning algorithm 510 based on historical application data and data transfer for a particular base station 105 or UE 115.

[0166]

[0175] In some examples, the input value 505 may be sent to a machine learning algorithm 510 for processing. In some examples, preprocessing may be performed on the input value 505 according to a set of operations so that the input value 505 may be in a format compatible with the machine learning algorithm 510. The input value 505 may be transformed into a set of k input layer nodes 530 in the input layer 515. In some cases, different measurements may be input to different input layer nodes 530 of the input layer 515. If the number of input layer nodes 530 exceeds the number of inputs corresponding to the input value 505, some input layer nodes 530 may be assigned a default value (e.g., a value of 0). As illustrated, the input layer 515 may contain three input layer nodes 530-a, 530-b, and 530-c. However, it should be understood that the input layer 515 may contain any number of input layer nodes 530 (e.g., 20 input nodes).

[0167]

[0176] The machine learning algorithm 510 can transform the input layer 515 into a hidden layer 520 based on the number of input-hidden weights between the k input layer nodes 530 and the n hidden layer nodes 535. The machine learning algorithm 510 may include any number of hidden layers 520 as intermediate steps between the input layer 515 and the output layer 525. Additionally or alternatively, each hidden layer 520 may contain any number of nodes. For example, as illustrated, the hidden layer 520 may contain four hidden layer nodes 535-a, 535-b, 535-c, and 535-d. However, it should be understood that the hidden layer 520 may contain any number of hidden layer nodes 535 (e.g., 10 input nodes). In a fully connected neural network, each node in a layer may be based on each node in the previous layer. For example, the value of hidden layer node 535-a can be based on input layer nodes 530-a, 530-b, and 530-c (for example, with different weights applied to each node value).

[0168]

[0177] The machine learning algorithm 510 can determine a value for an output layer node 540 of an output layer 525 that follows one or more hidden layers 520. For example, the machine learning algorithm 510 can transform a hidden layer 520 into an output layer 525 based on the number of hidden-output weights between n hidden layer nodes 535 and m output layer nodes 540. In some cases, n=m. Each output layer node 540 may correspond to a different output value 545 of the machine learning algorithm 510. As illustrated, the machine learning algorithm 510 may include three output layer nodes 540-a, 540-b, and 540-c that support three different threshold values. However, it should be understood that the output layer 525 may include any number of output layer nodes 540. In some examples, post-processing may be performed on the output value 545 according to a sequence of operations so that the output value 545 can be in a format suitable for reporting the output value 545.

[0169]

[0178] As described herein, a fully connected neural network (NN) includes a series of fully connected layers that connect all neurons in one layer to all neurons in another layer. A batch-normalized NN includes a normalization step that fixes the mean and variance of each layer of input to the neural network. A dropout NN may ignore randomly selected nodes during training of the neural network. For example, at each training stage, individual nodes are either dropped from the net with probability 1-p or retained with probability p. A convolutional neural network (NN) is also called a shift-invariant or spatially invariant artificial neural network (SIANN). A convolutional neural network includes an input layer, a hidden layer, and an output layer. In a feedforward neural network, the hidden layers may be called hidden because their inputs and outputs are masked by the activation function and final convolution. In a convolutional neural network, the hidden layer includes a layer that performs convolutions. Typically, the hidden layer includes a layer that performs a dot product of the convolution kernel and the layer's input matrix. This product is usually a Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the layer's input matrix, the convolution operation generates a feature map, which contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers. ReLU NN has an argument f(x) = x + This includes the ReLU activation function, which is defined as the positive part of max(0,x), where x is the input to the neuron. Residual neural networks can jump between layers of the NN using skip connections and can be implemented using two- or three-layer skips, including nonlinearity (e.g., ReLus) and batch normalization between layers.

[0170]

[0179] Figure 6 illustrates an example of a process flow 600 supporting interference distribution compression and reconstruction according to one or more aspects of the present disclosure. In some examples, the process flow 600 may be implemented by an aspect of the wireless communication system 100 or the wireless communication system 200, or may implement aspects thereof. The process flow 600 may include a UE 115-g, which may be an example of the UE 115 described herein. The process flow 600 may also include a network entity 205-f, which may include all or some components of the base station 105 described herein. In the following description of the process flow 600, the operations between the network entity 205-f and the UE 115-g may be transmitted in an order different from the exemplary order shown, or the operations performed by the network entity 205-f and the UE 115-g may be performed in a different order or at different times. Some operations may also be omitted from the process flow 600, and other operations may be added to the process flow 600.

[0171]

[0180] In 605, UE115-g may transmit instructions to network entity 205-f regarding UE115-g's ability to encode interference information.

[0172]

[0181] In 610, network entity 205-f may send control signaling associated with interference information reporting to UE115-g. In some examples, the control signaling may indicate an encoding configuration for encoding interference information. In some examples, the control signaling may indicate an index associated with a selected encoding configuration, where each encoding configuration in the set of encoding configurations is associated with a respective index in the set of indices. In some examples, network entity 205-f may select an encoding configuration based on a capability message. In some examples, the encoding configuration may be an autoencoder or an artificial neural network.

[0173]

[0182] In some examples, the control signaling may indicate one or more parameters associated with the compression scheme used by the coding configuration. In some examples, one or more parameters may include the code size, the number of layers, the number of nodes per layer, the loss function, or a combination thereof. In some examples, the control signaling may indicate the input format for coding interference information in UE115-g.

[0174]

[0183] In some examples, control signaling may indicate interference measurement resources for measuring interference in UE115-g. In some examples, control signaling may indicate one or more parameters associated with measuring interference in UE115-g across the indicated interference measurement resources. In some examples, one or more parameters include frequency granularity, time granularity, spatial granularity, or a combination thereof.

[0175]

[0184] In 615, the UE115-g can measure interference in the UE across a set of interference measurement resources. In some cases, the UE115-g can measure interference plus noise in the UE across a set of interference measurement resources.

[0176]

[0185] In 620, the UE115-g may encode interference information representing the distribution of interference in the UE based on measured interference across a set of interference measurement resources, according to a compression scheme. In some examples, the UE115-g may encode interference information by generating a mean vector and covariance matrix of latent random variables representing the distribution of interference in the UE across a set of interference measurement resources, the set of interference measurement resources including two or more sets of time resources, frequency resources, or spatial resources. In some cases, the interference distribution may be the distribution of interference plus noise across the set of interference measurement resources.

[0177]

[0186] In 625, UE115-g may transmit interference information encoded according to a compression scheme to network entity 205-f. In 630, network entity 205-f may decode the interference information according to a compression scheme. In some examples, UE115-g may transmit interference information encoded according to a compression scheme in a CSF report. In 635, network entity 205-f may transmit scheduling information for communication in UE115-g based on the decoded interference information.

[0178]

[0187] In some examples, UE115-g may use an artificial neural network associated with a compression scheme to determine one or more model parameters associated with the compression scheme and send one or more model parameters to network entity 205-f. Network entity 205-f may determine one or more parameters associated with the compression scheme based on one or more model parameters and send them to UE115-g (for example, via a configuration message).

[0179]

[0188] In some examples, network entity 205-f may use an artificial neural network associated with the compression scheme to determine one or more model parameters associated with the compression scheme. Based on one or more model parameters, network entity 205-f may send one or more parameters associated with the compression scheme to UE115-g (for example, via a configuration message).

[0180]

[0189] Figure 7 shows a block diagram 700 of a device 705 that supports interference distribution compression and reconstruction according to one or more embodiments of the present disclosure. Device 705 may be an example of an embodiment of UE115 described herein. Device 705 may include a receiver 710, a transmitter 715, and a communications manager 720. Device 705 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0181]

[0190] The receiver 710 may provide means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to the compression and reconstruction of interference distributions). The information may be passed to other components of device 705. The receiver 710 may utilize a single antenna or a set of multiple antennas.

[0182]

[0191] The transmitter 715 may provide means for transmitting signals generated by other components of device 705. For example, the transmitter 715 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction), user data, control information, or any combination thereof. In some examples, the transmitter 715 may be placed alongside the receiver 710 in a transceiver module. The transmitter 715 may utilize a single antenna or a set of multiple antennas.

[0183]

[0192] The communication manager 720, receiver 710, transmitter 715, or various combinations thereof or various components thereof may be examples of means for performing various modes of compression and reconstruction of interference distribution as described herein. For example, the communication manager 720, receiver 710, transmitter 715, or various combinations thereof or components thereof may support a method for performing one or more of the functions described herein.

[0184]

[0193] In some examples, the communications manager 720, the receiver 710, the transmitter 715, or various combinations or components thereof may be implemented in hardware (for example, in a communications management circuit). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, individual gates or transistor logic, individual hardware components, or any combination thereof that constitutes or otherwise supports means for performing the functions described herein. In some examples, a processor and memory coupled to the processor may be configured to perform one or more of the functions described herein (for example, by the processor executing instructions stored in memory).

[0185]

[0194] In addition or alternatively, in some embodiments, the communications manager 720, receiver 710, transmitter 715, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communications management software or firmware). When implemented in code executed by a processor, the functions of the communications manager 720, receiver 710, transmitter 715, or various combinations or components thereof may be performed by any combination of a general-purpose processor, DSP, central processing unit (CPU), ASIC, FPGA, or any other programmable logic device (e.g., configured as a means for performing the functions described in this disclosure, or otherwise supporting such means).

[0186]

[0195] In some examples, the communications manager 720 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 710, the transmitter 715, or both. For example, the communications manager 720 may receive information from the receiver 710, transmit information to the transmitter 715, or be integrated with the receiver 710, the transmitter 715, or both to receive information, transmit information, or perform various other operations as described herein.

[0187]

[0196] The communication manager 720 may support wireless communication in the UE according to embodiments disclosed herein. For example, the communication manager 720 may be configured as a means for measuring interference in the UE across a set of interference measurement resources, or may otherwise support it. The communication manager 720 may be configured as a means for encoding interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, or may otherwise support it, according to a compression scheme. The communication manager 720 may be configured as a means for transmitting the interference information encoded according to the compression scheme to a first network entity, or may otherwise support it.

[0188]

[0197] By including or configuring the communications manager 720 in accordance with the examples described herein, the device 705 (e.g., a processor controlling a receiver 710, a transmitter 715, the communications manager 720, or a combination thereof, or otherwise coupled thereto) may support techniques for more efficient use of communications resources by providing dynamic measurement and reporting of interference information in the UE.

[0189]

[0198] Figure 8 shows a block diagram 800 of a device 805 that supports interference distribution compression and reconstruction according to one or more embodiments of the present disclosure. Device 805 may be an example of an embodiment of device 705 or UE115 described herein. Device 805 may include a receiver 810, a transmitter 815, and a communications manager 820. Device 805 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0190]

[0199] Receiver 810 may provide means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction). The information may be passed to other components of device 805. Receiver 810 may utilize a single antenna or a set of multiple antennas.

[0191]

[0200] Transmitter 815 may provide means for transmitting signals generated by other components of device 805. For example, transmitter 815 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction), user data, control information, or any combination thereof. In some examples, transmitter 815 may be placed juxtaposed with receiver 810 within a transceiver module. Transmitter 815 may utilize a single antenna or a set of antennas.

[0192]

[0201] Device 805, or various components thereof, may be examples of means for performing various forms of interference distribution compression and reconstruction as described herein. For example, the communications manager 820 may include an interference measurement manager 825, a compression manager 830, an interference reporting manager 835, or any combination thereof. The communications manager 820 may be an example of a form of communications manager 720 as described herein. In some examples, the communications manager 820 or various components thereof may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 810, the transmitter 815, or both. For example, the communications manager 820 may receive information from the receiver 810, transmit information to the transmitter 815, or be integrated with the receiver 810, the transmitter 815, or both to receive information, transmit information, or perform various other operations as described herein.

[0193]

[0202] The communication manager 820 may support wireless communication in the UE according to embodiments disclosed herein. The interference measurement manager 825 may be configured as a means for measuring interference in the UE across a set of interference measurement resources, or may otherwise support it. The compression manager 830 may be configured as a means for encoding interference information representing the distribution of interference in the UE based on measured interference across a set of interference measurement resources, or may otherwise support it, according to a compression scheme. The interference reporting manager 835 may be configured as a means for transmitting the interference information encoded according to the compression scheme to a first network entity, or may otherwise support it.

[0194]

[0203] Figure 9 shows a block diagram 900 of a communications manager 920 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. Communications manager 920 may be an example of an aspect of communications manager 720, communications manager 820, or both thereof as described herein. Communications manager 920 or its various components may be an example of means for performing various aspects of interference distribution compression and reconstruction as described herein. For example, communications manager 920 may include an interference measurement manager 925, a compression manager 930, an interference reporting manager 935, a coding configuration manager 940, a CSF manager 945, a neural network manager 950, a trained parameter manager 955, a UE coding capability manager 960, or any combination thereof. Each of these components may communicate with one another directly or indirectly (e.g., via one or more buses).

[0195]

[0204] The communication manager 920 may support wireless communication in the UE according to embodiments disclosed herein. The interference measurement manager 925 may be configured as a means for measuring interference in the UE across a set of interference measurement resources, or may otherwise support it. The compression manager 930 may be configured as a means for encoding interference information representing the distribution of interference in the UE based on measured interference across a set of interference measurement resources, or may otherwise support it, according to a compression scheme. The interference reporting manager 935 may be configured as a means for transmitting the interference information encoded according to the compression scheme to a first network entity, or may otherwise support it.

[0196]

[0205] In some cases, interference in a UE can be interference plus noise, and the distribution of interference can be the distribution of interference plus noise across a set of interference measurement resources.

[0197]

[0206] In some examples, the interference distribution in a UE involves a probability mass function over a set of resources in time, frequency, and / or space.

[0198]

[0207] In some examples, a set of resources in time, frequency, and / or space includes a set of interference measurement resources.

[0199]

[0208] In some examples, the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference in the UE is based on the measured interference across the set of interference measurement resources.

[0200]

[0209] In some examples, the set of resources in time, frequency, and / or space includes resources that occur later than the set of interference measurement resources, and the distribution of interference in the UE is predicted based at least partially on the measured interference across the set of interference measurement resources.

[0201]

[0210] In some examples, to encode interference information, the compression manager 930 may be configured, or otherwise support, a means for generating a compressed, estimated, or predicted interference distribution across a set of interference measurement resources.

[0202]

[0211] In some examples, the compression method includes codeword-based compression or artificial neural network-based compression.

[0203]

[0212] In some examples, to encode interference information, the compression manager 930 may be configured as a means for generating mean vectors and covariance matrices of latent random variables representing the distribution of interference in the UE across a set of interference measurement resources, or may support it otherwise, and the set of interference measurement resources may include two or more sets of time, frequency, or spatial resources.

[0204]

[0213] In some examples, the coding configuration manager 940 may be configured as a means for receiving coding configuration instructions for coding interference information from a first network entity or one or more second network entities associated with the first network entity, or may otherwise support this.

[0205]

[0214] In some examples, the UE coding capability manager 960 may be configured, or otherwise support, for transmitting instructions to a first network entity or one or more second network entities associated with the first network entity regarding the UE's ability to encode interference information. In some examples, the coding configuration manager 940 may be configured, or otherwise support, for receiving instructions to an index associated with a coding configuration in response to transmitting instructions from the first network entity or one or more second network entities associated with the first network entity regarding the UE's ability to encode interference information, and a set of coding configurations, including coding configurations, is associated with a set of indices, including indices.

[0206]

[0215] In some examples, the coding configuration includes a configuration for either an autoencoder or an artificial neural network.

[0207]

[0216] In some examples, the coding configuration manager 940 may be configured as a means for selecting one coding configuration from a set of coding configurations, where each coding configuration in the set of coding configurations is associated with a respective index in a set of indices, or may otherwise support this. In some examples, the coding configuration manager 940 may be configured as a means for transmitting instructions for one index in a set of indices associated with the selected coding configuration from a first network entity or one or more second network entities associated with the first network entity, or may otherwise support this.

[0208]

[0217] In some examples, the encoding configuration manager 940 may be configured as a means for receiving instructions for one or more parameters associated with a compression scheme from a first network entity or one or more second network entities associated with the first network entity, or may otherwise support this.

[0209]

[0218] In some examples, one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

[0210]

[0219] In some examples, the interference measurement manager 925 may be configured as a means for receiving instructions for a set of interference measurement resources from a first network entity or one or more second network entities associated with the first network entity, or may otherwise support this.

[0211]

[0220] In some examples, the interference measurement manager 925 may be configured to receive, or otherwise support, instructions for one or more parameters associated with measuring interference in the UE across a set of interference measurement resources from a first network entity or one or more second network entities associated with the first network entity.

[0212]

[0221] In some examples, one or more parameters associated with measuring interference in a UE across a set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof.

[0213]

[0222] In some examples, the interference reporting manager 935 may be configured to receive, or otherwise support, instructions for an input format for encoding interference information representing the distribution of interference in the UE from a first network entity or one or more second network entities associated with the first network entity.

[0214]

[0223] In some examples, the CSF manager 945 may be configured as a means for transmitting channel state feedback reports containing encoded interference information, encoded according to a compression scheme, or may support it otherwise.

[0215]

[0224] In some examples, the neural network manager 950 may be configured, or otherwise support, for determining one or more model parameters associated with a compression scheme using an artificial neural network associated with the compression scheme. In some examples, the trained parameter manager 955 may be configured, or otherwise support, for sending one or more model parameters to a first network entity or one or more second network entities associated with the first network entity.

[0216]

[0225] In some examples, the coding configuration manager 940 may be configured, or otherwise support, means for receiving one or more parameters associated with a compression scheme based on the transmission of one or more model parameters from a first network entity or one or more second network entities associated with the first network entity.

[0217]

[0226] Figure 10 shows a diagram of a system 1000 including a device 1005 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. Device 1005 may be an example of, or include, a component of, device 705, device 805, or UE 115 as described herein. Device 1005 may communicate wirelessly with one or more network entities 205, UE 115, or any combination thereof. Device 1005 may include components for bidirectional voice and data communication, including components for transmitting and receiving communications, such as a communications manager 1020, an input / output (I / O) controller 1010, a transceiver 1015, an antenna 1025, a memory 1030, a code 1035, and a processor 1040. These components may communicate electronically or otherwise (e.g., operably, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 1045).

[0218]

[0227] The I / O controller 1010 can manage input and output signals for device 1005. The I / O controller 1010 can also manage peripheral devices not integrated with device 1005. In some cases, the I / O controller 1010 may represent physical connections or ports to external peripheral devices. In some cases, the I / O controller 1010 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or another known operating system. Additionally or alternatively, the I / O controller 1010 may represent or interact with a modem, keyboard, mouse, touchscreen, or similar device. In some cases, the I / O controller 1010 may be implemented as part of a processor, such as processor 1040. In some cases, the user may interact with device 1005 via the I / O controller 1010 or via hardware components controlled by the I / O controller 1010.

[0219]

[0228] In some cases, device 1005 may include a single antenna 1025. However, in some other cases, device 1005 may have two or more antennas 1025, and two or more antennas may be capable of simultaneously transmitting or receiving multiple wireless transmissions. Transceiver 1015 may communicate bidirectionally via one or more antennas 1025, a wired link, or a wireless link, as described herein. For example, transceiver 1015 may represent a wireless transceiver and communicate bidirectionally with another wireless transceiver. Transceiver 1015 may also include a modem for modulating packets and providing the modulated packets to one or more antennas 1025 for transmission, and for demodulating packets received from one or more antennas 1025. Transceiver 1015, or transceiver 1015 and one or more antennas 1025, may be examples of transmitters 715, 815, 710, 810, or any combination thereof or components thereof, as described herein.

[0220]

[0229] Memory 1030 may include random access memory (RAM) and read-only memory (ROM). Memory 1030 may store computer-readable computer-executable code 1035, which, when executed by processor 1040, causes device 1005 to perform various functions described herein. Code 1035 may be stored in a non-temporary computer-readable medium, such as system memory or another type of memory. In some cases, code 1035 may not be directly executable by processor 1040, but may cause the computer to perform the functions described herein (for example, when compiled and executed). In some cases, memory 1030 may include a basic I / O system (BIOS) that can control basic hardware or software operations, such as interactions with peripheral components or peripheral devices.

[0221]

[0230] The processor 1040 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, the processor 1040 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated with the processor 1040. The processor 1040 may be configured to execute computer-readable instructions stored in memory (e.g., memory 1030) to cause device 1005 to perform various functions (e.g., functions or tasks supporting interference distribution compression and reconstruction). For example, device 1005 or a component of device 1005 may include the processor 1040 and memory 1030 coupled to or connected to the processor 1040, and the processor 1040 and memory 1030 are configured to perform various functions described herein.

[0222]

[0231] The communication manager 1020 may support wireless communication in the UE according to embodiments disclosed herein. For example, the communication manager 1020 may be configured as a means for measuring interference in the UE across a set of interference measurement resources, or may otherwise support it. The communication manager 1020 may be configured as a means for encoding interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, or may otherwise support it, according to a compression scheme. The communication manager 1020 may be configured as a means for transmitting the interference information encoded according to the compression scheme to a first network entity, or may otherwise support it.

[0223]

[0232] By including or configuring the communications manager 1020 in accordance with the examples described herein, device 1005 may support techniques for improved communications reliability, more efficient use of communications resources, and improved inter-device coordination by providing dynamic measurement and reporting of interference information in the UE and scheduling communications that take interference information in the UE into account.

[0224]

[0233] In some examples, the communications manager 1020 may be configured to use, or otherwise cooperate with, the transceiver 1015, one or more antennas 1025, or any combination thereof, to perform various operations (e.g., receiving, monitoring, transmitting). Although the communications manager 1020 is shown as a separate component, in some examples, one or more functions described with reference to the communications manager 1020 may be supported or performed by the processor 1040, memory 1030, code 1035, or any combination thereof. For example, code 1035 may include instructions executable by the processor 1040 to cause device 1005 to perform various aspects of interference distribution compression and reconstruction described herein, or the processor 1040 and memory 1030 may otherwise be configured to perform or support such operations.

[0225]

[0234] Figure 11 shows a block diagram 1100 of a device 1105 that supports interference distribution compression and reconstruction according to one or more embodiments of the present disclosure. Device 1105 may be an example of a base station 105 or a network entity as described herein. Device 1105 may include a receiver 1110, a transmitter 1115, and a communications manager 1120. Device 1105 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0226]

[0235] Receiver 1110 may provide means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction). The information may be passed to other components of device 1105. Receiver 1110 may utilize a single antenna or a set of multiple antennas.

[0227]

[0236] The transmitter 1115 may provide means for transmitting signals generated by other components of device 1105. For example, the transmitter 1115 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction), user data, control information, or any combination thereof. In some examples, the transmitter 1115 may be placed alongside the receiver 1110 in a transceiver module. The transmitter 1115 may utilize a single antenna or a set of antennas.

[0228]

[0237] The communication manager 1120, receiver 1110, transmitter 1115, or various combinations thereof or various components thereof may be examples of means for performing various modes of compression and reconstruction of interference distribution as described herein. For example, the communication manager 1120, receiver 1110, transmitter 1115, or various combinations thereof or components thereof may support a method for performing one or more of the functions described herein.

[0229]

[0238] In some examples, the communications manager 1120, the receiver 1110, the transmitter 1115, or various combinations or components thereof may be implemented in hardware (for example, in a communications management circuit). The hardware may include a processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gates or transistor logic, discrete hardware components, or any combination thereof that constitutes or otherwise supports means for performing the functions described herein. In some examples, a processor and memory coupled to the processor may be configured to perform one or more of the functions described herein (for example, by the processor executing instructions stored in memory).

[0230]

[0239] In addition or alternatively, in some embodiments, the communications manager 1120, receiver 1110, transmitter 1115, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communications management software or firmware). When implemented in code executed by a processor, the functions of the communications manager 1120, receiver 1110, transmitter 1115, or various combinations or components thereof may be implemented by any combination of a general-purpose processor, DSP, CPU, ASIC, FPGA, or any other programmable logic device (e.g., configured as a means for performing the functions described herein, or otherwise supporting such means).

[0231]

[0240] In some examples, the communications manager 1120 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 1110, the transmitter 1115, or both. For example, the communications manager 1120 may receive information from the receiver 1110, transmit information to the transmitter 1115, or be integrated with the receiver 1110, the transmitter 1115, or both to receive information, transmit information, or perform various other operations as described herein.

[0232]

[0241] The communication manager 1120 may support wireless communication at a base station in accordance with the examples disclosed herein. For example, the communication manager 1120 may be configured as a means for obtaining encoded interference information representing the distribution of interference, or may otherwise support it. The communication manager 1120 may be configured as a means for decoding encoded interference information according to a compression scheme, or may otherwise support it, in order to output the decoded interference information.

[0233]

[0242] By including or configuring the communications manager 1120 in accordance with the examples described herein, the device 1105 (e.g., a processor controlling the receiver 1110, transmitter 1115, communications manager 1120, or a combination thereof, or otherwise coupled thereto) can support techniques for more efficient use of communications resources by providing dynamic measurement and reporting of interference information in the UE.

[0234]

[0243] Figure 12 shows a block diagram 1200 of a device 1205 that supports interference distribution compression and reconstruction according to one or more embodiments of the present disclosure. Device 1205 may be an example of an embodiment of device 1105, base station 105, or network entity as described herein. Device 1205 may include a receiver 1210, a transmitter 1215, and a communications manager 1220. Device 1205 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0235]

[0244] Receiver 1210 may provide means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction). The information may be passed to other components of device 1205. Receiver 1210 may utilize a single antenna or a set of multiple antennas.

[0236]

[0245] The transmitter 1215 may provide means for transmitting signals generated by other components of device 1205. For example, the transmitter 1215 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to interference distribution compression and reconstruction), user data, control information, or any combination thereof. In some examples, the transmitter 1215 may be placed alongside the receiver 1210 in a transceiver module. The transmitter 1215 may utilize a single antenna or a set of multiple antennas.

[0237]

[0246] Device 1205, or various components thereof, may be examples of means for performing various forms of interference distribution compression and reconstruction as described herein. For example, the communications manager 1220 may include an interference reporting manager 1230, an interference reporting decoding manager 1225, or any combination thereof. The communications manager 1220 may be an example of a form of communications manager 1120 as described herein. In some examples, the communications manager 1220, or various components thereof, may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the receiver 1210, the transmitter 1215, or both. For example, the communications manager 1220 may receive information from the receiver 1210 and transmit information to the transmitter 1215, or may be integrated with the receiver 1210, the transmitter 1215, or both, to receive information, transmit information, or perform various other operations as described herein.

[0238]

[0247] The communication manager 1220 may support wireless communication at a base station in accordance with the examples disclosed herein. The interference reporting manager 1225 may be configured as a means for obtaining encoded interference information representing the distribution of interference, or may otherwise support it. The interference reporting decoding manager 1230 may be configured as a means for decoding encoded interference information according to a compression scheme, or may otherwise support it, in order to output the decoded interference information.

[0239]

[0248] Figure 13 shows a block diagram 1300 of a communications manager 1320 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. Communications manager 1320 may be an example of communications manager 1120, communications manager 1220, or both, as described herein. Communications manager 1320 or its various components may be an example of means for performing various aspects of interference distribution compression and reconstruction as described herein. For example, communications manager 1320 may include interference reporting manager 1325, interference reporting decoding 1330, scheduling manager 1335, coding configuration manager 1340, interference measurement configuration manager 1345, CSF manager 1350, neural network manager 1355, trained parameter manager 1360, UE coding capability manager 1365, or any combination thereof. Each of these components may communicate with one another directly or indirectly (e.g., via one or more buses).

[0240]

[0249] The communication manager 1320 may support wireless communication at a base station in accordance with the examples disclosed herein. The interference reporting manager 1325 may be configured as a means for obtaining encoded interference information representing the distribution of interference across a set of interference measurement resources, or may otherwise support it. The interference reporting decoding manager 1330 may be configured as a means for decoding encoded interference information according to a compression scheme, or may otherwise support it, in order to output the decoded interference information.

[0241]

[0250] In some cases, the interference distribution may be an interference-plus-noise distribution across a set of interference measurement resources.

[0242]

[0251] In some examples, the scheduling manager 1335 may be configured as a means for outputting scheduling information for communications at the UE based on the decoded interference information, or may support it in other ways.

[0243]

[0252] In some examples, the encoded interference information includes a mean vector and covariance matrix of latent random variables representing the distribution of interference in the UE across a set of interference measurement resources. The interference reporting decoding manager 1330 may be configured, or otherwise support, for generating samples based on the mean vector and covariance matrix, and for decoding the encoded interference information based at least partially on the samples.

[0244]

[0253] In some examples, the coding configuration manager 1340 may be configured as a means for outputting instructions for coding a coding configuration for coding interference information in the UE, or may support it otherwise.

[0245]

[0254] In some examples, the UE coding capability manager 1365 may be configured as a means for obtaining instructions on the UE's ability to code interference information, or may otherwise support it. In some examples, the coding configuration manager 1340 may be configured as a means for outputting instructions on indices associated with coding configurations based on instructions on the UE's ability to code interference information, or may otherwise support it, and a set of coding configurations containing coding configurations is associated with a set of indices containing indices.

[0246]

[0255] In some examples, the coding configuration includes a configuration for either an autoencoder or an artificial neural network.

[0247]

[0256] In some examples, the coding configuration manager 1340 may be configured as a means for obtaining an instruction for one of the indexes in a set of indices associated with a selected coding configuration, or may support it otherwise, such that each coding configuration in the set of coding configurations is associated with the respective index in the set of indices.

[0248]

[0257] In some examples, the encoding configuration manager 1340 may be configured as a means for outputting instructions for one or more parameters associated with a compression scheme, or may support this otherwise.

[0249]

[0258] In some examples, one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

[0250]

[0259] In some examples, the interference measurement configuration manager 1345 may be configured, or otherwise support, for outputting instructions for one or more parameters associated with measuring interference across a set of interference measurement resources.

[0251]

[0260] In some examples, one or more parameters associated with measuring interference across a set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof.

[0252]

[0261] In some examples, the coding configuration manager 1340 may be configured as a means for outputting instructions for an input format for coding interference information representing the distribution of interference, or may support it otherwise.

[0253]

[0262] In some examples, the CSF manager 1350 may be configured as a means for obtaining a channel state feedback report containing encoded interference information, encoded according to a compression scheme, or may support it in other ways.

[0254]

[0263] In some examples, the neural network manager 1355 may be configured, or otherwise support, for determining one or more model parameters associated with a compression scheme using an artificial neural network associated with the compression scheme. In some examples, the coding configuration manager 1340 may be configured, or otherwise support, for outputting one or more parameters associated with a compression scheme based on one or more model parameters.

[0255]

[0264] In some examples, the trained parameter manager 1360 may be configured as a means for obtaining one or more model parameters associated with a compression scheme, or may support this otherwise. In some examples, the coding configuration manager 1340 may be configured as a means for outputting one or more parameters associated with a compression scheme based on one or more model parameters, or may support this otherwise.

[0256]

[0265] Figure 14 shows a diagram of a system 1400 including a device 1405 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. Device 1405 may be an example of, or include, a component of, device 1105, device 1205, network entity, or base station 105 as described herein. Device 1405 may communicate wirelessly with one or more base stations 105, UE 115, or any combination thereof. Device 1405 may include components for bidirectional voice and data communication, including components for transmitting and receiving communications, such as a communications manager 1420, a network communications manager 1410, a transceiver 1415, an antenna 1425, a memory 1430, a code 1435, a processor 1440, and an inter-station communications manager 1445. These components may communicate electronically or otherwise (e.g., operably, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 1450).

[0257]

[0266] The network communication manager 1410 may manage communication with the core network 130 (for example, via one or more wired backhaul links). For example, the network communication manager 1410 may manage the transfer of data communications for one or more client devices such as UE 115.

[0258]

[0267] In some cases, device 1405 may include a single antenna 1425. However, in some other cases, device 1405 may have two or more antennas 1425, and two or more antennas may be capable of simultaneously transmitting or receiving multiple wireless transmissions. Transceiver 1415 may communicate bidirectionally via one or more antennas 1425, a wired link, or a wireless link, as described herein. For example, transceiver 1415 may represent a wireless transceiver and communicate bidirectionally with another wireless transceiver. Transceiver 1415 may also include a modem for modulating packets and providing the modulated packets to one or more antennas 1425 for transmission, and for demodulating packets received from one or more antennas 1425. Transceiver 1415, or transceiver 1415 and one or more antennas 1425, may be examples of transmitters 1115, transmitters 1215, receivers 1110, receivers 1210, or any combination thereof or components thereof, as described herein.

[0259]

[0268] Memory 1430 may include RAM and ROM. Memory 1430 may store computer-readable computer-executable code 1435, which, when executed by processor 1440, causes device 1405 to perform various functions described herein. Code 1435 may be stored in a non-temporary computer-readable medium, such as system memory or another type of memory. In some cases, code 1435 may not be directly executable by processor 1440, but may cause the computer to perform the functions described herein (for example, when compiled and executed). In some cases, memory 1430 may include a BIOS that can control basic hardware or software operations, such as interactions with peripheral components or peripheral devices.

[0260]

[0269] The processor 1440 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, the processor 1440 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated with the processor 1440. The processor 1440 may be configured to execute computer-readable instructions stored in memory (e.g., memory 1430) to cause device 1405 to perform various functions (e.g., functions or tasks supporting interference distribution compression and reconstruction). For example, device 1405 or a component of device 1405 may include the processor 1440 and memory 1430 coupled to or connected to the processor 1440, and the processor 1440 and memory 1430 are configured to perform various functions described herein.

[0261]

[0270] The inter-station communication manager 1445 may manage communication with other base stations 105 and may include a controller or scheduler for coordinating communication with the UE 115 in cooperation with other base stations 105. For example, the inter-station communication manager 1445 may coordinate scheduling for transmissions to the UE 115 for various interference mitigation techniques such as beamforming or joint transmission. In some examples, the inter-station communication manager 1445 may provide an X2 interface within the LTE / LTE-A wireless communication network technology for communication between base stations 105.

[0262]

[0271] The communication manager 1420 may support wireless communication at a base station in accordance with the examples disclosed herein. For example, the communication manager 1420 may be configured as a means for receiving, or otherwise supporting, encoded interference information from the UE representing the distribution of measured interference at the UE across a set of interference measurement resources. The communication manager 1420 may be configured as a means for decoding the encoded interference information according to a compression scheme in order to output the decoded interference information.

[0263]

[0272] By including or configuring the communications manager 1420 in accordance with the examples described herein, the device 1405 may support techniques for improved communications reliability, more efficient use of communications resources, and improved inter-device coordination by providing dynamic measurement and reporting of interference information in the UE and scheduling communications that take interference information in the UE into account.

[0264]

[0273] In some examples, the communications manager 1420 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise cooperating with the transceiver 1415, one or more antennas 1425, or any combination thereof. Although the communications manager 1420 is shown as a separate component, in some examples, one or more functions described with reference to the communications manager 1420 may be supported or performed by the processor 1440, memory 1430, code 1435, or any combination thereof. For example, code 1435 may include instructions executable by the processor 1440 for causing device 1405 to perform various aspects of interference distribution compression and reconstruction described herein, or the processor 1440 and memory 1430 may be configured otherwise to perform or support such operations.

[0265]

[0274] Figure 15 shows a flowchart illustrating a method 1500 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The operation of method 1500 may be implemented by a UE or its components as described herein. For example, the operation of method 1500 may be performed by UE 115 as described with reference to Figures 1 to 10. In some examples, the UE may execute a set of instructions to control a functional element of the UE to perform the described function. Additionally or alternatively, the UE may perform aspects of the described function using dedicated hardware.

[0266]

[0275] In 1505, the method may include measuring interference in the UE across a set of interference measurement resources. Operation of 1505 may be carried out according to the examples disclosed herein. In some examples, the mode of operation of 1505 may be performed by the interference measurement manager 925, as described with reference to Figure 9.

[0267]

[0276] At 1510, the method may include encoding interference information representing a distribution of interference at a UE based at least in part on measured interference over a set of interference measurement resources in accordance with a compression scheme. The operations of 1510 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1510 may be performed by a compression manager 930 as described with reference to FIG. 9.

[0268]

[0277] At 1515, the method may include transmitting the interference information encoded in accordance with the compression scheme to a first network entity. The operations of 1515 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1515 may be performed by an interference report manager 935 as described with reference to FIG. 9.

[0269]

[0278] FIG. 16 shows a flowchart illustrating a method 1600 that supports interference distribution compression and reconstruction in accordance with one or more aspects of the present disclosure. The operations of method 1600 may be implemented by a UE or a component thereof as described herein. For example, the operations of method 1600 may be performed by a UE 115 as described with reference to FIGS. 1 through 10. In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may perform aspects of the described functions using special-purpose hardware.

[0270]

[0279] At 1605, the method may include receiving, from a first network entity, an indication of an encoding configuration configured to encode interference information. The operations of 1605 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1605 may be performed by an encoding configuration manager 940 as described with reference to FIG. 9.

[0271]

[0280] At 1610, the method may include measuring interference at a UE across a set of interference measurement resources. The operations of 1610 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1610 may be performed by an interference measurement manager 925, as described with reference to Figure 9.

[0272]

[0281] At 1615, the method may include encoding interference information representative of an interference distribution at the UE based at least in part on the measured interference across the set of interference measurement resources in accordance with a compression scheme. The operations of 1615 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1615 may be performed by a compression manager 930, as described with reference to Figure 9.

[0273]

[0282] At 1620, the method may include transmitting the interference information encoded in accordance with the compression scheme to a first network entity, or one or more second network entities associated with the first network entity. The operations of 1620 may be performed in accordance with examples disclosed herein. In some examples, aspects of the operations of 1620 may be performed by an interference report manager 935, as described with reference to Figure 9.

[0274]

[0283] Figure 17 shows a flowchart illustrating a method 1700 that supports compression and reconstruction of interference distribution in accordance with one or more aspects of the present disclosure. The operations of method 1700 may be implemented by a UE or a component thereof as described herein. For example, the operations of method 1700 may be performed by a UE 115 as described with reference to Figures 1 through 10. In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may perform aspects of the described functions using dedicated hardware.

[0275]

[0284] In 1705, the method may include measuring interference in the UE across a set of interference measurement resources. Operation of 1705 may be carried out according to the examples disclosed herein. In some examples, aspects of operation of 1705 may be performed by the interference measurement manager 925, as described with reference to Figure 9.

[0276]

[0285] In 1710, the method may include encoding interference information representing the distribution of interference in the UE, at least in part, based on measured interference across a set of interference measurement resources, according to a compression scheme. Operation of 1710 may be carried out according to the examples disclosed herein. In some examples, aspects of operation of 1710 may be carried out by a compression manager 930, as described with reference to Figure 9.

[0277]

[0286] In 1715, the method may include transmitting interference information encoded according to a compression scheme to a first network entity. The operation of 1715 may be carried out according to the examples disclosed herein. In some examples, the operation of 1715 may be carried out by an interference reporting manager 935, as described with reference to Figure 9.

[0278]

[0287] In 1720, the method may include determining one or more model parameters associated with the compression scheme using an artificial neural network associated with the compression scheme. The operation of 1720 may be carried out according to the examples disclosed herein. In some examples, aspects of the operation of 1720 may be carried out by a neural network manager 950, as described with reference to Figure 9.

[0279]

[0288] In 1725, the method may include transmitting one or more model parameters to a first network entity or one or more second network entities associated with the first network entity. The operation of 1725 may be carried out according to the examples disclosed herein. In some examples, the operation of 1725 may be carried out by a learned parameter manager 955 as described with reference to Figure 9.

[0280]

[0289] Figure 18 shows a flowchart illustrating a method 1800 supporting interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The operation of method 1800 may be implemented by a base station or its components (e.g., a network entity) as described herein. For example, the operation of method 1800 may be performed by a base station 105 or a network entity as described with reference to Figures 1-6 and 11-14. In some examples, the base station or network entity may execute a set of instructions to control functional elements of the base station or network entity to perform the functions described. Additionally or alternatively, the base station or network entity may perform aspects of the functions described using dedicated hardware.

[0281]

[0290] In 1805, the method may include obtaining encoded interference information representing the distribution of interference. Operation of 1805 may be carried out according to the examples disclosed herein. In some examples, aspects of operation of 1805 may be performed by the interference reporting manager 1325, as described with reference to Figure 13.

[0282]

[0291] In 1810, the method may include decoding the encoded interference information according to a compression scheme in order to output the decoded interference information. The operation of 1810 may be carried out according to the examples disclosed herein. In some examples, the operation of 1810 may be carried out by the interference reporting decoding manager 1330, as described with reference to Figure 13.

[0283]

[0292] Figure 19 shows a flowchart illustrating a method 1900 supporting interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The operation of method 1900 may be implemented by a base station or its components (e.g., a network entity) as described herein. For example, the operation of method 1900 may be performed by a base station 105 or a network entity as described with reference to Figures 1 to 6 and Figures 11 to 14. In some examples, the base station or network entity may execute a set of instructions to control functional elements of the base station or network entity to perform the functions described. Additionally or alternatively, the base station or network entity may perform aspects of the functions described using dedicated hardware.

[0284]

[0293] In 1905, the method may include obtaining encoded interference information representing the distribution of interference. The operation of 1905 may be carried out according to the examples disclosed herein. In some examples, the operation of 1905 may be carried out by the interference reporting manager 1325, as described with reference to Figure 13.

[0285]

[0294] In 1910, the method may include decoding the encoded interference information according to a compression scheme in order to output the decoded interference information. The operation of 1910 may be carried out according to the examples disclosed herein. In some examples, the operation of 1910 may be carried out by the interference reporting decoding manager 1330, as described with reference to Figure 13.

[0286]

[0295] In 1915, the method may include outputting scheduling information for communications at the UE based on the decoded interference information. Operation of 1915 may be carried out according to the examples disclosed herein. In some examples, the operation of 1915 may be carried out by the scheduling manager 1335, as described with reference to Figure 13.

[0287]

[0296] Figure 20 shows a flowchart illustrating a method 2000 that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The operation of method 2000 may be implemented by a base station or its components (e.g., a network entity) as described herein. For example, the operation of method 2000 may be performed by a base station 105 or a network entity as described with reference to Figures 1 to 6 and Figures 11 to 14. In some examples, the base station or network entity may execute a set of instructions to control functional elements of the base station or network entity to perform the functions described. Additionally or alternatively, the base station or network entity may perform aspects of the functions described using dedicated hardware.

[0288]

[0297] In 2005, the method may include outputting instructions for an encoding configuration to encode interference information in the UE. The operation of 2005 may be carried out according to the examples disclosed herein. In some examples, the operation of 2005 may be carried out by the encoding configuration manager 1340, as described with reference to Figure 13.

[0289]

[0298] In 2010, the method may include obtaining encoded interference information encoded according to a compression scheme, where the interference information represents the distribution of interference across a set of resources. The operation of 2010 may be carried out according to the examples disclosed herein. In some examples, the operation of 2010 may be carried out by the interference reporting manager 1325, as described with reference to Figure 13.

[0290]

[0299] In 2015, the method may include decoding the encoded interference information according to a compression scheme in order to output the decoded interference information. The operation of 2015 may be carried out according to the examples disclosed herein. In some examples, the operation of 2015 may be carried out by the interference reporting decoding manager 1330, as described with reference to Figure 13.

[0291]

[0300] Figure 21 illustrates an example of a network architecture 2100 (e.g., a non-aggregated base station architecture, a non-aggregated RAN architecture) that supports interference distribution compression and reconstruction according to one or more aspects of the present disclosure. The network architecture 2100 may illustrate an example for implementing one or more aspects of a wireless communication system 100. The network architecture 2100 may include one or more CU160-a that can communicate directly with the core network 130-a via a backhaul communication link 120-a, or indirectly with the core network 130-a via one or more non-aggregated network entities (e.g., a quasi-RT RIC175-b via an E2 link, or a non-RT RIC175-a associated with an SMO180-a (SMO framework), or both). The CU160-a may communicate with one or more DU165-a via their respective midhaul communication links 162-a (e.g., an F1 interface). A DU165-a may communicate with one or more RU170-a via their respective fronthaul communication links 168-a. A RU170-a may be associated with a respective coverage area 110-c and may communicate with a UE115-h via one or more communication links 125-a. In some implementations, a UE115-h may be serviced simultaneously by multiple RU170-a.

[0292]

[0301] Each of the network entities of the network architecture 2100 (e.g., CU160-a, DU165-a, RU170-a, non-RT RIC175-a, quasi-RT RIC175-b, SMO180-a, open cloud (O-cloud) 2105, open eNB (O-eNBs) 2110) may include one or more interfaces, or may be coupled with one or more interfaces configured to receive or transmit signals (e.g., data, information) via a wired or wireless transmission medium. Each network, or associated processor providing instructions to the interfaces of a network entity, may be configured to communicate with one or more other network entities via a transmission medium. For example, a network entity may include a wired interface configured to receive or transmit signals to one or more other network entities via a wired transmission medium. Additionally or alternatively, a network entity may include a wireless interface which may include a receiver, transmitter, or transceiver (such as an RF transceiver), and the wireless interface is configured to receive or transmit signals, or both, to one or more other network entities via a wireless transmission medium.

[0293]

[0302] In some examples, CU160-a may host one or more higher layer control functions. Such control functions may include RRC, PDCP, SDAP, and the like. Each control function may be implemented using an interface configured to communicate signals with other control functions hosted by CU160-a. CU160-a may be configured to handle user plane functions (e.g., CU-UP), control plane functions (e.g., CU-CP), or a combination thereof. In some examples, CU160-a may be logically divided into one or more CU-UP units and one or more CU-CP units. The CU-UP unit, when implemented in an O-RAN configuration, may communicate bidirectionally with the CU-CP unit via an interface such as the E1 interface. CU160-a may be implemented to communicate with DU165-a as needed for network control and signaling.

[0294]

[0303] DU165-a may correspond to a logical unit including one or more functions (e.g., base station functions, RAN functions) for controlling the operation of one or more RUs 170-a. In some examples, DU165-a may, based at least in part on function splitting such as that defined by the 3rd Generation Partnership Project (3GPP), at least partially host one or more of the RLC layer, MAC layer, and one or more aspects of the PHY layer (e.g., a high PHY layer including modules for FEC encoding and decoding, scrambling, modulation and demodulation, etc.). In some examples, DU165-a may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers hosted by DU165-a, or with control functions hosted by CU160-a.

[0295]

[0304] In some examples, low-layer functions may be hosted by one or more RU170-a. For example, a RU170-a controlled by a DU165-a may correspond to a logical node hosting RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, at least partially based on a functional partitioning such as lower-layer functional partitioning. In such an architecture, the RU170-a may be implemented to handle over-the-air (OTA) communication with one or more UE115-h. In some implementations, the real-time and non-real-time modes of control and user-plane communication with the RU170-a may be controlled by the corresponding DU165-a. In some examples, such a configuration may allow the DU165-a and CU160-a to be implemented in a cloud-based RAN architecture such as a vRAN architecture.

[0296]

[0305] The SMO180-a can be configured to support RAN deployment and provisioning of non-virtualized and virtualized network entities. For non-virtualized network entities, the SMO180-a can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operational and maintenance interfaces (such as the O1 interface). For virtualized network entities, the SMO180-a can be configured to interact with a cloud computing platform (e.g., O-Cloud 2105) to perform network entity lifecycle management (e.g., instantiating virtualized network entities) via a cloud computing platform interface (e.g., the O2 interface). Such virtualized network entities may include, but are not limited to, the CU160-a, DU165-a, RU170-a, and quasi-RT RIC175-b. In some implementations, the SMO180-a may communicate with components configured according to 4G RAN (e.g., via the O1 interface). Additionally or alternatively, in some implementations, the SMO180-a may communicate directly with one or more RU170-a via the O1 interface. The SMO180-a may also include a non-RT RIC175-a configured to support the functionality of the SMO180-a.

[0297]

[0306] Non-RT RIC175-a may be configured to include logical functions that enable non-real-time control and optimization of RAN elements and RAN resources, artificial intelligence (AI) or machine learning (ML) workflows including model training and updating, or policy-based guidance for applications / features in quasi-RT RIC175-b. Non-RT RIC175-a may be coupled to or communicate with quasi-RT RIC175-b (e.g., via the A1 interface). Quasi-RT RIC175-b may be configured to include logical functions that enable quasi-real-time control and optimization of RAN elements and RAN resources via data acquisition and actions via an interface connecting one or more CU160-a, one or more DU165-a, or both, and the O-eNB2110 to quasi-RT RIC175-b (e.g., via the E2 interface).

[0298]

[0307] In some examples, a non-RT RIC175-a may receive parameters or external enrichment information from an external server to generate an AI / ML model deployed to a quasi-RT RIC175-b. Such information may be utilized by the quasi-RT RIC175-b and may be received by the SMO180-a or non-RT RIC175-a from a non-network data source or network function. In some examples, a non-RT RIC175-a or quasi-RT RIC175-b may be configured to adjust RAN behavior or RAN performance. For example, a non-RT RIC175-a may monitor long-term trends and patterns in performance and employ an AI / ML model to implement corrective actions through the SMO180-a (e.g., reconfiguration via O1) or by creating a RAN management policy (e.g., A1 policy).

[0299]

[0308] The following provides an overview of the embodiments of this disclosure.

[0300]

[0309] Embodiment 1: A method for wireless communication in a UE, comprising: measuring interference in the UE across a set of interference measurement resources; encoding interference information representing the distribution of interference in the UE based on the measured interference across the set of interference measurement resources, according to a compression scheme; and transmitting the interference information encoded according to the compression scheme to a first network entity.

[0301]

[0310] Embodiment 2: The method according to Embodiment 1, wherein the interference distribution in the UE includes a probability mass function for a set of resources in time, frequency, and / or space.

[0302]

[0311] Embodiment 3: The method according to Embodiment 2, wherein the set of resources in time, frequency, and / or space includes a set of interference measurement resources.

[0303]

[0312] Embodiment 4: The method according to Embodiment 2, wherein the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference in the UE is at least partially based on measured interference across the set of interference measurement resources.

[0304]

[0313] Embodiment 5: The method according to Embodiment 2, wherein the set of resources in time, frequency, and / or space includes resources that occur later than the set of interference measurement resources, and the distribution of interference in the UE is predicted at least in part on the measured interference across the set of interference measurement resources.

[0305]

[0314] Embodiment 6: The method according to any one of Embodiments 1 to 5, wherein encoding according to a compression scheme comprises generating a compressed, estimated, or predicted interference distribution across a set of interference measurement resources.

[0306]

[0315] Embodiment 7: The method according to any one of Embodiments 1 to 6, wherein the compression method includes a codeword-based compression method or an artificial neural network-based compression method.

[0307]

[0316] Embodiment 8: The method according to any one of Embodiments 1 to 7, wherein encoding according to a compression scheme comprises generating a mean vector and covariance matrix of latent random variables representing the distribution of interferences across a set of interference measurement resources, the set of interference measurement resources comprising two or more sets of time resources, frequency resources, or spatial resources.

[0308]

[0317] Embodiment 9: The method according to any one of embodiments 1 to 8, further comprising receiving instructions for an encoding configuration for encoding interference information from a first network entity or one or more second network entities associated with the first network entity.

[0309]

[0318] Embodiment 10: The method according to Embodiment 9, further comprising transmitting instructions to a first network entity or one or more second network entities associated with the first network entity of the ability of the UE to encode interference information, wherein receiving instructions for encoding configurations to encode interference information comprises receiving instructions for indices associated with encoding configurations from the first network entity or one or more second network entities associated with the first network entity in response to transmitting instructions for the UE's ability to encode interference information, and the set of encoding configurations including encoding configurations is associated with a set of indices including indices.

[0310]

[0319] Embodiment 11: The method according to Embodiment 9 or 10, wherein the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network.

[0311]

[0320] Embodiment 12: The method according to any one of embodiments 1 to 11, further comprising: selecting one coding configuration from a set of coding configurations for coding interference information, wherein each coding configuration in the set of coding configurations is associated with a respective index in a set of indices; and transmitting to a first network entity or one or more second network entities associated with the first network entity an instruction for one index in a set of indices associated with the selected coding configuration.

[0312]

[0321] Embodiment 13: The method according to any one of embodiments 1 to 12, further comprising receiving instructions for one or more parameters associated with a compression scheme from a first network entity or one or more second network entities associated with the first network entity.

[0313]

[0322] Embodiment 14: The method according to Embodiment 13, wherein one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

[0314]

[0323] Embodiment 15: The method according to any one of embodiments 1 to 14, further comprising receiving instructions for a set of interference measurement resources from a first network entity or one or more second network entities associated with the first network entity.

[0315]

[0324] Embodiment 16: The method of any one of embodiments 1 to 15, further comprising receiving instructions from a first network entity or one or more second network entities associated with the first network entity for one or more parameters associated with measuring interference in a UE across a set of interference measurement resources.

[0316]

[0325] Embodiment 17: The method according to Embodiment 16, wherein one or more parameters associated with measuring interference in a UE across a set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof.

[0317]

[0326] Embodiment 18: The method according to any one of embodiments 1 to 17, further comprising receiving instructions for an input format for encoding interference information representing the distribution of interference in the UE from a first network entity or one or more second network entities associated with the first network entity.

[0318]

[0327] Embodiment 19: The method according to any one of Embodiments 1 to 18, wherein transmitting interference information encoded according to a compression scheme comprises transmitting a channel state feedback report containing encoded interference information encoded according to a compression scheme.

[0319]

[0328] Embodiment 20: The method according to any one of embodiments 1 to 19, further comprising determining one or more model parameters associated with a compression scheme using an artificial neural network associated with a compression scheme, and transmitting one or more model parameters to a first network entity or one or more second network entities associated with the first network entity.

[0320]

[0329] Embodiment 21: The method according to any one of embodiments 1 to 20, further comprising receiving one or more parameters associated with a compression scheme, at least in part, based on transmitting one or more model parameters from a first network entity or one or more second network entities associated with the first network entity.

[0321]

[0330] Embodiment 22: The method according to any one of Embodiments 1 to 120, wherein the interference in the UE includes interference plus noise, and the interference distribution includes the interference plus noise distribution across a set of interference measurement resources.

[0322]

[0331] Embodiment 23: A method for wireless communication in a network entity, comprising: obtaining encoded interference information representing the distribution of interference; and decoding the encoded interference information according to a compression scheme in order to output decoded interference information.

[0323]

[0332] Embodiment 24: The method according to Embodiment 23, further comprising outputting scheduling information for communication at the UE based at least in part on the decoded interference information.

[0324]

[0333] Embodiment 25: The method according to Embodiment 23, wherein the encoded interference information includes a mean vector and a covariance matrix of latent random variables representing the distribution of interference in the UE across a set of interference measurement resources, and the method further comprises generating a sample based on the mean vector and the covariance matrix, and decoding the encoded interference information based at least partially on the sample.

[0325]

[0334] Embodiment 26: The method according to Embodiment 23 or 24, further comprising outputting instructions for an encoding configuration for encoding interference information in a UE.

[0326]

[0335] Embodiment 27: The method of Embodiment 26, further comprising obtaining an instruction of the UE's ability to encode interference information, and outputting an instruction of an encoding configuration, which comprises outputting an instruction of an index associated with an encoding configuration, at least in part based on receiving an instruction of the UE's ability to encode interference information, wherein a set of encoding configurations including an encoding configuration is associated with a set of indices including an index.

[0327]

[0336] Embodiment 28: The method according to Embodiment 26 or 27, wherein the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network.

[0328]

[0337] Embodiment 29: The method according to any one of Embodiments 23 to 28, further comprising obtaining an indication of one index from a set of indices associated with a selected coding configuration, wherein each coding configuration from the set of coding configurations is associated with each index from the set of indices.

[0329]

[0338] Embodiment 30: The method according to any one of embodiments 23 to 29, further comprising outputting instructions for one or more parameters associated with a compression scheme.

[0330]

[0339] Embodiment 31: The method according to Embodiment 30, wherein one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

[0331]

[0340] Embodiment 32: The method according to any one of embodiments 23 to 31, further comprising outputting an indication of one or more parameters associated with measuring interference across a set of interference measurement resources.

[0332]

[0341] Embodiment 33: The method according to Embodiment 32, wherein one or more parameters associated with measuring interference across a set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof.

[0333]

[0342] Embodiment 34: The method according to any one of embodiments 23 to 33, further comprising outputting instructions for an input format of interference information representing the distribution of interference.

[0334]

[0343] Embodiment 35: Obtaining encoded interference information is any method of Embodiments 23 to 34, comprising obtaining a channel state feedback report containing encoded interference information encoded according to a compression scheme.

[0335]

[0344] Embodiment 36: The method according to any one of embodiments 23 to 35, further comprising: determining one or more model parameters associated with a compression scheme using an artificial neural network associated with a compression scheme; and outputting one or more parameters associated with a compression scheme based at least in part on one or more model parameters.

[0336]

[0345] Embodiment 37: The method according to any one of embodiments 23 to 36, further comprising obtaining one or more model parameters associated with a compression scheme, and outputting one or more parameters associated with a compression scheme based at least in part on one or more model parameters.

[0337]

[0346] Embodiment 38: The method according to any one of Embodiments 23 to 37, wherein the interference distribution includes an interference-plus-noise distribution across a set of interference measurement resources.

[0338]

[0347] Apparatus 39: Apparatus for wireless communication in a UE, comprising a processor and memory coupled to the processor, wherein the processor is configured to perform the method described in any of Apparatus 1 to 22.

[0339]

[0348] Embodiment 40: Apparatus for wireless communication in a UE, comprising at least one means for performing the method described in any of Embodiments 1 to 22.

[0340]

[0349] Embodiment 41: A non-temporary computer-readable medium for storing code for wireless communication in a UE, wherein the code comprises instructions executable by a processor to perform the method described in any of Embodiments 1 to 22.

[0341]

[0350] Embodiment 42: A device for wireless communication in a network entity, comprising a processor configured to cause the device to perform any of the methods described in Embodiments 23 to 38.

[0342]

[0351] Embodiment 43: An apparatus for wireless communication in a network entity, comprising at least one means for performing the method described in any of Embodiments 23 to 38.

[0343]

[0352] Embodiment 44: A non-temporary computer-readable medium for storing code for wireless communication in a network entity, wherein the code includes instructions executable by a processor to perform the method described in any of Embodiments 23 to 38.

[0344]

[0353] It should be noted that the methods described herein describe possible implementations, that the operations and steps may be reconfigured or otherwise modified, and that other implementations are possible. Furthermore, two or more embodiments of the methods may be combined.

[0345]

[0354] Embodiments of LTE, LTE-A, LTE-A Pro, or NR systems may be described as examples, and the terms LTE, LTE-A, LTE-A Pro, or NR may be used for the majority of the description; however, the techniques described herein are applicable to networks other than LTE, LTE-A, LTE-A Pro, or NR networks. For example, the techniques described may be applicable to various other wireless communication systems such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, as well as other systems and wireless technologies not expressly described herein.

[0346]

[0355] The information and signals described herein can be represented using a wide variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout this description can be represented by voltage, electric current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0347]

[0356] The various exemplary blocks and components described in this disclosure may be implemented or carried out using general-purpose processors, DSPs, ASICs, CPUs, FPGAs or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, a processor may be any processor, controller, microcontroller, or state machine. A processor may be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0348]

[0357] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or codes. Other examples and implementations are within the scope of this disclosure and the accompanying claims. For example, due to the nature of the software, the functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or any combination thereof. Features implementing the functions may also be physically located in various locations, including being distributed so that parts of the functions are implemented in different physical locations.

[0349]

[0358] Computer-readable media include both non-temporary computer storage media and communication media, including any media that facilitates the transfer of computer programs from one location to another. Non-temporary storage media may be any available media that can be accessed by a general-purpose or dedicated computer. Examples, but not limited to, non-temporary computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-temporary media that can be used to transport or store desired program code means in the form of instructions or data structures, and can be accessed by a general-purpose or dedicated computer or general-purpose or dedicated processor. Any connection is also appropriately referred to as computer-readable media. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of computer-readable media. As used herein, disk and disc include CD, laserdisc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc, where disk typically reproduces data magnetically, and disc optically reproduces data using a laser. Any combination of the above is also included in the scope of computer-readable media.

[0350]

[0359] When used herein, including within the claims, “or” as used in an enumeration of items (e.g., an enumeration of items followed by a phrase such as “at least one of” or “one or more of”) indicates an inclusive enumeration, such as the enumeration “at least one of A, B, or C” meaning A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Furthermore, the phrase “based on” as used herein should not be interpreted as a reference to a closed set of conditions. For example, an exemplary step described as “based on condition A” could be based on both condition A and condition B without departing from the scope of this disclosure. In other words, when used herein, the phrase “based on” shall be interpreted in the same way as the phrase “at least partially based on.”

[0351]

[0360] The term "decide" or "to decide" encompasses a wide variety of actions, and therefore "deciding" can include calculating, calculating, processing, deriving, investigating, looking up (such as through lookups in tables, databases, or other data structures), confirming, etc. It can also include receiving (such as receiving information), accessing (such as accessing data in memory), etc. Furthermore, "deciding" can include resolving, selecting, choosing, establishing, and other similar actions.

[0352]

[0361] As used herein, including in the claims, the term “set” refers to one or more groups.

[0353]

[0362] In the attached diagrams, similar components or features may have the same reference label. Furthermore, various components of the same type may be distinguished by adding a dash and a second label to distinguish similar components after the reference label. When only the first reference label is used herein, the description is applicable to any similar component having the same first reference label, regardless of the second reference label or any other subsequent reference labels.

[0354]

[0363] The descriptions provided herein with respect to the accompanying drawings are illustrative and do not necessarily represent all examples that may be implemented or fall within the scope of the claims. The term “example” as used herein means “to serve as an example, instance, or illustrative solution” and does not mean “preferred” or “advantageous over other examples.” Detailed descriptions include specific details to facilitate understanding of the described techniques. However, these techniques may be practiced without these specific details. In some instances, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0355]

[0364] The descriptions herein are provided to enable those skilled in the art to create or use this disclosure. Various modifications of this disclosure will become apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of this disclosure. Accordingly, this disclosure should be given the broadest scope that is consistent with the principles and novel features disclosed herein, and is not limited to the examples and designs described herein. The invention described in the original claims of this application is listed below. [C1] A device for wireless communication in user equipment (UE), Processor and The processor comprises a memory coupled to the processor, and the processor is Measure the interference in the UE across a set of interference measurement resources, According to the compression method, interference information representing the distribution of interference in the UE is encoded based at least in part on the measured interference across the set of interference measurement resources, A device configured to transmit the interference information, encoded according to the compression scheme, to a first network entity. [C2] The apparatus according to C1, wherein the interference in the UE includes interference plus noise, and the distribution of the interference includes the distribution of interference plus noise across the set of interference measurement resources. [C3] The apparatus according to C1, wherein the distribution of interference in the UE includes a probability mass function for a set of resources in time, frequency, and / or space. [C4] The apparatus according to C3, wherein the set of resources in time, frequency, and / or space includes the set of interference measurement resources. [C5] The apparatus according to C3, wherein the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference in the UE is at least partially based on the interference measured across the set of interference measurement resources. [C6] The apparatus according to C3, wherein the set of resources in time, frequency, and / or space includes resources that occur later than the set of interference measurement resources, and the distribution of the interference in the UE is predicted at least in part on the measured interference across the set of interference measurement resources. [C7] In order to encode the interference information, the processor The apparatus according to C1, further configured to generate a compressed, estimated, or predicted interference distribution across the set of interference measurement resources. [C8] The apparatus according to C1, wherein the compression method includes a codeword-based compression method or an artificial neural network-based compression method. [C9] In order to encode the interference information, the processor It is further configured to generate a mean vector and covariance matrix of latent random variables representing the distribution of interference in the UE across the set of interference measurement resources, The apparatus according to C1, wherein the set of interference measurement resources includes two or more sets of time resources, frequency resources, or spatial resources. [C10] The device described above is The apparatus according to C1, further comprising an antenna operable to receive instructions for an encoding configuration configured to encode interference information from the first network entity or one or more second network entities associated with the first network entity. [C11] The aforementioned processor is The antenna is further configured to transmit instructions to the first network entity or one or more second network entities associated with the first network entity regarding the UE's ability to encode interference information, and to receive the instructions for the encoding configuration configured to encode the interference information, The apparatus according to C10, which is further operable to receive instructions for indices associated with the coding configuration in response to the transmission of instructions for the UE's ability to encode interference information from the first network entity or one or more second network entities associated with the first network entity, wherein the set of coding configurations including the coding configuration is associated with the set of indices including the index. [C12] The apparatus according to C10, wherein the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network. [C13] The aforementioned processor is A set of coding configurations for encoding the interference information, wherein each coding configuration in the set of coding configurations is associated with each index in the set of indices, from which one coding configuration is selected. The apparatus according to C1, further configured to transmit to the first network entity or one or more second network entities associated with the first network entity an instruction for one index from the set of indices associated with the selected coding configuration. [C14] The aforementioned processor is The apparatus according to C1, further configured to receive instructions for one or more parameters associated with the compression scheme from the first network entity or one or more second network entities associated with the first network entity. [C15] The apparatus according to C14, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof. [C16] The aforementioned processor is The apparatus according to C1, further configured to receive instructions for the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity. [C17] The aforementioned processor is The apparatus according to C1, further configured to receive instructions from the first network entity or one or more second network entities associated with the first network entity for one or more parameters associated with measuring the interference in the UE across the set of interference measurement resources. [C18] The apparatus according to C17, wherein the one or more parameters associated with measuring the interference in the UE across the set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof. [C19] The aforementioned processor is The apparatus according to C1, further configured to receive instructions for an input format for encoding the interference information representing the distribution of interference in the UE from the first network entity or one or more second network entities associated with the first network entity. [C20] The aforementioned processor is The apparatus according to C1, further configured to transmit a channel state feedback report including the encoded interference information encoded according to the compression scheme. [C21] The aforementioned processor is Using the artificial neural network associated with the compression method, one or more model parameters associated with the compression method are determined. The apparatus according to C1, further configured to transmit the one or more model parameters to the first network entity or to one or more second network entities associated with the first network entity. [C22] The aforementioned processor is The apparatus according to C21, further configured to receive one or more parameters associated with the compression scheme from the first network entity or one or more second network entities associated with the first network entity, at least in part, based on the transmission of one or more model parameters. [C23] Device for wireless communication in a network entity, Processor and The processor comprises a memory coupled to the processor, and the processor is We obtain encoded interference information that represents the distribution of interference, A device configured to decode encoded interference information according to a compression scheme in order to output decoded interference information. [C24] The apparatus according to C23, wherein the interference distribution includes an interference-plus-noise distribution across a set of interference measurement resources. [C25] The apparatus according to C23, further comprising an antenna operable to transmit scheduling information for communications in a user equipment (UE) based at least in part on the decoded interference information. [C26] The encoded interference information includes a mean vector and a covariance matrix of latent random variables representing the distribution of the interference in the user equipment (UE) across a set of interference measurement resources, and the processor, Based on the mean vector and the covariance matrix, a sample is generated. The apparatus according to C23, further configured to decode the encoded interference information based at least partially on the sample. [C27] The aforementioned processor is The apparatus described in C23, further configured to output instructions for an encoding configuration for encoding interference information in a user device (UE). [C28] The aforementioned processor is Obtain instructions for the UE's ability to encode interference information, The apparatus according to C27, further configured to output an instruction for an index associated with the coding configuration, based at least in part on the instruction for the UE's ability to encode interference information, wherein the set of coding configurations, including the coding configuration, is associated with the set of indices, including the index. [C29] The apparatus according to C27, wherein the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network. [C30] The aforementioned processor is The apparatus according to C23, further configured to obtain an indication of one index from a set of indices associated with a selected coding configuration, wherein each coding configuration from the set of coding configurations is associated with each index from the set of indices. [C31] The aforementioned processor is The apparatus according to C23, further configured to output instructions for one or more parameters associated with the compression method. [C32] The apparatus according to C31, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof. [C33] The aforementioned processor is The apparatus described in C23, further configured to output indications for one or more parameters associated with measuring interference across a set of interference measurement resources. [C34] The apparatus according to C33, wherein the one or more parameters associated with measuring interference across the set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof. [C35] The aforementioned processor is The apparatus according to C23, further configured to output instructions for an input format for encoding interference information representing the distribution of the interference. [C36] The aforementioned processor is The apparatus according to C23, further configured to obtain a channel state feedback report including the encoded interference information. [C37] The aforementioned processor is Using the artificial neural network associated with the compression method, one or more model parameters associated with the compression method are determined. The apparatus according to C23, further configured to output one or more parameters associated with the compression scheme based at least in part on the one or more model parameters. [C38] The aforementioned processor is Obtain one or more model parameters associated with the compression method, The apparatus according to C23, further configured to output one or more parameters associated with the compression scheme based at least in part on the one or more model parameters. [C39] A method for wireless communication in user equipment (UE), Measuring interference in the UE across a set of interference measurement resources, Encoding interference information representing the distribution of interference in the UE, based at least partially on the measured interference across the set of interference measurement resources, according to a compression method, A method comprising transmitting the interference information, encoded according to the compression scheme, to a first network entity. [C40] The apparatus according to C39, wherein the interference in the UE includes interference plus noise, and the distribution of the interference includes the distribution of interference plus noise across the set of interference measurement resources. [C41] The method according to C39, wherein the distribution of interference in the UE includes a probability mass function for a set of resources in time, frequency, and / or space. [C42] The method according to C41, wherein the set of resources in time, frequency, and / or space includes the set of interference measurement resources. [C43] The method according to C41, wherein the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of the interference in the UE is at least partially based on the measured interference across the set of interference measurement resources. [C44] The method according to C41, wherein the set of resources in time, frequency, and / or space includes resources that occur later than the set of interference measurement resources, and the distribution of the interference in the UE is predicted at least in part on the measured interference across the set of interference measurement resources. [C45] Encoding according to the compression method is: The method according to C39, comprising generating a compressed, estimated, or predicted interference distribution across the set of interference measurement resources. [C46] The method according to C39, wherein the compression method includes a codeword-based compression method or a neural network-based compression method. [C47] Encoding according to the compression method is: The method according to C39, comprising generating a mean vector and a covariance matrix representing the distribution of the interference in the UE across the set of interference measurement resources, wherein the set of interference measurement resources includes two or more sets of time resources, frequency resources, or spatial resources. [C48] The method according to C39, further comprising receiving instructions for an encoding configuration for encoding the interference information from the first network entity or one or more second network entities associated with the first network entity. [C49] Further comprising transmitting instructions to the first network entity or one or more second network entities associated with the first network entity of the UE's ability to encode interference information, and receiving the instructions for the encoding configuration for encoding the interference information, The method according to C48, comprising receiving an instruction for an index associated with the coding configuration in response to transmitting the instruction for the UE's ability to encode interference information from the first network entity or one or more second network entities associated with the first network entity, wherein the set of coding configurations including the coding configuration is associated with the set of indices including the index. [C50] The method according to C48, wherein the coding configuration includes a configuration for one of an autoencoder or an artificial neural network. [C51] A set of coding configurations for encoding the interference information, wherein each coding configuration in the set of coding configurations is associated with each index in the set of indices, and one coding configuration is selected from this set of coding configurations. The method according to C39, further comprising transmitting to the first network entity or one or more second network entities associated with the first network entity an instruction for one of the indexes from the set of indices associated with the selected coding configuration. [C52] The method according to C39, further comprising receiving instructions for one or more parameters associated with the compression scheme from the first network entity or one or more second network entities associated with the first network entity. [C53] The method according to C52, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof. [C54] The method of C39, further comprising receiving instructions for the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity. [C55] The method according to C39, further comprising receiving instructions from the first network entity or one or more second network entities associated with the first network entity for one or more parameters associated with measuring the interference in the UE across the set of interference measurement resources. [C56] The method according to C55, wherein the one or more parameters associated with measuring the interference in the UE across the set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof. [C57] The method according to C39, further comprising receiving instructions from the first network entity or one or more second network entities associated with the first network entity for an input format for encoding the interference information representing the distribution of the interference in the UE. [C58] Transmitting the interference information encoded according to the compression method is: The method according to C39, comprising transmitting a channel state feedback report containing the interference information encoded according to the compression scheme. [C59] Using the artificial neural network associated with the compression method, determine one or more model parameters associated with the compression method, The method according to C39, further comprising transmitting the one or more model parameters to the first network entity or one or more second network entities associated with the first network entity. [C60] The method according to C59, further comprising receiving one or more parameters associated with the compression scheme, at least in part, based on transmitting the one or more model parameters from the first network entity or one or more second network entities associated with the first network entity. [C61] A method for wireless communication in a network entity, Obtaining encoded interference information that represents the distribution of interference, A method comprising decoding the encoded interference information according to a compression scheme in order to output the decoded interference information. [C62] The method according to C61, wherein the interference distribution includes an interference-plus-noise distribution across a set of interference measurement resources. [C63] The method of C61, further comprising outputting scheduling information for communication in a user device (UE) to the UE, based at least in part on the decoded interference information. [C64] The encoded interference information includes a mean vector and a covariance matrix of latent random variables representing the distribution of the interference in user equipment (UE) across a set of interference measurement resources, and the method is: To generate a sample based on the mean vector and the covariance matrix, The method according to C61, further comprising decoding the encoded interference information based at least partially on the sample. [C65] The method of C61, further comprising outputting instructions for an encoding configuration for encoding interference information in a user device (UE). [C66] Further comprising obtaining instructions for the UE's ability to encode interference information, and outputting the instructions for the encoding configuration, The method according to C65, comprising outputting an instruction for an index associated with the coding configuration, at least in part, based on the instruction for the UE's ability to encode interference information, wherein the set of coding configurations including the coding configuration is associated with the set of indices including the index. [C67] The method according to C65, wherein the encoding configuration includes a configuration for one of an autoencoder or an artificial neural network. [C68] The method according to C61, further comprising obtaining an indication of one index from a set of indices associated with a selected coding configuration, wherein each coding configuration from the set of coding configurations is associated with each index from the set of indices. [C69] The method of C61, further comprising outputting an instruction for one or more parameters associated with the compression scheme. [C70] The method according to C69, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof. [C71] The method of C61, further comprising outputting an indication of one or more parameters associated with measuring interference across a set of interference measurement resources. [C72] The method according to C71, wherein the one or more parameters associated with measuring interference across the set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof. [C73] The method of C71, further comprising outputting instructions for an input format for encoding interference information representing the distribution of the interference. [C74] The method according to C61, wherein obtaining the encoded interference information encoded according to the compression method comprises obtaining a channel state feedback report containing the encoded interference information. [C75] Using the artificial neural network associated with the compression method, determine one or more model parameters associated with the compression method, The method according to C61, further comprising outputting one or more parameters associated with the compression scheme based at least in part on the one or more model parameters. [C76] The method according to C61, further comprising obtaining one or more model parameters associated with the compression scheme, and outputting one or more parameters associated with the compression scheme based at least in part on the one or more model parameters. [C77] A device for wireless communication in user equipment (UE), Means for measuring interference in the UE across a set of interference measurement resources, and means for encoding interference information representing the distribution of interference in the UE, at least partially based on the measured interference across the set of interference measurement resources, according to a compression scheme. An apparatus comprising means for transmitting the interference information encoded according to the compression scheme to a first network entity. [C78] A device for wireless communication in a network entity, A means for obtaining encoded interference information representing the distribution of interference, An apparatus comprising means for decoding the encoded interference information according to a compression scheme in order to output the decoded interference information. [C79] A non-temporary computer-readable medium for storing a code for wireless communication in a user device (UE), wherein the code is: Measure the interference in the UE across a set of interference measurement resources, According to the compression method, interference information representing the distribution of interference in the UE is encoded based at least in part on the measured interference across the set of interference measurement resources, A non-temporary computer-readable medium containing instructions executable by a processor for transmitting the interference information, encoded according to the compression scheme, to a first network entity. [C80] A non-temporary computer-readable medium for storing a code for wireless communication in a network entity, wherein the code is: We obtain encoded interference information that represents the distribution of interference, A non-temporary computer-readable medium comprising instructions executable by a processor for decoding the encoded interference information according to a compression scheme in order to output the decoded interference information.

Claims

1. A method for wireless communication in user equipment (UE), Receiving instructions for one or more parameters associated with the compression method from the first network entity, Measuring interference in the UE across a set of interference measurement resources, Encoding interference information representing the distribution of interference in the UE based at least in part on the measured interference across the set of interference measurement resources according to the compression method, wherein the set of interference measurement resources includes channel state information (CSI) reference signals (CSI-RS), CSI interference measurements (CSI-IM), or interference measurement resources (IMR), the IMR comprising time-frequency resources. A method comprising transmitting the interference information encoded according to the compression scheme to the first network entity.

2. The method according to claim 1, wherein the interference in the UE includes interference plus noise, and the distribution of the interference includes the distribution of interference plus noise across the set of interference measurement resources.

3. The method according to claim 1, wherein the distribution of interference in the UE includes a probability mass function for a set of resources in time, frequency, and / or space, the set of resources in time, frequency, and / or space includes the set of interference measurement resources, or the set of resources in time, frequency, and / or space includes resources prior to the set of interference measurement resources, and the distribution of interference in the UE is at least partially based on the interference measured across the set of interference measurement resources, or the set of resources in time, frequency, and / or space includes resources later than the set of interference measurement resources, and the distribution of interference in the UE is predicted at least partially based on the interference measured across the set of interference measurement resources.

4. Encoding according to the compression method means This includes generating a compressed, estimated, or predicted interference distribution across the set of interference measurement resources, or encoding according to the compression scheme. The method according to claim 1, comprising generating a mean vector and a covariance matrix representing the distribution of interference in the UE across the set of interference measurement resources, wherein the set of interference measurement resources includes two or more sets of time resources, frequency resources, or spatial resources.

5. The further includes receiving instructions for an encoding configuration for encoding the interference information from the first network entity or one or more second network entities associated with the first network entity, The further includes transmitting instructions to the first network entity or one or more second network entities associated with the first network entity regarding the UE's ability to encode interference information, and receiving the instructions regarding the encoding configuration for encoding the interference information, The method according to claim 1, comprising receiving an instruction for an index associated with the coding configuration in response to transmitting the instruction for the UE's ability to encode interference information from the first network entity or one or more second network entities associated with the first network entity, wherein the set of coding configurations including the coding configuration is associated with the set of indices including the index, or the coding configuration includes a configuration for one of an autoencoder or an artificial neural network.

6. The method according to claim 1, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

7. The method according to claim 1, further comprising receiving instructions for the set of interference measurement resources from the first network entity or one or more second network entities associated with the first network entity.

8. Further includes receiving instructions from the first network entity or one or more second network entities associated with the first network entity for one or more parameters associated with measuring the interference in the UE across the set of interference measurement resources, The method according to claim 1, wherein the one or more parameters associated with measuring the interference in the UE across the set of interference measurement resources include frequency granularity, temporal granularity, spatial granularity, or a combination thereof.

9. A method for wireless communication in a network entity, Sending instructions for one or more parameters associated with the compression method to the user equipment (UE), The method involves obtaining encoded interference information representing the distribution of interference, wherein the interference distribution comprises a distribution across a set of interference measurement resources, the set of interference measurement resources includes channel state information (CSI) reference signals (CSI-RS), CSI interference measurements (CSI-IM), or interference measurement resources (IMR), and the IMR comprises time-frequency resources. A method comprising decoding the encoded interference information according to the compression scheme in order to output the decoded interference information.

10. The encoded interference information includes a mean vector and covariance matrix of latent random variables representing the distribution of the interference in user equipment (UE) across a set of interference measurement resources, and the method is: To generate a sample based on the mean vector and the covariance matrix, The method according to claim 9, further comprising decoding the encoded interference information based at least partially on the sample.

11. Further includes outputting instructions for an encoding configuration for encoding interference information in user equipment (UE), Further including obtaining instructions for the UE's ability to encode interference information, and outputting the instructions for the encoding configuration, The method according to claim 9, comprising outputting an index instruction associated with the coding configuration, at least in part on the instruction of the UE's ability to encode interference information, wherein a set of coding configurations including the coding configuration is associated with a set of indices including the index, or the coding configuration includes a configuration for one of an autoencoder or an artificial neural network.

12. The method according to claim 9, wherein the one or more parameters associated with the compression scheme include code size, number of layers, number of nodes per layer, loss function, or a combination thereof.

13. Further including outputting instructions for one or more parameters associated with measuring interference across a set of interference measurement resources, The one or more parameters associated with measuring interference across the set of interference measurement resources include frequency granularity, time granularity, spatial granularity, or a combination thereof, The method according to claim 9, further comprising outputting an instruction for an input format for encoding interference information representing the distribution of the interference.

14. A device for wireless communication in user equipment (UE), Means for receiving instructions for one or more parameters associated with a compression scheme from a first network entity, Means for measuring interference in the UE across a set of interference measurement resources, Means for encoding interference information representing the distribution of interference in the UE, based at least in part on the measured interference across the set of interference measurement resources, wherein the set of interference measurement resources includes channel state information (CSI) reference signals (CSI-RS), CSI interference measurements (CSI-IM), or interference measurement resources (IMR), the IMR comprising time-frequency resources. An apparatus comprising means for transmitting the interference information encoded according to the compression method to a first network entity.

15. A device for wireless communication in a network entity, Means for transmitting instructions for one or more parameters associated with a compression method to a user device (UE), Means for obtaining encoded interference information representing the distribution of interference, wherein the interference distribution comprises a distribution across a set of interference measurement resources, the set of interference measurement resources comprising channel state information (CSI) reference signals (CSI-RS), CSI interference measurements (CSI-IM), or interference measurement resources (IMR), the IMR comprising time-frequency resources. An apparatus comprising means for decoding the encoded interference information according to a compression scheme in order to output the decoded interference information.

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