Configuration for temporal-spatial-frequency domain channel state information (CSI) compression performance monitoring

Abstract

A method, system and apparatus are disclosed. According to some embodiments, a user equipment, UE (22), that is configured to communicate with a network node (16) is provided. The UE (22) is configured to: receive a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, where the plurality of target-CSI types includes a first target-CSI type and a second target-CSI type different from the first target CSI-type; perform a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types; and transmit at least one or both of: a target-CSI report; and a performance monitoring report associated with the performance monitoring operation.

Description

[0001] CONFIGURATION FOR TEMPORAL-SPATIAL-FREQUENCY DOMAIN CHANNEL STATE INFORMATION (CSI) COMPRESSION PERFORMANCE MONITORING FIELD

[0002] The present disclosure relates to wireless communications, and in particular, to performance monitoring operation using at least one channel state information, CSI, type.

[0003] BACKGROUND

[0004] The Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile user equipments (UE), as well as communication between network nodes and between UEs. The 3GPP is also developing standards for Sixth Generation (6G) wireless communication networks.

[0005] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the air interface design in wireless communication networks. One example of use of AI / ML implementation for the physical layer (Al PHY) is using AI / ML autoencoder to improve the channel compression accuracy and / or to reduce the channel state information (CSI) feedback overhead. This topic has been discussed throughout 3GPP Rel. 18 and continues to be discussed in Rel.

[0006] 19.

[0007] Some general aspects for AI / ML-based CSI compression

[0008] In the legacy mechanism (i.e., non- AI / ML-based CSI compression), the UE can be configured to report a suggested precoder to the NW (e.g., network node), e.g., suggest a precoder matrix indicator (PMI). The PMI is based on measured CSI-RS and sent to the NW as a CSI report, based on a certain mechanism, e.g., codebook. The codebook generally defines how the UE arranges the reported bits based on (the number of) beams and taps selected by the UE to be reported to the NW and how the UE quantizes the precoding matrix.

[0009] In AI / ML-based CSI compression, an AI / ML-based autoencoder (AE) replaces at least part of, the legacy mechanism. AEs can have different architectures. For example, AEs can be based on dense NNs, multi-dimensional convolution NNs, variational,recurrent NNs, transformer networks, or any combination thereof. However, all existing AE architectures possess an encoder-bottleneck-decoder structure illustrated in FIG. 1.

[0010] The codeword’s size (denoted by Y in FIG. 1) of an AE is smaller than the input data’s size (X in FIG. 1). The AE encoder thus reduces the dimensionality of the input features X down to K’s. The decoder part of the AE tries to invert the encoder and reconstruct X with minimal error, according to some predefined loss function, also known at target function in the general optimization literature.

[0011] FIG. 2 illustrates how an AE might be used for AVML-enhanced CSI reporting in NR. The UE measures the channel in the downlink using CSI-RS. The UE estimates the channel for each subcarrier (SC) from each base station TX antenna to each UE RX antenna. The estimate can be viewed as a three-dimensional channel matrix. The 3D channel matrix represents the MIMO channel (e.g., spatial propagation channel between multiple transmit antennas and multiple receive antennas) estimated over several SCs and is input to the encoder. However, there are different architectures where a processed version of the 3D MIMO channel, or a processed subset of the information, e.g., singular vectors of the MIMO channel, is input to the encoder.

[0012] The AE encoder is implemented in the UE, and the AE decoder is implemented in the NW, denoted BS for base station in FIG 2. The output of the AE encoder is signaled from the UE to the NW over the uplink. The codeword can be viewed as a learned latent representation of the channel. Properties of the data (e.g., CSI-RS channel estimates), the channel size, uplink feedback rate, and hardware limitations of the encoder and decoder need to be considered when optimizing the AE’s architecture.

[0013] The weights and biases of an AE (with a fixed architecture) are trained to minimize the reconstruction error (the error between the input X and output X) on some training datasets. For example, the weights and biases can be trained to minimize the mean squared error, MSE (mean(X — X)2). To achieve good performance during live operation, the training data set should represent the actual data the AE will encounter during live operation.

[0014] In the two-sided CSI compression, the output of the UE-side encoder needs to be communicated over the air interface to the network node decoder with the assigned CSI reporting payload and, therefore, needs to be quantized to a finite number of bits (e.g., 1-4 bits per encoder output’s neuron) to obtain an efficient transmission as shown in FIG. 3. Accordingly, a quantization layer is connected to the output of the encoder or directly included in the encoder. In an example, the quantization layer may implement scalarquantization which quantizes the output of each neuron of the encoder output layer (the bottleneck layer of AE) to generate bits to fit the CSI reporting payload in the uplink control information (UCI). Other quantization methods, e.g., vector quantization, may also be used.

[0015] Pre-processing for input data to the AE

[0016] A proper pre-processing on the input to the encoder can greatly reduce the size and complexity of designing and / or training an AI / ML model, and, in the meantime, improve the scalability and transferability of the model. In addition, pre-processing may reduce the need for multiple models depending on bandwidth variation and variation in the number of antenna ports at the network node. By using pre-processing, instead of directly compressing the channel (i.e., with dimensions of RX x TX x SC as in FIG. 2, the channels are first processed into another representation.

[0017] One example of the pre-processing may include transforming the channel into eigenvectors (FIG. 4). Here, the UE may conduct the following steps:

[0018] 1. Compute the covariance matrix of the channel and extract the relevant eigenvectors.

[0019] 2. The covariance matrix is summed over 4 “f-units”, to get 13 “sub-chunks” (subbands) in frequency.

[0020] 3. For each of the 13 averaged covariance matrices, compute an eigen-decomposition and extract the 4 eigenvectors corresponding to the 4 largest eigenvalues.

[0021] 4. Normalize the phase and magnitude of the eigenvectors.

[0022] 5. The number of eigenvectors to feedback should be the same for all sub-chunks and depends on the rank hypothesis testing with a value between 1 and 4.

[0023] In another example, pre-processing may include transforming the channel into the beam-delay domain. The feature extraction for beam-delay reduced eigenvector-based feedback is illustrated in FIG. 5. This is also called a “W2 compressor” as the feature extraction is similar to the standardized 3GPP procedures and the jargon for the matrix left to compress for the AI / ML is “W2”. The steps are as follows:

[0024] 1. The UE selects an orthogonal basis, one of a set of oversampled / rotated, spatial domain, DFT bases. In that basis the UE selects L vectors. These vectors represent the spatial domain (SD) basis DFT. This basis is wideband applicable, i.e., valid for all subbands, and applicable for both polarizations. This selection may happen jointly, e.g., the UE does a spatial domain DFT on the 32 x 4 (TX x RX) matrix, per RB, and selects the L strongest beams out of 16 (for one polarization). Thebeam-space channel is computed by multiplying the channel with the selected SD basis. The covariance of the beam-space channel is summed over, e.g., 4 RBs to produce a covariance matrix for each subband.

[0025] 2. For each covariance matrix (per subband) the UE extracts a number of eigenvectors and may select the rank, i.e. number of layers.

[0026] 3. The UE does a frequency domain DFT per layer, transforming to a delay domain, whereafter it selects the M strongest taps. The resulting tensor of dimensions 2L x number of layers x M is called the linear combination coefficients, in 3GPP jargon “W2”. The W2 matrix can be used to reconstruct the, by the UE suggested, precoding matrices.

[0027] 4. The data is used as input in the AI / ML model. This could be the raw linear combination coefficients, or it could be enhanced with information about the selected beams and taps, noise levels, etc.

[0028] Performance monitoring for Al-based spatial-frequency (SF) domain CSI compression

[0029] To make sure that the performance of the AI / ML is acceptable, monitoring the performance of the AI / ML model may be required. Note that the term “acceptable” may not only be in the form of absolute value but also the form of relative value, e.g., by comparing the performance with the (expected) performance of the legacy mechanism. Performance monitoring can be performed by using an intermediate KPI, e.g., normalized mean square error (NMSE), squared generalized cosine similarity (SGCS), etc.; eventual KPI, e.g., user perceived throughput (UPT), the expected block error rate (BLER), etc.; or by monitoring the data drift either in the AI / ML input or in the AI / ML output (or both). Performance monitoring can be performed either on the UE side or the NW side (or both).

[0030] UE-side monitoring

[0031] In UE-side monitoring, a UE can be configured to measure one or more channel samples and transmit the performance metric of the (UE part) model for the respective channels. In one example, the UE can achieve this by measuring the channel, conducting the required preprocessing, conducting the mode inference, and using the output of the UE part model to a nominal decoder. The output of the nominal decoder is then compared to the ground-truth / target-CSI (i.e., the measured channels or the pre-processed measured channels). The nominal decoder may be a reference decoder agreed in 3GPP standard(s) / specification(s), a decoder provided by the NW, a decoder produced by the UE during the model training, etc. In another alternative, the UE may also train a performancemetric estimator model which may use the measured channels as the input and directly output the (estimated) performance metric of the UE part model.

[0032] NW-side monitoring

[0033] To achieve reliable model performance assessment results, the UE can be configured to measure one or more channel samples and then report the target-CSI and the model output(s) of one or multiple UE-part model(s) associated with these one or more channel samples. In the case of which multiple samples of channels are used, the UE may first accumulate the target-CSI and the model output(s) for multiple samples within a time window and then report the accumulated data together, or the UE may report the target-CSI and model output(s) per sample.

[0034] This target-CSI, in return, may be used by the NW to do performance monitoring, model retraining, fine-tuning, etc. The NW, may, for example, first generate one or more reconstructed CSI samples by feeding the one or more UE-part model output samples as inputs to the NW-part model, and then calculate one or more intermediate KPI values by comparing the one or more generated reconstructed CSI with the target CSI samples. Some of the possible intermediate KPIs, for example, are the generalized cosine similarity (GCS), squared generalized cosine similarity (SGCS), etc.

[0035] To achieve higher accuracy in performance monitoring, data collection, etc., the target-CSI needs to be as accurate as possible. However, this may bring a significant amount of overhead. Therefore, the target-CSI may need to be quantized to reduce the overhead. The quantization may be performed, e.g., with scalar quantization, with e-Type Il-like quantization, etc.

[0036] Al-based TSF domain CSI compression with predicted CSI

[0037] The 3GPP Rel. 18 study on Al CSI compression focused on the compression of a channel measurement in the spatial and frequency domain. In Rel. 19, new use cases have been considered for CSI compression to also include the temporal domain compression aspects. One mechanism to consider the temporal domain of CSI compression is by compressing multiple CSI reports at once. The rationale is that given that the reports are close in time, they could have correlations to be learned from the Al model to reach greater levels of compression. The caveat in this case is that CSI measurements of previous slots are not readily useful for future transmissions at the network node, thus, the CSI reports must be for the future. Thus, CSI prediction at the UE is introduced.

[0038] FIG. 6 illustrates an example implementation. K previous channel measurements are used to predict CSI of N_4 future slots. The N_4 slots are then fed to an encoder whichtransforms all the measurements into a single report, with a size smaller than its input. Such self-contained report is then forwarded to the NW which uses the decoder to restore the report for the N_4 slots. As with the previous illustrated examples, other processes can be introduced, such as pre-processing, and different AI / ML methods can be used.

[0039] Note that prediction and compression can be seen as logical processes, and they can be implemented separately or combined in a single AI / ML model or even be implemented by non-AI, such as autoregressive models for prediction. FIG. 7 is an example of CSI compression with predicted CSI where a sperate block with channel / CSI block prediction is used.

[0040] To conduct robust performance monitoring for TSF compression, both measured CSI and predicted CSI may be needed as target-CSI. How to accommodate both target-CSIs in the configuration in an efficient manner needs to be defined.

[0041] SUMMARY

[0042] Some embodiments advantageously provide methods, systems, and apparatuses for performance monitoring operation using at least one channel state information, CSI, type.

[0043] One or more embodiments provide mechanisms for the performance monitoring configurations for temporal spatial frequency domain CSI compression using the legacy framework, e.g., a framework defined by existing 3GPP standard(s).

[0044] According to one aspect of the present disclosure, a method implemented in a user equipment, UE, that is configured to communicate with a network node is provided. A plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types is received where the plurality of target-CSI types includes: a first target-CSI type; a second target-CSI type different from the first target CSI-type. A performance monitoring operation is performed based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types. At least one or both of a target-CSI report and a performance monitoring report associated with the performance monitoring operation are transmitted.

[0045] According to one or more embodiments of this aspect, the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

[0046] According to one or more embodiments of this aspect, the first target-CSI type is obtained from a CSI prediction of a prediction window, where the CSI prediction of aprediction window is based on measurements in a measurement window that is positioned before the prediction window in a time domain.

[0047] According to one or more embodiments of this aspect, the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

[0048] According to one or more embodiments of this aspect, the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

[0049] According to one or more embodiments of this aspect, the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of: a different measurement resource; a different compression codebook; different restricted codebook parameters; and a different reporting periodicity.

[0050] According to one or more embodiments of this aspect, CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

[0051] According to one or more embodiments of this aspect, the first target-CSI type is derived differently from the second target-CSI type.

[0052] According to one or more embodiments of this aspect, the target-CSI report includes reference CSI information; and a performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

[0053] According to another aspect of the present disclosure, a user equipment, UE, that is configured to communicate with a network node, the UE is configured to: receive a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, where the plurality of target-CSI types includes: a first target-CSI type; and a second target-CSI type different from the first target CSI-type. The UE is configured to perform a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types; and transmit at least one or both of: a target-CSI report; and a performance monitoring report associated with the performance monitoring operation.

[0054] According to one or more embodiments of this aspect, the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

[0055] According to one or more embodiments of this aspect, the first target-CSI type is obtained from a CSI prediction of a prediction window, where the CSI prediction of aprediction window is based on measurements in a measurement window that is positioned before the prediction window in a time domain.

[0056] According to one or more embodiments of this aspect, the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

[0057] According to one or more embodiments of this aspect, the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

[0058] According to one or more embodiments of this aspect, the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of: a different measurement resource; a different compression codebook; different restricted codebook parameters; and a different reporting periodicity.

[0059] According to one or more embodiments of this aspect, CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

[0060] According to one or more embodiments of this aspect, the first target-CSI type is derived differently from the second target-CSI type.

[0061] According to one or more embodiments of this aspect, the target-CSI report includes reference CSI information; and a performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

[0062] According to another aspect of the present disclosure, a method implemented in a network node that is configured to communicate with a user equipment is provided. A plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types are transmitted where the plurality of target-CSI types includes: a first target-CSI type; and a second target-CSI type different from the first target CSI-type. At least one or both of: a target-CSI report and a performance monitoring report associated with the performance monitoring operation is received. The at least one or both of the target-CSI report and performance monitoring report is associated with on a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types.

[0063] According to one or more embodiments of this aspect, the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.According to one or more embodiments of this aspect, the first target-CSI type is obtained from a CSI prediction of a prediction window, where the CSI prediction of a prediction window is based on measurements in a measurement window that is positioned before the prediction window in a time domain.

[0064] According to one or more embodiments of this aspect, the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

[0065] According to one or more embodiments of this aspect, the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

[0066] According to one or more embodiments of this aspect, the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of: a different measurement resource; a different compression codebook; different restricted codebook parameters; and a different reporting periodicity.

[0067] According to one or more embodiments of this aspect, CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

[0068] According to one or more embodiments of this aspect, the first target-CSI type is derived differently from the second target-CSI type.

[0069] According to one or more embodiments of this aspect, the target-CSI report includes reference CSI information; and a performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

[0070] According to another aspect of the present disclosure, a network node that is configured to communicate with a user equipment is provided. The network node is configured to: transmit a plurality of performance monitoring configurations associated with a plurality of target- Channel State Information, CSI, types, where the plurality of target-CSI types includes a first target-CSI type; and a second target-CSI type different from the first target CSI-type; receive at least one or both of: a target-CSI report; and a performance monitoring report associated with the performance monitoring operation. The at least one or both of the target-CSI report and performance monitoring report is associated with on a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types.

[0071] According to one or more embodiments of this aspect, the first target-CSI type andthe second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

[0072] According to one or more embodiments of this aspect, the first target-CSI type is obtained from a CSI prediction of a prediction window, where the CSI prediction of a prediction window is based on measurements in a measurement window that is positioned before the prediction window in a time domain.

[0073] According to one or more embodiments of this aspect, the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

[0074] According to one or more embodiments of this aspect, the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

[0075] According to one or more embodiments of this aspect, the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of a different measurement resource; a different compression codebook; different restricted codebook parameters; and a different reporting periodicity.

[0076] According to one or more embodiments of this aspect, CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

[0077] According to one or more embodiments of this aspect, the first target-CSI type is derived differently from the second target-CSI type.

[0078] According to one or more embodiments of this aspect, the target-CSI report includes reference CSI information; and a performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

[0079] BRIEF DESCRIPTION OF THE DRAWINGS

[0080] A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

[0081] FIG. l is a diagram of an example fully connected autoencoder;

[0082] FIG. 2 is a diagram of example CSI compression using an autoencoder;

[0083] FIG. 3 is a diagram of an example quantization operation at the output of the encoder to fit the CSI payload over the air interface;FIG. 4 is a diagram of example pre-processing of the channel into eigenvector(s) before use as input(s) to the encoder;

[0084] FIG. 5 is a diagram of example pre-processing the channel into the beam delay domain before use as inputs to the autoencoder;

[0085] FIG. 6 is a diagram of an example of CSI compression with predicted CSI where a separate block with channel / CSI clock prediction is used;

[0086] FIG. 7 is a diagram of another example of CSI compression with predicted CSI where a separate block with channel / CSI clock prediction is used;

[0087] FIG. 8 is a schematic diagram of an example network architecture illustrating a communication system according to principles disclosed herein;

[0088] FIG. 9 is a block diagram of a network node in communication with a user equipment over a wireless connection according to some embodiments of the present disclosure;

[0089] FIG. 10 is a schematic diagram of another example network architecture illustrating a communication system according to principles disclosed herein;

[0090] FIG. 11 is a flowchart of an example process in a network node according to some embodiments of the present disclosure;

[0091] FIG. 12 is a flowchart of another example process in a network node according to some embodiments of the present disclosure;

[0092] FIG. 13 is a flowchart of an example process in a user equipment according to some embodiments of the present disclosure;

[0093] FIG. 14 is a flowchart of another example process in a user equipment according to some embodiments of the present disclosure; and

[0094] FIG. 15 is a flowchart of another example process in a network node according to some embodiments of the present disclosure.

[0095] DETAILED DESCRIPTION

[0096] As discussed above, how to accommodate both target-CSIs in the configuration in an efficient manner (e.g., reusing / enhancing the existing framework) needs to be defined.

[0097] The present disclosure solves this problem by, for example, providing mechanisms for the performance monitoring configurations for temporal spatial frequency domain CSI compression using the legacy framework.

[0098] Before describing in detail exemplary embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processingsteps related to performance monitoring operation using at least one channel state information, CSI, type. Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0099] As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and / or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0100] In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

[0101] In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and / or wireless connections.

[0102] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and / or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and / orcomponents, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0103] The term “network node” used herein can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi -cell / multicast coordination entity (MCE), relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” used herein may be used to also denote a user equipment (UE) such as a wireless device (WD) or a radio network node.

[0104] The concept of ‘network (NW)’ and / or a network node (described above) can be understood as a generic network node, gNB, base station, unit within the base station, relay node, core network node, a core network node, or a device supporting D2D communication. The node may be deployed in a 5G network, or a 6G network. Moreover, although the term AI / ML model uses a single form, it should be well understood that it should not prevent the implementation of more than one AI / ML model. The UE may either be configured or autonomously switch between the models depending on certain conditions and / or proprietary implementations.

[0105] In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The UE herein can be any type of user equipment capable of communicating with a network node or another UE over radio signals, such as a wireless device (WD). The UE may also be a radio communication device, target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine communication (M2M), low-cost and / or low-complexity UE, a sensor equipped with UE, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device etc.The UE (and the NW for two-sided model cases) is assumed to have the capability of running AI / ML models supporting the AI / ML-enabled features and its respected performance monitoring procedures.

[0106] Note that although the mechanisms described below may mention Al-based CSI compression as a method to compress the CSI, this does not limit the implementation of one or more embodiments toward non-AI-based CSI compression, should performance monitoring be required for such compression mechanism. CSI may generally refer to information or knowledge of current radio channel conditions, where such information alios the network to adapt, for example, transmission and reception to the channel.

[0107] Similarly, in one or more embodiments, Al-based CSI prediction may be assumed to be used in the CSI prediction block. This, however, does not limit the implementation of one or more embodiments toward non-AI-based CSI prediction. Further, various examples or embodiments below may mention a separated CSI prediction and CSI compression block. The present disclosure, however, is also applicable to joint CSI prediction CSI compression approach, where in such approach, the CSI prediction and the CSI compression are performed by a single unified model. In such an approach, the model input is the CSI-RS measurements in the measurement window and the output is the compressed CSI of the predicted channels.

[0108] Further, the term predicted CSI may be used interchangeably with similar terms such as, for example, predicted channel, predicted PMI (e.g., predicted channel -related indicator that identifies a configuration (preferred configuration) for communication signals), predicted eigenvector, or the alike.

[0109] Also, in some embodiments the generic term “radio network node” is used. It can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell / multicast Coordination Entity (MCE), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).

[0110] Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and / or New Radio (NR) and / or 6G, may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. It is contemplated that other 3GPP systems may make use of the concepts and arrangements disclosed herein. For example, a disclosure relating to NR may also be implementable in a 6G system and / or an LTE system, a disclosure relating to 6G may alsobe implementable in a NR and / or LTE system, and a disclosure relating to LTE may also be implementable in a NR and / or 6G system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

[0111] Note further, that functions described herein as being performed by a user equipment or a network node may be distributed over a plurality of user equipments and / or network nodes. In other words, it is contemplated that the functions of the network node and user equipment described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

[0112] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0113] Some embodiments are directed to performance monitoring operation using at least one channel state information, CSI, type.

[0114] Referring to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 8 a schematic diagram of a communication system 10, according to an embodiment, such as a 3 GPP -type cellular network that may support standards such as LTE and / or NR (5G) and / or 6G, which comprises an access network 12, such as a radio access network, and a core network 14. The core network 14 includes one or more network nodes 15. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18). Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first user equipment (UE) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second UE 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of UEs 22a, 22b (collectively referred to as user equipments 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situationwhere a sole UE is in the coverage area or where a sole UE is connecting to the corresponding network node 16. Note that although only two UEs 22 and three network nodes 16 are shown for convenience, the communication system may include many more UEs 22 and network nodes 16.

[0115] As one example, in certain embodiments, access network 12 may contain some access network nodes 16 that support 3 GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 16 support (or the same access network nodes 16 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, communication system 10 may support multiple generations of related communication standards (e.g., 4G, 5G and 6G 3 GPP communication standards) and, as a result, may include an access network 12 and / or a core network 14 that supports multiple different standard generations or may include multiple access networks 12 and / or multiple core networks 14 with individual networks supporting different standards generations.

[0116] Also, it is contemplated that a UE 22 can be in simultaneous communication and / or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a UE 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, UE 22 can be in communication with an eNB for LTEZE-UTRAN, a gNB for NR / NG-RAN (i.e. being configured for multiradio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC) and / or Wi-Fi.

[0117] A network node 16 (eNB or gNB) is configured to include a configuration unit 24 which is configured to perform one or more network node 16 functions as described herein such as those functions with respect to, for example, a performance monitoring operation using at least one channel state information, CSI, type. A user equipment 22 is configured to include a CSI unit 26 which is configured to perform one or more UE 22 functions as described herein such as those functions with respect to, for example, a performance monitoring operation using at least one channel state information, CSI, type.

[0118] Example implementations, in accordance with an embodiment, of the UE 22 and network node 16 discussed in the preceding paragraphs will now be described with reference to FIG. 9.

[0119] The communication system 10 includes a network node 16 provided in a communication system 10 and including hardware 28 enabling it to communicate with the UE 22. The hardware 28 may include a communication interface 29 comprising a radiointerface 30 for setting up and maintaining at least a wireless connection 32 with a UE 22 located in a coverage area 18 served by the network node 16. The radio interface 30 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The radio interface 30 includes an array of antennas 34 to radiate and receive signal(s) carrying electromagnetic waves.

[0120] In the embodiment shown, the hardware 28 of the network node 16 further includes processing circuitry 36. The processing circuitry 36 may include a processor 38 and a memory 40. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 36 may comprise integrated circuitry for processing and / or control, e.g., one or more processors and / or processor cores and / or FPGAs (Field Programmable Gate Array) and / or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 38 may be configured to access (e.g., write to and / or read from) the memory 40, which may comprise any kind of volatile and / or nonvolatile memory, e.g., cache and / or buffer memory and / or RAM (Random Access Memory) and / or ROM (Read-Only Memory) and / or optical memory and / or EPROM (Erasable Programmable Read-Only Memory).

[0121] Thus, the network node 16 further has software 42 stored internally in, for example, memory 40, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 42 may be executable by the processing circuitry 36. The processing circuitry 36 may be configured to control any of the methods and / or processes described herein and / or to cause such methods, and / or processes to be performed, e.g., by network node 16.

[0122] Processor 38 corresponds to one or more processors 38 for performing network node 16 functions described herein. The memory 40 is configured to store data, programmatic software code and / or other information described herein. In some embodiments, the software 42 may include instructions that, when executed by the processor 38 and / or processing circuitry 36, causes the processor 38 and / or processing circuitry 36 to perform the processes described herein with respect to network node 16. For example, processing circuitry 36 of the network node 16 may include configuration unit 24 which is configured to perform one or more network node 16 functions described herein.

[0123] The network node 16 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 16 comprises multiple such entities (e.g., BTS and BSC), one or more of theseparate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 16 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 40 or portions of memory 40 for different RATs) and some components may be reused (e.g., a same antenna may be shared by different RATs). The network node 16 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 16, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 16.

[0124] In certain alternative embodiments, network node 16 may be capable of wireless communication but does not include separate radio front-end circuitry, instead, the processing circuitry 36 includes radio front-end circuitry and is connected to the antenna 34. Similarly, in some embodiments, all or some of the RF receivers, transmitters and / or transceivers are part of the radio interface 30. In still other embodiments, the communication interface 29 includes one or more ports or terminals, the radio interface 30, and the RF receiver, transmitter and / or transceiver, and the communication interface 31 communicates with baseband processing circuitry, which is part of a digital unit (not shown).

[0125] The antenna 34 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 34 may be coupled to the radio front-end circuitry in radio interface 30 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 34 is separate from the network node 16 and connectable to the network node 16 through one or more interfaces or ports.

[0126] Network node 15 can include one or more components described above with respect to network node 16, e.g., communication interface 29, radio interface 30, antenna 34, ports, processing circuitry 36, processor 38, memory 40 and software 42. These elements of network node 15 can be arranged such that network node 15 can perform various core network functions. Network node 15 can communicate wirelessly or via a wired connection with network nodes 16 via communication link 59.The communication system 10 further includes the UE 22 already referred to. The UE 22 may have hardware 44 that may include a radio interface 46 configured to set up and maintain a wireless connection 32 with a network node 16 serving a coverage area 18 in which the UE 22 is currently located. The radio interface 46 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The radio interface 46 includes an array of antennas 48 to radiate and receive signal(s) carrying electromagnetic waves.

[0127] Communication functions of the radio interface 46 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0128] The hardware 44 of the UE 22 further includes processing circuitry 50. The processing circuitry 50 may include a processor 52 and memory 54. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 50 may comprise integrated circuitry for processing and / or control, e.g., one or more processors and / or processor cores and / or FPGAs (Field Programmable Gate Array) and / or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 52 may be configured to access (e.g., write to and / or read from) memory 54, which may comprise any kind of volatile and / or nonvolatile memory, e.g., cache and / or buffer memory and / or RAM (Random Access Memory) and / or ROM (Read-Only Memory) and / or optical memory and / or EPROM (Erasable Programmable Read-Only Memory).

[0129] Thus, the UE 22 may further comprise software 56, which is stored in, for example, memory 54 at the UE 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the UE 22. The software 56 may beexecutable by the processing circuitry 50. The software 56 may include a client application 58. The client application 58 may be operable to provide a service to a human or non-human user via the UE 22.

[0130] The processing circuitry 50 may be configured to control any of the methods and / or processes described herein and / or to cause such methods, and / or processes to be performed, e.g., by UE 22. The processor 52 corresponds to one or more processors 52 for performing UE 22 functions described herein. The UE 22 includes memory 54 that is configured to store data, programmatic software code and / or other information described herein. In some embodiments, the software 56 and / or the client application 58 may include instructions that, when executed by the processor 52 and / or processing circuitry 50, causes the processor 52 and / or processing circuitry 50 to perform the processes described herein with respect to UE 22. For example, the processing circuitry 50 of the user equipment 22 may include CSI unit 26 which is configured to perform one or more UE 22 functions described herein.

[0131] In some embodiments, the inner workings of the network node 16 and UE 22 may be as shown in FIG. 9 and independently, the surrounding network topology may be that of FIG. 8.

[0132] The wireless connection 32 between the UE 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and / or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc. In some embodiments, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.

[0133] Although FIGS. 8 and 9 show various “units” such as configuration unit 24 and CSI unit 26 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

[0134] FIG. 10 is another example of a communication system 10 according to some embodiments. As used herein, the communication system 10 of FIG. 10 includes multiple access points (APs) 60 (with four example APs 60a, 60b, 60c, and 60d being depicted) and multiple wireless devices, referred to in the context of communication system 10 of FIG.10 as stations (STAs) 62 (referred to individually as STA 62a, STA 62b, STA 62c, STA 62d, and STA 62e). STA 62a is served by AP 60a in a first basic service set (BSS) 64a. STA 62b and STA 62c are served by AP 60b in a second BSS, BSS 64b. STA 62d is served by AP 60c in a third BSS, BSS 64c. STA 62e is served by AP 60d in a fourth BSS, BSS 64d. Stations 62 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like, including UEs 22 that are shown and described with respect to FIGS. 8 and 9. In other words, in some embodiment, STA 62 is a UE 22. Further, stations 62 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0135] Each of STAs 62 may connect through a radio link to one of APs 60. For example, depending on location or channel conditions experienced by a given STA 62, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0136] Each AP 60 may provide data connectivity to STAs 62 connected to a particular AP 60. As illustrated, APs 60 may be connected to a data network 66. In this way, APs 60 may also provide data connectivity between STAs 62 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like.

[0137] Accordingly, the radio link established between a given STA 62 and its serving AP 60 may be used for providing various kinds of services to STA 62, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 62 and / or on a device linked to STA 62. By way of example, FIG. 10 illustrates an application service platform 68 provided in data network 66. The application(s) executed on STA 62 and / or on one or more other devices linked to STA 62 may use the radio link for data communication with one or more other STA 62 and / or the application service platform 68, thereby enabling utilization of the corresponding service(s) at STA 62.

[0138] FIG. 11 is a flowchart of an example process in a network node 16 according to some embodiments of the present disclosure. One or more blocks described herein may beperformed by one or more elements of network node 16 such as by one or more of processing circuitry 36 (including the configuration unit 24), processor 38, and / or radio interface 30. Network node 16 is configured to transmit (Block S100) performance monitoring configurations for performing a performance monitoring operation, where the performance monitoring operation is based on at least one of a first target-channel state information, CSI, type and a second target-CSI type, as described herein. Network node 16 is configured to receive (Block SI 02) at least one of a target-CSI report or performance monitoring report, where the at least one of a target-CSI report or performance monitoring report is based on the performance monitoring operation, as described herein.

[0139] According to one or more embodiments, the first target-CSI type is based on a CSI prediction of a prediction window from channels measured in a measurement window.

[0140] According to one or more embodiments, the second target-CSI type is based on CSI-reference signal, RS, measurements in the prediction window.

[0141] According to one or more embodiments, the first target-CSI type has a different compression codebook than the second target-CSI type.

[0142] According to one or more embodiments, the performance monitoring configurations support temporal-spatial-frequency domain compression.

[0143] FIG. 12 is a flowchart of another example process in a network node 16 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 36 (including the configuration unit 24), processor 38, and / or radio interface 30. Network node 16 is configured to transmit (Block S104) a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, where the plurality of target-CSI types includes a first target-CSI type and a second target-CSI type different from the first target CSI-type, as described herein. Network node 16 is configured to receive (Block S106) at least one or both of a target-CSI report; and a performance monitoring report associated with the performance monitoring operation, and where the at least one or both of the target-CSI report and performance monitoring report being associated with on a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types, as described herein.

[0144] According to one or more embodiments, the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.According to one or more embodiments, the first target-CSI type is obtained from a CSI prediction of a prediction window, where the CSI prediction of a prediction window is based on measurements in a measurement window that is positioned before the prediction window in a time domain.

[0145] According to one or more embodiments, the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

[0146] According to one or more embodiments, the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

[0147] According to one or more embodiments, the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of a different measurement resource; a different compression codebook; different restricted codebook parameters; and a different reporting periodicity.

[0148] According to one or more embodiments, CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

[0149] According to one or more embodiments, the first target-CSI type is derived differently from the second target-CSI type.

[0150] According to one or more embodiments, the target-CSI report includes reference CSI information; and a performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

[0151] FIG. 13 is a flowchart of an example process in a user equipment 22 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of user equipment 22 such as by one or more of processing circuitry 50 (including the CSI unit 26), processor 52, and / or radio interface 46. UE 22 is configured to perform (Block SI 08) a performance monitoring operation based on at least one of a first target-channel state information, CSI, type and a second target-CSI type, as described herein. UE 22 is configured to transmit (SI 10) at least one of a target-CSI report or performance monitoring report, where the at least one of a target-CSI report or performance monitoring report is based on the performance monitoring operation, as described herein.

[0152] According to one or more embodiments, the first target-CSI type is based on a CSI prediction of a prediction window from channels measured in a measurement window.According to one or more embodiments, the second target-CSI type is based on CSI-reference signal, RS, measurements in the prediction window.

[0153] According to one or more embodiments, the first target-CSI type has a different compression codebook than the second target-CSI type.

[0154] According to one or more embodiments, the UE is further configured to receive performance monitoring configurations for supporting temporal-spatial-frequency domain compression, where the performance monitoring operation is based on the performance monitoring configurations.

[0155] FIG. 14 is a flowchart of an example process in a user equipment 22 according to some embodiments of the present disclosure. One or more blocks described herein may be performed by one or more elements of user equipment 22 such as by one or more of processing circuitry 50 (including the CSI unit 26), processor 52, and / or radio interface 46. UE 22 is configured to receive (Block SI 12) a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, where the plurality of target-CSI types include a first target-CSI type and a second target-CSI type different from the first target CSI-type, as described herein. UE 22 is configured to perform (Block SI 14) a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types, as described herein. UE 22 is configured to transmit (Block SI 16) at least one or both of a target-CSI report and a performance monitoring report associated with the performance monitoring operation.

[0156] According to one or more embodiments, the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

[0157] According to one or more embodiments, the first target-CSI type is obtained from a CSI prediction of a prediction window, where the CSI prediction of a prediction window is based on measurements in a measurement window that is positioned before the prediction window in a time domain.

[0158] According to one or more embodiments, the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

[0159] According to one or more embodiments, the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.According to one or more embodiments, the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of: a different measurement resource; a different compression codebook; different restricted codebook parameters; and a different reporting periodicity.

[0160] According to one or more embodiments, CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

[0161] According to one or more embodiments, the first target-CSI type is derived differently from the second target-CSI type.

[0162] According to one or more embodiments, the target-CSI report includes reference CSI information; and

[0163] a performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

[0164] Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for performance monitoring operation using at least one channel state information, CSI, type. CSI type may refer to a category of CSI information.

[0165] Some embodiments provide performance monitoring operation using at least one channel state information, CSI, type. One or more UE 22 functions described below may be performed by one or more of processing circuitry 50, processor 52, CSI unit 26, radio interface 46, etc. One or more network node 16 functions described below may be performed by one or more of processing circuitry 36, processor 38, configuration unit 24, radio interface 30, etc.

[0166] FIG. 15 is a flowchart an example process performed by UE 22 to conduct the performance monitoring:

[0167] Step 200 Receiving performance monitoring configuration from the NW Step 210 Performing performance monitoring operations

[0168] Step 220A Transmitting target-CSI report to the NW

[0169] Step 220B Transmitting the performance monitoring report to the NW [Step 200] Receiving performance monitoring configuration from the NW (e.g., network node 16)

[0170] To conduct performance monitoring, several parameters may need to be configured. The configuration may include one or more of target-CSI types, the resourceconfiguration for performance monitoring, performance monitoring periodicity, etc. This configuration may reuse the existing CSI report configuration framework, e.g., under CSI-ReportConfig information element (IE) as defined in 3GPP TS 38.331 V18.4.0 or under another lE / configuration that is able to provide the same functions. To accommodate the transmission of target-CSI (either or both target-CSI types of predicted CSI and measured CSI), however, some modifications / enhancements to the CSI reporting framework are needed.

[0171] Each CSI report, including the CSI report for performance monitoring, ties with resource for channel measurement (resourcesForChannelMeasurement parameter under the CSI-ReportConfig IE as defined in 3 GPP TS 38.331, or other param eter(s) in 6G with similar functionality).

[0172] In one or more embodiments, the resource for channel measurement for the target-CSI type of predicted CSI is independent to the resource for channel measurement for the target-CSI type of measured CSI, i.e., each of this target-CSI type will have its own CSI report config ID (reportConfigld parameter under CSI-ReportConfig IE) where each reportConfigldC associated to different CSI-ResourceConfigld under resourcesForChannelMeasurement parameter. In this setup, the CSI-ResourceConfigld that is associated with the target-CSI type of predicted CSI contains CSI-RSs in the measurement window while the CSI-ResourceConfigld that is associated with the target-CSI type of measured CSI contains CSI-RS in the prediction window. Note that in one or more embodiments, the CSI-ResourceConfigld that is associated with the target-CSI type of predicted CSI may be the same with the CSI-ResourceConfigld that is used for the regular CSI reporting that is configured to the UE. Further, the target-CSI types may be derived differently, e.g., a prediction based CSI-type is derived differently from a measurement-based CSI-type.

[0173] In one or more embodiments, the resource for channel measurement for the target-CSI type of predicted CSI and target-CSI type of measured CSI may be configured under a single ID. In this setup, the resourcesForChannelMeasurement parameter (or other parameter in 6G that provides such functionality) contains the CSI-RS of the measurement window and the prediction window. The determination of whether to utilize the CSI-RS in the measurement window (for the target-CSI type of predicted-CSI) or the CSI-RS in the prediction window (for the target-CSI type of measured-CSI) may depend on other configurations / parameters inside the CSI-ReportConfig. In one embodiment, theresourcesForChannelMeasurement refers to CSI-ResourceConfig as defined in 3GPP TS 38.331. Within the CSI-ResourceConfig,

[0174] • a first NZP-CSI-RS-ResourceSet identifier refers to a first NZP-CSI-RS- ResourceSet which contains information about the CSI-RS resources associated with the measurement window, and

[0175] • a second NZP-CSI-RS-ResourceSet identifier refers to a second NZP-CSI-RS- ResourceSet which contains information about the CSI-RS resources associated with the prediction window.

[0176] The determination of whether to utilize the CSI-RSs in the first NZP-CSI-RS-ResourceSet (for the target-CSI type of predicted-CSI) or the CSI-RSs in the second NZP-CSI-RS-ResourceSet (for the target-CSI type of measured-CSI) may depend on other configurations / parameters inside the CSI-ReportConfig.

[0177] In one or more embodiments, the CSI-ReportConfig (IE) (or other IE providing such functionality) may contain information on which target-CSI type the UE is to use for its performance monitoring operations. The candidate values of target-CSI may be either measured-CSI or predicted-CSI. Such information, in one example, may be included as one or more new report quantities (written as reportQuantity in 3GPP TS 38.331). The following can serve as an example of a CSI-ReportConfig where reportQuantity can be set to a value of targetCSI wherein the enumerated values of measured-CSI and predicted-CSI.

[0178] Different for the case of the UE is configured with target-CSI type of measured-CSI.

[0179] CSI-ReportConfig ::= SEQUENCE )

[0180] reportQuantity-rxx CHOICE {

[0181] targetCSI ENUMERATED {measured-CSI, predicted-CSI} }

[0182] }

[0183] For transmitting the target-CSI to the NW (e.g., network node 16), a certain compression mechanism may be needed, i.e., to minimize the overhead of the target-CSI report. Here, the same or similar codebook (CB) as used in Rel. 18 (i.e., Rel. 18 Type II Doppler codebook defined in Section 5.2.2.2.10 of 3GPP TS38.214 V14.0.0) may be used.In Rel. 18, the Type II Doppler CB, the fidelity of the compression is determined by one or more of the number of selected spatial domain basis vectors, number of frequency domain basis vectors, the number of Doppler domain basis vectors, and the number of non-zero combining coefficients to be included in the CSI report that are configured via, e.g., paramCombination-Doppler-r 18 (or other parameter(s) with such functionality) under CodebookConfig (or other parameter(s) with such functionality) parameter. As this target-CSI serves as ground truth (e.g., reference) to evaluate the (Al) TSF compression performance, the target-CSI may have sufficient fidelity.

[0184] In one or more embodiments, a restriction on the value in the paramCombination parameter is introduced for performance monitoring purpose. For example, for performance monitoring purpose, only paramCombination value of larger than a first value (e.g., 7) is allowed.

[0185] In one or mor embodiments, a new codebook under codebookConfig (or other configuration parameter with such functionality) may be introduced, where it differs on the possible paramCombination value (e.g., one or more of with higher number of spatial domain basis vectors, number of frequency domain basis vectors, the number of Doppler domain basis vectors, and the number of non-zero combining coefficients to be included in the CSI report, etc.). In one example, a new codebook type (e.g., typell-Doppler-r 18-monitoring may be introduced within the CodebookConfig IE.

[0186] In another approach, target-CSI from multiple slots (either from the prediction or measurement) may be compressed independently, e.g., by using Rel. 16 eType II CB. Similar to the above, to guarantee the fidelity of the target-CSI, a value restriction to the paramCombination parameter may be introduced for performance monitoring purpose. Note, however, in TSF compression, multiple slots of CSI are being compressed (i.e., the target-CSI contains one or more CSIs).

[0187] In one or more embodiments, a new codebook type under codebookConfig is introduced (e.g., typel I-moniloring-r 19) which contains one or multiple Rel- 16 eTypell codebooks associated to the predicted CSIs over the one or multiple prediction time instances. In yet another embodiment, multiple parameter combinations (e.g., a list of paramCombination) is configured and are associated to the predicted CSIs over the one or multiple time instances.

[0188] The two target-CSI types may entail two different fidelity requirements and two different inputs (from prediction and from measurement). In one or more embodiments, therefore, the codebook used for target-CSI type of measured CSI and target-CSI type ofpredicted CSI may be different. This may be beneficial, e.g., when the CSI-RS resources for performance monitoring is configured under one CSI-RS resource configuration. For example, at least one of new codebook, e.g., typell-measured-rl9 and typell-predicted-rl9 may be introduced. Details on the typell-measured-rl9 and typell-predicted-rl9 may follow the details on the typeII-Doppler-rl8-monitoring or typell-monitoring-rl9 mentioned before.

[0189] For the case of only one new codebook type is introduced, one target-CSI type may use the new codebook while the other target-CSI type may reuse the existing codebook type. For example, the target-CSI type of predicted CSI may be compressed with the typeII-Doppler-rl8 codebook while the target CSI type of measured CSI may be configured with typell-measured-rl9, etc.

[0190] Note that the UE 22 may be configured with either or both codebook type that is applicable for the target-CSI type of predicted CSI and the target-CSI type of measured CSI. If the UE 22 is configured with two codebook types, then the UE 22 infers that it is to conduct performance monitoring operations based on both target-CSI type of predicted CSI and measured CSI.

[0191] In one or more embodiments, a new parameter under CSI-ReportConfig (e.g., performance-monitoring-output-type) may be added where candidate values like ‘measured-CSF and ‘predicted-CSI’ are defined to indicate whether the performance monitoring output is to be calculated based on measured CSI or / and predicted CSI.

[0192] In a periodic CSI reporting, the periodicity of the CSI report is configured via CSI-ReportPeriodicityAndOffset parameter inside CSI-ReportConfig IE. In one approach, this parameter may also be used as the reporting periodicity for the performance monitoring purpose.

[0193] Different target-CSI types, however, may need to be configured with different reporting (and CSI-RS resources) periodicities. In one embodiment, the existing CSI-ReportPeriodicityAndOffset may be used to configure the reporting periodicity of the performance monitoring report / target-CSI report of one target-CSI type. In one example, the mentioned parameter may configure the reporting periodicity of performance monitoring with the target-CSI type of predicted CSI. Here, an additional parameter, e.g., CSI-ReportPeriodicityAndOffset-measured may be introduced to configure the reporting periodicity of performance monitoring with the target-CSI type of measured CSI. Note that this is only an example, and the opposite may also apply, i.e., the existing parameter isassociated with the target-CSI type of measured CSI and the new parameter may be introduced and is associated with the target-CSI type of predicted CSI.

[0194] In one or more embodiments, the reporting periodicity of performance monitoring with a certain target-CSI type may be formulated according to standard text (e.g., 3GPP standard(s)) and is associated with the performance monitoring periodicity with another target-CSI type. For example, the existing CSI-ReportPeriodicityAndOffset may be used to configure the performance monitoring report periodicity for a first target-CSI type. The performance monitoring report periodicity for a second target-CSI type is then derived from the mentioned parameter, e.g., X times the value of the mentioned parameter. In an example, X may be an integer.

[0195] [Step 210] Performing performance monitoring operations

[0196] In one example performance monitoring mechanism, the UE 22 may be configured to report the target CSI to the NW (e.g., for NW-side performance monitoring). In the case of the UE 22 configured to conduct performance monitoring operations with the predicted CSI, the UE 22 may measure the CSI-RS transmitted by the NW in the measurement window and then predict the CSI based on this measurement. This prediction result then serves as the target-CSI that will be reported to the NW. In the case of the UE 22 configured to conduct performance monitoring operations with the measured CSI, the UE 22 may measure the CSI-RS transmitted by the NW in the prediction window and use it as the target-CSI that will be reported to the NW.

[0197] In another example performance monitoring mechanism, the UE 22 may be configured to report the performance monitoring metric / results / output to the NW. In the case of the UE 22 configured to use the predicted CSI as the target CSI type, the UE 22 conducts a measurement to the CSI-RS in the measurement window and conduct CSI prediction. The result of this prediction (the predicted CSI) is then compressed using the CSI compression block. The output of the CSI compression block is then decompressed with a nominal decoder (that can be obtained via model transfer, model training, standardized reference model, etc.). The output of the decompression block is then compared to the predicted CSI obtained before to result in, e.g., an intermediate KPI (e.g., SGCS).

[0198] For the case of the UE 22 is configured to use the measured CSI as the target CSI type, the UE 22 may conduct a measurement to the CSI-RS in the measurement window and conduct CSI prediction. The result of this prediction (the predicted CSI) is then compressed using the CSI compression block. Besides measuring the CSI-RS in themeasurement window, the UE 22 also measures the CSI-RS in the prediction window to obtain the measured CSI.

[0199] The output of the CSI compression block is then decompressed with a nominal decoder (that can be obtained via model transfer, model training, standardized reference model, etc.). The output of the decompression block is then compared to the measured CSI to result in, e.g., an intermediate KPI (e.g., SGCS).

[0200] In another example, the UE 22 may measure the CSI-RS in the prediction window to obtain the measured CSI. This measured CSI is then used as inputs for the CSI compression block. The output of the CSI compression block is then decompressed with a nominal decoder (that can be obtained via one or more of model transfer, model training, standardized reference model, etc.). The output of the decompression block is then compared to the measured CSI to result in, e.g., an intermediate KPI (e.g., SGCS).

[0201] For the case of the UE 22 is configured to use both the measured CSI and the predicted CSI as the target CSI type, the UE 22 conducts two sets of performance monitoring metric / results / output for this TSF compression feature, with one set corresponding to using the predicted CSI as target CSI, and the other set corresponds to using the measured CSI as target CSI.

[0202] [Step 220A] Transmitting target-CSI report to the NW

[0203] For the case of the UE 22 configured with NW side monitoring, the UE 22 then transmit the target-CSI to the NW alongside the compressed predicted CSI. Note that these two may be reported in a single or a separate container. The container can be a CSI report, a MAC CE, or a RRC message.

[0204] The transmission of the target-CSI report may depend on the codebook configured for the respected target-CSI. For example, if the UE 22 is configured with Rel. 18 eType II (like) compression, the target-CSI across multiple slots is transmitted as a single target-CSI. On the other hand, for the case of the UE 22 is configured with Rel. 16 eType II (like) compression, the target-CSI across multiple slots is transmitted as multiple target-CSI. Note that this multiple target-CSI may be transmitted in a single transmission.

[0205] [Step 220B] Transmitting the performance monitoring report to the NW For the case where UE 22 is configured with multiple target-CSI types, the UE 22 may transmit the target-CSI report in a single or multiple container. In the case where the UE 22 configured with UE-side monitoring, the UE 22 then transmits the performance monitoring reports to the NW. The performance monitoring report may be in terms of the performance metric (e.g., SGCS) or maybe in terms of performance monitoring output(e.g., an indication of whether the performance of the AI / ML model is acceptable, above a certain threshold, etc. The performance monitoring report may also contain information on which target-CSI type the UE 22 used for producing the performance monitoring metric / output.

[0206] The following may be considered examples of one or more embodiments in UE 22. Similar examples but from the network node 16 perspective may mirror the below examples.

[0207] 1. Methods, implementing in the UE 22 capable of supporting temporal-spatial- frequency domain compression with prediction and its associated performance monitoring operation, the method comprising:

[0208] - receiving performance monitoring configurations

[0209] - performing performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type.

[0210] - transmitting at least one of target-CSI report or performance monitoring report to the NW.

[0211] 2. Example 1 where the first target-CSI type is obtained from CSI prediction of the prediction window from the measured channels in the measurement window and the second target-CSI type is obtained from the CSI-RS measurement in the prediction window.

[0212] 3. Any one of Examples 1-2 where the CSI-RS resources for the first target-CSI type is separate from the CSI-RS resources for the second target-CSI type.

[0213] 4. Any one of Examples 1-3 where the CSI-RS resources for the first target-CSI type and the CSI-RS resources for the second target-CSI type are under one configuration.

[0214] 5. Any one of Examples 1-4 where the target-CSI type is defined under CSI-report quantity parameter.

[0215] 6. Any one of Examples 1-5 where the first target-CSI type has a different compression codebook than the second target-CSI type.

[0216] 7. Any one of Examples 1-6 where for performance monitoring purpose, the values for the parameters inside the existing codebook are restricted to a subset of values.

[0217] 8. Any one of Examples 1-7 where the codebook for the first target-CSI type and the second target-CSI type is a codebook specified for performance monitoring purposes.9. Example 8 where the target-CSI of multiple slots are compressed independently using the existing codebook in a per slot basis.

[0218] 10. Example 9 where a new parameter is defined inside the CSI report configuration to indicate the target-CSI type.

[0219] 11. Any one of Examples 1-9 where the reporting periodicities of the performance monitoring for the first and the second target-CSI types are different Therefore, one or more embodiments described herein provides one or more mechanisms for the performance monitoring configurations for temporal spatial frequency domain CSI compression using the legacy framework. Specifically, the framework is modified / enhanced to allow both measurement-based target-CSI and prediction-based target-CSI.

[0220] One or more embodiments provide the following advantages: the existing framework can be reused to an extent to accommodate performance monitoring configurations for temporal spatial frequency domain CSI compression.

[0221] Some Examples

[0222] Example Al . A method implemented in a user equipment, UE 22, that is configured to communicate with a network node 16, the method comprising:

[0223] performing a performance monitoring operation based on at least one of a first target-channel state information, CSI, type and a second target-CSI type; and transmitting at least one of a target-CSI report or performance monitoring report, the at least one of a target-CSI report or performance monitoring report being based on the performance monitoring operation.

[0224] Example A2. The method of Example Al, wherein the first target-CSI type is based on a CSI prediction of a prediction window from channels measured in a measurement window.

[0225] Example A3. The method of Example A2, wherein the second target-CSI type is based on CSI-reference signal, RS, measurements in the prediction window.

[0226] Example A4. The method of any one of Examples A1-A3, wherein the first target-CSI type has a different compression codebook than the second target-CSI type.

[0227] Example A5. The method of any one of Examples A1-A4, further comprising receiving performance monitoring configurations for supporting temporal-spatial-frequency domain compression, the performance monitoring operation being based on the performance monitoring configurations.Example Bl. A user equipment, UE 22, configured to communicate with a network node 16, the UE 22 configured to, and / or comprising a radio interface 46 and / or processing circuitry 50 configured to:

[0228] perform a performance monitoring operation based on at least one of a first targetchannel state information, CSI, type and a second target-CSI type; and

[0229] transmit at least one of a target-CSI report or performance monitoring report, the at least one of a target-CSI report or performance monitoring report being based on the performance monitoring operation.

[0230] Example B2. The UE 22 of Example Bl, wherein the first target-CSI type is based on a CSI prediction of a prediction window from channels measured in a measurement window.

[0231] Example B3. The UE 22 of Example B2, wherein the second target-CSI type is based on CSI-reference signal, RS, measurements in the prediction window.

[0232] Example B4. The UE 22 of any one of Examples B1-B3, wherein the first target-CSI type has a different compression codebook than the second target-CSI type.

[0233] Example B5. The UE 22 of any one of Examples B1-B4, wherein the UE 22 is further configured to receive performance monitoring configurations for supporting temporal-spatial-frequency domain compression, the performance monitoring operation being based on the performance monitoring configurations.

[0234] Example Cl . A method implemented by a network node 16 that is configured to communicate with a user equipment, UE 22, the method comprising:

[0235] transmitting performance monitoring configurations for performing a performance monitoring operation, the performance monitoring operation being based on at least one of a first target-channel state information, CSI, type and a second target-CSI type; and receiving at least one of a target-CSI report or performance monitoring report, the at least one of a target-CSI report or performance monitoring report being based on the performance monitoring operation.

[0236] Example C2. The method of Example Cl, wherein the first target-CSI type is based on a CSI prediction of a prediction window from channels measured in a measurement window.

[0237] Example C3. The method of Example C2, wherein the second target-CSI type is based on CSI-reference signal, RS, measurements in the prediction window.

[0238] Example C4. The method of any one of Examples C1-C3, wherein the first target-CSI type has a different compression codebook than the second target-CSI type.Example C5. The method of any one of Examples C1-C4, wherein the performance monitoring configurations support temporal-spatial-frequency domain compression.

[0239] Example DI . A network node 16 configured to communicate with a user equipment, UE 22, the network node 16 configured to, and / or comprising a radio interface 30 and / or comprising processing circuitry 36 configured to:

[0240] transmit performance monitoring configurations for performing a performance monitoring operation, the performance monitoring operation being based on at least one of a first target-channel state information, CSI, type and a second target-CSI type; and receive at least one of a target-CSI report or performance monitoring report, the at least one of a target-CSI report or performance monitoring report being based on the performance monitoring operation.

[0241] Example D2. The network node 16 of Example DI, wherein the first target-CSI type is based on a CSI prediction of a prediction window from channels measured in a measurement window.

[0242] Example D3. The network node 16 of Example D2, wherein the second target-CSI type is based on CSI-reference signal, RS, measurements in the prediction window.

[0243] Example D4. The network node 16 of any one of Examples D1-D3, wherein the first target-CSI type has a different compression codebook than the second target-CSI type.

[0244] Example D5. The network node 16 of any one of Examples D1-D4, wherein the performance monitoring configurations support temporal-spatial-frequency domain compression.

[0245] As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and / or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and / or functionality described herein may be performed by, and / or associated to, a corresponding module, which may be implemented in software and / or firmware and / or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computerreadable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

[0246] Some embodiments are described herein with reference to flowchart illustrations and / or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0247] These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0248] The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0249] It is to be understood that the functions / acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

[0250] Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java®or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0251] Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments can be combined in any way and / or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

[0252] It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.

Claims

CLAIMS1. A method implemented in a user equipment, UE (22), that is configured to communicate with a network node (16), the method comprising:receiving (SI 12) a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, the plurality of target-CSI types including:a first target-CSI type;a second target-CSI type different from the first target CSI-type; performing (SI 14) a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types; and transmitting (SI 16) at least one or both of:a target-CSI report; anda performance monitoring report associated with the performance monitoring operation.

2. The method of Claim 1, wherein the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

3. The method of any one of Claims 1-2, wherein the first target-CSI type is obtained from a CSI prediction of a prediction window, the CSI prediction of a prediction window being based on measurements in a measurement window that is positioned before the prediction window in a time domain.

4. The method of Claim 3, wherein the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

5. The method of any one of Claims 1-4, wherein the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

6. The method of Claim 5, wherein the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of:a different measurement resource;a different compression codebook;different restricted codebook parameters; anda different reporting periodicity.

7. The method of any one of Claims 1-6, wherein CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

8. The method of any one of Claims 1-7, wherein the first target-CSI type is derived differently from the second target-CSI type.

9. The method of any one of Claims 1-8, wherein the target-CSI report includes reference CSI information; anda performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

10. A user equipment, UE (22), that is configured to communicate with a network node (16), the UE (22) is configured to:receive a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, the plurality of target-CSI types including:a first target-CSI type; anda second target-CSI type different from the first target CSI-type; perform a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types; and transmit at least one or both of:a target-CSI report; anda performance monitoring report associated with the performance monitoring operation.

11. The UE (22) of Claim 10, wherein the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

12. The UE (22) of any one of Claims 10-11, wherein the first target-CSI type is obtained from a CSI prediction of a prediction window, the CSI prediction of a prediction window being based on measurements in a measurement window that is positioned before the prediction window in a time domain.

13. The UE (22) of Claim 12, wherein the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

14. The UE (22) of any one of Claims 10-13, wherein the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

15. The UE (22) of Claim 14, wherein the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of:a different measurement resource;a different compression codebook;different restricted codebook parameters; anda different reporting periodicity.

16. The UE (22) of any one of Claims 10-15, wherein CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

17. The UE (22) of any one of Claims 10-16, wherein the first target-CSI type is derived differently from the second target-CSI type.

18. The UE (22) of any one of Claims 10-17, wherein the target-CSI report includes reference CSI information; anda performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

19. A method implemented in a network node (16) that is configured to communicate with a user equipment, UE (22), the method comprising:transmitting (SI 04) a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, the plurality of target-CSI types including:a first target-CSI type;a second target-CSI type different from the first target CSI-type; receiving (SI 06) at least one or both of:a target-CSI report; anda performance monitoring report associated with the performance monitoring operation; andthe at least one or both of the target-CSI report and performance monitoring report being associated with on a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types.

20. The method of Claim 19, wherein the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

21. The method of any one of Claims 19-20, wherein the first target-CSI type is obtained from a CSI prediction of a prediction window, the CSI prediction of a prediction window being based on measurements in a measurement window that is positioned before the prediction window in a time domain.

22. The method of Claim 21, wherein the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

23. The method of any one of Claims 19-22, wherein the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

24. The method of Claim 23, wherein the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of:a different measurement resource;a different compression codebook;different restricted codebook parameters; anda different reporting periodicity.

25. The method of any one of Claims 19-24, wherein CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

26. The method of any one of Claims 19-25, wherein the first target-CSI type is derived differently from the second target-CSI type.

27. The method of any one of Claims 19-26, wherein the target-CSI report includes reference CSI information; anda performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

28. A network node (16) that is configured to communicate with a user equipment, UE (22), the network node (16) configured to:transmit a plurality of performance monitoring configurations associated with a plurality of target-Channel State Information, CSI, types, the plurality of target-CSI types including:a first target-CSI type; anda second target-CSI type different from the first target CSI-type; receive at least one or both of:a target-CSI report; anda performance monitoring report associated with the performance monitoring operation; andthe at least one or both of the target-CSI report and performance monitoring report being associated with on a performance monitoring operation based on at least one of a first target-CSI type and a second target-CSI type of the plurality of target-CSI types.

29. The network node (16) of Claim 28, wherein the first target-CSI type and the second target-CSI type are associated with performing monitoring for Temporal Spatial Frequency, TSF, CSI compression.

30. The network node (16) of any one of Claims 28-29, wherein the first target-CSI type is obtained from a CSI prediction of a prediction window, the CSI prediction of a prediction window being based on measurements in a measurement window that is positioned before the prediction window in a time domain.

31. The network node (16) of Claim 30, wherein the second-target CSI type is obtained from CSI-Reference Signal, RS, measurements in the prediction window.

32. The network node (16) of any one of Claims 28-31, wherein the performance monitoring configuration indicates a different at least one of report setting and compression setting for each target CSI type.

33. The network node (16) of Claim 32, wherein the different at least one of reporting setting and compression setting for each target CSI type comprises one or more of:a different measurement resource;a different compression codebook;different restricted codebook parameters; anda different reporting periodicity.

34. The network node (16) of any one of Claims 28-33, wherein CSI-Reference Signal, CSI-RS, resources for the first target-CSI type are separate from the CSI-RS resources for the second target-CSI type.

35. The network node (16) of any one of Claims 28-34, wherein the first target-CSI type is derived differently from the second target-CSI type.

36. The network node (16) of any one of Claims 28-35, wherein the target-CSI report includes reference CSI information; anda performance monitoring report includes at least one performance metric associated with the first target-CSI type and the second target-CSI type.

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