Interference measurement resource capability and configuration reporting

Abstract

Systems and techniques for wireless communication are provided. For example, a process can include obtaining information indicating one or more configured performance values corresponding to interference prediction by a user equipment (UE). A recommended configuration of an interference measurement resource (IMR) and an interference prediction resource for interference prediction by the UE can be determined, where the recommended configuration is associated with the one or more configured performance values and one or more performance capabilities of an interference prediction machine learning network associated with the UE. The UE can transmit information indicating the recommended configuration for interference prediction by the UE to a network entity.

Description

Technical Field

[0001] This disclosure relates in general to wireless communications. For example, aspects of this disclosure relate to systems and techniques for reporting information associated with interference measurements and / or forecasting resources. Background Technology

[0002] Wireless communication systems are deployed to provide a variety of telecommunications and data services, including telephone, video, data, messaging, and broadcasting. Broadband wireless communication systems have evolved through several generations, including first-generation analog wireless telephone service (1G), second-generation (2G) digital wireless telephone service (including the transitional 2.5G networks), third-generation (3G) high-speed data wireless devices with internet capabilities, and fourth-generation (4G) services (e.g., LTE, WiMax). Examples of wireless communication systems include Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, Orthogonal Frequency Division Multiple Access (OFDMA) systems, and the Global System for Mobile Communications (GSM) system. Other wireless communication technologies include 802.11 Wi-Fi, Bluetooth, etc.

[0003] The fifth-generation (5G) mobile standard demands higher data transmission speeds, a greater number of connections, better coverage, and other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard (also known as "New Radio" or "NR") is designed to provide tens of megabits per second of data rate to each of tens of thousands of users, and 1 gigabits per second to dozens of employees on an office floor. To support large-scale sensor deployments, it should support hundreds of thousands of simultaneous connections. Summary of the Invention

[0004] The following is a simplified summary of the invention relating to one or more aspects disclosed herein. Therefore, this summary should not be considered an exhaustive overview relating to all conceived aspects, nor should it be considered to identify key or decisive elements relating to all conceived aspects or to depict the scope associated with any particular aspect. Accordingly, the following outline presents certain concepts in a simplified form relating to one or more aspects of the mechanisms disclosed herein, preceding the detailed description that follows.

[0005] Wireless communication networks utilize various techniques to perform uplink and downlink transmissions between network entities and / or user equipment (UEs). Transmissions to and / or from different network entities, UEs, cells, etc., may interfere with one or more other transmissions. For example, if an uplink transmission by a first UE and a downlink reception by a second UE are scheduled to use the same frequency simultaneously, the uplink transmission may interfere with the downlink reception. In some cases, interference may vary over time. For example, interference associated with uplink and / or downlink transmissions by UEs and / or other network entities (e.g., base stations, gNBs, etc.) may experience interference variations corresponding to changes in interference between UEs and network entities over time. In some cases, interference variations may degrade the performance of the wireless communication network.

[0006] For example, interference measurements may be determined by the UE and reported (e.g., transmitted) to the base station. The base station may use the interference measurements received from the UE to determine and / or perform scheduling for the UE, wherein the upcoming scheduling for the UE is at least partially based on the interference measurements for the UE. Scheduling performed for the UE and based on the last interference measurement for the UE may be referred to as sample-and-hold. Using sample-and-hold to serve the UE may degrade the performance of the wireless communication network (e.g., may reduce throughput, increase latency, etc.). For example, sample-and-hold may be associated with the time delay between the UE first determining the interference measurement and later receiving scheduling based on the UE's last reported interference measurement (e.g., from the base station). The delay associated with using sample-and-hold to serve the UE may result in a mismatch between the actual interference at the UE and the last reported interference used by the base station to perform scheduling for the UE. In some examples, the mismatch or different interference measurements between the UE and the base station may correspond to reduced wireless network performance and / or increased interference variations and / or relatively rapid interference variations (e.g., interference changes within a time period shorter than the delay between the UE's interference measurement report and the corresponding scheduling performed by the base station).

[0007] In some examples, interference prediction can be performed by the UE and / or the base station to reduce the variation between interference measured and reported by the UE and UE scheduling based on the determined interference. For example, the base station may perform UE scheduling based on interference predictions for the UE (e.g., one or more predicted interference measurements for the UE) rather than interference measurements determined and reported to the base station by the UE. In some cases, interference prediction may be based on predicted interference covariance matrices and / or predicted interference power, which may be determined using one or more artificial intelligence (AI) and / or machine learning (ML) networks. Using ML-based interference prediction for the UE, more advanced scheduling and / or link adaptation techniques can be used to serve the UE and increase network throughput and / or latency. For example, interference prediction can be a nonlinear task where various configuration parameters at multiple different network locations (e.g., neighboring cells, etc.) may influence the temporal, frequency, and / or spatial correlation of inter-cell interference observed at the UE. For example, inter-cell interference for a UE can vary with the configuration of scheduling behavior for each corresponding cell in a set of neighboring cells, and / or can vary based on configuration parameters such as the number of active UEs, service types in neighboring cells, load and / or resource utilization (RU), beam management, etc. In some aspects, the interference variation associated with a UE can be further based on channel variations (e.g., between the interfering cell and the UE).

[0008] Interference prediction accuracy and / or confidence level information associated with interference prediction may be based on the type of interference measurement resource (IMR), the periodicity of the IMR, the number of IMRs, and / or the pattern of the IMRs. In some cases, interference prediction accuracy and / or confidence level information may additionally be based on the time interval between the interference measurement resource and the interference prediction resource. Serving a UE based on inaccurate or low-confidence interference predictions (e.g., performing scheduling for the UE) may degrade network performance (e.g., throughput, latency, etc.). Systems and techniques are needed to provide reporting information indicating the UE's IMR capabilities between the UE and network entities (e.g., base stations, gNBs, etc.). Furthermore, systems and techniques are needed to provide reporting information indicating the accuracy and / or confidence level associated with UE interference prediction.

[0009] This document describes systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively, “Systems and Technologies”) for improving and enhancing reporting of interference prediction capability and performance information associated with a UE. In some aspects, these systems and technologies can be used to provide interference measurement resource (IMR) capability and configuration reports between a UE and a network entity (e.g., a base station, gNB, etc.). In some aspects, IMR reports generated and transmitted by the UE can indicate the UE’s IMR capabilities. In some examples, IMR reports can indicate the accuracy and / or confidence level associated with interference prediction corresponding to the UE. In some aspects, the UE can transmit information indicating a request or recommended IMR configuration for interference measurements and / or interference predictions performed by the UE. In some aspects, the UE can transmit information indicating the accuracy or confidence level associated with interference measurement predictions performed by the UE based on one or more of the type, periodicity, quantity, etc., of the IMR and / or interference prediction resources configured by the network entity. In some respects, the UE may send an IMR report or other information indicating the UE's interference prediction capability, wherein the UE's interference prediction capability corresponds to the corresponding accuracy and / or confidence level of the UE's interference prediction when the network entity allocates different combinations or configurations of IMR and / or interference prediction resources for performing UE interference prediction.

[0010] In some aspects, the UE can be configured to perform interference prediction based on a combination of one or more previous interference measurements and / or one or more previous interference predictions performed by the UE. Different configurations of the type, periodicity, number, and pattern of the IMRs used by the UE to obtain interference measurements, and the time intervals between the interference measurement resources and interference prediction resources allocated to the UE, can correspond to different performance levels (e.g., accuracy and / or confidence) of the corresponding interference predictions performed by the UE. In some aspects, a network entity can configure the UE using IMRs or interference prediction resources for interference prediction based on one or more IMR configurations and / or capabilities reported by the UE to the network entity. In some aspects, the UE can report recommended or requested configurations of measurement and prediction resources to the network entity, which can be used by the UE to meet configured interference prediction performance and / or confidence level thresholds. In some aspects, the UE can send information indicating its interference measurement and interference prediction capabilities to the network entity, and the network entity can determine and configure interference measurement and interference prediction resources for the UE based on analysis of the reported UE interference measurement and interference prediction capabilities. In some aspects, the UE can be configured to report recommended or requested configurations of interference measurement resources and interference prediction resources corresponding to various thresholds regarding the interval between interference prediction resources and interference measurement resources, and / or information about the UE's interference prediction capabilities. For example, the UE can report resource configuration recommendations and / or interference prediction performance information corresponding to different time values ​​of the interval between consecutive IMRs and interference prediction resources. In some cases, the UE interference prediction performance information can indicate the mean square error (MSE) of the UE's interference prediction or various other accuracy measures. In some examples, the UE interference prediction performance information can indicate the confidence level in the UE's interference prediction.

[0011] According to at least one exemplary example, a method for wireless communication performed at a user equipment (UE) is provided. The method includes: obtaining information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; determining a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and sending the information indicating the recommended configuration for the interference prediction performed by the UE to a network entity.

[0012] In another exemplary example, an apparatus for a user equipment (UE) for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory and configured to: obtain information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; determine a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and send the information indicating the recommended configuration for the interference prediction performed by the UE to a network entity.

[0013] In another exemplary example, a non-transitory computer-readable storage medium includes instructions stored thereon that, when executed by at least one processor, cause the at least one processor to: obtain information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; determine a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and send the information indicating the recommended configuration for the interference prediction performed by the UE to a network entity.

[0014] In another exemplary example, an apparatus for wireless communication is provided. The apparatus includes: components for obtaining information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; components for determining a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and components for sending the information indicating the recommended configuration for the interference prediction performed by the UE to a network entity.

[0015] According to at least one exemplary example, a method for wireless communication performed at a network entity is provided. The method includes: sending to a user equipment (UE) information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; receiving from the UE information indicating a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and configuring a plurality of IMRs and interference prediction resources for the UE based on the information indicating the recommended configuration.

[0016] In another exemplary example, an apparatus for a network entity for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory and configured to: send information to a user equipment (UE) indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; receive from the UE a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and configure a plurality of IMR and interference prediction resources for the UE based on the information indicating the recommended configuration.

[0017] In another exemplary example, a non-transitory computer-readable storage medium includes instructions stored thereon that, when executed by at least one processor, cause the at least one processor to: send information to a user equipment (UE) indicating one or more configured performance values ​​corresponding to interference predictions performed by the UE; receive from the UE a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference predictions performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and configure a plurality of IMRs and interference prediction resources for the UE based on the information indicating the recommended configuration.

[0018] In another exemplary example, an apparatus for wireless communication is provided. The apparatus includes: means for transmitting to a user equipment (UE) information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; means for receiving from the UE information indicating a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and means for configuring a plurality of IMRs and interference prediction resources for the UE based on the information indicating the recommended configuration.

[0019] The aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, wireless communication devices and / or processing systems, as fully described herein with reference to the accompanying drawings and description, and as illustrated in the accompanying drawings and description.

[0020] The features and technical advantages of the examples according to this disclosure have been summarized rather extensively above in order to better understand the detailed description below. Additional features and advantages will be described below. The disclosed concepts and specific examples can be readily utilized as the basis for modifying or designing other structures for achieving the same purpose of this disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, in both their organization and manner of operation, and the associated advantages, will be better understood by considering the following description in conjunction with the accompanying drawings. Each drawing provided in the drawings is for illustrative and descriptive purposes and not as a limitation of the definitions in the claims.

[0021] While aspects are described herein by way of example, those skilled in the art will understand that such aspects can be implemented in many different arrangements and scenarios. The techniques described herein can be implemented using different platform types, devices, systems, shapes, sizes, and / or package arrangements. For example, some aspects can be implemented via integrated chip examples or specific implementations or other devices based on non-modular components (e.g., end-user equipment, vehicles, communication equipment, computing devices, industrial equipment, retail / shopping devices, medical devices, and / or artificial intelligence devices). Aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and / or system-level components. Devices incorporating the described aspects and features may include additional components and features for implementing and practicing the claimed and described aspects. For example, the transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders, and / or summers). The aspects described herein are intended to be practiced in a wide variety of devices, components, systems, distributed arrangements, and / or end-user equipment of various sizes, shapes, and configurations.

[0022] Based on the accompanying drawings and detailed description, other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art. Attached Figure Description

[0023] Examples of specific implementations are described in detail below with reference to the accompanying figures:

[0024] Figure 1 This is a block diagram illustrating an example of a wireless communication network based on some examples;

[0025] Figure 2 These are illustrations of base station and user equipment (UE) designs based on some examples, which enable the transmission and processing of signals exchanged between the UE and the base station;

[0026] Figure 3 This is a diagram illustrating an example of a decomposed base station based on some examples;

[0027] Figure 4 This is a block diagram illustrating the components of a user device based on some examples;

[0028] Figure 5 This is a diagram illustrating examples of physical channels and reference signals in some example wireless networks;

[0029] Figure 6 This is a diagram illustrating examples of configurations of wireless communication networks that may experience interference, based on some examples.

[0030] Figure 7 This is a diagram illustrating examples of interference predictions associated with a base station and a UE, based on some examples;

[0031] Figure 8A This is a diagram illustrating examples of interference measurement and prediction resource configuration based on first UE interference measurement resource (IMR) capability information;

[0032] Figure 8B This is a diagram illustrating examples of interference measurement and predictive resource allocation based on second UE IMR capability information, using some examples.

[0033] Figure 9 It is a signaling diagram corresponding to the process of configuring wireless communication and interference prediction between network entities and UEs based on some examples;

[0034] Figure 10 This is a flowchart illustrating an example process for wireless communication by a UE, based on some examples;

[0035] Figure 11 This is a flowchart illustrating an example process for wireless communication by a network entity, based on some examples;

[0036] Figure 12 This is a block diagram illustrating an example of a computing system used to implement some of the aspects described in this article. Detailed Implementation

[0037] Certain aspects of this disclosure are provided below for illustrative purposes. Alternative aspects may be devised without departing from the scope of this disclosure. Furthermore, well-known elements of this disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of this disclosure. Some of the aspects described herein can be applied independently, and some of them can be combined, as will be apparent to those skilled in the art. In the following description, specific details are set forth for illustrative purposes to provide a thorough understanding of various aspects of this application. However, it will be apparent that various aspects can be practiced without these specific details. The accompanying drawings and descriptions are not intended to be limiting.

[0038] The following description provides only exemplary aspects and is not intended to limit the scope, applicability, or configuration of this disclosure. Rather, the following description of the exemplary aspects will provide those skilled in the art with a description that can be used to implement the exemplary aspects. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope of this application as set forth in the appended claims.

[0039] Wireless multiple access communication networks can employ various techniques to perform uplink transmissions from different user equipment (UEs) and downlink receptions to different UEs. In some examples, neighboring cells within a wireless communication network may have different configurations, which can lead to overlap in conflicting communications, including overlap between transmissions and / or receptions to and from UEs. For example, a wireless communication network utilizing time division duplex (TDD) may include neighboring cells with different TDD configurations, and a network utilizing full duplex (FD) may include neighboring cells with different FD configurations, etc. For example, if an uplink transmission by a first UE and a downlink reception by a second UE are scheduled to use the same frequency simultaneously, the uplink transmission may interfere with the downlink reception (e.g., overlap with the downlink reception).

[0040] Interference variations can occur when the interference associated with uplink and / or downlink transmissions of a particular UE and / or other network entities (e.g., base stations, gNBs, etc.) changes over time. Interference variations degrade the performance of wireless communication networks. For example, interference measurements may be determined by the UE and subsequently reported (e.g., transmitted) from the UE to the base station. The base station may then use the interference measurements received from the UE to determine and / or perform scheduling for the UE, where the upcoming scheduling for the UE is at least partially based on the interference measurements for the UE. UE scheduling performed based on the last measured interference for the UE may be referred to as sample-and-hold.

[0041] Using sample-and-hold to serve a UE can degrade the performance of the wireless communication network (e.g., may reduce throughput, increase latency, etc.). For example, sample-and-hold is associated with the time delay between the UE initially determining an interference measurement and later receiving a schedule based on the UE's last reported interference measurement (e.g., from the base station). The delay associated with using sample-and-hold to serve a UE can lead to a mismatch between the actual interference at the UE and the last reported interference used by the base station to perform scheduling for the UE. Mismatch between the UE and the base station, or different interference measurements, can correspond to the degraded wireless network performance described above. In some cases, interference mismatch can be associated with increased interference variation and / or relatively rapid interference variation (e.g., interference changes within a shorter time period than the delay between the UE's interference measurement report and the corresponding scheduling performed by the base station).

[0042] In some examples, interference prediction may be performed by the UE and / or by the base station to reduce the latency between interference determination and the implementation of UE scheduling based on the determined interference. For example, the base station may perform UE scheduling based on interference predictions for the UE (e.g., one or more predicted interference measurements for the UE). In some cases, interference prediction may be based on predicted interference covariance matrices and / or predicted interference power. One or more artificial intelligence (AI) and / or machine learning (ML) networks may be used to determine the predicted interference covariance matrix and / or predicted interference power. For example, interference prediction can be a highly nonlinear task because various configuration parameters at multiple different network locations (e.g., neighboring cells, etc.) may affect the temporal, frequency, and / or spatial correlation of inter-cell interference observed at a particular UE. For example, inter-cell interference measured for a particular UE may vary with the configuration of scheduling behavior for each corresponding cell in a set of neighboring cells (e.g., the type of scheduler, such as proportional fairness, round-robin, etc.; scheduling granularity, such as micro-slots, slots, multi-slots, etc.). Inter-cell interference measured for a specific UE can be additionally varied based on configuration parameters such as the number of active UEs, service types in neighboring cells, load and / or resource utilization (RU), beam management, etc. In some respects, interference variations associated with a specific UE can be further based on channel variations (e.g., between the interfering cell and the UE).

[0043] In some aspects, one or more AI and / or ML networks can be configured to learn interference variation patterns from previous interference measurement resources (IMR) and / or previous interference prediction resources for a specific UE. Using ML-based interference prediction for a specific UE, more advanced scheduling and / or link adaptation techniques can be used to serve the specific UE and increase network throughput and / or latency.

[0044] Interference prediction accuracy and / or confidence level information associated with interference prediction may be based on the type, periodicity, number, and / or pattern of IMRs. In some cases, interference prediction accuracy and / or confidence level information may additionally be based on the time interval between interference measurement resources and interference prediction resources. Serving a UE based on inaccurate or low-confidence interference predictions (e.g., performing scheduling for the UE) may degrade network performance (e.g., throughput, latency, etc.). Systems and techniques are needed to provide reporting information indicating UE IMR capabilities between the UE and network entities (e.g., base stations, gNBs, etc.). Furthermore, systems and techniques are needed to provide reporting information indicating the accuracy and / or confidence level associated with UE interference prediction.

[0045] This document describes systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively, “systems and techniques”) that can be used to provide interference measurement resource (IMR) capability and configuration reports between a UE and network entities such as base stations, gNBs, etc. In some aspects, IMR reports generated and transmitted by the UE can indicate the UE’s IMR capabilities. In some examples, IMR reports can indicate the accuracy and / or confidence level associated with interference predictions corresponding to the UE.

[0046] In some aspects, interference prediction can be performed by the UE and / or by a network entity (e.g., a base station, gNB) associated with the UE. In an exemplary example, the UE may send an IMR report indicating one or more IMR configurations for the UE. For example, the UE may send an IMR configuration indicating a request or recommendation for interference measurement and / or interference prediction performed by the UE (e.g., configured and / or sent and / or scheduled by a network entity). In some aspects, the IMR report or UE-sent information may indicate an accuracy or confidence level associated with the interference measurement prediction performed by the UE, wherein the UE accuracy or confidence level in the interference prediction is based on one or more of the type, periodicity, and / or quantity of interference measurement resources (IMRs) and / or interference prediction resources configured by the network entity. For example, a UE with relatively high interference prediction capability may use fewer IMRs than a UE with relatively low interference prediction capability to satisfy the same configured interference prediction accuracy or confidence level in its interference prediction.

[0047] In some aspects, the UE may send an IMR report or other information indicating its interference prediction capabilities, wherein the UE's interference prediction capabilities correspond to the corresponding accuracy and / or confidence level of the interference prediction when a network entity (e.g., to or for the UE) allocates different combinations or configurations of IMRs and / or interference prediction resources for performing UE interference prediction. For example, an IMR may be allocated to the UE as a physical or virtual element (e.g., a network resource or resource element, etc.) for measuring and / or determining interference levels during the corresponding time slot of the IMR. Interference prediction resources may be allocated or configured for the UE as time slots or other time periods during which the UE may perform interference prediction based on various parameters such as historical data, traffic load forecasts, and / or machine learning-based prediction outputs to estimate (e.g., predict) future interference conditions.

[0048] In some aspects, the UE can be configured to perform interference prediction based on a combination of one or more previous interference measurements performed by the UE (e.g., using one or more previous IMRs and / or historical data associated with one or more previous IMRs) and / or one or more previous interference predictions performed by the UE (e.g., interference predictions determined during one or more previous interference prediction resource or time allocations from network entities to the UE). Different configurations of the type, periodicity, number, and pattern of the IMRs used by the UE to obtain interference measurements, and the time intervals between the interference measurement resources allocated to the UE and the interference prediction resources, can correspond to different performance levels (e.g., accuracy and / or confidence) of the corresponding interference predictions performed by the UE.

[0049] In an exemplary example, a network entity may configure a UE using IMR and / or interference prediction resources for interference prediction, wherein the interference measurement resources are configured based on one or more IMR configurations and / or capabilities reported by the UE to the network entity. For example, the UE may report interference prediction recommendations (e.g., recommended or requested configurations of measurement and prediction resources for the UE), which the UE may use to meet configured (e.g., target) interference prediction performance and / or confidence level thresholds. The UE may generate and report interference prediction recommendations or configurations based on its interference measurement and prediction capabilities. In some aspects, the UE may send information indicating its interference measurement and prediction capabilities to the network entity, and the network entity may determine and configure interference measurement and prediction resources for the UE based on the reported UE interference measurement and prediction capabilities analyzed (e.g., by the network entity).

[0050] In some examples, the UE may be configured to report recommended or requested configurations of interference measurement resources and interference prediction resources corresponding to various thresholds regarding the interval between interference prediction resources and interference measurement resources, and / or information about the UE's interference prediction capabilities. For example, the UE may report resource configuration recommendations and / or interference prediction performance information corresponding to different time values ​​(e.g., in ms, number of slots, etc.) of the interval between consecutive IMR and interference prediction resources. In some cases, the UE interference prediction performance information may indicate the mean square error (MSE) of the UE's interference prediction or various other accuracy measures. In some examples, the UE interference prediction performance information may indicate the confidence level in the UE's interference prediction.

[0051] Other aspects of the system and technology will be described in relation to the accompanying drawings.

[0052] As used in this article, the phrase “based on” should not be interpreted as referring to a closed set of information, one or more conditions, one or more factors, etc. In other words, the phrase “based on A” (where “A” can be information, conditions, factors, etc.) should be interpreted as “based on at least A”, unless specifically stated differently.

[0053] As used herein, the terms “User Equipment” (UE) and “Network Entity” are not intended to be specific to or otherwise limited to any particular Radio Access Technology (RAT) unless otherwise specified. In general, a UE can be any wireless communication device (e.g., mobile phone, router, tablet computer, laptop computer, and / or tracking device, etc.), wearable device (e.g., smartwatch, smart glasses, wearable ring, and / or extended reality (XR) device (such as virtual reality (VR) headset, augmented reality (AR) headset or glasses, or mixed reality (MR) headset)), vehicle (e.g., car, motorcycle, bicycle, etc.), and / or Internet of Things (IoT) device, etc., for a user to communicate over a wireless communication network. A UE can be mobile or can (e.g., at certain times) be stationary and can communicate with a Radio Access Network (RAN). As used herein, the term "UE" can be interchangeably referred to as "access terminal" or "AT," "client device," "wireless device," "subscriber device," "subscriber terminal," "subscriber station," "user terminal," or "UT," "mobile device," "mobile terminal," "mobile station," or variations thereof. Generally, a UE can communicate with the core network via the RAN, and through the core network, the UE can connect to external networks such as the Internet and other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for the UE, such as through wired access networks, wireless local area network (WLAN) networks (e.g., based on the IEEE 802.11 communication standard), etc.

[0054] Network entities can be implemented in a converged or monolithic base station architecture, or alternatively, in a decomposed base station architecture, and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. A base station (e.g., with a converged / monolithic or decomposed base station architecture) may operate according to one of several RATs communicating with the UE (depending on the network in which it is deployed), and may alternatively be referred to as an access point (AP), network node, NodeB (NB), evolved NodeB (eNB), next-generation eNB (ng-eNB), new radio (NR) NodeB (also known as gNB or gNodeB), etc. The base station may primarily be used to support the UE's radio access, including supporting data, voice, and / or signaling connections for the supported UE. In some systems, the base station may provide edge node signaling functions, while in others, it may provide additional control and / or network management functions. The communication link through which a UE transmits signals to a base station is called an uplink (UL) channel (e.g., reverse traffic channel, reverse control channel, access channel, etc.). The communication link through which a base station transmits signals to a UE is called a downlink (DL) or forward link channel (e.g., paging channel, control channel, broadcast channel, or forward traffic channel, etc.). As used herein, the term traffic channel (TCH) can refer to uplink, reverse or downlink, and / or forward traffic channel.

[0055] The terms "network entity" or "base station" (e.g., having a converged / monolithic or decomposed base station architecture) can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs that may or may not be co-located. For example, when the term "network entity" or "base station" refers to a single physical TRP, that physical TRP may be a base station antenna corresponding to a cell (or several cell sectors) of the base station. When the term "network entity" or "base station" refers to multiple co-located physical TRPs, these physical TRPs may be an antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). When the term "base station" refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected via a transmission medium to a common source) or a remote radio headend (RRH) (a remote base station connected to a serving base station). Alternatively, a non-co-located physical TRP may be a serving base station receiving measurement reports from a UE and a neighboring base station where the UE is measuring its reference radio frequency (RF) signal (or simply "reference signal"). As used in this article, a TRP is the point by which a base station transmits and receives wireless signals, so any mention of transmitting from or receiving at a base station should be understood as referring to a specific TRP of the base station.

[0056] In some specific implementations supporting UE positioning, network entities or base stations may not support the UE's radio access (e.g., may not support data, voice, and / or signaling connections regarding the UE), but instead may transmit reference signals to the UE for measurement, and / or receive and measure signals transmitted by the UE. Such a base station may be referred to as a positioning beacon (e.g., in the case of transmitting signals to the UE) and / or as a location measurement unit (e.g., in the case of receiving and measuring signals from the UE).

[0057] As described herein, a node (which may be referred to as a node, network node, network entity, or wireless node) may include, may be included in, or may be a component of: a base station (e.g., any base station described herein), a UE (e.g., any UE described herein), a network controller, apparatus, device, computing system, integrated access and backhaul (IAB) node, distributed unit (DU), central unit (CU), remote / radio unit (RU) (which may also be referred to as a remote radio unit (RRU)), and / or another processing entity configured to perform any of the techniques described herein. For example, a network node may be a UE. Alternatively, a network node may be a base station or a network entity. Furthermore, a first network node may be configured to communicate with a second or third network node. In one aspect of this example, the first network node may be a UE, the second network node may be a base station, and the third network node may be a UE. In another aspect of this example, the first network node may be a UE, the second network node may be a base station, and the third network node may be a base station. In other aspects of this example, the first network node, the second network node, and the third network node may differ from these examples. Similarly, references to UE, base station, device, equipment, computing system, etc., may include disclosures of UE, base station, device, equipment, computing system, etc., as network nodes. For example, a disclosure of a UE being configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node. Consistent with this disclosure, once a particular example is extended according to this disclosure (e.g., a disclosure of a UE being configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node), a wider example of a narrower example may be interpreted in reverse, but in a broad, open-ended manner. In the above example where the UE is configured to receive information from the base station and the first network node is configured to receive information from the second network node, the first network node may refer to the first UE configured to receive information, the first base station, the first device, the first equipment, the first computing system, a first set of one or more components or a first processing entity, etc.; and the second network node may refer to the second UE, the second base station, the second device, the second equipment, the second computing system, a second set of one or more components or a second processing entity, etc.

[0058] As described herein, different terms may be used in various contexts to describe the transmission of information (e.g., any information, signal, etc.). Disclosure of one communication term includes disclosure of other communication terms. For example, a first network node may be described as being configured to send information to a second network node. In this example and consistent with this disclosure, disclosure that a first network node is configured to send information to a second network node includes disclosure that the first network node is configured to provide, transmit, output, communicate, or send information to the second network node. Similarly, in this example and consistent with this disclosure, disclosure that a first network node is configured to send information to a second network node includes disclosure that the second network node is configured to receive, obtain, or decode information provided, transmitted, output, communicate, or sent by the first network node.

[0059] RF signals comprise electromagnetic waves of a given frequency that transmit information across the space between a transmitter and a receiver. As used herein, a transmitter may send a single “RF signal” or multiple “RF signals” to a receiver. However, due to the propagation characteristics of RF signals through multipath channels, a receiver may receive multiple “RF signals” corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between the transmitter and receiver can be referred to as a “multipath” RF signal. As used herein, where the context clearly indicates that the term “signal” refers to a wireless signal or RF signal, an RF signal may also be referred to as a “wireless signal” or simply a “signal.”

[0060] Various aspects of the systems and technologies described herein will be discussed below with reference to the accompanying drawings. According to these aspects, Figure 1 An example of a wireless communication system 100 is illustrated. The wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 and various UEs 104. In some aspects, base station 102 may also be referred to as a "network entity" or "network node". One or more base stations in base station 102 may be implemented in an aggregated or monolithic base station architecture. Additionally or alternatively, one or more base stations in base station 102 may be implemented in a decomposed base station architecture and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. Base station 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, macro cell base stations may include eNB and / or ng-eNB (where wireless communication system 100 corresponds to a Long Term Evolution (LTE) network), or gNB (where wireless communication system 100 corresponds to an NR network), or a combination of both, and small cell base stations may include femtocells, picocells, microcells, etc.

[0061] Base station 102 can collectively form a RAN and interface with core network 170 (e.g., evolved packet core (EPC) or 5G core (5GC)) via backhaul link 122, and interface with one or more location servers 172 (which may be part of core network 170 or external to core network 170) via core network 170. Among other functions, base station 102 can perform functions related to one or more of the following: delivering user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment tracking, RAN information management (RIM), paging, location, and delivery of warning messages. Base station 102 can communicate with each other directly or indirectly (e.g., via EPC or 5GC) via backhaul link 134 (which may be wired and / or wireless).

[0062] Base station 102 can wirelessly communicate with UE 104. Each base station in base station 102 can provide communication coverage for a corresponding geographical coverage area 110. In one aspect, base station 102 in each coverage area 110 can support one or more cells. A “cell” is a logical communication entity used to communicate with a base station (e.g., on a frequency resource, referred to as a carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier (e.g., Physical Cell Identifier (PCI), Virtual Cell Identifier (VCI), Cell Global Identifier (CGI)) to distinguish cells operating via the same or different carrier frequencies. In some cases, different cells can be configured according to different protocol types that can provide access for different types of UEs (e.g., Machine Type Communication (MTC), Narrowband IoT (NB-IoT), Enhanced Mobile Broadband (eMBB), or other protocol types). Because a cell is supported by a specific base station, the term “cell” can refer to either or both of the logical communication entity and the base station supporting the logical communication entity, depending on the context. Furthermore, since the TRP is typically the physical transmission point of the cell, the terms “cell” and “TRP” can be used interchangeably. In some cases, the term "cell" may also refer to the geographic coverage area (e.g., sector) of a base station, provided that a carrier frequency can be detected within a portion of the geographic coverage area 110 and that carrier frequency is used for communication within that portion.

[0063] While the geographic coverage areas 110 of adjacent macro cell base stations 102 may partially overlap (e.g., in handover areas), some areas within geographic coverage areas 110 may substantially overlap with larger geographic coverage areas 110. For example, a small cell base station 102' may have a coverage area 110' that substantially overlaps with the coverage areas 110 of one or more macro cell base stations 102. A network that includes both small cell base stations and macro cell base stations can be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB) that can provide service to a restricted group referred to as a Closed Subscriber Group (CSG).

[0064] The communication link 120 between base station 102 and UE 104 may include uplink (also known as reverse link) transmission from UE 104 to base station 102 and / or downlink (also known as forward link) transmission from base station 102 to UE 104. Communication link 120 may use MIMO antenna techniques, including spatial multiplexing, beamforming, and / or transmit diversity. Communication link 120 may use one or more carrier frequencies. Carrier allocation may be asymmetric for downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink compared to the uplink).

[0065] The wireless communication system 100 may also include a WLAN AP 150 communicating with a WLAN station (STA) 152 via a communication link 154 in unlicensed spectrum (e.g., 5 GHz). When communicating in unlicensed spectrum, the WLAN STA 152 and / or WLAN AP 150 may perform a Free Channel Assessment (CCA) or Listen-After-Talk (LBT) process before communication to determine if the channel is available. In some examples, the wireless communication system 100 may include devices (e.g., UEs, etc.) that communicate with one or more UEs 104, base stations 102, APs 150, etc., using ultra-wideband (UWB) spectrum. The UWB spectrum may range from 3.1 GHz to 10.5 GHz.

[0066] Small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in unlicensed spectrum, small cell base station 102' can employ LTE or NR technology and use the same 5 GHz unlicensed spectrum as WLAN AP 150. Small cell base station 102' employing LTE and / or 5G in unlicensed spectrum can enhance coverage of the access network and / or increase the capacity of the access network. NR in unlicensed spectrum can be referred to as NR-U. LTE in unlicensed spectrum can be referred to as LTE-U, Licensed Assisted Access (LAA), or MulteFire.

[0067] The wireless communication system 100 may further include a millimeter-wave (mmW) base station 180, which can operate at mmW and / or near-mmW frequencies to communicate with the UE 182. The mmW base station 180 may be implemented in a converged or monolithic base station architecture, or alternatively, in a decomposed base station architecture (e.g., including one or more of a CU, DU, RU, near-RT RIC, or non-RT RIC). Extremely high frequency (EHF) is a portion of the electromagnetic spectrum that contains radio frequency (RF). EHF has a range of 30 GHz to 300 GHz, with wavelengths between 1 mm and 10 mm. Radio waves in this band can be referred to as millimeter waves. Near-mmW extends down to frequencies of 3 GHz with wavelengths of 100 mm. Ultra-high frequency (SHF) bands extend between 3 GHz and 30 GHz, and are also referred to as centimeter waves. Communication using mmW and / or near-mmW radio bands has high path loss and relatively short range. mmW base station 180 and UE 182 can utilize beamforming (transmit and / or receive) on mmW communication link 184 to compensate for extremely high path loss and short range. Furthermore, it should be understood that in alternative configurations, one or more base stations 102 may also use mmW or near-mmW and beamforming for transmission. Therefore, it should be understood that the foregoing illustrations are merely examples and should not be construed as limiting the various aspects disclosed herein.

[0068] In some aspects related to 5G, the spectrum operated by wireless network nodes or entities (e.g., base station 102 / 180, UE 104 / 182) is divided into multiple frequency ranges: FR1 (from 450 MHz to 6000 MHz), FR2 (from 24250 MHz to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In multi-carrier systems such as 5G, one of the carrier frequencies is referred to as the "primary carrier," "anchor carrier," "primary serving cell," or "PCell," and the remaining carrier frequencies are referred to as "secondary carriers," "secondary serving cells," or "SCell." In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) used by UE 104 / 182 and the cell, where UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure in that cell. The primary carrier carries all common control channels as well as UE-specific control channels and can be a carrier on a licensed frequency (however, this is not always the case). The secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured and used to provide additional radio resources once an RRC connection is established between UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier on an unlicensed frequency. The secondary carrier may contain only the necessary signaling information and signals; for example, since the primary uplink and primary downlink carriers are typically UE-specific, those UE-specific signaling information and signals may not be present on the secondary carrier. This means that different UEs 104 / 182 in a cell can have different downlink primary carriers. The same applies to the uplink primary carrier. The network can change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a “serving cell” (whether PCell or SCell) corresponds to the carrier frequency and / or component carrier through which some base stations are communicating, the terms “cell,” “serving cell,” “component carrier,” “carrier frequency,” etc., can be used interchangeably.

[0069] For example, still refer to Figure 1One of the frequencies used by macro cell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used by macro cell base station 102 and / or mmW base station 180 may be secondary carriers ("SCell"). In carrier aggregation, each carrier of base station 102 and / or UE 104 may use up to Y MHz of spectrum (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz), with up to a total of Yx MHz (x component carriers) for transmission in each direction. Component carriers may or may not be adjacent to each other in the spectrum. Carrier allocation may be asymmetric with respect to downlink and uplink (e.g., more or fewer carriers may be allocated to downlink compared to uplink). Simultaneous transmission and / or reception on multiple carriers enables UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, two aggregated 20 MHz carriers in a multi-carrier system would theoretically result in a doubling of the data rate (i.e., 40 MHz) compared to the data rate obtained by a single 20 MHz carrier.

[0070] To operate on multiple carrier frequencies, base station 102 and / or UE 104 may be equipped with multiple receivers and / or transmitters. For example, UE 104 may have two receivers, namely "Receiver 1" and "Receiver 2", where "Receiver 1" is a multi-band receiver that can be tuned to band "X" or band "Y", and "Receiver 2" is a single-band receiver that can be tuned to only band "Z". In this example, if UE 104 is being served in band "X", then band "X" will be referred to as PCell or active carrier frequency, and "Receiver 1" will need to tune from band "X" to band "Y" (SCell) to measure band "Y" (and vice versa). In contrast, regardless of whether UE 104 is being served in band "X" or band "Y", due to the separate "Receiver 2", UE 104 can measure band "Z" without interrupting service on band "X" or band "Y".

[0071] The wireless communication system 100 may also include a UE 164, which can communicate with the macro cell base station 102 on the communication link 120 and / or with the mmW base station 180 on the mmW communication link 184. For example, the macro cell base station 102 may support PCells and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0072] The wireless communication system 100 may further include one or more UEs, such as UE 190, which are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as "side links"). Figure 1 In one example, UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of the base stations in base station 102 (e.g., UE 190 can indirectly obtain cellular connectivity through this D2D P2P link), and has a D2D P2P link 194 with a WLAN STA 152 connected to WLANAP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity through this D2D P2P link). In one example, D2D P2P links 192 and 194 can use any known D2D RAT (such as LTE Direct (LTE-D), Wi-Fi Direct (Wi-Fi-D), Bluetooth). ® (etc.) to support.

[0073] Figure 2 A block diagram of a base station 102 and a UE 104 designed according to some aspects of this disclosure is shown, which enables the transmission and processing of signals exchanged between the UE and the base station. Design 200 includes components of base station 102 and UE 104, which may be... Figure 1 The base station 102 is a base station and the UE 104 is a UE. The base station 102 may be equipped with T antennas 234a to 234t, and the UE 104 may be equipped with R antennas 252a to 252r, wherein typically T≥1 and R≥1.

[0074] At base station 102, transmitting processor 220 can receive data for one or more UEs from data source 212, select one or more modulation and decoding schemes (MCS) for each UE based at least in part on channel quality indicators (CQI) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS selected for the UE, and provide data symbols for all UEs. Transmitting processor 220 can also process system information (e.g., semi-static resource allocation information (SRPI), etc.) and control information (e.g., CQI requests, grants, upper-layer signaling, channel state information, channel state feedback, etc.), and provide overhead symbols and control symbols. Transmitting processor 220 can also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signal (PSS) and secondary synchronization signal (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 can perform spatial processing (e.g., pre-decoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and can provide T output symbol streams to T modulators (MODs) 232a to 232t. The modulators 232a to 232t are shown as combined modulator-demodulators (MOD-DEMODs). In some cases, the modulators and demodulators can be separate components. Each modulator in the modulators 232a to 232t can process a corresponding output symbol stream (e.g., for an orthogonal frequency division multiplexing (OFDM) scheme, etc.) to obtain an output sample stream. Each modulator in the modulators 232a to 232t can further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. The T downlink signals can be transmitted from the modulators 232a to 232t respectively via T antennas 234a to 234t. Based on some aspects described in more detail below, position coding can be used to generate synchronization signals to transmit additional information.

[0075] At UE 104, antennas 252a to 252r can receive downlink signals from base station 102 and / or other base stations and can provide the received signals to demodulators (DEMODs) 254a to 254r respectively. Demodulators 254a to 254r are shown as combined modulator-demodulators (MOD-DEMODs). In some cases, the modulator and demodulator can be separate components. Each demodulator in demodulators 254a to 254r can condition (e.g., filter, amplify, down-convert, and digitize) the received signal to obtain an input sample. Each demodulator in demodulators 254a to 254r can further process the input sample (e.g., for OFDM, etc.) to obtain the received symbols. MIMO detector 256 can obtain the received symbols from all R demodulators 254a to 254r, perform MIMO detection on these received symbols where applicable, and provide the detected symbols. The receiver processor 258 can process (e.g., demodulate and decode) the detected symbols, provide the decoded data for UE 104 to the data sink 260, and provide the decoded control information and system information to the controller / processor 280. The channel processor can determine the Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), and / or Channel Quality Indicator (CQI), etc.

[0076] On the uplink, at UE 104, the transmit processor 264 can receive and process data from data source 262 and control information from controller / processor 280 (e.g., reports including RSRP, RSSI, RSRQ, CQI, channel state information, and / or channel state feedback, etc.). The transmit processor 264 can also generate reference symbols for one or more reference signals (e.g., based at least in part on β values ​​or sets of β values ​​associated with the one or more reference signals). Symbols from the transmit processor 264 can be pre-decoded by the TX MIMO processor 266, further processed by modulators 254a to 254r (e.g., for DFT-s-OFDM and / or CP-OFDM, etc.), and transmitted to base station 102. At base station 102, uplink signals from UE 104 and other UEs can be received by antennas 234a to 234t, processed by demodulators 232a to 232t, detected by MIMO detector 236 where applicable, and further processed by receiver processor 238 to obtain decoded data and control information transmitted by UE 104. Receiver processor 238 can provide the decoded data to data sink 239 and the decoded control information to controller (processor) 240. Base station 102 may include communication unit 244 and communicates with network controller 231 via communication unit 244. Network controller 231 may include communication unit 294, controller / processor 290, and memory 292.

[0077] In some respects, one or more components of UE 104 may be included in the housing. These include the controller 240 of base station 102, the controller / processor 280 of UE 104, and / or... Figure 2 Any other component may perform one or more techniques associated with determining the implicit uplink control information (UCI) beta value for NR.

[0078] Memory 242 and memory 282 may store data and program code for base station 102 and UE 104, respectively. Scheduler 246 may schedule UE for data transmission on downlink, uplink and / or sidelink.

[0079] In some respects, the deployment of communication systems (such as 5G New Radio (NR) systems) can involve a variety of components or constituent parts. In a 5G NR system or network, network nodes, network entities, network mobility elements, radio access network (RAN) nodes, core network nodes, network elements or network equipment (such as base stations (BS)), or one or more units (or components) performing base station functionality can be implemented in aggregated or decomposed architectures. For example, BSs (such as Node B (NB), evolved NB (eNB), NR BS, 5G NB, access point (AP), transmit / receive point (TRP), or cell, etc.) can be implemented as aggregated base stations (also known as standalone BS or monolithic BS) or decomposed base stations.

[0080] Aggregated base stations can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. Decomposed base stations can be configured to utilize a protocol stack that is physically or logically distributed across two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs) (i.e., central or distributed units). In some aspects, the CU may be implemented within a RAN node, and one or more DUs may co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other RAN nodes. DUs may be implemented to communicate with one or more RUs. Each of the CU, DU, and RU may also be implemented as a virtual unit (e.g., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU)).

[0081] Base station type operation or network design can take into account the aggregation characteristics of base station functionality. For example, decomposed base stations can be utilized in Integrated Access Backhaul (IAB) networks, Open Radio Access Networks (O-RAN (such as network configurations initiated by the O-RAN Alliance)), or Virtualized Radio Access Networks (vRAN, also known as Cloud Radio Access Networks (C-RAN)). Decomposition can include distributing functionality across two or more units in various physical locations, as well as virtually distributing the functionality of at least one unit, which enables flexibility in network design. Individual units in a decomposed base station or decomposed RAN architecture can be configured to communicate wirelessly with at least one other unit.

[0082] Figure 3A diagram illustrating an example of a decomposed base station 300 architecture is shown. The decomposed base station 300 architecture may include one or more central units (CUs) 310, which may communicate directly with the core network 320 via a backhaul link, or indirectly with the core network 320 via one or more decomposed base station units, such as a near real-time (near-RT) RAN Intelligent Controller (RIC) 325 via an E2 link, or a non-real-time (non-RT) RIC 315 associated with a Service Management and Orchestration (SMO) framework 305, or both. CUs 310 may communicate with one or more distributed units (DUs) 330 via corresponding midhaul links (such as F1 interfaces). DUs 330 may communicate with one or more radio units (RUs) 340 via corresponding fronthaul links. RUs 340 may communicate with a corresponding UE 104 via one or more radio frequency (RF) access links. In some implementations, UE 104 may be served simultaneously by multiple RUs 340.

[0083] Each unit in the array (e.g., CU 310, DU 330, RU 340, and near-RT RIC 325, non-RT RIC 315, and SMO frame 305) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via wired or wireless transmission media. Each of these units, or an associated processor or controller providing instructions to the communication interfaces of these units, may be configured to communicate with one or more other units via transmission media. For example, these units may include wired interfaces configured to receive signals or transmit signals to one or more other units via wired transmission media. Additionally, these units may include wireless interfaces that may include receivers, transmitters, or transceivers (such as radio frequency (RF) transceivers) configured to receive signals via wireless transmission media or transmit signals to one or more other units, or both.

[0084] In some aspects, the CU 310 can host one or more higher-level control functions. Such control functions may include Radio Resource Control (RRC), Packet Data Convergence Protocol (PDCP), or Service Data Adaptation Protocol (SDAP), etc. Each control function can be implemented using an interface configured to signal to other control functions hosted by the CU 310. The CU 310 can be configured to handle user plane functionality (i.e., Central Unit-User Plane (CU-UP)), control plane functionality (i.e., Central Unit-Control Plane (CU-CP)), or a combination thereof. In some specific implementations, the CU 310 can be logically divided into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP units can communicate bidirectionally with the CU-CP units via an interface such as an E1 interface. The CU 310 can be implemented to communicate with the DU 330 for network control and signaling purposes, as needed.

[0085] DU 330 may correspond to a logic unit that includes one or more base station functions for controlling the operation of one or more RU 340s. In some aspects, DU 330 may at least partially host one or more of the Radio Link Control (RLC) layer, Medium Access Control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) according to functional splits (such as those defined by the 3rd Generation Partnership Project (3GPP). In some aspects, DU 330 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signaling with other layers (and modules) hosted by DU 330 or with control functions hosted by CU 310.

[0086] Lower-layer functionality can be implemented by one or more RU 340s. In some deployments, an RU340 controlled by a DU 330 may correspond to a logical node that is at least partially based on functional decomposition, such as lower-layer functional decomposition, to host RF processing functions or low-PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, Physical Random Access Channel (PRACH) extraction and filtering, or both). In such architectures, the RU 340 may be implemented to handle over-the-air (OTA) communications with one or more UE 104s. In some specific implementations, the real-time and non-real-time aspects of control plane and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration enables the implementation of DU 330 and CU 310 in cloud-based RAN architectures such as vRAN architectures.

[0087] The SMO framework 305 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 305 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operation and maintenance interfaces such as the O1 interface. For virtualized network elements, the SMO framework 305 can be configured to interact with a cloud computing platform such as the Open Cloud (O-Cloud) 390 to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface such as the O2 interface. Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, and near-RT RIC 325. In some implementations, the SMO framework 305 can communicate with hardware aspects of the 4G RAN, such as the Open eNB (O-eNB) 311, via the O1 interface. Additionally, in some implementations, the SMO framework 305 can communicate directly with one or more RUs 340 via the O1 interface. SMO framework 305 may also include a non-RT RIC 315 configured to support the functionality of SMO framework 305.

[0088] The non-RT RIC 315 can be configured to include logical functions that enable non-real-time control and optimization of RAN elements and resources, including AI / ML workflows for model training and updates, or policy-based guidance for applications / features in the near-RT RIC 325. The non-RT RIC 315 can be coupled to or communicate with the near-RT RIC 325, such as via an A1 interface. The near-RT RIC 325 can be configured to include logical functions that enable near real-time control and optimization of RAN elements and resources via an interface, such as an E2 interface, through data collection and action, connecting one or more CU 310s, one or more DU 330s, or both, and O-eNBs to the near-RT RIC 325.

[0089] In some implementations, to generate AI / ML models to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. This information can be utilized by the near-RT RIC 325 and may be received from non-network data sources or network functions at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns in performance and employ AI / ML models to perform corrective actions via the SMO framework 305 (such as reconfiguration via O1) or by creating RAN management policies (such as A1 policies).

[0090] Figure 4 An example of a computing system 470 for a wireless device 407 is illustrated. The wireless device 407 may include client devices such as UEs (e.g., UE 104, UE 152, UE 190) or other types of devices usable by an end user (e.g., a station (STA) configured to communicate using a Wi-Fi interface). For example, the wireless device 407 may include mobile phones, routers, tablet computers, laptop computers, tracking devices, wearable devices (e.g., smartwatches, glasses, extended reality (XR) devices such as virtual reality (VR), augmented reality (AR), or mixed reality (MR) devices), Internet of Things (IoT) devices, vehicles, aircraft, and / or another device configured to communicate via a wireless communication network. The computing system 470 includes software and hardware components that may be electrically coupled or communicatively coupled (e.g., or may otherwise communicate, as applicable) via a bus 489. For example, the computing system 470 includes one or more processors 484. One or more processors 484 may include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, special-purpose hardware, any combination thereof, and / or other processing devices or systems. One or more processors 484 may use bus 489 to communicate between cores and / or with one or more memory devices 486.

[0091] The computing system 470 may also include one or more memory devices 486, one or more digital signal processors (DSPs) 482, one or more SIMs 474, one or more modems 476, one or more wireless transceivers 478, antennas 487, one or more input devices 472 (e.g., camera, mouse, keyboard, touchscreen, touchpad, keypad and / or microphone, etc.) and one or more output devices 480 (e.g., display, speaker and / or printer, etc.).

[0092] In some aspects, computing system 470 may include one or more RF interfaces configured to transmit and / or receive radio frequency (RF) signals. In some examples, the RF interface may include components such as modem 476, wireless transceiver 478, and / or antenna 487. One or more wireless transceivers 478 may transmit and receive wireless signals (e.g., signal 488) from one or more other devices via antenna 487, such as other wireless devices, network devices (e.g., base stations such as eNBs and / or gNBs, Wi-Fi access points (APs) such as routers or range extenders, etc.), and / or cloud networks, etc. In some examples, computing system 470 may include multiple antennas or antenna arrays that facilitate simultaneous transmission and reception functionality. Antenna 487 may be an omnidirectional antenna, allowing radio frequency (RF) signals to be received and transmitted in all directions. Wireless signal 488 may be transmitted via a wireless network. The wireless network may be any wireless network, such as cellular or telecommunications networks (e.g., 3G, 4G, 5G, etc.), wireless local area networks (e.g., Wi-Fi networks), Bluetooth, etc. ™ Networks and / or other networks.

[0093] In some examples, wireless signal 488 can be transmitted directly to other wireless devices using sidelink communication (e.g., using a PC5 interface, using a DSRC interface, etc.). Wireless transceiver 478 can be configured to transmit RF signals via antenna 487 for performing sidelink communication according to one or more transmit power parameters that can be associated with one or more regulated modes. Wireless transceiver 478 can also be configured to receive sidelink communication signals with different signal parameters from other wireless devices.

[0094] In some examples, one or more wireless transceivers 478 may include an RF front end, which includes one or more components such as amplifiers, mixers for down-conversion of signals (e.g., also referred to as signal multipliers), frequency synthesizers (e.g., also referred to as oscillators) that supply signals to the mixers, baseband filters, analog-to-digital converters (ADCs), one or more power amplifiers, and other components. The RF front end typically handles the selection of wireless signals 488 and the conversion of wireless signals to baseband frequencies or intermediate frequencies, and can convert RF signals to the digital domain.

[0095] In some cases, computing system 470 may include a decoder-decoder device (or codec) configured to encode and / or decode data transmitted and / or received using one or more wireless transceivers 478. In some cases, computing system 470 may include an encryption-decryption device or component configured (e.g., according to AES and / or DES standards) to encrypt and / or decrypt data transmitted and / or received by one or more wireless transceivers 478.

[0096] One or more SIMs 474 may each securely store an International Mobile Subscriber Identity (IMSI) number and associated key assigned to a user of a wireless device 407. The IMSI and key can be used to identify and authenticate the subscriber when accessing a network provided by a network service provider or operator associated with one or more SIMs 474. One or more modems 476 may modulate one or more signals to encode information to be transmitted using one or more wireless transceivers 478. One or more modems 476 may also demodulate signals received by one or more wireless transceivers 478 to decode the transmitted information. In some examples, one or more modems 476 may include a Wi-Fi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. One or more modems 476 and one or more wireless transceivers 478 may be used to transmit data from one or more SIMs 474.

[0097] The computing system 470 may also include one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices 486) (and / or communicate with them), which may include, but are not limited to, local and / or network-accessible storage devices, disk drives, drive arrays, optical storage devices, solid-state storage devices such as RAM and / or ROM, which may be programmable, flash-updatable, etc. Such storage devices may be configured to implement any suitable data storage, including but not limited to various file systems and / or database structures.

[0098] In various aspects, functionality may be stored in memory device 486 as one or more computer program products (e.g., instructions or code) and executed by one or more processors 484 and / or one or more DSPs 482. Computing system 470 may also include software elements (e.g., residing within one or more memory devices 486) including, for example, operating systems, device drivers, executable libraries, and / or other code, such as one or more applications that may include computer programs implementing the functionality provided by various aspects, and / or may be designed to implement methods and / or configure systems as described herein.

[0099] Figure 5 This is a schematic diagram illustrating example 500 of physical channels and reference signals in a wireless network. In some examples, one or more downlink channels and one or more downlink reference signals may carry information from base station 102 to UE 104. One or more uplink channels and one or more uplink reference signals may carry information from UE 104 to base station 102.

[0100] In some respects, downlink channels may include one or more of the following: a Physical Downlink Control Channel (PDCCH) carrying downlink control information (DCI), a Physical Downlink Shared Channel (PDSCH) carrying downlink data, and / or a Physical Broadcast Channel (PBCH) carrying system information. In some respects, PDSCH communication may be scheduled by PDCCH communication.

[0101] In some examples, the uplink channel may include one or more of the following: a Physical Uplink Control Channel (PUCCH) carrying uplink control information (UCI), a Physical Uplink Shared Channel (PUSCH) carrying uplink data, and / or a Physical Random Access Channel (PRACH) for initial network access. In some aspects, UE 104 may send acknowledgment (ACK) or negative acknowledgment (NACK) feedback (e.g., ACK / NACK feedback or ACK / NACK information) in the UCI on the PUCCH and / or PUSCH.

[0102] In some cases, the downlink reference signal may include one or more of the following: Synchronization Block (SSB), Channel State Information (CSI) Reference Signal (CSI-RS), Demodulation Reference Signal (DMRS), Positioning Reference Signal (PRS), and / or Phase Tracking Reference Signal (PTRS). In some examples, the uplink reference signal may include one or more of the following: Sounding Reference Signal (SRS), DMRS, and / or PTRS.

[0103] The SSB may carry or include information for initial network acquisition and synchronization. For example, the SSB may carry or include one or more of the primary synchronization signal (PSS), secondary synchronization signal (SSS), PBCH, and / or PBCH DMRS. The SSB may be referred to as a synchronization signal / PBCH (SS / PBCH) block. In some aspects, base station 102 may transmit multiple SSBs on multiple corresponding beams, and the SSBs may be used for beam selection.

[0104] The CSI-RS can carry information for downlink channel estimation (e.g., downlink CSI acquisition), which can be used for scheduling, link adaptation, or beam management. For example, base station 102 can configure a CSI-RS set for UE 104, and UE 104 can measure the configured CSI-RS set. Based on the CSI-RS measurement, UE 104 can perform channel estimation and report the channel estimation parameters to base station 102 (e.g., in a CSI report). For example, the channel estimation parameters may include one or more of the following: Channel Quality Indicator (CQI), Pre-decoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), Layer Indicator (LI), Rank Indicator (RI), and / or Reference Signal Received Power (RSRP).

[0105] In some examples, base station 102 may use CSI reports to select transmission parameters for downlink communication to UE 104. For example, base station 102 may use CSI reports to select transmission parameters including one or more of the following: the number of transmission layers (e.g., rank), the pre-decoding matrix (e.g., pre-decoder), the modulation and decoding scheme (MCS), and / or refined downlink beams (e.g., using beam refinement or beam management procedures), etc.

[0106] The DMRS can carry information used to estimate the radio channel for demodulating the associated physical channel (e.g., PDCCH, PDSCH, PBCH, PUCCH, or PUSCH). The design and mapping of the DMRS can be specific to the physical channel it is used to estimate. The DMRS is UE-specific, can be beamformed, can be confined to scheduled resources (e.g., not transmitted over broadband), and can be transmitted only when necessary. As shown, the DMRS is used for both downlink and uplink communication.

[0107] PTRS can carry information for compensating oscillator phase noise. In some cases, oscillator phase noise can increase with increasing oscillator carrier frequency. In some examples, PTRS can be used at high carrier frequencies (e.g., millimeter-wave frequencies) to mitigate oscillator phase noise. PTRS can be used to track the phase of a local oscillator and to achieve suppression of phase noise and common phase error (CPE). Figure 5 As shown, in some examples, one or more PTRS can be used for both downlink communication (e.g., on PDSCH) and uplink communication (e.g., on PUSCH).

[0108] The PRS may carry information associated with timing or ranging measurements of UE 104. For example, UE 104 may utilize one or more signals (e.g., PRS) transmitted by base station 102 to improve Observed Time Difference of Arrival (OTDOA) positioning performance. In some examples, the PRS may be a pseudo-random quadrature phase shift keying (QPSK) sequence mapped diagonally with frequency and time offsets to avoid conflicts with cell-specific reference signals and control channels (e.g., PDCCH). The PRS may be designed to improve the detectability of UE 104, which may need to detect downlink signals from multiple neighboring base stations to perform OTDOA-based positioning. Therefore, UE 104 may receive PRS from multiple cells (e.g., a reference cell and one or more neighboring cells) and may report Reference Signal Time Difference (RSTD) based on OTDOA measurements associated with the PRS received from the multiple cells. In some aspects, base station 102 may calculate the positioning of UE 104 based on the RSTD measurements reported by UE 104.

[0109] In some examples, the SRS may carry information for uplink channel estimation, which can be used for scheduling, link adaptation, pre-decoder selection, and / or beam management, etc. Base station 102 may configure one or more SRS resource sets for UE 104, and UE 104 may transmit SRS on the configured SRS resource sets. The SRS resource sets may have configurable uses, such as uplink CSI acquisition, downlink CSI acquisition for reciprocity-based operation, uplink beam management, etc. Base station 102 may measure the SRS, perform channel estimation based on the measurement, and / or use the SRS measurement to configure communication with UE 104.

[0110] As previously mentioned, the systems and techniques described herein can be used to provide interference measurement resource (IMR) capabilities and / or configuration reporting information corresponding to interference predictions performed by the UE and / or by the UE. Various types of interference can occur in wireless communication networks. For example, Figure 6 This is an example diagram illustrating the configuration of a wireless communication network 600 that may experience interference, based on some examples. The wireless communication network 600 may include multiple cells, including cell 0 (e.g., cell 610-0), cell 1 (e.g., cell 610-1), and cell 2 (e.g., cell 610-2), etc. Each of the multiple cells may include one or more network entities, such as a base station, gNB, etc. For example, cell 610-0 includes network entity 615-0, cell 610-1 includes network entity 615-1, cell 610-2 includes network entity 615-2, etc.

[0111] In some respects, Figure 6The wireless communication network 600 can be connected with Figure 1 The wireless communication network 170 or other wireless communication networks are the same as or similar. In some examples, Figure 6 Multiple residential areas 610-0, 610-1, and 610-2 can be connected with Figure 1 The cell or geographic coverage area 110 or 110' is the same as or similar to one or more of them. In some cases, one or more network entities 615-0, 615-1, 615-2 may be related to Figure 1 Base station 102 and / or mmW base station 180, Figure 2 Base station 102 Figure 5 The network entity 102 is the same as or similar to it. UE 604 can be with Figure 1 UE, Figure 2 UE 104 Figure 3 UE 104 Figure 4 UE 407 Figure 5 One or more of UE 104 and others are the same or similar.

[0112] UE located in a specific cell (e.g., located in Figure 6 UE 604 (located in cell 610-0) may experience inter-cell interference based on receiving radio signals from multiple cells (e.g., from corresponding network entities located within multiple cells). For example, inter-cell interference may be associated with signals from neighboring cells that interfere with signals within the UE's current cell. In the example where UE 604 is located in cell 610-0, inter-cell interference may correspond to interference between one or more of the following: signal 632 (e.g., between cell 0 base station 615-0 and UE 604), signal 634 (e.g., associated with cell 1 and cell 1 base station 615-1), and / or signal 636 (e.g., associated with cell 2 and cell 2 base station 615-2).

[0113] In some cases, inter-cell interference may occur more frequently toward the corresponding edge of each of the multiple cells within the wireless communication network 600, where the signal from the serving cell is relatively weak. For example, toward Figure 6At the edge of serving cell 0, the signal 632 received by UE 604 from cell 0 base station 615-0 is relatively weak. Towards the edge of serving cell 0, interference signals 634 and 636 (e.g., from cell 1 base station 615-1 and cell 2 base station 615-2, respectively) are relatively strong, and inter-cell interference can be observed by UE 604 with greater amplitude and / or greater probability. For example, inter-cell interference can be based on frequency reuse, where cellular networks (e.g., wireless communication network 600) utilize the same frequency band across different cells (e.g., cells 610-0, 610-1, 610-2, etc.) to improve spectral efficiency. In examples where neighboring cells transmit on one or more of the same frequencies, a UE towards the edge of the serving cell may experience inter-cell interference from neighboring cells transmitting on the same frequency. Higher density deployments of base stations, cells, and / or other network entities can additionally be associated with increased inter-cell interference occurrence, as well as various other factors such as irregular cell geometry or layout.

[0114] Inter-cell interference (among other types of interference that can be observed by a UE such as UE 604) can correspond to reduced signal quality or degraded (e.g., lower) signal-to-interference-plus-noise ratio (SINR), which may reduce the data rate of UE 604 and / or may increase the error rate of wireless communications to or from UE 604. In some examples, based on multiple UEs experiencing inter-cell interference at the same or similar times, inter-cell interference may be associated with a reduction in overall network throughput (e.g., reduced throughput of wireless communication network 600).

[0115] Among the techniques used for inter-cell interference mitigation or reduction, inter-cell interference coordination (ICIC) can be used to manage interference in wireless communication networks (e.g., wireless communication network 600). In ICIC, neighboring cells (e.g., cells 610-0, 610-1, and 610-2) can be configured to coordinate resource allocation to reduce or minimize inter-cell interference, including for users and UEs located near or toward the edge of a corresponding cell within the cell. In some aspects, beamforming can be used to focus transmitted signal energy toward a intended UE, which can reduce the transmitted signal strength toward unintended UEs (e.g., UEs in different and / or adjacent cells). In some cases, dynamic frequency allocation can be performed to adapt frequency allocations within different cells and / or for different UEs based on measured interference information.

[0116] Interference variations can occur when the interference associated with uplink and / or downlink transmissions of a particular UE and / or other network entities (e.g., base stations, gNBs, etc.) changes over time. Interference variations degrade the performance of wireless communication networks. For example, interference measurements may be determined by the UE and subsequently reported (e.g., transmitted) from the UE to the base station. The base station may then use the interference measurements received from the UE to determine and / or perform scheduling for the UE, where the upcoming scheduling for the UE is at least partially based on the interference measurements for the UE. UE scheduling performed based on the last measured interference for the UE may be referred to as sample-and-hold.

[0117] Using sample-and-hold to serve a UE can degrade the performance of the wireless communication network (e.g., may reduce throughput, increase latency, etc.). For example, sample-and-hold is associated with the time delay between the UE initially determining an interference measurement and later receiving a schedule based on the UE's last reported interference measurement (e.g., from the base station). The delay associated with using sample-and-hold to serve a UE can lead to a mismatch between the actual interference at the UE and the last reported interference used by the base station to perform scheduling for the UE. Mismatch between the UE and the base station, or different interference measurements, can correspond to the degraded wireless network performance described above. In some cases, interference mismatch can be associated with increased interference variation and / or relatively rapid interference variation (e.g., interference changes within a shorter time period than the delay between the UE's interference measurement report and the corresponding scheduling performed by the base station).

[0118] In some examples, interference prediction may be performed by the UE and / or by the base station to reduce the latency between interference determination and the implementation of UE scheduling based on the determined interference. For example, the base station may perform UE scheduling based on interference predictions for the UE (e.g., one or more predicted interference measurements for the UE). In some cases, interference prediction may be based on predicted interference covariance matrices and / or predicted interference power. One or more artificial intelligence (AI) and / or machine learning (ML) networks may be used to determine the predicted interference covariance matrix and / or predicted interference power. For example, interference prediction can be a highly nonlinear task because various configuration parameters at multiple different network locations (e.g., neighboring cells, etc.) may affect the temporal, frequency, and / or spatial correlation of inter-cell interference observed at a particular UE. For example, inter-cell interference measured for a particular UE may vary with the configuration of scheduling behavior for each corresponding cell in a set of neighboring cells (e.g., the type of scheduler, such as proportional fairness, round-robin, etc.; scheduling granularity, such as micro-slots, slots, multi-slots, etc.). Inter-cell interference measured for a specific UE can be additionally varied based on configuration parameters such as the number of active UEs, service types in neighboring cells, load and / or resource utilization (RU), beam management, etc. In some respects, interference variations associated with a specific UE can be further based on channel variations (e.g., between the interfering cell and the UE).

[0119] As previously mentioned, in some respects, one or more artificial intelligence (AI) and / or machine learning (ML) networks can be used to perform interference prediction, which can be configured to learn interference variation patterns from previous interference measurement resources (IMR) and / or previous interference prediction resources allocated for a particular UE.

[0120] In some respects, an IMR can be a physical or virtual element used to measure real-time interference levels in a wireless communication network. For example, in a cellular communication network, configured subcarriers, symbols, and / or resource elements (REs) can be used to measure interference as an interference-plus-noise ratio (INR) value, a signal-to-interference-plus-noise ratio (SINR) value, etc. Interference prediction resources can be allocated or configured for a UE as time slots or other time periods during which the UE can perform interference prediction based on various parameters such as historical data, traffic load predictions, and / or machine learning-based prediction outputs to estimate (e.g., predict) future interference conditions. In some cases, interference prediction resources can refer to a configured time period for which the UE performs interference prediction without transmitting physical signals or using network resources.

[0121] In some cases, the UE may be configured to perform interference prediction based on a combination of one or more previous interference measurements performed by the UE (e.g., using one or more previous IMRs and / or historical data associated with one or more previous IMRs) and / or one or more previous interference predictions performed by the UE (e.g., interference predictions determined during one or more previous interference prediction resource or time allocations from network entities to the UE). Different configurations of the type, periodicity, number, and pattern of the IMRs used by the UE to obtain interference measurements, and the time intervals between the interference measurement resources allocated to the UE and the interference prediction resources, may correspond to different performance levels (e.g., accuracy and / or confidence) of the corresponding interference predictions performed by the UE. In some aspects, during the allocated or corresponding time period for interference prediction resources associated with the UE, the UE may perform interference prediction indicating predicted or estimated interference at a future time later than the current interference prediction resource time (e.g., after the current interference prediction resource time).

[0122] In some cases, interference prediction determined by and / or for a specific UE can indicate interference on future resources scheduled for the UE (e.g., interference values, interference changes, etc.). In other cases, interference prediction can be used by the network and / or its network entities to determine updated scheduling for serving the UE based on predicted interference on future resources, where updated scheduling based on predicted interference can be used to increase network throughput and / or latency.

[0123] For example, Figure 7 This is an illustration of an example of a wireless communication system 700 that can be configured to utilize interference prediction and / or interference prediction information determined by interference prediction engine 750. In some aspects, interference prediction engine 750 can be used to determine one or more interference predictions corresponding to a specific one or more UEs at one or more future times. In some examples, interference prediction engine 750 may be implemented by and / or included in a network entity such as a base station, gNB, etc. For example, in some cases, interference prediction engine 750 may be included in and / or implemented by gNB 715 and / or UE 704. In some cases, interference prediction engine 750 may be included in the wireless communication network 700 and may be implemented outside of gNB 715 and / or UE 704. In some aspects, gNB 715 may be... Figure 6 The network entity 704 is identical or similar to one or more of network entities 615-0, 615-1, and 615-2. In some examples, UE 704 may be... Figure 6 The same as or similar to UE 604.

[0124] In one exemplary example, the wireless communication system 700 may include a UE 704, a gNB 715, an interference prediction engine 750, a scheduler 720, an RF / digital front end 710, a demodulation engine 732, and a channel state feedback (CSF) engine.

[0125] In some aspects, network scheduler 720 may be configured to generate scheduling decisions associated with wireless communication between gNB 715 and UE 704, wherein scheduler 720 makes scheduling decisions based on predicted interference determined by interference prediction engine 750 (e.g., at UE 704 and / or for communication between UE 704 and gNB 715). For example, scheduler 720 may be implemented as a gNB-side network entity and may utilize predicted interference against UE 704 to determine improved scheduling decisions for transmissions from gNB 715 to UE 704. For example, scheduler 720 may use predicted interference information from interference prediction engine 750 to exclude a subset of resources associated with a relatively high interference indication within the predicted interference information from resource allocation. In some examples, scheduler 720 may use predicted interference information to adjust one or more of the modulation and decoding scheme (MCS) and / or rank used for communication between UE 704 and gNB 715. Increasing the MCS and / or rank may correspond to an increase in rate. In some respects, the scheduler 720 may not be able to increase the MCS and rank for the UE 704 based on an increased MCS or rank corresponding to a better SNR (e.g., lower interference power). In some cases, interference prediction information may be used to activate and / or deactivate one or more additional Rx blocks (e.g., front-end linearization, additional filters, etc.) associated with one or more of the UE 704 and / or RF front-end 710.

[0126] In some respects (e.g., using interference prediction engine 750, etc.), interference prediction information determined at gNB 715 or UE 704 can be used to improve network performance on the UE side. For example, interference prediction information can be used by the RF / digital front-end 710 included in or associated with UE 704 to perform automatic gain control (AGC) gain state prediction. AGC can be implemented by UE 704 and / or RF front-end 710 to adjust the gain value used by RF front-end 710 based on the input signal power value. In an exemplary example, high interference prediction may correspond to high input power at UE 704 and / or RF front-end 710 (e.g., high input power at a future time associated with interference prediction), and interference prediction information from interference prediction engine 750 can be used to generate a relatively low gain configuration for AGC gain state prediction. Low interference prediction may correspond to low input power at UE 704 and / or RF front end 710 (e.g., low input power at a future time associated with interference prediction), and interference prediction information from interference prediction engine 750 may be used to generate a relatively high gain configuration for AGC gain state prediction.

[0127] In another exemplary example, interference prediction information from interference prediction engine 750 can be used by CSF engine 736 to implement channel state feedback (CSF) for UE 704. For example, UE 704 can utilize CSF engine 736 based on one or more received reference signals (e.g., such as...) Figure 5 The gNB 715 determines Channel State Information (CSI) using one or more reference signals from the UE 704 and / or CSF engine 736, and provides the measured CSI as feedback. Based on the CSI feedback from the UE 704 and / or CSF engine 736, the gNB 715 can determine adapted transmission parameters (e.g., modulation scheme, beamforming vector, power level, etc.) to improve network throughput and / or reliability.

[0128] In some respects, the CSF engine 736 can utilize interference prediction information to reduce interference measurement resource (IMR) overhead. For example, interference prediction indicating predicted interference at a future time t1 can be used to reduce the number of IMRs that need to be allocated to perform real-time interference measurements at t1 (e.g., based on using earlier interference predictions for time t1 combined with a smaller number of IMRs to determine real-time interference measurements at time t1, a smaller number of IMRs can be used to perform real-time interference measurements at time t1).

[0129] In some examples, the CSF engine 736 may utilize interference prediction information to perform CSI prediction and / or determine predicted CSI, where the CSI prediction and interference prediction correspond to the same future time. In some aspects, the CSI measured at time t0 can be used to determine the interference prediction for time t1, and the interference prediction for time t1 can be used to determine the CSI prediction for the same future time t1. In some cases, the CSF engine 736 may use interference prediction information to perform CSI prediction and compression, and may additionally perform channel prediction based at least in part on the interference prediction information.

[0130] In another exemplary example, demodulation engine 732 may be included in and / or associated with one or more of RF front-end 710 and / or UE 704. Demodulation engine 704 may also be referred to as a “demback”. In some aspects, demodulation engine 732 may implement advanced receivers using interference prediction information (e.g., from interference prediction engine 750) and / or associated smoothing filters of that interference prediction information. For example, demodulation engine 732 may select a receiver algorithm for use by UE 704 based on predicted interference information (e.g., predicted SINR, etc.) determined by interference prediction engine 750. In some examples, demodulation engine 732 may be configured to implement a receiver algorithm selected from at least maximum likelihood (ML) estimation or minimum mean square error (MMSE) estimation. In some cases, MMSE estimation may be used to implement a receiver algorithm with relatively low computational complexity and relatively low performance, while ML estimation may be used to implement a receiver algorithm with relatively high computational complexity and relatively high performance.

[0131] In some cases, when the interference covariance matrix is ​​close to the diagonal, the performance (e.g., accuracy) of MMSE estimation can be the same as or similar to that of ML estimation. In some examples, demodulation engine 732 can utilize interference prediction information to select an MMSE estimation receiver algorithm based on interference prediction information indicating an interference covariance matrix close to the diagonal. Demodulation engine 732 can also select an ML estimation receiver algorithm based on interference prediction information indicating an interference covariance matrix not close to the diagonal.

[0132] In some examples, (e.g., implemented at UE 704, implemented at network entity 715, or both), the interference prediction engine can be configured to perform interference prediction based on a combination of one or more previous interference measurements and / or one or more previous interference predictions (e.g., interference predictions determined during one or more previous interference prediction resource or time allocations used for interference prediction). Different configurations of the type, periodicity, number, and pattern of the IMRs used by the interference prediction engine to obtain the interference measurements can correspond to different levels of interference prediction accuracy and / or confidence (e.g., where one or more past interference measurements using configured IMRs are used to determine the interference prediction).

[0133] For example, when Channel State Information-Reference Signal (CSI-RS) is used as IMR, a UE configured to perform interference prediction (e.g., configured to implement) Figure 7 The interference prediction engine (UE, etc.) of the 750 can be correlated with the interference prediction accuracy within the range of ±5dB error of the correct interference value at a 90% confidence level.

[0134] When using different types of IMRs, the same UE can be associated with different (e.g., larger or smaller) interference prediction accuracies. For example, when Channel State Information-Interference Measurement (CSI-IM) is used as the IMR, the same UE can be associated with an error range of ±3 dB with the correct interference value at 90% confidence.

[0135] In some respects, the systems and techniques described herein can be used to determine interference prediction information for wireless communication between a UE and a network entity. The interference prediction information can be determined based on one or more prior interference measurements determined for wireless communication between the UE and the network entity. For example, the interference prediction information can be based on prior measurements associated with one or more IMRs configured by a network entity (e.g., a base station, gNB, etc.) associated with the UE. In an exemplary example, the interference prediction information may include a predicted interference covariance matrix (e.g., also referred to as R) determined based on one or more prior interference measurements of a configured IMR. nn (matrix) and / or predicted interference power (e.g., trace(R)) nn )).

[0136] As mentioned above, the accuracy and / or confidence level of interference predictions derived from past interference measurements can be based on parameters of the IMR associated with those past interference measurements, such as type, periodicity, quantity, and / or pattern. In some respects, the accuracy and / or confidence level of interference predictions can be further based on the time interval between the interference measurement resource and the interference prediction resource. For example, when a large time interval is used between the IMR and the interference prediction resource, the accuracy of interference predictions may be relatively low when generating interference predictions based on relatively old IMR measurements. When a small time interval is used between the IMR and the interference prediction resource, the accuracy of interference predictions can be relatively high when generating interference predictions based on relatively new IMR measurements. For example, a shorter time interval between the interference measurement resource and the interference prediction resource can be associated with higher accuracy in interference predictions, based on the stronger correlation between them.

[0137] In one exemplary example, the UE may be configured (e.g., to a network entity, such as a base station, gNB, etc.) to indicate capability information indicating the UE's ability to perform interference prediction within one or more configured interference prediction performance thresholds and / or one or more configured interference prediction confidence thresholds. For example, the UE may be configured (e.g., pre-configured, configured based on signaling from a network entity, etc.) to have an interference prediction performance threshold corresponding to the minimum target accuracy or performance among the errors (e.g., MSE, etc.) in the interference prediction determined by the UE. For example, interference prediction with an error of ±3 dB relative to the correct interference value may be within a configured interference prediction performance threshold of ±5 dB error. In another example, interference prediction with an error of ±3 dB relative to the correct interference value may not be within a configured interference prediction performance threshold of ±1 dB error.

[0138] In some examples, the UE may use one or more machine learning networks and / or rely on one or more machine learning-based techniques for interference prediction to achieve interference prediction. In some aspects, machine learning interference prediction may be associated with a corresponding confidence level (e.g., percentage, etc.) for each corresponding interference value predicted by the machine learning interference prediction network. In some cases, the UE may be configured (e.g., pre-configured, configured based on signaling from network entities, etc.) with an interference prediction confidence threshold that corresponds to a minimum or target confidence level for the interference prediction determined by the machine learning interference prediction network implemented by the UE. For example, an interference prediction with a 90% confidence level falls within a configured interference prediction confidence threshold of 80%, but outside a configured interference prediction confidence threshold of 95%. In another example, an interference prediction with an 80% confidence level falls within a configured interference prediction confidence threshold of 60%, but not within a configured interference prediction confidence level of 90% or 95%, and so on.

[0139] In an exemplary example, a network entity may configure a UE using IMR and / or interference prediction resources for interference prediction, wherein the interference measurement resources are configured based on one or more IMR configurations and / or capabilities reported by the UE to the network entity. For example, the UE may report interference prediction recommendations (e.g., recommended or requested configurations of measurement and prediction resources for the UE), which the UE may use to meet configured (e.g., target) interference prediction performance and / or confidence level thresholds. The UE may generate and report interference prediction recommendations or configurations based on its interference measurement and prediction capabilities. In some aspects, the UE may send information indicating its interference measurement and prediction capabilities to the network entity, and the network entity may determine and configure interference measurement and prediction resources for the UE based on the reported UE interference measurement and prediction capabilities analyzed (e.g., by the network entity).

[0140] In some respects, the “interference prediction configuration” and / or “IMR capability” associated with the UE can be used interchangeably. For example, the UE may (e.g., to a network entity) send information indicating one or more interference prediction configurations for the UE to meet configured interference prediction performance and / or confidence thresholds associated with communication between the UE and the network entity.

[0141] In some cases, interference prediction configuration information can indicate whether the UE can utilize (e.g., from network entities) the current or recent configuration of IMR and interference prediction resources to meet configured interference prediction performance (e.g., accuracy or MSE) thresholds, interference prediction confidence thresholds, or both.

[0142] In some respects, interference prediction configuration information may indicate the maximum interference prediction performance level or value (e.g., accuracy, MSE, etc.) associated with the interference prediction determined by the UE when utilizing the current or most recent configuration of IMR and interference prediction resources from network entities.

[0143] In some examples, interference prediction configuration information may indicate IMR and interference prediction resource configuration, which can be used by the UE to perform interference prediction within a configured performance (e.g., accuracy or MSE) threshold and within a configured confidence threshold.

[0144] In an exemplary example, a UE may report a recommended configuration of IMRs and interference prediction resources to be scheduled by network entities for future use by the UE to perform interference measurements and interference prediction based on those measurements. For example, the configuration information reported by the UE for interference prediction may indicate one or more types of IMRs that can be used (e.g., by the UE) to obtain interference measurements and perform interference prediction within a configured accuracy threshold and / or a configured confidence threshold.

[0145] In some respects, interference measurement resources (IMRs) can be configured as one or more of the following: Channel State Information-Reference Signal (CSI-RS), Channel State Information-Interference Measurement (CSI-IM), Physical Downlink Shared Channel-Demodulation Reference Signal (PDSCH-DMRS), and PDSCH-Open Tone. Different types of IMRs (e.g., CSI-RS, CSI-IM, PDSCH-DMRS, PDSCH-Open Tone, etc.) can correspond to different interference estimation techniques, which can be associated with different corresponding interference measurement accuracies. For example, the accuracy of interference measurements determined using CSI-RS as an IMR may differ from the accuracy of interference measurements determined using CSI-IM, the accuracy of interference measurements determined using CSI-IM as an IMR may differ from the accuracy of interference measurements determined using PDSCH-DMRS, and the accuracy of interference measurements determined using PDSCH-DMRS as an IMR may differ from the accuracy of interference measurements determined using PDSCH-Open Tone, etc. As previously mentioned, the accuracy of the interference measurements determined by the UE can correspond to the accuracy of the interference predictions performed by the UE, since the interference predictions can be based on earlier interference measurements.

[0146] In another example, UE interference prediction configuration and / or capability information may indicate the number of interference measurements. For example, when the UE measures interference on more IMRs before performing interference prediction, the UE can determine interference prediction information with higher performance (e.g., higher accuracy or MSE, and a higher confidence level or percentage). In some cases, UE interference prediction configuration and / or capability information may indicate the number of future time slots (e.g., the offset from the current time slot reported by the UE to network entities), within which the UE can perform interference prediction with a performance no less than either a configured accuracy / MSE threshold or a configured confidence threshold for interference prediction performance. For interference predictions corresponding to future times exceeding the indicated number of future time slots, the UE prediction performance may be lower than either or both of the configured accuracy / MSE threshold and the configured confidence threshold.

[0147] In another example, UE interference prediction configuration and / or capability information may indicate the periodicity of the reference signal used as a measurement resource (e.g., IMR). For example, as the periodicity of the reference signal used when measuring interference (e.g., CSI-RS, CSI-IM, PDSCH-DMRS, PDSCH-empty tone, etc.) decreases, the UE interference prediction performance can improve for predicted interference values ​​determined for future interference on the resource. In some cases, the increased periodicity of the reference signal used for IMR measurements may be associated with increased overhead for wireless communication on the network and / or between the UE and associated network entities. In an exemplary example, the UE may generate and send interference prediction configuration and / or capability information to a network entity (e.g., gNB, base station, etc.), where the interference prediction configuration or capability information indicates the periodicity of the measurement resource determined by the UE based on an interference prediction performance-signaling overhead tradeoff. For example, the UE may select the minimum periodicity of the IMR corresponding to interference predictions made by the UE, which have performance greater than or equal to a configured accuracy or MSE threshold, a configured confidence threshold, or both. In some cases, the UE may select the periodicity of the IMR for interference measurements and predictions performed by the UE, wherein the periodicity indicated by the UE in its reports to network entities is smaller than the minimum periodicity of the IMR that allows the UE to meet the configured accuracy / MSE threshold and the configured confidence level threshold (e.g., a shorter periodicity).

[0148] As mentioned above, in some aspects, a UE can be configured to generate an interference prediction configuration and send it to a network entity, wherein the interference prediction configuration indicates one or more of the following: a request configuration for interference measurement resources and interference prediction resources scheduled for the UE (e.g., scheduled by a network entity receiving the data from the UE) and / or the UE's interference measurement capabilities and interference prediction capabilities. In an exemplary example, the request configuration indicated by the UE and / or the UE's interference measurement capabilities and interference prediction capabilities may be based on, or compared with, one or more configured thresholds for interference prediction accuracy (e.g., MSE, etc.) and interference prediction confidence level (e.g., confidence percentage, etc.).

[0149] In an exemplary example, a UE may be configured to report its interference prediction configuration request and / or capability to indicate one or more measurement resource (e.g., IMR) types that the UE can use to perform interference prediction within configured thresholds of accuracy (e.g., MSE) and / or confidence. For example, the UE may use the measurement interference covariance matrix R... nnThe type of resource used (e.g., CSI-RS, CSI-IM, PDSCH-DMRS, PDSCH-empty tone, etc.) may correspond to the quality (e.g., accuracy and / or confidence) of the measured interference value determined by the UE. Higher quality (e.g., higher accuracy and / or higher confidence) interference measurements performed by the UE may correspond to higher quality (e.g., higher accuracy and / or higher confidence) interference predictions made by the UE based on higher quality interference measurements. Lower quality (e.g., lower accuracy and / or lower confidence) interference measurements performed by the UE may correspond to lower quality (e.g., lower accuracy and / or lower confidence) interference predictions made by the UE based on lower quality interference measurements.

[0150] For example, interference measurements determined based on the CSI-IM reference signal used as IMR and / or interference measurements determined based on the empty tone embedded in the scheduled resource block can be used to determine the interference covariance matrix R. nn The interference measurement, the interference covariance matrix is ​​based on:

[0151] Equation (1)

[0152] Here, y represents the received signal received by the UE (e.g., for a UE receiving an IMR including CSI-IM or empty tone), and y H The matrix y represents the transpose or conjugate transpose of the received IMR signal. For example, the term y can be calculated by determining the transpose of matrix y. H Then, the transpose and conjugate are used to obtain y. H In some aspects, during downlink time slots associated with scheduled or allocated CSI-IM or neutral tone IMRs used as IMRs, network entities do not transmit physical signals to the UE (e.g., do not transmit data to the UE). The UE's reception or received signal y during the downlink time slot associated with the CSI-IM or neutral tone IMR can indicate interference at the UE from signals not transmitted by the entity (e.g., in the example of inter-cell interference). Figure 6 The received signal y by UE 604 may correspond to interference from neighboring cell signals 634 and / or 636 at UE 604. In this example, the signal y received by the UE may be an interference signal, and the interference covariance matrix can be determined using equation (1).

[0153] In another illustrative example, interference measurements can be obtained based on the IMR, which includes a DMRS or CSI-RS reference signal, by determining the interference covariance matrix as follows:

[0154] Equation (2)

[0155] In equation (2), y represents the IMR or interference measurement resource signal received at the UE; term x represents the DMRS or CSI-RS sequence used for IMR (e.g., sent by a network entity and received by the UE); and This is a channel estimate, which can be based on DMRS, CSI-RS, or various other channel estimates. For example, the interference measurement in equation (2) can be determined as the first term. (For example, indicating the received signal y and the signal to be received with zero interference) The expected value of the product of the difference between the two terms and the second term, which includes the conjugate transpose of the first term.

[0156] In another illustrative example, interference measurements can be determined based on the covariance matrix of the PDSCH data pitch:

[0157] Equation (3)

[0158] In equation (3), And it is an Rx covariance matrix estimate (e.g., a covariance estimate obtained or received by the UE from the IMR). In some aspects, Rx covariance matrix estimation... It can be determined based on symbol-by-symbol or sub-band averaging of the data tones in the PDSCH data tones used for interference measurement, which include those sent by network entities and / or received by the UE for IMR. In some respects, the interference measurement and interference covariance matrix of equation (3) may not be guaranteed. The positive definiteness of the interference measurement, and in some cases, additional processing (such as regularization) can be performed by the UE to obtain the final interference measurement or interference covariance matrix for the IMR.

[0159] In some aspects, the UE can be configured to report interference prediction configuration requests and / or capability information that indicates one or more thresholds regarding the interval (e.g., time interval or time offset) between interference prediction resources and IMRs scheduled and sent to the UE from a network entity. For example, the UE may send or report interference prediction configuration requests and / or capability information to the same network entity that schedules and sends the interference prediction resources and IMRs used by the UE for interference prediction and measurement. In some examples, the UE interference prediction reporting information may indicate resource configurations that the UE can use to meet or exceed configured interference prediction performance thresholds (e.g., accuracy or MSE thresholds, confidence level thresholds, etc.).

[0160] In some examples, the UE may report a requested or recommended interference resource configuration (e.g., IMR configuration and / or interference prediction resource configuration) to a network entity, which indicates a specific time interval value (e.g., in milliseconds or the number of time slots in the network) between the interference prediction resource and the IMR. For example, the interference prediction resource and IMR configured and / or scheduled by the network entity at a requested time interval (e.g., the requested or recommended interference resource configuration reported by the UE to the network entity) may be used by the UE to perform interference measurement and prediction with an accuracy (e.g., interference prediction MSE or other accuracy value) and / or confidence level greater than or equal to the corresponding configured accuracy threshold and / or configured confidence level value used for interference prediction at the UE.

[0161] In some respects, a large time interval between the interference prediction resource and an earlier measurement resource (e.g., the IMR used for interference prediction at the interference prediction resource time slot) can be associated with lower accuracy in interference prediction. For example, given a long time interval between the IMR and a subsequent interference prediction resource (e.g., a weak correlation between the interference prediction resource and the interference measurement resource), the interference values ​​measured by the IMR and the last measurement of the subsequent interference prediction resource may exhibit significant variations in interference.

[0162] For example, Figure 8A This is a diagram illustrating an example of an interference measurement and prediction resource configuration 800a associated with a first time interval 845a between interference measurement resources and interference prediction resources. Figure 8B This is a diagram illustrating an example of an interference measurement and prediction resource configuration 800b associated with a second time interval 855 between interference measurement resources and interference prediction resources, wherein... Figure 8B The second time interval value is 855 compared to Figure 8A The first time interval value of 845 is longer (e.g., larger).

[0163] In an exemplary example, Figure 8A The interference measurement and prediction resource configuration 800a can correspond to a 20ms time interval 845 between consecutive or adjacent IMRs 832 and interference prediction resources 836 (e.g., a first time interval value 845 equal to 20ms). Figure 8B The interference measurement and prediction resource configuration 800b can correspond to a 40ms time interval 855 (e.g., a second time interval value 855 equal to 40ms) between consecutive or adjacent IMRs 832 and interference prediction resources 836. Figure 8A and Figure 8B The IMR 832 can be identical or similar to each other. In some cases, Figure 8A and Figure 8B The interference prediction resources 836 may be the same as or similar to each other.

[0164] In some aspects, the time interval between interference measurement resources and interference prediction resources (e.g., respectively) Figure 8A and Figure 8B The time intervals 845 and / or 855 may differ from the periodicity between consecutive IMRs 832 and / or may differ from the periodicity between consecutive disturbance prediction resources 836. For example, Figure 8A The continuous IMR 832 can have a periodicity of 40 ms for the measurement resource 848a (e.g., unlike...). Figure 8A The 20ms time interval 845 between the IMR 832 of resource configuration 800a and the interference prediction resource 836. Figure 8B The continuous IMR 832 can have a periodicity of 80 ms equal to that of the measurement resource 858 (e.g., unlike...). Figure 8B The 40ms time interval 855 between the IMR 832 of the resource configuration 800b and the interference prediction resource 836.

[0165] In an exemplary example, Figure 8B A longer time interval 855 between the IMR 832 and the predicted resource 836 may be associated with lower accuracy (e.g., a larger MSE), which is related to the accuracy determined by the UE in Figure 8B The predicted interference values ​​determined at 836 locations in the interference prediction resource are correlated. For example, using... Figure 8B Measurement resources and prediction resources (e.g., Figure 8B During a 40ms time interval 855 between the IMR 832 and the interference prediction resource 836, the UE or other interference prediction engine can determine the interference measurement value within ±5dB MSE of the baseline true interference value 812b corresponding to the interference prediction resource 836. In some aspects, the 40ms time interval 855 between the measurement resource and the prediction resource, and the 80ms periodicity 858 of the measurement resource (e.g., IMR 832), can correspond to UE interference prediction performance with 80% interference prediction accuracy (e.g., 80% confidence level) within 5dB of the baseline true interference value 812b (e.g., within ±5dB MSE accuracy).

[0166] Figure 8A The 20ms time interval between the measured resources and the predicted resources 845 and Figure 8A The 40ms periodicity of the measurement resources (e.g., IMR 832) corresponds to an improved UE interference prediction performance with 90% interference prediction accuracy (e.g., 90% confidence level) within 3dB of the baseline true interference value 812a (e.g., within ±3dB MSE accuracy).

[0167] In another illustrative example, when the UE is configured with a prediction resource 50 ms away from the measurement resource (e.g., by a network entity), it can achieve ±3 dB of interference prediction MSE (e.g., ±3 dB MSE interference prediction accuracy associated with the 50 ms time interval between the interference measurement resource and the interference prediction resource scheduled by the network entity for the UE). When the network entity configures the IMR and interference prediction resource with a time interval of 100 ms, the same UE can achieve lower or reduced interference prediction performance with an interference prediction MSE accuracy dropping to ±5 dB.

[0168] In some respects, the accuracy and / or confidence level of UE interference prediction can be based on the frequency and / or beam spacing between the interference measurement resource and the interference prediction resource. For example, the accuracy and / or confidence level of UE interference prediction can increase or decrease as the frequency and / or beam spacing between the IMR and the interference prediction resource increases or decreases, and vice versa.

[0169] In an exemplary example, the UE may be configured to transmit configuration information 822a or 822b indicating interference prediction capabilities and / or performance values ​​implemented at the UE (e.g., relative to a configured MSE or accuracy threshold, and / or relative to a configured confidence level threshold, etc.) of interference measurement capabilities and interference prediction capabilities. For example, Figure 8A Configuration information 822a may indicate that, in order to achieve interference prediction performance within a configured threshold with ±3dB MSE accuracy of 90% confidence, the UE recommends or requests an interference resource configuration with a 20ms time interval 845 between the IMR 832 and the interference prediction resource 836. In some cases, configuration information 822a may indicate a UE recommendation or request for an interference resource configuration that additionally utilizes the 40ms periodicity of the IMR 832 scheduled by the network entity receiving configuration information 822a.

[0170] In another example, Figure 8B Configuration information 822b may indicate that the UE may, based on a request from the UE (e.g., to a network entity) for configuration of interference resources utilizing the 40ms time interval 855 between IMR 832 and interference prediction resource 836, implement a configured threshold (e.g., different from the one configured for interference resources) with ±5dB MSE accuracy at 80% confidence level. Figure 8A The example demonstrates interference prediction performance within the configured threshold. In some cases, Figure 8B The configuration information 822b can indicate a UE recommendation or request for an interference resource configuration, which additionally utilizes the 80ms periodicity of the IMR832 scheduled by the network entity that receives the configuration information 822b.

[0171] Figure 9This is a signaling diagram corresponding to the process 900 for configuring wireless communication and interference prediction between network entity 905 and UE 904, based on some examples. In one illustrative example, Figure 9 The UE 904 can be used with Figure 1 One or more of the various UEs in Figure 8 (including) Figure 6 UE 604 Figure 7 The same as or similar to UE 704, etc. In some respects, Figure 9 The network entity 905 can be a base station, gNB, etc. In some cases, Figure 9 Network entity 905 can be with Figure 1 To one or more of the various network entities in Figure 8 (including) Figure 6 Base stations 615-0, 615-1 and / or 615-21, Figure 7 (same as or similar to gNB 715, etc.)

[0172] In some examples, network entity 905 may be associated with the serving cell of UE 904 (e.g., network entity 905 may be associated with...). Figure 6 The serving base station 615-0 of cell 0 is the same, and UE 904 can be with Figure 6 (The same applies to UE 604 in cell 0, etc.). In an exemplary example, network entity 905 may send configuration information 920 to UE 904 indicating one or more interference prediction performance thresholds and / or one or more interference prediction confidence thresholds. For example, configuration information 920 may be used to configure UE 904 with thresholds for one or more upcoming interference prediction resources or time windows (e.g., determined and / or requested by network entity 905) that will be scheduled by network entity 905 for UE 904 to determine interference predictions.

[0173] In some respects, configuration information 920 may indicate a configured accuracy value threshold or MSE threshold for the maximum difference configured by network entity 905, which is the difference between the predicted interference value determined by UE 904 and the actual interference measurement observed by UE 904 for the same time slot associated with the interference prediction.

[0174] At box 930, UE 904 may determine a recommended or requested configuration of interference resources corresponding to upcoming interference measurement resources (e.g., IMRs) and / or upcoming interference prediction resources, which will be configured or scheduled by network entity 905 for interference measurement and prediction to be performed by UE 904. For example, the recommended or requested configuration of interference measurement resources and interference prediction resources 930 may indicate one or more of the following: the type of reference signal for the IMR (e.g., CSI-RS, CSI-IM, PDSCH-DMRS, PDSCH-empty tone, etc.), the number of IMRs, the number of interference prediction resources, the number of IMRs used for interference prediction at each interference prediction resource, the periodicity between consecutive IMRs, the periodicity between consecutive interference prediction resources, the pattern of IMRs and interference prediction resources scheduled by the network entity for the UE, and / or the time interval value between the IMRs used by the UE for interference measurement and prediction and the interference prediction resources (e.g., in milliseconds, in the number of time slots, etc.).

[0175] In some examples, UE 904 may analyze its interference measurement and prediction capabilities against the configured interference prediction performance and confidence threshold 920 indicated by network entity 905 to determine one or more recommended or requested configurations of IMR and interference prediction resources to be scheduled by network entity 905.

[0176] For example, in an exemplary example, network entity 905 may indicate a configured interference prediction performance threshold 920 that requires UE 904 to meet, and UE 904 may determine at block 930 one or more IMR and interference prediction resource configurations corresponding to interference prediction performance of UE 904 that is greater than or equal to the corresponding configured performance threshold 920 indicated by network entity 905.

[0177] In another illustrative example, network entity 905 may instruct UE 904 to attempt to meet a configured interference prediction performance threshold 920 without requiring UE 904 to achieve interference prediction performance greater than or equal to the corresponding configured performance threshold 920. For example, the recommended configuration for IMR and interference prediction resources determined by the UE at box 930 may include a best-effort recommendation or configuration for the IMR and interference prediction resources that achieves interference prediction performance close to the configured threshold and / or within a configured maximum distance or offset from the configured threshold (e.g., in an example where UE performance (e.g., MSE prediction accuracy value and / or prediction confidence level) is not (e.g., below) one or both of the configured interference prediction performance thresholds 930).

[0178] At box 940, UE 904 may send interference prediction configuration information to network entity 905, wherein the transmitted interference prediction configuration information is based on and / or indicates one or more recommended or requested configurations of the IMR and interference prediction resources determined by UE 904 at box 930. In some aspects, the interference prediction configuration information 940 sent from UE 904 to network entity 905 may include each of the one or more interference resource configurations determined by UE 904 at box 930.

[0179] At box 950, network entity 905 may configure, schedule and / or allocate multiple IMR and / or interference prediction resources for UE 904, wherein the configured multiple IMR and interference prediction resources of box 950 are provided by network entity 905 based on interference prediction configuration information 940 sent by UE 904 (e.g., and received by network entity 905 from UE 904).

[0180] Based on the configured IMR and / or interference prediction resources scheduled by network entity 905 for UE 904 at box 950, UE 904 can then determine one or more interference values ​​for each of the one or more IMRs scheduled by network entity 905. For example, based on the type of reference signal used for the IMR, UE 904 can use various equations in equations (1) to (3) to determine one or more interference measurements for each IMR, etc.

[0181] Based on the number of configured IMRs for each interference prediction resource, UE 904 can then use the scheduled time period or time slot corresponding to the interference prediction resource to determine interference prediction information, wherein the determined interference prediction information is based at least in part on interference values ​​measured using one or more earlier IMRs.

[0182] In some respects, interference prediction configuration information 940 reported by UE 904 (e.g., sent by UE 904 to network entity 905) can be reported statically. For example, UE 904 can perform static reporting of UE interference prediction configuration information 940 based on sending one or more Radio Resource Control (RRC) messages to network entity 905 indicating the determined interference prediction configuration information 940 associated with UE 904.

[0183] In another exemplary example, UE 904 may perform a semi-static reporting of interference prediction configuration information 940, for example, by sending one or more Media Access Control (MAC)-Control Elements (MAC-CEs) to network entity 905 instructing the network entity to perform a semi-static reporting of interference prediction configuration information 940 determined for UE 904.

[0184] In some respects, UE 904 may perform dynamic reporting of interference prediction configuration information 940, for example, by sending uplink control information (UCI) to network entity 905 indicating the determined interference prediction configuration information 940 for UE 904.

[0185] Figure 10 This is a flowchart illustrating a process 1000 for performing wireless communication. Process 1000 can be performed by a wireless device (e.g., Figure 1 One or more of UEs 104, 152, 164, 182, and 190; Figure 2 UE 104; Figure 3 UE 104; Figure 4 UE407; Figure 12 The wireless device may be a component or system (e.g., a chipset) of a computing system 1200; etc. The wireless device may be a mobile device (e.g., a mobile phone), a network-connected wearable device such as a watch, an extended reality (XR) device such as a virtual reality (VR) device or an augmented reality (AR) device, a vehicle or a component or system of a vehicle, or other types of computing devices. The operation of process 1000 may be implemented in one or more processors (e.g., Figure 4 processor 484, Figure 12 Software components that execute and run on processor 1210 and / or other processors. Furthermore, they may be transmitted, for example, via one or more antennas (e.g., Figure 2 Antenna 252, Figure 4 Antenna 487, etc.) and / or one or more transceivers (e.g., Figure 4 (e.g., wireless transceiver 478) to enable wireless devices to send and receive signals in process 1000.

[0186] At box 1002, process 1000 includes obtaining information indicating one or more configured performance values ​​corresponding to interference predictions made by the UE. For example, the UE may interact with... Figure 9 The information is the same as or similar to UE 904, and the information indicating one or more configured performance values ​​can be from... Figure 9 The network entity 905 is the same as or similar to the network entity.

[0187] In some cases, one or more configured performance values ​​include one or more of the configured prediction accuracy or configured prediction confidence associated with interference prediction performed by the UE. In some cases, values ​​such as Figure 7 The interference prediction engine 750 performs interference predictions performed by the UE. In some examples, the configured prediction accuracy includes a configured threshold for the mean square error (MSE) associated with the interference predictions performed by the UE.

[0188] In some cases, one or more configured performance values ​​include the minimum interference prediction accuracy or the minimum interference prediction confidence for interference prediction using the interference prediction machine learning network. In some examples, the interference prediction machine learning network may be included... Figure 7 The interference prediction engine 750 and / or the interference prediction engine is implemented.

[0189] In some cases, information indicating one or more configured performance values ​​can be combined with... Figure 9 The configuration information is the same as or similar to that of 920.

[0190] At box 1004, process 1000 includes determining a recommended configuration of interference measurement resources (IMR) and interference prediction resources for interference prediction performed by the UE, wherein the recommended configuration is associated with one or more configured performance values ​​and one or more performance capabilities of the interference prediction machine learning network associated with the UE.

[0191] For example, recommended configurations can be included in configuration information determined by the UE (such as...). Figure 8A Configuration information 822a, Figure 8B The configuration information (e.g., 822b) is provided. In some cases, the recommended configuration can be found in... Figure 9 The recommended configuration is determined at box 930 and / or from Figure 9 The recommended interference prediction configuration information 940 sent by UE 904 to network entity 905 is the same as or similar to that of UE 904.

[0192] In some cases, IMR can be combined with Figure 8A and / or Figure 8B The IMR 832 is the same as or similar. In some examples, the interference prediction resources can be the same as... Figure 8A and / or Figure 8B The interference prediction resources are the same as or similar to those in resource 836.

[0193] In some cases, the performance capability of an interference prediction machine learning network indicates the duration for which one or more configured performance values ​​are valid. In some examples, it is recommended to configure the type of reference signal for the IMR, and the type of reference signal for the IMR includes one of the following: Channel State Information (CSI)-Reference Signal (CSI-RS), CSI-Interference Measurement (CSI-IM), Physical Downlink Shared Channel (PDSCH)-Demodulation Reference Signal (PDSCH-DMRS), or PDSCH-Open Tone.

[0194] In some cases, the recommended configuration indicates one or more of the corresponding periodicity of the IMR or the corresponding periodicity of the interference prediction resource. In some examples, the recommended configuration indicates the number of IMRs associated with each of the multiple interference prediction resources for the UE. In some cases, the recommended configuration indicates the time interval between the IMR and the interference prediction resource scheduled by the network entity for the UE based on the recommended configuration.

[0195] At box 1006, process 1000 includes sending information to a network entity indicating a recommended configuration for interference prediction performed by the UE. For example, the information indicating the recommended configuration may include... Figure 8A Configuration information 822a and / or Figure 8B The configuration information in 822b and / or may be the same as or similar to that configuration information. In some cases, the information indicating the recommended configuration may be the same as the information indicating... Figure 9 The recommended configuration 930 has the same or similar interference prediction configuration information as 940.

[0196] In some cases, to send information indicating the recommended configuration, the UE is configured to perform static reporting using a Radio Resource Control (RRC) message indicating the recommended configuration. In some examples, to send information indicating the recommended configuration, the UE is configured to perform semi-static reporting using a Media Access Control (MAC)-Control Element (MAC-CE) message indicating the recommended configuration. In some cases, to send information indicating the recommended configuration, the UE is configured to perform dynamic reporting using Uplink Control Information (UCI) indicating the recommended configuration.

[0197] In some examples, process 1000 also includes sending information to the network entity indicating one or more performance capabilities of the interference prediction machine learning network. For example, one or more performance capabilities of the interference prediction machine learning network, along with recommended configurations of the IMR and interference prediction resources, may be included in an interference prediction report sent by the UE to the network entity.

[0198] In some cases, process 1000 also includes receiving scheduling information from a network entity corresponding to multiple IMRs and interference prediction resources scheduled by the network entity for the UE in response to a recommended configuration sent by the UE. For example, the scheduling information may be related to... Figure 9 The network entity 905 sends a message to UE 904, which is related to... Figure 9 The scheduling information associated with the configured interference measurement resource 950 is the same or similar.

[0199] In some examples, the UE may use an interference prediction machine learning network to determine the predicted interference value, where the predicted interference value is determined using multiple IMRs and interference prediction resources. In some examples, one or more configured performance values ​​include a configured interference prediction accuracy threshold and a configured interference prediction confidence threshold. In some cases, the predicted interference value is associated with an accuracy value greater than or equal to the configured interference prediction accuracy threshold. In some cases, the predicted interference value is associated with a confidence value greater than or equal to the configured interference prediction confidence threshold.

[0200] Figure 11 This is a flowchart illustrating a process 1100 for performing wireless communication. Process 1100 may be performed by components or systems (e.g., chipsets) of a network entity (e.g., a base station, gNB, etc.). For example, the network entity may interact with... Figure 1 Base station 102, Figure 1 mmW base station 180 Figure 1 AP 150, Figure 2 Base station 102, Figure 3 One or more base stations or network entities Figure 5 Base station 102, Figure 12 The operation of process 1100 may be the same as or similar to one or more of the computing systems 1200, etc. The operation of process 1100 may be implemented on one or more processors (e.g., Figure 4 processor 484, Figure 12 Software components that execute and run on processor 1210 and / or other processors. Furthermore, they may be transmitted, for example, via one or more antennas (e.g., Figure 2 Antenna 252, Figure 4 Antenna 487, etc.) and / or one or more transceivers (e.g., Figure 4 (e.g., wireless transceiver 478) to enable network entities to send and receive signals in process 1100.

[0201] At box 1102, process 1100 includes sending information to the user equipment (UE) indicating one or more configured performance values ​​corresponding to interference predictions performed by the UE. For example, the information indicating one or more configured performance values ​​may include... Figure 9 The configuration information is in 920 and / or may be the same as or similar to that configuration information. In some cases, one or more configured performance values ​​include a configured interference prediction accuracy threshold and a configured interference prediction confidence threshold.

[0202] In some cases, network entities may receive predicted interference values ​​from the UE, determined based on multiple IMRs and interference prediction resources. In some examples, the predicted interference value is associated with an accuracy value that is greater than or equal to a configured interference prediction accuracy threshold. In some cases, the predicted interference value is associated with a confidence value that is greater than or equal to a configured interference prediction confidence threshold.

[0203] At box 1104, process 1100 includes receiving from the UE information indicating a recommended configuration of interference measurement resources (IMR) for interference prediction performed by the UE and interference prediction resources, wherein the recommended configuration is associated with one or more configured performance values ​​and one or more performance capabilities of the interference prediction machine learning network associated with the UE.

[0204] In some cases, process 1100 also includes receiving information from the UE indicating one or more performance capabilities of the interference prediction machine learning network. In some cases, the performance capabilities of the interference prediction machine learning network indicate future time slots in which interference predictions performed by the UE are associated with corresponding performance values ​​lower than one or more configured performance values.

[0205] In some cases, network entities may determine a second configuration of IMR and interference prediction resources for a UE, wherein the second configuration is based on the performance capabilities of the interference prediction machine learning network, one or more configured performance values, and at least a portion of a recommended configuration from the UE. In some examples, to configure multiple IMR and interference prediction resources, the network entity is configured to send scheduling information to the UE indicating a second configuration of the IMR and interference prediction resources in response to a recommended configuration received from the UE.

[0206] In some examples, it is recommended to configure an indication of the type of reference signal for the IMR, wherein the type of reference signal for the IMR includes one of the following: Channel State Information (CSI)-Reference Signal (CSI-RS), CSI-Interference Measurement (CSI-IM), Physical Downlink Shared Channel (PDSCH)-Demodulation Reference Signal (PDSCH-DMRS), or PDSCH-Open Tone.

[0207] In some cases, the recommended configuration indicates one or more of the corresponding periodicity of the IMR or the corresponding periodicity of the interference prediction resource. In some examples, the recommended configuration indicates one or more of the following: the number of IMRs associated with each of the multiple interference prediction resources for the UE, or the time interval between the IMRs scheduled for the UE by the network entity based on the recommended configuration and the interference prediction resources. In some examples, the information indicating the recommended configuration is included in Radio Resource Control (RRC) messages, Medium Access Control (MAC)-Control Element (MAC-CE), or Uplink Control Information (UCI) received from the UE.

[0208] At box 1106, process 1100 includes configuring multiple IMR and interference prediction resources for the UE based on information indicating recommended configurations. For example, a network entity may send with Figure 9 950 identical or similar configured interference measurement resources have been configured.

[0209] Figure 12 This is a diagram illustrating an example of a system used to implement certain aspects of this technology. Specifically, Figure 12 An example of a computing system is illustrated, which can be any computing device, such as an internal computing system, a remote computing system, a camera, or any component thereof, wherein the components of the system communicate with each other using connection 1205. Connection 1205 can be a physical connection using a bus, or a direct connection to processor 1210, such as in a chipset architecture. Connection 1205 can also be a virtual connection, a networking connection, or a logical connection.

[0210] In some aspects and examples, computing system 1200 is a distributed system in which the functions described herein may be distributed across a data center, multiple data centers, a peer-to-peer network, etc. In some aspects, one or more system components described represent a plurality of such components, each performing some or all of the functions described for which the component is used. In some aspects and examples, components may be physical or virtual devices.

[0211] Example system 1200 includes at least one processing unit (CPU or processor) 1210 and a connection 1205 that communicatively couples various system components, including system memories 1215 such as read-only memory (ROM) 1220 and random access memory (RAM) 1225, to processor 1210. Computing system 1200 may include a cache 1212 of high-speed memory that is directly connected to, closely proximate to, or integrated into processor 1210.

[0212] Processor 1210 may include any general-purpose processor and hardware or software services, such as services 1232, 1234, and 1236 stored in storage device 1230, which are configured to control processor 1210 and dedicated processors in which software instructions are incorporated into the actual processor design. Processor 1210 may be a substantially completely independent computing system containing multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0213] To enable user interaction, the computing system 1200 includes an input device 1245 that can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, speech, etc. The computing system 1200 may also include an output device 1235 that can be one or more of a plurality of output mechanisms. In some instances, a multimodal system allows a user to provide multiple types of input / output to communicate with the computing system 1200.

[0214] The computing system 1200 may include a communication interface 1240, which typically controls and manages user input and system output. The communication interface may perform or facilitate the receiving and / or transmitting of wired or wireless communications using wired and / or wireless transceivers, including utilizing audio jacks / plugs, microphone jacks / plugs, Universal Serial Bus (USB) ports / plugs, Apple... ™ Lightning ™Ports / plugs, Ethernet ports / plugs, fiber optic ports / plugs, dedicated wired ports / plugs, wireless signal transmission for 3G, 4G, 5G and / or other cellular data networks, Bluetooth™ wireless signal transmission, Bluetooth™ Low Energy (BLE) wireless signal transmission, IBEACON™ wireless signal transmission, Radio Frequency Identification (RFID) wireless signal transmission, Near Field Communication (NFC) wireless signal transmission, Dedicated Short Range Communication (DSRC) wireless signal transmission, 802.11 Wi-Fi wireless signal transmission, Wireless Local Area Network (WLAN) signal transmission, Visible Light Communication (VLC), Microwave Access Global Interoperability (WiMAX), Infrared (IR) wireless signal transmission, Public Switched Telephone Network (PSTN) signal transmission, Integrated Services Digital Network (ISDN) signal transmission, Self-organizing Network signal transmission, Radio wave signal transmission, Microwave signal transmission, Infrared signal transmission, Visible light signal transmission, Ultraviolet light signal transmission, Wireless signal transmission along the electromagnetic spectrum, or those communications in some combination thereof. The communication interface 1240 may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers for determining the location of the computing system 1200 based on one or more signals received from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the U.S. Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), China's BeiDou Navigation Satellite System (BDS), and Europe's Galileo GNSS. There are no limitations on operation on any particular hardware arrangement, and therefore the basic features herein can be easily replaced to obtain improved hardware or firmware arrangements as they are developed.

[0215] Storage device 1230 may be a non-volatile and / or non-transitory and / or computer-readable storage device, and may be a hard disk or other type of computer-readable medium capable of storing data accessible by a computer, such as magnetic tape, flash memory cards, solid-state storage devices, digital multifunction discs, cartridges, floppy disks, hard disks, magnetic tapes, magnetic stripes, any other magnetic storage media, flash memory, memristor memory, any other solid-state storage, CD-ROM, rewritable CD, digital video disc (DVD), Blu-ray Disc (BDD), holographic disc, another optical medium, secure digital card (SD card), micro secure digital card (microSD card), Memory Stick. ®Cards, smart card chips, EMV chips, Subscriber Identity Module (SIM) cards, mini / micro / nano / micro SIM cards, another integrated circuit (IC) chip / card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM, cache memory (e.g., level 1 (L1) cache, level 2 (L2) cache, level 3 (L3) cache, level 4 (L4) cache, level 5 (L5) cache or other (L#) cache), resistive random access memory (RRAM / ReRAM), phase change memory (PCM), spin-transfer torque RAM (STT-RAM), another memory chip or cassette and / or combinations thereof.

[0216] Storage device 1230 may include software services, servers, services, etc., which enable the system to perform functions when the code defining such software is executed by processor 1210. In some aspects and examples, hardware services that perform specific functions may include software components stored in a computer-readable medium connected to necessary hardware components, such as processor 1210, connection 1205, output device 1235, etc., to perform the functions. The term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing, or carrying instructions and / or data. Computer-readable media may include non-transitory media in which data can be stored and which does not include carrier waves and / or transient electronic signals propagated wirelessly or via a wired connection. Examples of non-transitory media may include, but are not limited to, magnetic disks or magnetic tapes, optical storage media (such as compact discs (CDs) or digital versatile discs (DVDs)), flash memory, memory, or memory devices. Computer-readable media may store code and / or machine-executable instructions thereon, which may represent procedures, functions, subroutines, programs, routines, subroutines, modules, software packages, classes, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or hardware circuitry by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc., may be passed, forwarded, or transmitted via any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0217] Specific details have been provided in the foregoing description to offer a thorough understanding of the aspects and examples presented herein, but those skilled in the art will recognize that this application is not limited thereto. Therefore, although illustrative aspects of this application have been described in detail herein, it is to be understood that the various inventive concepts may be embodied and employed in various other ways, and the appended claims are not intended to be construed as including these variations unless limited by prior art. The various features and aspects of the applications described above may be used individually or in combination. Furthermore, without departing from the broader scope of the specification, aspects may be utilized in any number of environments and applications beyond those described herein. Therefore, the specification and drawings should be considered illustrative rather than restrictive. For illustrative purposes, the methods are described in a particular order. It should be understood that, in alternative aspects, the methods may be performed in a different order than described.

[0218] For clarity, in some cases, this technology may be presented as comprising individual functional blocks, which include devices, device components, steps, or routines embodied in a method, either in software or a combination of hardware and software. Additional components may be used in addition to those shown in the figures and / or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form to avoid obscuring these aspects in unnecessary detail. In other cases, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the aspects.

[0219] Furthermore, those skilled in the art will understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been described above in general terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in different ways for each specific application, but such specific implementation decisions should not be construed as departing from the scope of this disclosure.

[0220] The aspects and examples described above can be presented as processes or methods, depicted as flowcharts, diagrams, data flow graphs, structure diagrams, or block diagrams. While a flowchart may describe operations as a sequential process, many operations within an operation can be executed in parallel or concurrently. Furthermore, the order of operations can be rearranged. A process terminates when its operations are completed, but a process may have additional steps not included in the accompanying diagrams. Processes can correspond to methods, functions, procedures, subroutines, subroutines, etc. When a process corresponds to a function, the termination of the process may correspond to the function returning to the calling function or the main function.

[0221] The processes and methods described in the examples above can be implemented using stored computer-executable instructions or computer-executable instructions otherwise available from a computer-readable medium. Such instructions may include, for example, instructions and data that configure, or otherwise configure, a general-purpose computer, special-purpose computer, or processing device to perform a function or group of functions. The portion may be accessible via a network of the computer resources used. The computer-executable instructions may be, for example, binary, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that can be used to store the instructions, the information used, and / or information created during the methods according to the described examples include disks or optical discs, flash memory, USB devices with non-volatile memory, networked storage devices, etc.

[0222] In some respects, computer-readable storage devices, media, and memories may include cables or wireless signals containing bit streams, etc. However, when referred to, non-transitory computer-readable storage media explicitly exclude media such as energy, carrier signals, electromagnetic waves, and the signals themselves.

[0223] Those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and arts. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to throughout the above description may, in some cases, be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or light particles, or any combination thereof, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, etc.

[0224] The various exemplary logic blocks, modules, and circuits described in conjunction with the aspects disclosed herein can be implemented or executed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any form factor of various form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., computer program products) for performing necessary tasks can be stored in a computer-readable or machine-readable medium. A processor can perform the necessary tasks. Examples of form factors include: laptop computers, smartphones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rack-mounted devices, self-contained devices, etc. The functionality described herein can also be embodied in peripheral devices or interlocking cards. By further example, such functionality can also be implemented on circuit boards in different chips or different processes running on a single device.

[0225] Instructions, media for transmitting such instructions, computing resources for executing them, and other structures for supporting such computing resources are example components for providing the functionality described in this disclosure.

[0226] The techniques described herein can also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques can be implemented in any of a variety of devices, such as general-purpose computers, wireless communication devices (mobile phones), or integrated circuit devices with multiple uses, including applications in wireless communication devices (mobile phones) and other devices. Any feature described as a module or component can be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques can be implemented at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, perform one or more of the methods, algorithms, and / or operations described above. The computer-readable data storage medium can form part of a computer program product, which may include packaging material. The computer-readable medium may include memory or data storage media, such as random access memory (RAM) (such as synchronous dynamic random access memory (SDRAM)), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, etc. Additionally or alternatively, the technology may be implemented at least in part by a computer-readable communication medium that carries or conveys program code in the form of instructions or data structures that can be accessed, read and / or executed by a computer, such as propagated signals or waves.

[0227] The program code can be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such processors can be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; however, in alternatives, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Therefore, as used herein, the term "processor" may refer to any of the foregoing structures, any combination of the foregoing structures, or any other structure or means suitable for implementing the techniques described herein.

[0228] Those skilled in the art will understand that, without departing from the scope of this description, the less than (“<”) and greater than (“>”) symbols or terms used herein may be represented by less than or equal to (“>”) respectively. The sign "") and greater than or equal to (" The symbol ) is used instead.

[0229] When a component is described as being “configured” to perform certain operations, such configuration can be achieved, for example, by designing electronic circuits or other hardware to perform the operations, by programming programmable electronic circuits (e.g., microprocessors or other suitable electronic circuits) to perform the operations, or any combination thereof.

[0230] The phrase “coupled to” or “communicatively coupled to” means that any component is physically connected directly or indirectly to another component, and / or that any component is in communication with another component directly or indirectly (e.g., connected to that other component via a wired or wireless connection and / or other suitable communication interface).

[0231] Claim language or other languages ​​that state "at least one of" and / or "one or more of" in a set indicate that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language stating "at least one of A and B" or "at least one of A or B" means A, B, or A and B. In another example, claim language stating "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, or A and C, or B and C, A and B and C, or any repetition is information or data (e.g., A and A, B and B, C and C, A and A and B, etc.), or any other ordering, repetition, or combination of A, B, and C. The language "at least one of" and / or "one or more of" in a set does not limit the set to the items listed in the set. For example, the language of a claim that expresses “at least one of A and B” or “at least one of A or B” may mean A, B or A and B, and may additionally include items not listed in the set of A and B.

[0232] Claims using language such as "at least one processor, which is configured to," or other languages, indicate that one or more processors (in any combination) are capable of performing associated operations. For example, a claim using language stating "at least one processor, which is configured to: X, Y, and Z" means that a single processor can be used to perform operations X, Y, and Z; or that multiple processors are each assigned a specific subset of tasks of operations X, Y, and Z, such that the multiple processors together perform X, Y, and Z; or that a group of multiple processors work together to perform operations X, Y, and Z. In another example, a claim using language stating "at least one processor, which is configured to: X, Y, and Z" may mean that any single processor can perform only at least one subset of operations X, Y, and Z.

[0233] The exemplary aspects of this disclosure include:

[0234] Aspect 1. An apparatus for a user equipment (UE) for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, wherein the at least one processor is configured to: obtain information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; determine a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and send the information indicating the recommended configuration for the interference prediction performed by the UE to a network entity.

[0235] Aspect 2. The apparatus according to aspect 1, wherein the at least one processor is further configured to: send information to the network entity indicating the one or more performance capabilities of the interference prediction machine learning network.

[0236] Aspect 3. The apparatus according to aspect 2, wherein the one or more performance capabilities of the interference prediction machine learning network and the recommended configuration of the IMR and interference prediction resources are included in the interference prediction report sent by the UE to the network entity.

[0237] Aspect 4. The apparatus according to any one of Aspects 1 to 3, wherein the at least one processor is further configured to: receive, in response to the recommended configuration sent by the UE, scheduling information from the network entity corresponding to a plurality of IMRs and interference prediction resources scheduled by the network entity for the UE.

[0238] Aspect 5. The apparatus according to aspect 4, wherein the at least one processor is further configured to: use the interference prediction machine learning network to determine a predicted interference value, wherein the predicted interference value is determined using the plurality of IMRs and interference prediction resources.

[0239] Aspect 6. The apparatus according to Aspect 5, wherein: the one or more configured performance values ​​include a configured interference prediction accuracy threshold and a configured interference prediction confidence threshold; the predicted interference value is associated with an accuracy value greater than or equal to the configured interference prediction accuracy threshold; and the predicted interference value is associated with a confidence value greater than or equal to the configured interference prediction confidence threshold.

[0240] Aspect 7. The apparatus according to any one of Aspects 1 to 6, wherein the one or more configured performance values ​​include one or more of configured prediction accuracy or configured prediction confidence associated with the interference prediction performed by the UE.

[0241] Aspect 8. The apparatus according to aspect 7, wherein the configured prediction accuracy includes a configured threshold for the mean square error (MSE) associated with the interference prediction performed by the UE.

[0242] Aspect 9. The apparatus according to any one of Aspects 1 to 8, wherein the one or more configured performance values ​​include minimum interference prediction accuracy for interference prediction using the interference prediction machine learning network, or minimum interference prediction confidence for interference prediction using the interference prediction machine learning network.

[0243] Aspect 10. The apparatus according to any one of Aspects 1 to 9, wherein the performance capability of the interference prediction machine learning network indicates the duration for which the one or more configured performance values ​​are valid.

[0244] Aspect 11. The apparatus according to any one of Aspects 1 to 10, wherein the recommended configuration indicates the type of reference signal for the IMR, and wherein the type of reference signal for the IMR includes one of: Channel State Information (CSI)-Reference Signal (CSI-RS), CSI-Interference Measurement (CSI-IM), Physical Downlink Shared Channel (PDSCH)-Demodulation Reference Signal (PDSCH-DMRS), or PDSCH-Open Tone.

[0245] Aspect 12. The apparatus according to any one of Aspects 1 to 11, wherein the recommended configuration indicates one or more of the corresponding periodicity of the IMR or the corresponding periodicity of the interference prediction resource.

[0246] Aspect 13. The apparatus according to any one of Aspects 1 to 12, wherein the recommended configuration indicates the number of IMRs associated with each of a plurality of interference prediction resources for the UE.

[0247] Aspect 14. The apparatus according to any one of Aspects 1 to 13, wherein the recommended configuration indicates the time interval between the IMR and interference prediction resources scheduled by the network entity for the UE based on the recommended configuration.

[0248] Aspect 15. The apparatus according to any one of Aspects 1 to 14, wherein, in order to transmit the information indicating the recommended configuration, the at least one processor is configured to perform a static report using a Radio Resource Control (RRC) message indicating the recommended configuration.

[0249] Aspect 16. The apparatus according to any one of Aspects 1 to 15, wherein, in order to send the information indicating the recommended configuration, the at least one processor is configured to perform a semi-static report using a Media Access Control (MAC)-Control Element (MAC-CE) indicating the recommended configuration.

[0250] Aspect 17. The apparatus according to any one of Aspects 1 to 16, wherein, in order to send the information indicating the recommended configuration, the at least one processor is configured to perform dynamic reporting using uplink control information (UCI) indicating the recommended configuration.

[0251] Aspect 18. An apparatus for a network entity for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, wherein the at least one processor is configured to: send to a user equipment (UE) information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; receive from the UE information indicating a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and configure a plurality of IMR and interference prediction resources for the UE based on the information indicating the recommended configuration.

[0252] Aspect 19. The apparatus according to aspect 18, wherein the at least one processor is further configured to: receive from the UE information indicating the one or more performance capabilities of the interference prediction machine learning network.

[0253] Aspect 20. The apparatus according to aspect 19, wherein the performance capability of the interference prediction machine learning network indicates a future time slot in which the interference prediction performed by the UE is associated with a corresponding performance value lower than one or more configured performance values.

[0254] Aspect 21. The apparatus according to any one of Aspects 19 to 20, wherein the at least one processor is configured to: determine a second configuration of IMR and interference prediction resources for the UE, wherein the second configuration is based on the performance capabilities of the interference prediction machine learning network, the one or more configured performance values, and at least a portion of the recommended configuration from the UE.

[0255] Aspect 22. The apparatus according to aspect 21, wherein, in order to configure the plurality of IMRs and interference prediction resources, the at least one processor is configured to: in response to the recommended configuration received from the UE, send scheduling information to the UE indicating the second configuration of the IMRs and interference prediction resources.

[0256] Aspect 23. The apparatus according to any one of Aspects 18 to 22, wherein: the one or more configured performance values ​​include a configured interference prediction accuracy threshold and a configured interference prediction confidence threshold; and the at least one processor is configured to receive from the UE a predicted interference value determined based on the plurality of IMRs and interference prediction resources.

[0257] Aspect 24. The apparatus according to aspect 23, wherein: the predicted interference value is associated with an accuracy value greater than or equal to the configured interference prediction accuracy threshold; and the predicted interference value is associated with a confidence value greater than or equal to the configured interference prediction confidence threshold.

[0258] Aspect 25. The apparatus according to any one of Aspects 18 to 24, wherein the recommended configuration indicates the type of reference signal for the IMR, and wherein the type of reference signal for the IMR includes one of: Channel State Information (CSI)-Reference Signal (CSI-RS), CSI-Interference Measurement (CSI-IM), Physical Downlink Shared Channel (PDSCH)-Demodulation Reference Signal (PDSCH-DMRS), or PDSCH-Open Tone.

[0259] Aspect 26. The apparatus according to any one of Aspects 18 to 25, wherein the recommended configuration indicates one or more of the corresponding periodicity of the IMR or the corresponding periodicity of the interference prediction resource.

[0260] Aspect 27. The apparatus according to any one of Aspects 18 to 26, wherein the recommended configuration indicates one or more of the following: the number of IMRs associated with each of a plurality of interference prediction resources for the UE, or the time interval between IMRs scheduled by the network entity for the UE based on the recommended configuration and interference prediction resources.

[0261] Aspect 28. The apparatus according to any one of Aspects 18 to 27, wherein the information indicating the recommended configuration is included in a Radio Resource Control (RRC) message, Medium Access Control (MAC)-Control Element (MAC-CE), or Uplink Control Information (UCI) received from the UE.

[0262] Aspect 29. A method for wireless communication by a user equipment (UE), the method comprising: obtaining information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; determining a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and sending the information indicating the recommended configuration for the interference prediction performed by the UE to a network entity.

[0263] Aspect 30. A method for wireless communication by a network entity, the method comprising: sending to a user equipment (UE) information indicating one or more configured performance values ​​corresponding to interference prediction performed by the UE; receiving from the UE information indicating a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with the one or more configured performance values ​​and one or more performance capabilities of an interference prediction machine learning network associated with the UE; and configuring a plurality of IMRs and interference prediction resources for the UE based on the information indicating the recommended configuration.

[0264] Aspect 31. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions causing the at least one processor, when executed by at least one processor, to perform an operation according to any one of Aspects 1 to 17.

[0265] Aspect 32. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions causing the at least one processor, when executed by at least one processor, to perform any one of aspects 18 to 28.

[0266] Aspect 33. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions causing the at least one processor, when executed by at least one processor, to perform the operations described in aspect 29.

[0267] Aspect 34. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions causing the at least one processor, when executed by at least one processor, to perform the operations described in aspect 30.

[0268] Aspect 35. An apparatus comprising one or more components for performing operations according to any one of aspects 1 to 17.

[0269] Aspect 36. An apparatus comprising one or more components for performing operations according to any one of aspects 18 to 28.

[0270] Aspect 37. An apparatus comprising one or more components for performing the operations described in aspect 29.

[0271] Aspect 38. An apparatus comprising one or more components for performing the operations described in aspect 30.

Claims

1. An apparatus for a user equipment (UE) for wireless communication, the apparatus comprising: At least one memory; and At least one processor, said at least one processor being coupled to said at least one memory, said at least one processor being configured to: Obtain information indicating one or more configured performance values ​​corresponding to interference predictions made by the UE; Determine a recommended configuration for interference measurement resources (IMR) and interference prediction resources used for the interference prediction performed by the UE, wherein the recommended configuration is associated with one or more configured performance values ​​and one or more performance capabilities of the interference prediction machine learning network associated with the UE; as well as Send information to the network entity indicating the recommended configuration for the interference prediction performed by the UE.

2. The apparatus of claim 1, wherein the at least one processor is further configured to: Send information to the network entity indicating the one or more performance capabilities of the interference prediction machine learning network.

3. The apparatus of claim 2, wherein the one or more performance capabilities of the interference prediction machine learning network and the recommended configuration of the IMR and interference prediction resources are included in the interference prediction report sent by the UE to the network entity.

4. The apparatus of claim 1, wherein the at least one processor is further configured to: In response to the recommended configuration sent by the UE, scheduling information corresponding to multiple IMRs and interference prediction resources scheduled by the network entity for the UE is received from the network entity.

5. The apparatus of claim 4, wherein the at least one processor is further configured to: The interference prediction machine learning network is used to determine the predicted interference value, wherein the predicted interference value is determined using the plurality of IMRs and interference prediction resources.

6. The apparatus according to claim 5, wherein: The one or more configured performance values ​​include a configured interference prediction accuracy threshold and a configured interference prediction confidence threshold; The predicted interference value is associated with an accuracy value that is greater than or equal to the configured interference prediction accuracy threshold; and The predicted interference value is associated with a confidence value that is greater than or equal to the configured interference prediction confidence threshold.

7. The apparatus of claim 1, wherein the one or more configured performance values ​​include one or more of a configured prediction accuracy or a configured prediction confidence associated with the interference prediction performed by the UE.

8. The apparatus of claim 7, wherein the configured prediction accuracy includes a configured threshold for the mean square error (MSE) associated with the interference prediction performed by the UE.

9. The apparatus of claim 1, wherein the one or more configured performance values ​​include minimum interference prediction accuracy for interference prediction using the interference prediction machine learning network, or minimum interference prediction confidence for interference prediction using the interference prediction machine learning network.

10. The apparatus of claim 1, wherein the performance capability of the interference prediction machine learning network indicates the duration for which the one or more configured performance values ​​are valid.

11. The apparatus of claim 1, wherein the recommended configuration indicates the type of reference signal for the IMR, and wherein the type of reference signal for the IMR includes one of: Channel State Information (CSI)-Reference Signal (CSI-RS), CSI-Interference Measurement (CSI-IM), Physical Downlink Shared Channel (PDSCH)-Demodulation Reference Signal (PDSCH-DMRS), or PDSCH-Open Tone.

12. The apparatus of claim 1, wherein the recommended configuration indicates one or more of the corresponding periodicity of the IMR or the corresponding periodicity of the interference prediction resource.

13. The apparatus of claim 1, wherein the recommended configuration indicates the number of IMRs associated with each of a plurality of interference prediction resources for the UE.

14. The apparatus of claim 1, wherein the recommended configuration indicates the time interval between the IMR and interference prediction resources scheduled by the network entity for the UE based on the recommended configuration.

15. The apparatus of claim 1, wherein, in order to send the information indicating the recommended configuration, the at least one processor is configured to perform a static report using a Radio Resource Control (RRC) message indicating the recommended configuration.

16. The apparatus of claim 1, wherein, in order to send the information indicating the recommended configuration, the at least one processor is configured to perform a semi-static report using a Media Access Control (MAC)-Control Element (MAC-CE) indicating the recommended configuration.

17. The apparatus of claim 1, wherein, in order to send the information indicating the recommended configuration, the at least one processor is configured to perform dynamic reporting using uplink control information (UCI) indicating the recommended configuration.

18. An apparatus for a network entity for wireless communication, the apparatus comprising: At least one memory; and At least one processor, said at least one processor being coupled to said at least one memory, said at least one processor being configured to: Send information to the user equipment (UE) indicating one or more configured performance values ​​corresponding to the interference predictions made by the UE; The UE receives information indicating a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with one or more configured performance values ​​and one or more performance capabilities of the interference prediction machine learning network associated with the UE. as well as Configure multiple IMR and interference prediction resources for the UE based on the information indicating the recommended configuration.

19. The apparatus of claim 18, wherein the at least one processor is further configured to: The UE receives information indicating one or more performance capabilities of the interference prediction machine learning network.

20. The apparatus of claim 19, wherein the performance capability of the interference prediction machine learning network indicates a future time slot in which the interference prediction performed by the UE is associated with a corresponding performance value lower than one or more of the configured performance values.

21. The apparatus of claim 19, wherein the at least one processor is configured to: A second configuration for the IMR and interference prediction resources for the UE is determined, wherein the second configuration is based on the performance capabilities of the interference prediction machine learning network, the one or more configured performance values, and at least a portion of the recommended configuration from the UE.

22. The apparatus of claim 21, wherein, in order to configure the plurality of IMRs and interference prediction resources, the at least one processor is configured to: In response to the recommended configuration received from the UE, scheduling information indicating the second configuration of IMR and interference prediction resources is sent to the UE.

23. The apparatus according to claim 18, wherein: The one or more configured performance values ​​include a configured interference prediction accuracy threshold and a configured interference prediction confidence threshold; and The at least one processor is configured to receive from the UE a predicted interference value determined based on the plurality of IMRs and interference prediction resources.

24. The apparatus according to claim 23, wherein: The predicted interference value is associated with an accuracy value that is greater than or equal to the configured interference prediction accuracy threshold; and The predicted interference value is associated with a confidence value that is greater than or equal to the configured interference prediction confidence threshold.

25. The apparatus of claim 18, wherein the recommended configuration indicates the type of reference signal for the IMR, and wherein the type of reference signal for the IMR includes one of: Channel State Information (CSI)-Reference Signal (CSI-RS), CSI-Interference Measurement (CSI-IM), Physical Downlink Shared Channel (PDSCH)-Demodulation Reference Signal (PDSCH-DMRS), or PDSCH-Open Tone.

26. The apparatus of claim 18, wherein the recommended configuration indicates one or more of the corresponding periodicity of the IMR or the corresponding periodicity of the interference prediction resource.

27. The apparatus of claim 18, wherein the recommended configuration indicates one or more of the following: the number of IMRs associated with each of a plurality of interference prediction resources for the UE, or the time interval between IMRs scheduled by the network entity for the UE based on the recommended configuration and the interference prediction resources.

28. The apparatus of claim 18, wherein the information indicating the recommended configuration is included in a Radio Resource Control (RRC) message, Medium Access Control (MAC)-Control Element (MAC-CE), or Uplink Control Information (UCI) received from the UE.

29. A method for wireless communication by a user equipment (UE), the method comprising: Obtain information indicating one or more configured performance values ​​corresponding to interference predictions made by the UE; Determine a recommended configuration for interference measurement resources (IMR) and interference prediction resources used for the interference prediction performed by the UE, wherein the recommended configuration is associated with one or more configured performance values ​​and one or more performance capabilities of the interference prediction machine learning network associated with the UE; as well as Send information to the network entity indicating the recommended configuration for the interference prediction performed by the UE.

30. A method for wireless communication by a network entity, the method comprising: Send information to the user equipment (UE) indicating one or more configured performance values ​​corresponding to the interference predictions made by the UE; The UE receives information indicating a recommended configuration of interference measurement resources (IMR) and interference prediction resources for the interference prediction performed by the UE, wherein the recommended configuration is associated with one or more configured performance values ​​and one or more performance capabilities of the interference prediction machine learning network associated with the UE. as well as Configure multiple IMR and interference prediction resources for the UE based on the information indicating the recommended configuration.

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