Methods for csi-reporting during performance monitoring

Abstract

A method performed by a user equipment (UE) is provided. The method comprises receiving a first set of configurations related to an artificial intelligence or machine learning (AI / ML) based channel state information (CSI) report, receiving a second set of configurations related to a target-CSI report, and receiving an indication to send the target-CSI report and an indication to send the AI / ML-based CSI report. The method further comprises sending the AI / ML-based CSI report according to a third set of configurations. The method further comprises sending the target-CSI report. At least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations

Description

METHODS FOR CSI-REPORTING DURING PERFORMANCE MONITORINGCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 532,339 filed on August 11, 2023, titled “METHODS FOR CSI-REPORTING DURING PERFORMANCE MONITORING”. The content of the application is hereby incorporated by reference in its entirety for all purposes.FIELD

[0002] The present disclosure relates generally to communication systems and, more specifically, to methods and systems for channel state information (CSI) reporting during performance monitoring.BACKGROUND

[0003] The 5thgeneration (5G) mobile wireless communication system (e.g., New Radio or NR) uses OFDM (Orthogonal Frequency Division Multiplexing) with configurable bandwidths and subcarrier spacing to efficiently support a diverse set of use cases and deployment scenarios. With respect to Long-Term Evolution (LTE), NR improves deployment flexibility, user throughputs, latency, and reliability.

[0004] Artificial intelligence or machine learning (AI / ML) models are used in 5G technologies for performance enhancements and / or complexity / overhead reductions for the user equipment (UE) and / or network nodes. The AI / ML models used for NR air interface can be categorized into the two types: a one-sided AI / ML model and a two-sided AI / ML model. In a two-sided AI / ML model, UE has flexibility in sending its channel state information (CSI) report. For example, the UE may select the ParComb, preferred ranks, etc., that suits its condition (e.g., its link quality, anticipated traffic, etc.). In an AI / ML-based CSI report, the flexibility may be in terms of payload size, Uplink Control Information (UCI) quantization size, compression ratio, encoder output (latent space) size, selected model (or model pair), etc. While the existing AI / ML-based CSI reporting mechanism may bring extra flexibility and efficiency in the UL transmission, it may give undesirable consequences for the AI / ML-based CSI report for the performance monitoring and data collection procedures.

[0005] In performance monitoring, for example, the network node may want to know the performance of an AI / ML model for a certain model and / or certain configuration (e.g.,performance of an AI / ML model for configurations like certain payloads, certain quantization sizes, certain layers, certain ranks, etc.). To achieve it, the UE may be configured or indicated by the network node to send a target-CSI report according to configurations that the network node wants to monitor. This target-CSI report is then compared with the reconstructed CSI generated by the network node-side model using an AI / ML-based CSI report as the -network node-side model input, to observe the performance of the AI / ML model. Due to the flexibility of the transmission of the CSI report, however, the AI / ML-based CSI report and the target-CSI report may not always be aligned.

[0006] In addition, as mentioned above, the network node may be interested in monitoring the performance of the AI / ML model with certain configurations. However, the UE may not send the CSI report according to the configurations that the -network node is interested in. Considering the above, mechanisms are needed to ensure the alignment between the target-CSI report and the CSI-report.SUMMARY

[0007] Various computer-implemented systems, methods, and articles for CSI reporting during performance monitoring are described herein. In one embodiment, a method performed by a user equipment (UE) is provided. The method comprises receiving a first set of configurations related to an artificial intelligence or machine learning (AI / ML) based CSI report, receiving a second set of configurations related to a target-CSI report, and receiving an indication to send the target-CSI report and an indication to send the AI / ML-based CSI report. The method further comprises sending the AI / ML-based CSI report according to a third set of configurations. The method further comprises sending the target-CSI report. At least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

[0008] In one embodiment, a method performed by a network node is provided. The method comprises sending a first set of configurations related to an AI / ML based CSI report, sending a second set of configurations related to a target-CSI report, and sending an indication to send the target-CSI report and an indication to send the AI / ML-based CSI report. The method further comprises receiving the AI / ML-based CSI report according to a third set of configurations. The method further comprises receiving the target-CSI report. At least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combinationof at least two of the first set of configurations, the second set of configurations, and the third set of configurations.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] For a better understanding of the various described embodiments, reference should be made to the Detailed Description below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.

[0010] Figure 1 illustrates an example of a communication system in accordance with some embodiments.

[0011] Figure 2 illustrates an exemplary user equipment in accordance with some embodiments.

[0012] Figure 3 illustrates an exemplary network node in accordance with some embodiments.

[0013] Figure 4 is a block diagram of an exemplary host, which may be an embodiment of the host of Figure 1, in accordance with various aspects described herein.

[0014] Figure 5 is a block diagram illustrating an exemplary virtualization environment in which functions implemented by some embodiments may be virtualized.

[0015] Figure 6 illustrates a communication diagram of an exemplary host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.

[0016] Figure 7 illustrates an example of multi-user, multiple input multiple output (MU-MIMO) operations comprising a multi-antenna base station transmitting information to several UEs in accordance with some embodiments.

[0017] Figure 8 illustrates a structure of the Type II CSI in accordance with some embodiments.

[0018] Figure 9 illustrates an example of auto encoders (AEs) used for AI / ML-based CSI reporting in accordance with some embodiments.

[0019] Figure 10 illustrates another example of AEs used for AI / ML-based CSI reporting in accordance with some embodiments.

[0020] Figure 11 illustrates an example of a quantization layer used in a CSI compression based on a two-sided AI / ML model in accordance with some embodiments.

[0021] Figure 12 illustrates an example of pre-processing for input data to AE in accordance with some embodiments.

[0022] Figure 13 illustrates a flowchart showing an example method performed by a UE for CSI reporting during performance monitoring according to some embodiments.

[0023] Figure 14 illustrates a flowchart showing an example method performed by a network node for CSI reporting during performance monitoring according to some embodiments.DETAILED DESCRIPTION

[0024] To provide a more thorough understanding of the present invention, the following description sets forth numerous specific details, such as specific configurations, parameters, examples, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is intended to provide a better description of the exemplary embodiments.

[0025] Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise:

[0026] The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention.

[0027] As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and / or,” unless the context clearly dictates otherwise.

[0028] The term “based on” is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise.

[0029] As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of a networked environment where two or more components or devices are able to exchange data, the terms “coupled to” and “coupled with” are also used to mean “communicatively coupled with”, possibly via one or more intermediary devices.

[0030] In addition, throughout the specification, the meaning of “a”, “an”, and “the” includes plural references, and the meaning of “in” includes “in” and “on”.

[0031] Although some of the various embodiments presented herein constitute a single combination of inventive elements, it should be appreciated that the inventive subject matter isconsidered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. Further, the transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

[0032] Figure 1 shows an example of a communication system 100 in accordance with some embodiments.

[0033] In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 102, including one or more network nodes 110 and / or core network nodes 108.

[0034] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul userplane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the 0-RAN Alliance or comparable technologies. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0035] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0036] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0037] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes includefunctions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0038] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0039] As a whole, the communication system 100 of Figure 1 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0040] In some examples, the telecommunication network 102 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0041] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi -standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, e.g. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN- DC).

[0042] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0043] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the networknodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0044] Figure 2 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0045] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0046] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 2. The level of integration betweenthe components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0047] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general -purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0048] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0049] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or othermodification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0050] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0051] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD- DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0052] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / ora receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0053] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0054] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0055] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0056] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examplesof such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0057] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0058] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0059] Figure 3 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment,in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g, O-RU, O-DU, O-CU).

[0060] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0061] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi -standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0062] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared bydifferent RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0063] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0064] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0065] The memory 304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computerexecutable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0066] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0067] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0068] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0069] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signalsmay be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0070] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0071] Embodiments of the network node 300 may include additional components beyond those shown in Figure 3 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0072] Figure 4 is a block diagram of a host 400, which may be an embodiment of the host 116 of Figure 1, in accordance with various aspects described herein. As used herein, the host 400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0073] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input / output interface 406, a network interface 408, a power source 410, and a memory 412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices ofprevious figures, such as Figures 2 and 3, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0074] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0075] Figure 5 is a block diagram illustrating a virtualization environment 500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 500 includes components defined by the 0-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0076] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0077] Hardware 504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0078] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0079] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0080] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0081] Figure 6 shows a communication diagram of a host 602 communicating via a network node 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of Figure 1 and / or UE 200 of Figure 2), network node (such as network node 110a of Figure 1 and / or network node 300 of Figure 3), and host (such as host 116 of Figure 1 and / or host 400 of Figure 4) discussed in the preceding paragraphs will now be described with reference to Figure 6.

[0082] Like host 400, embodiments of host 602 include hardware, such as a communication interface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host 602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 650.

[0083] The network node 604 includes hardware enabling it to communicate with the host 602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of Figure 1) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0084] The UE 606 includes hardware and software, which is stored in or accessible by UE 606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. In providing the service to the user, the UE's client application may receive request data from the host's host applicationand provide user data in response to the request data. The OTT connection 650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 650.

[0085] The OTT connection 650 may extend via a connection 660 between the host 602 and the network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0086] As an example of transmitting data via the OTT connection 650, in step 608, the host 602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user data that was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0087] In some examples, the UE 606 executes a client application which provides user data to the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0088] One or more of the various embodiments improve the performance of OTT services provided to the UE 606 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the e.g., data rate, latency, power consumption and thereby provide benefits such as e.g., reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, extended battery lifetime, and a more efficient transmission of the target-CSI.

[0089] In an example scenario, factory status information may be collected and analyzed by the host 602. As another example, the host 602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0090] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 650 between the host 602 and UE 606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 602 and / or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alterthe operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0091] The 5th generation (5G) mobile wireless communication system (as known as New Radio or NR) uses OFDM (Orthogonal Frequency Division Multiplexing) with configurable bandwidths and subcarrier spacing to efficiently support a diverse set of use cases and deployment scenarios. With respect to Long-Term Evolution (LTE), NR improves deployment flexibility, user throughputs, latency, and reliability. NR also enhanced support for spatial multiplexing in which time-frequency resources are spatially shared across users, commonly referred to as multi-user, multiple input multiple output (MU-MIMO).

[0092] Example MU-MIMO operations are illustrated in Figure 7. As shown in Figure 7, a multi-antenna base station 702 with NTXantenna ports is spatially transmitting information to several UEs 712 withreceive antennas, in which sequence 5^ is aimed for UE(1), is aimed for UE(2), etc. The base station 702 includes a precoder 704 and a modulator 706. The base station 702 can be a network node as described above (e.g., a gNB) and the UEs 712 can be any type of UEs described above. Before modulation and transmission, precoding W is applied to each sequence to spatially separate the transmissions, e.g., to mitigate multiplexing interference.

[0093] At the receiver sides, each UE 712 includes a demodulator 714 that demodulates its received signal and a combiner 714 that combines receive antenna signals to obtain an estimate of the transmitted sequence. This estimate S can be expressed as [ LEq il] J

[0094] In the above [Eql], the second term represents the spatial multiplexing interference seen by UE(i). The goal for the base station is to construct the set of precoders is large, whereas the norm A i is small.In other words, the precodercan correlate well with the channel observed by UE(i), whereas it correlates poorly with other channels.

[0095] To construct precoders (e.g., precoder 704) for efficient MU-MIMO transmissions, the base station 702 needs to acquire detailed knowledge of channels H (i). In deployments where channel reciprocity holds, channel knowledge can be acquired from sounding reference signals (SRS) that are transmitted periodically, or on demand, by active UEs. Based on these SRS, the base station 702 estimatesHowever, when channel reciprocity does not hold or when SRS coverage is limited, active UEs 712 need to provide feedbacks like channel details to the base station 702. In NR (as well as in LTE), this is done by having the base station periodically transmit Channel State Information reference signals (CSI-RS) from which a UE can estimate its channel. The UE then reports CSI from which the base station can determine suitable precoders for MU-MIMO.

[0096] The CSI feedback mechanism targeting MU-MIMO operations in NR is referred to as CSI type II, in which a UE reports CSI feedback with a high CSI resolution. It is based on specifying sets of Discrete Fourier Transform (DFT) base functions (grid of beams) from which the UE selects those that best match its channel conditions (like classical codebook Precoder Matrix Indicator (PMI)). The number of beams, which the UE reports, is configurable via Radio Resource Control (RRC) signaling and may be 2 or 4 for Rel-15 Type II or 2, 4, or 6 for Rel-16 Type II. In Rel-16 Type II, the CSI report can be further compressed in the frequency domain (FD), where a set of FD DFT basis vectors are selected by the UE. The number of selected FD basis vectors is a function of a number of Channel Quality Information (CQI) subbands, a number of PMI subbands per CQI subband, and a ratio that determines the FD compression (termed as pv, where v is the layer index), which is configured by a next generation Node B (gNB) via RRC signaling. In addition, the UE also reports non-zero coefficients (NZCs) associated with the selected beams for Rel-15 Type II, which informs the gNB how these beams should be combined in terms of relative amplitude scaling and cophasing for each subband. In Rel-16, the reported NZCs are then associated with selected beams and FD basis vectors. In Rel-16, to further compress the CSI report, the gNB also configures a ratio, termed as ?, to the UE via RRC signaling, that determines the maximum number of NZCs to be reported. For example, for a single-layer transmission where 2L beams and M FD basis vectors are configured by gNB, there are in total of 2LM linear combination coefficients. Then, only 2LM / 3 NZCs will be reported at most, the remaining 2LM — 2LM / 3 are treated as zeros and are not reported. The selected beams are commonly used for all subbands and all transmission layers, whereas the NZCs (for both Rel-15 and Rel-16 Type II) and FD basis vectors (for Rel-16 Type II) are layer-specific.

[0097] To further explain the structure of the Type II CSI, an example of the Rel-15 CSI type II is illustrated in Figure 8. Figure 8 is an illustration of CSI Type II feedback, from which it can be observed that the selection of DFT beam vectors bnand their relative amplitudes anare determined from a wideband (801) perspective, whereas the co-phasing is per subband (802). Here, wideband 801 means that the selected DFT beam vectors are the same for all subcarriers used in the OFDM transmission, whereas subband 802 means that co-phasing parameters are determined over subsets of contiguous subcarriers. The co-phasing parameters are quantized such that e70nis taken from either a Quadrature Phase Shift Keying (QPSK) or 8PSK signal constellation.

[0098] With references to Figures 7 and 8, the precoder reported by the UE can be expressed in [Eq2] below.Wv[k] =nbnanejenW _[Eq2]

[0099] In the above [Eq2], VF [k] denotes precoder, k denotes a sub-band index, bndenotes the DFT beam vectors, andenotes their relative amplitudes, and e70nis taken from either a Quadrature Phase Shift Keying (QPSK) or 8PSK signal constellation. Note that the reporting overhead for Type II CSI is generally large, especially when compared to the Type I CSI. A dominant part of the reporting overhead is from subband reporting, e.g., the layerspecific NZCs. For instance, it requires about 7 bits (the actual number depends on the release version and parameter configuration) to report the phase and amplitude for one coefficient.

[0100] In NR, a UE can be configured with one or multiple CSI Report Settings, each configured by a higher layer parameter CSI-ReportConfig. Each CSI-ReportConfig is associated with a bandwidth part (BWP) and contains one or more of the following: a CSI resource configuration for channel measurement; a CSI-Interference Management (CSI-IM) resource configuration for interference measurement; a reporting configuration type, e.g., aperiodic CSI on Physical Uplink Shared Channel (PUSCH), periodic CSI on Physical Uplink Control Channel (PUCCH), or semi-persistent CSI on PUCCH or PUSCH; report quantity specifying what to be reported, such as rank indicator (RI), PMI, and CQI; codebook configuration, such as type I or type II CSI; a frequency domain configuration, e.g., subband vs. wideband CQI or PMI, and subband size; and a CQI table to be used.

[0101] A UE can be configured with one or multiple CSI resource configurations for channel measurement and one or more CSI-IM resources for interference measurement. Each CSI resource configuration for channel measurement can contain one or more nonzero power (NZP) CSI-RS resource sets. Each NZP CSI-RS resource set can further contain one or moreNZP CSI-RS resources. An NZP CSI-RS resource can be periodic, semi -persistent, or aperiodic.

[0102] Similarly, each CSI-IM resource configuration for interference measurement can contain one or more CSI-IM resource sets. Each CSI-IM resource set can further contain one or more CSI-IM resources. A CSI-IM resource can be periodic, semi -persistent, or aperiodic.

[0103] Type II CSI reporting on PUSCH is described next. In some embodiments, a UE performs aperiodic CSI reporting using PUSCH upon successful decoding of a Downlink Control Information (DCI) format 0 1 or DCI format 0 2, which triggers an aperiodic CSI trigger state. When a DCI format 0 1 schedules two PUSCH allocations, the aperiodic CSI report is carried on the second scheduled PUSCH. When a DCI format 0 1 schedules more than two PUSCH allocations, the aperiodic CSI report is carried on the penultimate scheduled PUSCH.

[0104] A UE performs semi-persistent CSI reporting on the PUSCH upon successful decoding of a DCI format 0 1 or DCI format 0 2 which activates a semi-persistent CSI trigger state. DCI format 0 1 and DCI format 0 2 contain a CSI request field which indicates the semi-persistent CSI trigger state to activate or deactivate. The PUSCH resources and Modulation and Coding Scheme (MCS) can be allocated semi-persistently by an uplink DCI.

[0105] CSI reporting on PUSCH can be multiplexed with uplink data on PUSCH. CSI reporting on PUSCH can also be performed without any multiplexing with uplink data from the UE.

[0106] Auto encoders (AEs) for AI / ML-based CSI reporting are shown in Figure 9. In Figure 9, an example use-case of two-sided AI / ML model is illustrated.

[0107] In one example of AI / ML-based CSI reporting, AEs may play a central part. Specifically, an AE is a type of artificial neural network (NN) that can be used to compress and decompress data in an unsupervised manner. Figure 9 illustrates a simple fully connected (dense) AE. As shown in Figure 9, the AE is divided into two parts: an encoder 901, which is used to compress the input data X; and a decoder 902, which is used to decompress the input data X. AEs can have different architectures. For example, AEs can be based on dense NNs, multi-dimensional convolution NNs, variational, recurrent NNs, transformer networks, or any combination thereof. However, AE architectures may possess an encoder-bottleneck-decoder structure as illustrated in Figure 9.

[0108] As shown in Figure 9, the size of the codeword (denoted by Y in Figure 9) of an AE is typically a lot smaller than the size of the input data (denoted by X in Figure 9).The AE encoder, thus, reduces the dimensionality of the input data X down to the size of the codeword Y. The decoder part of the AE tries to invert the encoder and reconstruct the input data X with minimal error, according to some predefined loss function.

[0109] The weights and biases of an AE (with a fixed architecture) are trained to minimize the reconstruction error (the error between the input data X and output data X) on some training datasets. For example, the weights and biases can be trained to minimize the mean squared error (MSE) (X — X)2. Model training is typically done using some variant of the gradient descent algorithm on a large training data set. To achieve good performance during live operation, the training data set should be representative of the actual data the AE encounters during live operation.

[0110] Figure 10 illustrates an AE 1002 used for AI / ML-based CSI reporting in NR. The UE 1002 in Figure 10 includes an encoder and measures the channel in the downlink using CSI-RS. For a plurality of subcarriers, a plurality of TX antennas, and a plurality of RX antennas associated with the estimated channel, the UE estimates the channel for each subcarrier (e.g., SC 1001 in Figure 10) from each base station’s transmitter (TX) antenna and at each UE’s receiver (RX) antenna. The estimation result can be represented by a three- dimensional (3D) channel matrix. The 3D channel matrix represents the MIMO channel estimated over several SCs and is input to the encoder of UE 1002 in Figure 10.[OHl] In 3GPP, a two-sided AEML model is defined as paired AI / ML model(s), over which a joint inference can be performed. A joint inference may comprise an inference using AI / ML models, and the inference can be performed jointly across the UE and the network node. For example, the first part of inference is performed by UE and then the remaining part is performed by a network node like a gNB, or vice versa. The AE -based CSI reporting is an example use case of two-sided AI / ML models. The AE can be divided into two parts, e.g., the UE-side of the model (e.g., including encoder 1002) and the network node-side of the model (e.g., including decoder 1003).

[0112] With reference to Figure 10, the AE encoder at UE 1002 is implemented in the UE, and the AE decoder at network node 1003 is implemented in the network node. The output of the AE encoder is signalled from the UE to the network node over the uplink. The codeword can be viewed as a learned latent representation of the channel. The architecture of an AE (e.g., the number of layers, the nodes per layer, activation function, etc.) typically needs to be numerically optimized for CSI reporting via a process called hyperparameter tuning. Properties of the data (e.g., CSLRS channel estimates), the channel size, the uplinkfeedback rate, and hardware limitations of the encoder and decoder may all need to be considered when optimizing the AE’s architecture.

[0113] In the CSI compression using a two-sided AI / ML model, the output of the UE-side encoder is communicated over the air interface to the decoder at the network node side with the assigned CSI reporting payload. Therefore, the output of the UE-side encoder is quantized to a finite number of bits (e.g., 1-4 bits per sample for the UCI) to obtain an efficient transmission as shown in Figure 11. Figure 11 illustrates an example of a quantization layer 1102 used in a CSI compression based on a two-sided AI / ML model in accordance with some embodiments. As shown in Figure 11, a quantization layer 1102 is usually connected to the output of the encoder 1101 or directly included in the encoder. As shown in Figure 11, the quantization layer 1102 can implement scalar quantization which quantizes the output of each neuron of the encoder output layer (the bottleneck layer of AE) to generate bits to fit the CSI reporting payload in the UCI 1103. Other quantization methods, e.g., vector quantization, may also be used. As shown in Figure 11, a dequantization layer 1104 , which dequantizes the UCI 1103, is connected to the input of the network node (e.g., gNB)’s decoder 1105 or directly included in the decoder. In Figure 11, encoder 1101 and quantization layer 1102 may be included in a UE; and dequantization layer 1104 and decoder 1105 may be included in a network node like a gNB.

[0114] Figure 12 illustrates an example of pre-processing for input data to an AE. Using pre-processing on the input to the encoder can greatly reduce the size and complexity of designing and / or training an AI / ML model, and in the meantime, improve the scalability and transferability of the model. In the CSI compression, pre-processing of the encoder input can include a transformation of the channel from the antenna-frequency domain to the beam-delay domain, or from the antenna-frequency-time domain to the beam-delay-doppler domain. In addition, pre-processing is used to reduce the need for multiple models depending on bandwidth variation and variation in the number of antenna ports at the network node (e.g., gNB).

[0115] The transformation in the pre-processing is further explained. The channel representation in the antenna-frequency domain is usually rich and hard to compress (e.g., a large amount of data that are hard to compress). However, its equivalent form in the beamdelay domain is sparse and easier to compress. Such sparsity, to some extent, reflects the physical interpretation of a propagation channel. That is, it reflects how the numerous sinusoidal signals traverse from the transmitting end, along different paths, to the receiving end.Essentially, each beam can be associated with a certain direction of a propagation path, and each delay can reflect the relative difference in the distance if a signal propagates along different paths. Ideally, each pair of beam and delay is associated with a single propagation path if there are infinite spatial resolution and delay resolution.

[0116] In a real propagation environment, dominant paths that contribute to conveying a signal are usually sparse if the whole 3D space is viewed, because the signal cannot reach the receiver end from any direction. Among other reasons, this is mainly limited by the antenna directivity and the number of antenna elements deployed at both the transmitter and the receiver, as well as the number of objects in the propagation environment that can reflect a signal without introducing significant loss. The above sparsity can be exploited to assist an AI / ML model. For example, the beam-delay domain transformation can help the AI / ML model with initial feature extraction. Another advantage of this pre-processing is that the beam-delay transformation can be achieved using Fast Fourier Transform (FFTs), for which there are fast implementations with hardware support. The sparsity can be further exploited by removing a number of insignificant beams and delays, so that the input dimensions could also be reduced with a marginal loss, likely resulting in smaller AI / ML models. The beam-delay transformation and feature extraction can be applied to both cases of explicit channel feedback and eigenvector-based feedback.

[0117] An example for pre-processing of the eigenvector-based feedback is described using Figure 12. Figure 12 illustrates one of possible processes in transforming the measured / estimated channels to provide the encoder inputs. As shown in Figure 12, the UE measures the channel on CSI-RS. For example, the UE may have 4 RX-ports, the configured CSI-format has 32 virtual TX-ports, and the bandwidth is 52 Resource Blocks (RBs) corresponding to 10 MHz at 15 kHz subcarrier spacing. The feature extraction for eigenvectorbased is illustrated using Figure 12. In the first step, the UE does a spatial domain (SD) DFT on the 32 x 4 matrix per RB and selects the L strongest beams out of 16 (for one polarization). This is done in a wideband manner, including the spatial oversampling of the SD basis, and the same beams are used for both polarizations. The covariance of the beam-space channel is summed over, e.g., 4 RBs to produce a covariance matrix for each subband. In the second step, for each covariance matrix (per subband), the UE extracts a number of eigenvectors and may select the rank, e.g., the number of layers. In the third step, the UE does a frequency domain DFT per layer, transforming to a delay domain, whereafter it selects the M strongest taps. The resulting tensor of dimensions 2L x number of layers x M is called the linear combinationcoefficients and can be used to reconstruct precoding matrices. In the fourth step, the tensor of linear combination coefficients is used as input in the AI / ML model. The input could be further enhanced with information about the selected beams and taps, noise levels, etc.

[0118] To achieve reliable model performance assessment results, the UE can be configured to measure one or more channel samples, and then report ground truth / label(s) (also referred to as target CSI sample(s)) and model output(s) of one or multiple UE-side model(s) associated with these one or more channel samples. In the case where multiple samples of channels are used, the UE may first accumulate the ground truth / label(s) and the model output(s) for multiple samples within a time window and then report the accumulated data together, or the UE may report the ground truth / label(s) (also referred to as the target CSI samples) and model output(s) per sample. In some embodiments, the UE reports the target CSI without using AI / ML model(s), without compression, or with non- AI / ML based compression, For example, because the target CSI is used as the input of the encoder, there may be no AI / ML model involved in determining the target CSI. In one example, the target CSI may be simply uncompressed but it may require additional overhead. In another example, the target CSI can be compressed but not using AI / ML model(s). The compression can be done, e.g., with scalar quantization, or using Rel. 16 eType Il-like quantization / codebook, possibly with higher fidelity (e.g., a larger number of selected beams and taps compared to the maximum number of selected beams and taps standardized in Rel. 16 eType II). The target CSI samples (e.g., ground truth / labels) may be included or represented in a target-CSI report sent from the UE to the network node.

[0119] The ground truth / label(s) (also referred to as target CSI sample(s)), in return, may be used by the network node to do performance monitoring, model retraining, fine-tuning, etc. The network node, for example, may first generate one or more reconstructed CSI samples by feeding the one or more UE-side model output samples as inputs to the network node-side model, and then calculates one or more intermediate Key Performance Indicator (KPI) values by comparing the one or more generated reconstructed CSI with the target CSI samples included or represented in the target-CSI report. Some of the possible intermediate KPIs, for example, are the generalized cosine similarity (GCS), squared generalized cosine similarity (SGCS), etc.

[0120] To achieve a higher accuracy in performance monitoring, data collection, etc., the target-CSI report needs to be as accurate as possible. However, this may cause a significant amount of overhead. Therefore, the target-CSI report may need to be quantized to reduce theoverhead. For example, the quantization can be done with scalar quantization, e-Type Il-like quantization, etc.

[0121] There currently exist certain challenge(s). As mentioned above, the UE has some flexibility in transmitting its CSI report. For example, the UE may select the ParComb, preferred ranks, etc., that suits its condition (e.g., its link quality, anticipated traffic, etc.). In an AI / ML-based CSI report, the flexibility may be in terms of payload size, UCI quantization size, compression ratio, encoder output (latent space) size, selected model (or model pair), etc. While this mechanism may bring extra flexibility and efficiency in the UL transmission, it may give undesirable consequences for the AI / ML-based CSI report for the performance monitoring and data collection procedures.

[0122] In performance monitoring, for example, the network node may want to know the performance of the AI / ML model for a certain model and / or a certain configuration (e.g., performance for certain payloads, certain quantization sizes, certain layers, certain ranks, etc.). To achieve it, the UE may be configured or indicated by the network node to send target-CSI report according to configurations that the network node wants to monitor. This target-CSI report is then compared with the reconstructed CSI generated by the network node-side AI / ML model using the reported CSI as the network node-side model input, to determine the performance of the AI / ML model. Due to the flexibility of the transmission of the CSI report, however, the AI / ML-based CSI report and the target-CSI report may not be aligned. For example, the UE is configured to report target-CSI with rank = 4, while the UE sends an AI / ML-based CSI report with a rank = 2. In this case, the last 2 layers (e.g., layers 3 or 4) of the target-CSI report may not be useful for performance monitoring.

[0123] In addition, as mentioned above, the network node may be interested in monitoring the performance of the AI / ML model with certain configurations. However, the UE may not send the CSI report according to the configurations that the network node is interested in. For example, the network node may want to know the performance of the AI / ML model when it is configured with a quantization size of 4 bits. The UE, on the other hand, may send CSI with a quantization size of 2 bits.

[0124] Considering the above, mechanisms are needed to ensure an alignment between the target-CSI report and the AI / ML-based CSI-report.

[0125] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. A method is proposed to provide / facilitate / enable the alignment between the target-CSI report and its corresponding AEML-based CSI report. The methodmay be used to enable the network node to obtain a CSI-report according to configurations that the network node intends to monitor. This can be achieved by limiting the flexibility of the configurations that can be used by the UE in the CSI report when the UE is indicated to (also) report the target-CSI for the received CSI-RSs. Alternatively, the UE may use at least a part of information on its AI / ML-based CSI report to determine the configurations for the target-CSI report.

[0126] Figure 13 is a flowchart providing an overview of an example method 1300 performed by a UE for CSI reporting during performance monitoring according to some embodiments. Figure 14 is a flowchart providing an overview of an example method 1400 performed by a network node to receive the CSI reporting during performance monitoring according to some embodiments. Detailed examples and descriptions for illustrating the methods 1300 and 1400 follow the description of Figures 13 and 14.

[0127] Figure 13 illustrates a flowchart showing an example method 1300 performed by a UE for CSI reporting during performance monitoring according to some embodiments. In block 1310 of method 1300, the UE receives a first set of configurations related to an AI / ML based CSI report. In some embodiments, the AI / ML-based CSI report collects data for performance monitoring of one or more AI / ML models by a network node.

[0128] In block 1320 of method 1300, the UE receives a second set of configurations related to a target-CSI report. In some embodiments, the target-CSI report collects data for performance monitoring of one or more AI / ML models by the network node.

[0129] In block 1330 of method 1300, the UE receives an indication to send the target- CSI report and an indication to send the AI / ML-based CSI report. In some embodiments, the indication to send the AI / ML-based CSI report and the indication to send the target-CSI report are received simultaneously on a same occasion of indication. In some embodiments, the indication to send the AI / ML-based CSI report is received within at least a first duration after the reception of the indication to send the target-CSI report.

[0130] In block 1340 of method 1300, the UE sends the AI / ML-based CSI report according to a third set of configurations. In some embodiments, the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

[0131] In block 1350 of method 1300, the UE sends the target-CSI report. At least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combinationof at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

[0132] In some embodiments, the UE further receives an explicit indication from a network node that a configuration related to the AI / ML-based CSI report should be used for generating the AI / ML-based CSI report. For example, the configuration is not derived from the second set of configurations related to a target-CSI report.

[0133] In some embodiments, at least one of sending the AI / ML-based CSI report and sending the target-CSI report comprises: sending the AI / ML-based CSI report according to network node configured values.

[0134] In some embodiments, a bitfield indicating a configuration used by the UE in the AI / ML-based CSI report has the same values as a configuration indicated by the network node. In some embodiments, one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is not sent by the UE. In some embodiments, one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is sent with a value of 0. In some embodiments, one or more bitfield indicating a configuration used by the UE in the AI / ML-based CSI report is ignored by the network node.

[0135] Figure 14 illustrates a flowchart showing an example method 1400 performed by a network node for CSI reporting during performance monitoring according to some embodiments. In block 1410 of method 1400, the network node sends a first set of configurations related to an AI / ML based CSI report. In some embodiments, the AI / ML-based CSI report collects data for performance monitoring of one or more AI / ML models by the network node.

[0136] In block 1420 of method 1400, the network node sends a second set of configurations related to a target-CSI report. In some embodiments, the target-CSI report collects data for performance monitoring of one or more AI / ML models by the network node.

[0137] In block 1430 of method 1400, the network node sends an indication to send the target-CSI report and an indication to send the AI / ML-based CSI report.

[0138] In block 1440 of method 1400, the network node receives the AI / ML-based CSI report according to a third set of configuration. In some embodiments, the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

[0139] In block 1450 of method 1400, the network node receives the target-CSI report. At least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

[0140] Certain embodiments described herein may provide one or more of the following technical advantage(s). The disclosed mechanism may enable the network node to monitor the performance of a certain (set of) configurations that is the main interest for the network node. Alternatively, the disclosed mechanism may also enable a more efficient transmission of the target-CSI report, e.g., the UE only sends the target-CSI report that is aligned with its AI / ML- based CSI report. Note that the disclosed method may also be useful for data collection.

[0141] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0142] In this disclosure, the concept of ‘network’ and / or a gNB can be understood as a generic network node, a gNB, a base station, a unit within the base station to handle at least some AI / ML operations, a relay node, a core network node, a core network node that handles at least some AI / ML operations, or a device supporting Device to Device (D2D) communication. The network node may be deployed in a 5G network, or a 6G network. Moreover, the AI / ML encoder (or decoder) described here can be a single AI / ML model, or functionality using multiple AI / ML models in a way that is transparent to a decoder (or an encoder). In cases of layer-specific models, an encoder (or a decoder) can refer to each such model for each layer, e.g., an encoder (or a decoder) model for Layer 1, or an encoder (or a decoder) may refer to the collection of such models together with the logic for switching between them.

[0143] An AI / ML-based CSI report may be an AE-based CSI report. The AE-based CSI report used here is an example two-sided AI / ML-based feature (e.g., Figures 9-11) to explain the methods proposed for pairing the UE-side and network node-side of a two-sided model. In these examples, a UE-side AI / ML model is an encoder, and a network node-side AI / ML model is a decoder.

[0144] In this disclosure, an AE-based CSI report is used as an example of an AI / ML models or implementation to show the benefit of target-CSI (ground truth) reporting / data collection. There may be other AI / ML models or implementations. It is understood that thetechnologies described in this disclosure are not limited to just the AE-based CSI reporting, and can be used for other usages / implementations, e.g., CSI prediction, beam management, etc.

[0145] In addition, as mentioned above, target-CSI transmission from the UE to the network node may be used for different purposes, e.g., for performance monitoring, data collection, etc. Therefore, this target-CSI transmission may be referred to under different names, e.g., target-CSI report, target-CSI data collection, target-CSI-based monitoring, ground truth, ground truth reporting, etc. For simplicity, this mechanism is referred to as “target-CSI” or “target-CSI report” in most parts of this invention.

[0146] Further, in the disclosure, the examples may use a simple configuration. For example, the configuration related to the payload size is mentioned as X bits. It is understood that the simple configurations described in this disclosure are not limiting and the technologies described herein can be applied to a more complex configuration. For example, the UE may be configured with different payload sizes for different layers, e.g., the configuration of the payload size is a set of configurations rather than a single configuration.

[0147] In this disclosure, the UE may be configured to send one AL / ML-based CSI report and one target-CSI report. That is, there may be a one-to-one relationship between the AI / ML- based CSI report and target-CSI report. In other embodiments, the UE may be configured to send more than one CSI report in relation to one target-CSI report. For example, for one CSI- RS resource, the UE may be configured to send one target-CSI report and more than one AI / ML-based CSI reports. Multiple AI / ML-based CSI reports sent by the UE may have different configuration on its payload size, quantization size, encoder output size, latent space size, compression ratio, rank, etc. For example, the network node may be interested in the worst performance, the best performance, or all performance levels of the AI / ML model. The network node may want to know how the performance changes if the AE compression level is changed (e.g., from 8 bits to 2 bits). If the performance expectation from the network node has a certain value (e.g., 0.8), then the configuration for the compression level may be set at certain level (e.g., 4 bits). Therefore, the network node may desire to receive multiple AI / ML-based CSI reports with a target-CSI report. The multiple AI / ML-based CSI reports and the target- CSI report can be used to find an optimal configuration or setting. In some examples, the target-CSI report may not be compressed unlike the AI / ML-based CSI report. In some examples, the target-CSI report may be compressed, for example, with scalar quantization or Rel. 16 eType-like quantization / codebook, to reduce the overhead of the target CSI transmission.

[0148] In the disclosed method, the UE is capable and configured to provide AI / ML-based CSI reports. In addition, the UE is also capable and configured to conduct performance monitoring of the AI / ML models used for AEML-based CSI reports. In general, the UE conducts one or more of the following steps to support network node-side performance monitoring: receiving a first set of configurations related to the AI / ML-based CSI reporting and a second set of configurations related to target-CSI report; receiving an indication to send the target-CSI report; receiving an indication to send the AI / ML-based CSI report; sending the AI / ML-based CSI report according to a third set of configurations; and sending the target-CSI. At least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations. In some embodiments, the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

[0149] With reference back to Figure 13, as described above, in block 1310 of method 1300, the UE receives a first set of configurations related to an AI / ML based CSI report.

[0150] To enable the AI / ML-based CSI report, the UE is first configured by the network node with a first set of configurations related to the AI / ML-based CSI report. The first set of configurations may, for example, include at least one of, model (or model-pair) ID, quantization size, encoder output (latent space) size, puncturing rate, payload size, rank, CSI report type ID, etc. In some examples, each parameter of the configurations may have one or multiple values. In some embodiments, the CSI report type ID may indicate at least a part of the aforementioned configurations. For example, the CSI report type ID may indicate the information of at least one of model (or model-pair) ID, quantization size, encoder output (latent space) size, puncturing rate, payload size, and rank. The CSI report type ID may also indicate a format of phase information, a format of amplitude information, a format of modulation and Coding Scheme (MCS), channel coding information, and so on for the AI / ML- based CSI report, where similarly, each parameter may have one or multiple values. The definition of CSI report types can be pre-known or pre-determined by the network node and the UE. The CSI report types may be indicated by the report type ID. For example, the network node and the UE may agree, that the first report type corresponds to report type ID 1 ; the second report type corresponds to report type ID 2; and so forth. Different report types may have different configurations. For instance, the first report type with report type ID 1 may indicatethat the configurations use 4 bits quantization and rank 2; while the second report type with report type ID 2 may indicate that the configurations use 8 bits quantization and rank 4.

[0151] For the case of the parameters having one value for each parameter, the value may be the maximum (or minimum) value that can be used by the UE in its CSI report. For example, the UE may be configured with a maximum payload size of 400 bits. In its report, the UE may use payload sizes of 400 bits, 300 bits, 200 bits, etc. The UE may include information on the selected (used) payload size in the CSI report.

[0152] For the case of the parameters having multiple values, the UE may select one value to be used and include the selected value in the CSI report. For example, the UE may be configured with a payload size of 200 bits, 300 bits, and 400 bits. The UE may then select one value of the multiple values (e.g., 300 bits). The UE may report the selected value in the CSI report or may implicitly indicate the information of the selected value in the CSI report. For example, the CSI report type ID may indicate the information of the payload size, and the report of the CSI report type ID implicitly indicate the selected payload size. In another example, the UE may report the payload level (e.g., low, middle, high) or may report the payload size by 1, 2, 3, 4; where 1 means the size of 100 bits, 2 means the size of 200 bits, 3 means the size of 300 bits and 4 means the size of 400 bits.

[0153] In some embodiments, the UE can be configured with multiple maximum values. The UE may be further indicated by the network node (for example with a lower layer indication, e.g., DCI, Medium Access Control-Control Element (MAC-CE), etc.) to inform the UE which maximum value should be considered by the UE when constructing its CSI report.

[0154] With reference back to Figure 13, in block 1320 of method 1300, the UE receives a second set of configurations related to a target-CSI report. To have an alignment between the UE and the network node on the target-CSI report, the UE may be configured by the network node with a second set of configurations related to the target-CSI report. The second set of configurations may be, for example, include the rank, layer, pre-processing type, encoder input type, quantization method, etc. The configurations may be indicated via at least one CSI report type ID for target-CSI reporting. The CSI report type ID for target-CSI reporting may or may not be the same as the CSI report type ID for AI / ML-based CSI reporting. In some examples, one set of configurations (e.g., the second set of configurations for the target-CSI report) may overwrite, replace, and / or supplement, fully or partially, the another set of configuration (e.g., the first set of configurations for the AI / ML-based CSI report). In another example, to differentiate with the existing CSI report type definition, the configurations maybe indicated via at least one target-CSI report type ID for target-CSI reporting, where it means that there are pre-defined CSI report types and pre-defined target-CSI report types at the network node and the UE sides.

[0155] With reference back to Figure 13, in block 1340 of method 1300, the UE sends the AI / ML-based CSI report according to a third set of configurations. In some embodiments, the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

[0156] To ensure or improve the alignment between the target-CSI report and the AI / ML- based CSI report, in one embodiment, the third set of configurations may include a part of the first set of configurations related to the AI / ML-based CSI-report (e.g., payload size, quantization size, compression rate, puncturing rate, CSI report type ID (or target-CSI report type ID), etc.).

[0157] In another embodiment, the third set of configurations may include a part of the second set of configurations related to the target-CSI report.

[0158] In another embodiment, the third set of configurations may include configurations generated according to one or more predetermined rules. In some embodiments, a part of the second set of configurations related to the target-CSI report is determined according to the AI / ML-based CSI report generated by the UE. For example, a part of the second set of configurations related to the target-CSI report may depend on the AI / ML-based CSI-report generated by the UE-side model. For example, if the UE sends an AI / ML-based CSI report with rank = 2, the UE may also decide that it sends a target-CSI report with rank = 2. In such case, the rank configuration for target-CSI reporting may not be configured by the network node explicitly, since it is decided by the UE autonomously based on its generated AI / ML- based CSI report.

[0159] In some embodiment, the UE further determines the target-CSI report according to at least a part of the first set of configurations related to the AI / ML-based CSI report. In one embodiment, a part of the AI / ML-based CSI report configuration parameters is reused for target-CSI reporting configuration. For example, the UE can reuse the rank configured for the AI / ML-based CSI report when generating the target-CSI report. Therefore, at the UE, at least part of the third set of configurations used to generate the AI / ML-based CSI report may be reused by the UE to generate the target-CSI report. For instance, while the second set of configurations related to the target-CSI report may indicate that the UE should use a rank of 4,the UE may have a better understanding of the quality of the channel, and therefore, uses a rank of 2 in the third set of configuration for generating the AI / ML-based CSI report. The configuration for generating the target-CSI report may use a rank of 2 as well. In this case, the UE determines that the configurations for the target-CSI report should follow the configurations of the AI / ML-based CSI report.

[0160] With reference still to Figure 13, in block 1330 of method 1300, the UE receives an indication to send the AI / ML-based CSI report and an indication to send the target-CSI report. In one embodiment, the indication to send the target-CSI report may be included in the indication to send the AI / ML-based CSI report.

[0161] In some embodiments, a bitfield indicating a configuration used by the UE in the AI / ML-based CSI report has the same values as a configuration indicated by the network node. In some embodiments, one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is not sent by the UE. In some embodiments, one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is sent with a value of 0. In some embodiments, one or more bitfield indicating a configuration used by the UE in the AI / ML-based CSI report is ignored by the network node.

[0162] In one example, an additional bitfield may be added in the aperiodic or semi- persistent CSI-report indication in the DCI or in MAC-CE. In the bitfield, one value (e.g., 0) may represent an indication to not report the target-CSI; while another value (e.g., 1) may represent an indication to report the target-CSI. Note that the size of the bitfield may be more than one bit if more than one format / configuration of target-CSI are supported. Here, for example, 00, 01, 10, and 11, may represent an indication to not report the target-CSI, to report the target-CSI according to a first configuration, to report the target-CSI according to a second configuration, and to report the target-CSI according to a third configuration, respectively. Note that if there are only two target-CSI configurations, the value of 11 may be reserved.

[0163] In some embodiments, the indication to send the AI / ML-based CSI report and the indication to send the target-CSI report are received simultaneously on a same occasion of indication. In some embodiments, the indication to send the AI / ML-based CSI report is received within at least a first duration after the reception of the indication to send the target- CSI report. The first duration may be pre-determined in the standard or may be configurable (e.g., in terms of slot, symbols, ms, etc.). The first duration may depend on the UE capability, e.g., because a more capable UE may decode the indication to send the target-CSI report faster. As the flexibility owned by the UE on the CSI-report may be different depending on whetherthe UE needs to send the target-CSI report, it is important that the UE finishes decoding the indication on whether it needs to send target-CSI report.

[0164] As mentioned above, the UE may need to follow certain configurations of the CSI report when the UE also receives an indication to send its target-CSI report (e.g., ground-truth).

[0165] In some embodiments, the AI / ML-based CSI report is generated by overwriting at least a part of the first set of configurations related to the AI / ML-based CSI report with at least a part of the second set of configurations related to the target-CSI report. For example, one or more configurations embedded in the target-CSI configuration (e.g., the second set of configurations related to the target-CSI report) may overwrite the AI / ML-based CSI report configurations (e.g., the first set of configurations related to the AI / ML-based CSI report). For example, the UE may be configured with a maximum payload size of 400 bits in the AI / ML- based CSI report configuration. However, the UE may also be configured with target-CSI configuration which may include the payload size of CSI-report that should be used by the UE during the performance monitoring, e.g., 600 bits (e.g., because the network node wants to check whether changing CSI report configurations is justified). In that case, the UE needs to use 600 bits in its CSI-report when the UE also receives an indication to send its target-CSI report.

[0166] In another embodiment, the UE may be configured with multiple values that can be used for its AI / ML-based CSI report. For example, the UE may be configured with multiple values of payload sizes. In the normal condition, e.g., no indication to send its target-CSI report, the UE may select one of the possible payload sizes for its CSI report (and report the selected values in the CSI report). During performance monitoring, the UE may then receive an indication (e.g., embedded in the indication to send a target-CSI report) from the network node on which payload size should be used by the UE for its AI / ML-based CSI report.

[0167] In some embodiments, the AI / ML-based CSI report is generated by applying a configuration in generating the AI / ML-based CSI report according to a first index. In some embodiments, the first index is different from an index used in generating the AI / ML-based CSI report, if the UE does not receive an indication to send the target-CSI report. For example, the UE may apply for a value according to a certain index in the configuration. The index may be, for example, index 0. In an example, the UE may be configured with payload sizes of 600 bits (index 0), 400 bits (index 1), 300 bits (index 2), and 200 bits (index 3). When the UE does not receive an indication to send target-CSI report, the UE may select one of the configured values for its CSI report. When the UE receives an indication to send target-CSI report, the UEmay need to use 600 bits for its CSI report, e.g., the value with index 0 in the configuration. Note that index 0 is only an example, and the standard may use another index, for example, the largest index, etc.

[0168] In some embodiments, the AI / ML-based CSI report is generated by applying a configuration having a maximum value. For example, the maximum value can be a maximum quantization size, a maximum payload size, a largest encoder output size, or a largest rank, etc. In one embodiment, the UE may apply the maximum (or minimum) value that is included in the configuration. For example, the UE may be configured with payload sizes of 400 bits (index 0), 600 bits (index 1), 300 bits (index 2), and 200 bits (index 3). Here, the UE may use the payload size of 600 bits (e.g., max value of {400, 600, 300, 200} bits). Note that except for network node configurable, the maximum value may also be a predetermined value, e.g., written in the standard text.

[0169] In another embodiment, the UE may be configured with a maximum or minimum value of one or more parameters in the CSI report configuration. When the UE is not indicated to send its ground truth, the UE may use a value that is different from the network node configured value if it satisfies the threshold. For example, the UE may be configured with a maximum payload size of 600 bits. The UE may then send CSI with a payload size of < 600 bits. When the UE also receives an indication to send the target-CSI report, the ground truth, however, a restriction may be applied, e.g., the UE may need to send a CSI report with a payload size of 600 bits (as configured by the network node in the CSI-report configuration as the maximum payload size).

[0170] In the embodiments above, the payload size is mainly used as an example. It is understood that other possible configurations, e.g., quantization size, encoder output size, latent space size, model ID, rank, etc., may also be used.

[0171] With reference back to Figure 13, in block 1340 of method 1300, the UE sends the AI / ML-based CSI report to the network node. In block 1340 of method 1300, the UE sends the target-CSI report to the network node.

[0172] After determining the configuration that should be used for its AI / ML-based CSI report, the UE then sends the AI / ML-based CSI report. As the flexibility of the AI / ML-based CSI report may be less flexible (compared to when the UE does not receive an indication to send the target-CSI report during performance monitoring), some information in the AI / ML- based CSI report may not be needed (e.g., information on what quantization size, payload size, rank, etc., that are used by the UE in the CSI report).

[0173] In one embodiment, the UE may still send the information of the configuration used by the UE in its AI / ML-based CSI report although the UE does not have the flexibility to select / use other values in the configuration. Here, the information contained in the AI / ML- based CSI report is the same as the values of the configuration that is and should be used by the UE.

[0174] In one embodiment, at least one of the bitfields in the CSI report may not be sent by the UE. For example, when the UE needs to use the maximum possible payload size from more than one configured payload size, information on the payload size used by the UE may be omitted by the UE as anyway, the network node understands which payload size used by the UE in its AI / ML-based CSI report.

[0175] In another embodiment, the UE may send zeros for the bitfield in the CSI report that should indicate the value that is used by the UE. For example, the UE may send 00 in the payload size bitfield regardless of the payload size that is currently used by the UE. On another side, the network node may then ignore such information, e.g., as the network node already knows which value in the configuration is used by the UE in its CSI report.

[0176] In an embodiment, at least part of the target-CSI configuration follows the configuration used by the UE in its AI / ML-based CSI report. In such case, the UE sends the target-CSI report according to the aforementioned configurations.

[0177] The above descriptions provide detailed examples for illustrating the method 1300 performed by the UE shown in Figure 13. It is understood that the counterpart method 1400 is performed by the network node as illustrated in Figure 14. And thus detailed description using Figure 14 is not repeated here. For instance, if the UE receives a first set of configurations (block 1310), the network node correspondingly sends the first set of configurations to the UE (block 1410).

[0178] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or moreoperations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0179] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0180] ADDITIONAL EMBODIMENTS

[0181] Group A Embodiments

[0182] 1. A method performed by a user equipment (UE), the method comprising: receiving a first set of configurations related to an artificial intelligence or machine learning (AI / ML) based channel state information (CSI) report; receiving a second set of configurations related to a target-CSI report; receiving an indication to send the target-CSI report and an indication to send the AI / ML- based CSI report; sending the AI / ML-based CSI report according to a third set of configurations; and sending the target-CSI report, wherein at least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

[0183] 2. The method of embodiment 1, wherein the AI / ML-based CSI report and target-CSI report collect data for performance monitoring of one or more AI / ML models by a network node.

[0184] 3 The method of embodiment 1, wherein the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

[0185] 4. The method of embodiment 1, wherein the indication to send the AI / ML- based CSI report and the indication to send the target-CSI report are received simultaneously on a same occasion of indication.

[0186] 5. The method of embodiment 1, wherein the indication to send the AI / ML- based CSI report is received within at least a first duration after the reception of the indication to send the target-CSI report.

[0187] 6. The method of embodiment 1, further comprising: determining the target-CSI report according to at least a part of the first set of configurations related to the AI / ML-based CSI report.

[0188] 7. The method of embodiment 1, wherein a part of the second set of configurations related to the target-CSI report is determined according to the AI / ML-based CSI report generated by the UE.

[0189] 8. The method of embodiment 1, wherein the AI / ML-based CSI report is generated by: overwriting at least a part of the first set of configurations related to the AI / ML-based CSI report with at least a part of the second set of configurations related to the target-CSI report.

[0190] 9. The method of embodiment 1, further comprising: receiving an explicit indication from a network node that a configuration related to the AI / ML-based CSI report should be used for generating the AI / ML-based CSI report.

[0191] 10. The method of embodiment 1, wherein the AI / ML-based CSI report is generated by: applying a configuration in generating the AI / ML-based CSI report according to a first index.

[0192] 11. The method of embodiment 10, wherein the first index is different from an index used in generating the AI / ML-based CSI report if the UE does not receive an indication to send the target-CSI report.

[0193] 12. The method of embodiment 1, wherein the AI / ML-based CSI report is generated by: applying a configuration having a maximum value.

[0194] 13. The method of embodiment 1, wherein at least one of sending the AI / ML- based CSI report and sending the target-CSI report comprises: sending the AI / ML-based CSI report according to network node configured values.

[0195] 14. The method of embodiment 1, wherein a bitfield indicating a configuration used by the UE in the AI / ML-based CSI report has the same values as a configuration indicated by the network node.

[0196] 15. The method of embodiment 1, where one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is not sent by the UE.

[0197] 16. The method of embodiment 1, where one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is sent with a value of 0.

[0198] 17. The method of embodiment 1, where one or more bitfield indicating a configuration used by the UE in the AI / ML-based CSI report is ignored by the network node.

[0199] 18. The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.

[0200] Group B Embodiments

[0201] 19. A method performed by a network node for enabling the network node to obtain a Channel State Information (CSI) report, the method comprising: sending a first set of configurations related to an artificial intelligence or machine learning (AI / ML) based channel state information (CSI) report; sending a second set of configurations related to a target-CSI report; sending an indication to send the target-CSI report and an indication to send the AI / ML- based CSI report; receiving the AI / ML-based CSI report according to a third set of configurations; and receiving the target-CSI report, wherein at least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

[0202] 20. The method of embodiment 19, wherein the AI / ML-based CSI report and target-CSI report collect data for performance monitoring of one or more AI / ML models by a network node.

[0203] 21. The method of embodiment 19, wherein the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

[0204] 22. The method of embodiment 19, wherein the indication to send the AI / ML- based CSI report and the indication to send the target-CSI report are received simultaneously on a same occasion of indication.

[0205] 23. The method of embodiment 19, wherein the indication to send the AI / ML- based CSI report is received within at least a first duration after the reception of the indication to send the target-CSI report.

[0206] 24. The method of embodiment 19, wherein a part of the second set of configurations related to the target-CSI report is determined according to the AI / ML-based CSI report generated by the UE.

[0207] 25. The method of embodiment 19, wherein the AI / ML-based CSI report is generated by: overwriting at least a part of the first set of configurations related to the AI / ML-based CSI report with at least a part of the second set of configurations related to the target-CSI report.

[0208] 26. The method of embodiment 19, further comprising: sending an explicit indication that a configuration related to the AI / ML-based CSI report should be used for generating the AI / ML-based CSI report.

[0209] 27. The method of embodiment 19, wherein the AI / ML-based CSI report is generated by: applying a configuration in generating the AI / ML-based CSI report according to a first index.

[0210] 28. The method of claim 27, wherein the first index is different from an index used in generating the AI / ML-based CSI report if the UE does not receive an indication to send the target-CSI report.

[0211] 29. The method of embodiment 19, wherein the AI / ML-based CSI report is generated by: applying a configuration having a maximum value.

[0212] 30. The method of embodiment 19, wherein at least one of sending the AI / ML- based CSI report and sending the target-CSI report comprises: sending the AI / ML-based CSI report according to network node configured values.

[0213] 31. The method of embodiment 19, wherein a bitfield indicating a configuration used by the UE in the AI / ML-based CSI report has the same values as a configuration indicated by the network node.

[0214] 32. The method of embodiment 19, where one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is not sent by the UE.

[0215] 33. The method of embodiment 19, where one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is sent with a value of 0.

[0216] 34. The method of embodiment 19, where one or more bitfield indicating a configuration used by the UE in the AI / ML-based CSI report is ignored by the network node.

[0217] 35. The method of any of the previous embodiments 19-34, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0218] Group C Embodiments

[0219] 36. A user equipment for enabling a network node to obtain a Channel StateInformation (CSI) report, comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0220] 37. A network node for enabling the network node to obtain a Channel StateInformation (CSI) report, the network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.

[0221] 38. A user equipment (UE) for enabling a network node to obtain a Channel StateInformation (CSI) report, the UE comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry;an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

[0222] 39. A host configured to operate in a communication system to provide an over- the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0223] 40. The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

[0224] 41. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0225] 42. The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

[0226] 43. The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

[0227] 44. A communication system configured to provide an over-the-top (OTT) service, the communication system comprising: a host comprising:processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0228] 45. The communication system of the previous embodiment, further comprising: the network node; and / or the UE.

[0229] 46. A host configured to operate in a communication system to provide an over- the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host.

[0230] 47. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0231] 48. The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data.

[0232] 49. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.

[0233] 50. The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

[0234] 51. A host configured to operate in a communication system to provide an over- the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host.

[0235] 52. The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

[0236] 53. The host of the previous embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0237] 54. A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host.

[0238] 55. The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application.

[0239] 56. The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0240] 57. A host configured to operate in a communication system to provide an over- the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; anda network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0241] 58. The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

[0242] 59. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0243] 60. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0244] 61. The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

[0245] 62. The method of the previous 2 embodiments, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A method performed by a user equipment (UE), the method comprising: receiving (1310) a first set of configurations related to an artificial intelligence or machine learning (AI / ML) based channel state information (CSI) report; receiving (1320) a second set of configurations related to a target-CSI report; receiving (1330) an indication to send the target-CSI report and an indication to send the AI / ML-based CSI report; sending (1340) the AI / ML-based CSI report according to a third set of configurations; and sending (1350) the target-CSI report, wherein at least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

2. The method of claim 1, wherein the AI / ML-based CSI report and target-CSI report collect data for performance monitoring of one or more AI / ML models by a network node.

3. The method of any of claims 1-2, wherein the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

4. The method of any of claims 1-3, wherein the indication to send the AEML-based CSI report and the indication to send the target-CSI report are received simultaneously on a same occasion of indication.

5. The method of any of claims 1-3, wherein the indication to send the AEML-based CSI report is received within at least a first duration after the reception of the indication to send the target-CSI report.

6. The method of any of claims 1-5, further comprising: determining the target-CSI report according to at least a part of the first set ofconfigurations related to the AI / ML-based CSI report.

7. The method of any of claims 1-6, wherein a part of the second set of configurations related to the target-CSI report is determined according to the AI / ML-based CSI report generated by the UE.

8. The method of any of claims 1-7, wherein the AI / ML-based CSI report is generated by: overwriting at least a part of the first set of configurations related to the AI / ML-basedCSI report with at least a part of the second set of configurations related to the target-CSI report.

9. The method of any of claims 1-8, further comprising: receiving an explicit indication from a network node that a configuration related to the AI / ML-based CSI report should be used for generating the AI / ML-based CSI report.

10. The method of any of claims 1-9, wherein the AI / ML-based CSI report is generated by: applying a configuration in generating the AI / ML-based CSI report according to a first index, and optionally, wherein the first index is different from an index used in generating the AI / ML-based CSI report if the UE does not receive an indication to send the target-CSI report.

11. The method of any of claims 1-10, wherein the AI / ML-based CSI report is generated by: applying a configuration having a maximum value.

12. The method of any of claims 1-11, wherein at least one of sending the AEML-based CSI report and sending the target-CSI report comprises: sending the AI / ML-based CSI report according to network node configured values.

13. The method of any of claims 1-12, wherein a bitfield indicating a configuration used by the UE in the AI / ML-based CSI report has the same values as a configuration indicated by the network node.

14. The method of any of claims 1-13, wherein one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is not sent by the UE; and / or wherein one or more bitfields indicating a configuration used by the UE in the AI / ML- based CSI report is sent with a value of 0; and / orwherein one or more bitfield indicating a configuration used by the UE in the AI / ML- based CSI report is ignored by the network node.

15. A method performed by a network node for enabling the network node to obtain a Channel State Information (CSI) report, the method comprising: sending (1410) a first set of configurations related to an artificial intelligence or machine learning (AI / ML) based channel state information (CSI) report; sending (1420) a second set of configurations related to a target-CSI report; sending (1430) an indication to send the target-CSI report and an indication to send the AI / ML-based CSI report; receiving (1440) the AI / ML-based CSI report according to a third set of configurations; and receiving (1450) the target-CSI report, wherein at least one of the AI / ML-based CSI report and the target-CSI report is generated according to a combination of at least two of the first set of configurations, the second set of configurations, and the third set of configurations.

16. The method of claim 15, wherein the AI / ML-based CSI report and target-CSI report collect data for performance monitoring of one or more AI / ML models by a network node.

17. The method of any of claims 15-16, wherein the third set of configurations comprises a part of the first set of configurations related to the AI / ML-based CSI report, a part of the second set of configurations related to the target-CSI report, or configurations generated according to one or more predetermined rules.

18. The method of any of claims 15-17, wherein the indication to send the AI / ML-based CSI report and the indication to send the target-CSI report are received simultaneously on a same occasion of indication.

19. The method of any of claims 15-17, wherein the indication to send the AI / ML-based CSI report is received within at least a first duration after the reception of the indication to send the target-CSI report.

20. The method of any of claims 15-19, wherein a part of the second set of configurations related to the target-CSI report is determined according to the AI / ML-based CSI reportgenerated by the UE.

21. The method of any of claims 15-20, wherein the AI / ML-based CSI report is generated by: overwriting at least a part of the first set of configurations related to the AI / ML-based CSI report with at least a part of the second set of configurations related to the target-CSI report.

22. The method of any of claims 15-21, further comprising: sending an explicit indication that a configuration related to the AI / ML-based CSI report should be used for generating the AI / ML-based CSI report.

23. The method of any of claims 15-22, wherein the AI / ML-based CSI report is generated by: applying a configuration in generating the AI / ML-based CSI report according to a first index, and optionally, wherein the first index is different from an index used in generating the AI / ML-based CSI report if the UE does not receive an indication to send the target-CSI report.

24. The method of any of claims 15-23, wherein the AI / ML-based CSI report is generated by: applying a configuration having a maximum value.

25. The method of any of claims 15-24, wherein at least one of sending the AI / ML-based CSI report and sending the target-CSI report comprises: sending the AI / ML-based CSI report according to network node configured values.

26. The method of any of claims 15-25, wherein a bitfield indicating a configuration used by the UE in the AI / ML-based CSI report has the same values as a configuration indicated by the network node.

27. The method of any of claims 15-26, wherein one or more bitfields indicating a configuration used by the UE in the AI / ML-based CSI report is not sent by the UE; and / or wherein one or more bitfields indicating a configuration used by the UE in the AI / ML- based CSI report is sent with a value of 0; and / or wherein one or more bitfield indicating a configuration used by the UE in the AI / ML-based CSI report is ignored by the network node.

28. A user equipment (UE) for enabling a network node to obtain a Channel State Information (CSI) report, comprising: processing circuitry configured to perform any of the steps of any of the claims 1-14; and power supply circuitry configured to supply power to the processing circuitry.

29. A network node for enabling the network node to obtain a Channel State Information (CSI) report, the network node comprising: processing circuitry configured to perform any of the steps of any of the claims 15-27; power supply circuitry configured to supply power to the processing circuitry.

Images