Method, user equipment, and access network node

Abstract

A system (1) is disclosed in which an access network node (base station) (5) performs channel adaptation of an air interface between a user equipment (UE) (3) and the radio access network node. Access network node information for the UE to determine whether the channel quality of the air interface has changed. If the UE determines that a current channel quality value of the air interface has changed from a previous channel quality value sent to the access network node, the access network node receives periodic channel quality reports from the UE at channel quality reporting occasions, the periodic channel quality reports including information indicative of the current channel quality value.

Description

Technical Field

[0001] The present disclosure relates to a wireless communication system operating according to the Third Generation Partnership Project (3GPP (registered trademark)) standard or its equivalents or derivatives, and devices thereof.

Background Art

[0002] The present disclosure is particularly relevant to, but not exclusively related to, improvements in channel state feedback in so-called "5G" or "New Radio" systems (also referred to as "next-generation" systems).

[0003] Under the 3GPP standard, NodeB (or "eNB" in LTE and "gNB" in 5G) is a base station for a communication device (user equipment or "UE") to connect to a core network and communicate with other communication devices or remote servers. The communication device may be, for example, a mobile communication device such as a mobile phone, smartphone, smartwatch, personal digital assistant, laptop / tablet computer, web browser, e-book reader, etc. Such mobile (or generally fixed) devices are usually operated by a user (thus, they are often collectively referred to as user equipment "UE"), but it is also possible to connect Internet of Things (IoT) devices and similar machine type communication (MTC) devices to the network. For simplicity, this application uses the term base station to refer to any such base station, and the terms mobile device or UE to refer to any such communication device.

[0004] The latest development in 3GPP standards is the so-called "5G" or "New Radio (NR)" standard, which refers to an evolving communication technology expected to support a variety of applications and services, including MTC, IoT communications, vehicle-to-vehicle communications and autonomous vehicles, high-definition video streaming, and smart city services. 3GPP intends to support 5G through the so-called 3GPP Next Generation (NextGen) Radio Access Network (RAN) and 3GPP NextGen Core (NGC) networks. Various details of 5G networks are described, for example, in Non-Patent Document 1 by the Next Generation Mobile Networks (NGMN) Alliance, which is available at https: / / www.ngmn.org / 5g-white-paper.html.

[0005] End-user communication devices are commonly referred to as User Equipment (UE) and may be operated by humans or may comprise automated (MTC / IoT) devices. Base stations of 5G / NR communication systems are commonly called new radio base stations ("NR-BS") or "gNBs," but it will be understood that they may also be referred to using the term "eNB" (or 5G / NR eNB), typically associated with Long Term Evolution (LTE) base stations (commonly also called "4G" base stations). Non-Patent Documents 2 and 3 define, among other things, the following nodes: A node that provides protocol termination for the NR user plane and control plane toward the gNB:UE and is connected to the 5G core network (5GC) via the NG interface. ng-eNB: A node that provides protocol termination for the E-UTRA (Evolved Universal Terrestrial Radio Access) user plane and control plane toward the UE, and is connected to 5GC via the NG interface. A node that provides protocol termination for the NR user plane and control plane toward En-gNB:UE, and functions as a secondary node in EN-DC (E-UTRA-NR Dual Connectivity). NG-RAN node: Either gNB or ng-eNB.

[0006] The terms base station or RAN node are used herein to refer to any such node. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] 'NGMN 5G White Paper' V1.0, Next Generation Mobile Networks (NGMN) Alliance,<https: / / www.ngmn.org / 5g-white-paper.html> [Non-Patent Document 2] 3GPP TS 38.300 V16.7.0 [Non-Patent Document 3] 3GPP TS 37.340 V16.7.0 [Non-Patent Document 4] 3GPP TS 38.214 V16.8.0 [Overview of the project] [Problems that the invention aims to solve]

[0008] Next-generation mobile networks support diverse service requirements categorized into three categories by the International Telecommunication Union (ITU): Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). eMBB aims to provide enhanced support for conventional mobile broadband, focusing on services requiring high capacity and guaranteed bandwidth, such as High Definition (HD) video, Virtual Reality (VR), and Augmented Reality (AR). URLLC is a requirement for critical applications such as autonomous driving and factory automation, which require guaranteed access within extremely short timeframes. MMTC needs to support a vast number of connected devices, such as smart meters and environmental monitoring, but can typically tolerate certain access latency. It should be understood that some of these applications may have relatively loose Quality of Service (QoS) or Quality of Experience (QoE) requirements, while others may have relatively stringent QoS / QoE requirements (e.g., high bandwidth and / or low latency).

[0009] The Physical Uplink Control Channel (PUCCH) carries a set of information called Uplink Control Information (UCI). The format of the PUCCH depends on what information the UCI carries. The PUCCH format used is determined by how many bits of information should be carried and how many symbols are assigned. The UCI used in NR (5G) includes one or more of the following information: Channel State Information (CSI), ACK / NAK, and Scheduling Request (SR). This is generally the same as in LTE (4G), as will be explained in detail below.

[0010] The Physical Downlink Control Channel (PDCCH) carries a set of information called Downlink Control Information (DCI). DCI used in NR (5G), depending on its format, includes information indicating resource allocation in the uplink (UL) or downlink (DL) to a single Radio Network Temporary Identifier (RNTI), for example, a UE. There are various DCI formats used in LTE and NR (5G), each a predetermined format in which downlink control information is packed / formed in the PDCCH and transmitted. DCI is used to schedule transmissions from the base station to the UE (downlink) and from the UE to the base station (uplink), and to provide such scheduling information to the UE.

[0011] In communications, the UE is configured to estimate and report the CSI of the communication channel between the UE and the base station, which is used in the CSI feedback framework to enable the base station to select an appropriate Modulation and Coding Scheme (MCS) based on the channel conditions across all or part of the bandwidth. The MCS defines the number of useful bits that can be carried by a single symbol, i.e., precisely for NR (5G), the number of useful bits that can be transmitted per Resource Element (RE). The MCS depends on the radio signal quality in the radio link; the better the link quality, the higher the MCS and the more useful bits can be transmitted within a symbol or RE. The assigned MCS is signaled to the UE using DCI on the PDCCH to define the modulation and coding rates. 3GPP TS 38.214 V 16.8.0 defines various MCS tables for 5G NR physical layer procedures for data.

[0012] Generally, NR (as with LTE) utilizes an implicit rank indicator (RI) / precoding matrix indicator (PMI) / channel quality indicator (CQI) feedback framework for CSI feedback. In summary, the combination of RI, PMI, and CQI forms the channel status report, and the CSI feedback framework is "implicit" in the form of CQI / PMI / RI (and channel rank indicator (CRI) in the relevant LTE and NR specifications) derived from the codebook. The rank indicator (RI) is information about the channel rank, indicating the number of streams / layers that can be received over the same time-frequency resource. Since the RI is determined by the long-term fading of the channel, it may generally be fed back at a longer period than the PMI or CQI. The PMI is a value that indicates the spatial characteristics of the channel, indicating the precoding matrix index of the network device (base station) preferred by each terminal device (UE). CQI is information that indicates the channel strength and the signal-to-interference-plus-noise ratio (SINR) when a base station uses PMI.

[0013] Accurate (or frequent) CSI feedback enables precise MCS selection and adaptation of links to current channel conditions. Enhancing CSI feedback to improve link adaptation is beneficial for reliability and overall system efficiency. The CSI reporting configuration for CSI can be periodic using PUCCH (P-CSI), aperiodic using PUSCH (A-CSI), or semi-permanent using PUCCH and DCI-activated PUSCH (SP-CSI). In periodic CSI reporting, the reporting period (i.e., the period defining the reporting point) is determined at the upper layer using RRC signaling, and at appropriate junctions, CSI data is transmitted by the UE to the scheduler (base station) using PUCCH, while in aperiodic reporting, CSI feedback is triggered by the base station as needed using DCI on PDCCH. In this case, CSI data is transmitted by the UE via PUSCH. A-CSI may form the primary CSI feedback framework of the communication system or be a supplementary configuration, for example, to address failed detection of P-CSI or SP-CSI reporting.

[0014] To improve accuracy and enable rapid adaptation to changing channel conditions, relatively short CSI reporting periodicity is required (resulting in increased CQI reporting frequency), which leads to increased signaling overhead and higher power consumption. For example, relatively frequent periodic CSI feedback from the UE to the scheduler may require CQI reporting every few transmission time intervals (TTIs), even if there is little (or no) fluctuation in CQI during a particular period. In other words, there may be periods in which the same or similar CQI values ​​are reported at relatively short intervals. In the case of aperiodic CSI feedback, frequent feedback requires frequent DCI transmissions from the base station to the UE without the guarantee that changes in channel conditions will be detected in a timely manner. Therefore, current CSI feedback methods have limited applicability in certain situations and are not ideal for relatively fast link adaptation. [Means for solving the problem]

[0015] Accordingly, this disclosure seeks to provide a method and related apparatus for CSI feedback enhancement to address or at least mitigate the above-mentioned problems. Several discussions have taken place in 3GPP regarding the use of Artificial Intelligence (AI) / Machine Learning (ML) in Release 18 to improve procedures related to wireless interfaces. One possible use case is CSI feedback enhancement. Accordingly, this disclosure also seeks to provide a method and related apparatus based on AI / ML-based CSI feedback enhancement.

[0016] In one embodiment, the Disclosure provides a method performed by a User Equipment (UE) of a Wireless Access Network for channel adaptation of a wireless interface between the UE and a node, the method comprising: receiving information indicating at least one condition for triggering the transmission of a periodic channel quality report; and, when at least one condition is met, transmitting a periodic channel quality report indicating the current channel quality value to the Wireless Access Network at a particular channel quality reporting opportunity, wherein the information is: - Channel quality change conditions that indicate the required difference between the current channel quality value and the previous channel quality value transmitted to the wireless access network, - Travel conditions indicating the travel required by the UE to transmit periodic channel quality reports to the radio access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the required difference between the UE's current location and the location when transmitting the previous channel quality report to the radio access network, - Time conditions indicating the validity period of previous channel quality reports that had the same channel quality value as the current channel quality value. Show at least one of them.

[0017] In one aspect, the present disclosure provides a method performed by a node of a radio access network for channel adaptation of a radio interface between a user equipment (UE) and the node of the radio access network. The method includes transmitting information indicating at least one condition for triggering the transmission of periodic channel quality reports, and receiving, from the UE, a periodic channel quality report indicating a current channel quality value at a specific channel quality reporting opportunity when the at least one condition is satisfied. The information includes - a channel quality change condition indicating a required difference between the current channel quality value and a previous channel quality value transmitted to the radio access network, - a mobility condition indicating a movement required by the UE to transmit a periodic channel quality report to the radio access network, - a location condition indicating a location or area where a periodic channel quality report needs to be transmitted to the radio access network, - a location change condition indicating a required difference between the current location of the UE and the location when a previous channel quality report was transmitted to the radio access network, - a time condition indicating the validity period of a previous channel quality report having the same channel quality value as the current channel quality value and indicates at least one of.

[0018] In one aspect, the present disclosure provides a method performed by a node of a radio access network for channel adaptation of a radio interface between a user equipment (UE) and the node of the radio access network. The method includes using an artificial intelligence / machine learning model to predict at least one inflection point regarding a channel quality value associated with a channel, and transmitting, to the UE, information indicating at least one condition regarding the at least one inflection point to trigger at least one periodic channel quality report by the UE to report an actual channel quality value associated with the at least one inflection point.

[0019] In one aspect, the present disclosure provides a user equipment (UE) for channel adaptation of a radio interface between the UE of a radio access network and a node. The UE includes means (e.g., a memory, a controller, and a transceiver) for receiving information indicating at least one condition for triggering the transmission of periodic channel quality reports, and means for transmitting a periodic channel quality report indicating a current channel quality value to the radio access network at a specific channel quality reporting opportunity when at least one condition is met. The information includes - a channel quality change condition indicating a required difference between the current channel quality value and a previous channel quality value transmitted to the radio access network; - a movement condition indicating a movement required by the UE to transmit a periodic channel quality report to the radio access network; - a location condition indicating a location or area where a periodic channel quality report needs to be transmitted to the radio access network; - a location change condition indicating a required difference between the current location of the UE and the location when a previous channel quality report was transmitted to the radio access network; - a time condition indicating the validity period of a previous channel quality report having the same channel quality value as the current channel quality value and indicates at least one of them.

[0020] In one aspect, the present disclosure provides a node of a radio access network for channel adaptation of a radio interface between a user equipment (UE) of the radio access network and the node. The node includes means (e.g., a memory, a controller, and a transceiver) for transmitting information indicating at least one condition for triggering the transmission of periodic channel quality reports, and means for receiving a periodic channel quality report indicating a current channel quality value from the UE at a specific channel quality reporting opportunity when at least one condition is met. The information includes - a channel quality change condition indicating a required difference between the current channel quality value and a previous channel quality value transmitted to the radio access network; - Travel conditions indicating the travel required by the UE to transmit periodic channel quality reports to the radio access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the required difference between the UE's current location and the location when transmitting the previous channel quality report to the radio access network, - Time conditions indicating the validity period of previous channel quality reports that had the same channel quality value as the current channel quality value. Show at least one of them.

[0021] In one embodiment, the Disclosure provides a node for a radio access network relating to channel adaptation of a radio interface between a user equipment (UE) of the radio access network and the node, the node including means (e.g., memory, controller, and transceiver) that use an artificial intelligence / machine learning model to predict at least one fluctuation point relating to a channel quality value associated with the channel, and means that transmit information to the UE indicating at least one condition relating to the at least one fluctuation point in order to trigger at least one periodic channel quality report by the UE to report the actual channel quality value associated with the at least one fluctuation point.

[0022] Aspects of the present disclosure extend to computer program products such as computer-readable storage media storing corresponding systems, devices, and instructions, the instructions being operable to program a programmable processor to perform the methods of the aspects and possibilities described above, and / or to program a computer appropriately adapted to provide the devices described in any of the claims.

[0023] To facilitate understanding for those skilled in the art, this disclosure will be described in detail in the context of a 3GPP system (5G network), but the principles of this disclosure can also be applied to other systems.

[0024] This disclosure is defined by the attached claims. The aspects of this disclosure are as described in the independent claims. Some optional features are described in the dependent claims.

[0025] Each feature disclosed herein (this term includes the claims) and / or each feature shown in the drawings may be incorporated into this disclosure independently of (or in combination with) any other disclosed and / or illustrated features. In particular, but not limited to, any feature of a claim dependent on a particular independent claim may be introduced into that independent claim in any combination or individually.

[0026] Herein, embodiments of the present disclosure will be described as examples with reference to the attached drawings. [Brief explanation of the drawing]

[0027] [Figure 1] This is a schematic diagram of a mobile (cellular or wireless) telecommunications system to which embodiments of the present disclosure may be applied. [Figure 2] Figure 1 is a schematic block diagram of mobile devices that form part of the system shown. [Figure 3] This is a schematic block diagram of an access network node (e.g., a base station) that forms part of the system shown in Figure 1. [Figure 4] Figure 1 is a schematic block diagram of the core network nodes that form part of the system shown. [Figure 5] This is a schematic diagram of a framework for CSI feedback based on artificial intelligence / machine learning models. [Modes for carrying out the invention]

[0028] overview Figure 1 is a schematic diagram of a mobile (cellular or wireless) telecommunications system 1 to which embodiments of the present disclosure may be applied.

[0029] In this system 1, users of mobile devices 3 (UEs) can communicate with each other and with other users via base stations 5 (and other access network nodes) and the core network 7, using appropriate 3GPP radio access technologies (RATs), such as E-UTRA (Evolved Universal Terrestrial Radio Access) and / or 5G RAT. It will be understood that multiple base stations 5 form a (radio) access network or (R)AN. As will be understood by those skilled in the art, one mobile device 3 and three base stations 5A-5C are shown in Figure 1 for illustrative purposes, but the system, when implemented, would typically include other base stations / (R)AN nodes and mobile devices (UEs).

[0030] Each base station 5 controls one or more associated cells (directly or via other nodes such as home base stations, relays, remote radio heads, and distributed units). Base stations 5 that support next-generation / 5G protocols are sometimes referred to as "gNBs". It will be understood that some base stations 5 may be configured to support both 4G and 5G protocols, and / or any other 3GPP or non-3GPP communication protocols.

[0031] The mobile device 3 and its serving base station 5 are connected via appropriate radio interfaces (e.g., so-called "NR" radio interfaces and / or "Uu" interfaces). Adjacent base stations 5 are connected to each other via appropriate inter-base station interfaces (e.g., so-called "Xn" interfaces, "X2" interfaces, etc.). Base stations 5 are also connected to core network nodes via appropriate interfaces (e.g., so-called "NG-U" interfaces (for the user plane), so-called "NG-C" interfaces (for the control plane), etc.).

[0032] The core network 7 (e.g., EPC in the case of LTE, or NGC in the case of NR / 5G) typically includes logical nodes (or “functions”) for subscriber management, mobility management, billing, security, call / session management (among other things) to support communications in the telecommunications system 1. For example, the core network 7 of a “next-generation” / 5G system includes user plane entities and control plane entities, such as one or more control plane functions (CPFs) 10 and one or more user plane functions (UPFs) 11. The so-called Access and Mobility Management Function (AMF) in 5G, or Mobility Management Entity (MME) in 4G, is responsible for handling connection and mobility management tasks for mobile devices 3. The so-called Session Management Function (SMF) is responsible for handling communication sessions for mobile devices 3, including establishing, modifying, and releasing sessions. Core network 7 may typically include, among other things, an Authentication Server Function (AUSF), a Unified Data Management (UDM) entity, a Policy Control Function (PCF), and an Application Function (AF). It will be understood that nodes or functions may have different names in different systems. Core network 7 connects (via UPF11) to a Data Network (DN), such as the Internet or a similar Internet Protocol (IP) based network. Core network 7 may also connect to Operations and Maintenance (OAM) functions (not shown).

[0033] It will be understood that each mobile device 3 may support one or more services that fall under any of the categories defined above (URLLC / eMBB / mMTC). Each service typically has associated requirements (e.g., latency / data rate / packet loss requirements), which may differ from service to service.

[0034] In this system, UE3 is configured to provide periodic CQI feedback relatively frequently (with relatively short CQI reporting cycles) to enable rapid link adaptation to changing radio conditions on the radio interface between UE3 and the serving base station 5. However, UE3 is also configured with one or more conditions to control whether or not to send CQI feedback on a given reporting opportunity. Effectively, the configured conditions allow UE3 to skip some periodic CQI reports when the report does not result in a change in link characteristics (e.g., when the CQI to be reported has not changed from a previous report, or when a previously transmitted CQI is still considered valid).

[0035] This reduces signaling overhead because, when certain conditions (at least one condition) are met, UE3 can skip (or delay) the transmission of CQI reports (one or more CQI reports). Specifically, if the measured CQI does not result in a CQI level update, the CQI report may not need to be sent to the scheduler (base station 5). For example, if UE3 is not operating or does not measure changes in channel conditions (or, for example, relatively small changes within relevant thresholds), UE3 may determine that the relevant CQI report is unlikely to result in a change in the link configuration used by base station 5. Accordingly, in accordance with this determination, UE3 may decide not to transmit a CQI report on a particular CQI reporting opportunity.

[0036] especially, - Channel quality change conditions that indicate the required difference between the current channel quality (CQI) value and the previous channel quality value transmitted to the radio access network (base station 5), - Movement conditions indicating the movement required by UE3 to transmit periodic channel quality reports to the wireless access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the difference required between the current location of UE3 and the location when transmitting the previous channel quality report to the wireless access network, - A time condition indicating the validity period of a previous channel quality report that had the same (or substantially the same) channel quality value as the current channel quality value. You may use one or more of these conditions.

[0037] Alternatively, artificial intelligence (AI) / machine learning (ML) is employed to predict or indicate expected points of channel fluctuation, as opposed to entirely computation-based channel estimation. Therefore, this solution can contribute to reduced complexity and improved CQI accuracy. This AI / ML-based approach allows for adjustment of the CSI feedback rate / CSI reporting pattern based on the predicted CSI fluctuation points. For example, a UE may be configured to send CSI feedback only when it reaches a point of significant fluctuation (which may be determined by time, location, or distance). Beneficially, the CSI feedback to the base station includes the actual CQI value measured by the UE. This is quite different from other AI / ML-based solutions that predict CSI from data acquired from other UEs (which can be less accurate because CQI itself is a predicted value, not a measured value). Conventional periodic CSI reporting reports every few milliseconds, regardless of whether the CQI has changed or whether reporting is required at that time or location (e.g., no data traffic).

[0038] User Equipment (UE) Figure 2 is a block diagram showing the main components of the mobile device (UE) 3 shown in Figure 1. As illustrated, the UE 3 includes a transceiver circuit 31 capable of transmitting signals to and receiving signals from nodes connected via one or more antennas 33. Although not necessarily shown in Figure 2, the UE 3 naturally has all the usual functions of a conventional mobile device (such as a user interface 35), which may be provided by hardware, software, and firmware, or any combination thereof, as needed. The controller 37 controls the operation of the UE 3 according to software stored in memory 39. The software may be pre-installed in memory 39 and / or downloaded, for example, via a telecommunications network 1 or from a removable data storage device (RMD). The software includes, among other things, an operating system 41, a communication control module 43, and (optionally) an AI / ML module 100.

[0039] The communication control module 43 is responsible for processing (generating / transmitting / receiving) signaling messages and uplink / downlink data packets between UE3 and other nodes, including (R)AN node 5 and core network nodes. Signaling may include control signaling (including UCI and DCI), particularly related to PUCCH and / or PDCCH and CSI feedback. The communication control module 43 is also responsible for determining the resource set and codebook to be used for a particular channel.

[0040] If present, the AI / ML module 100 is responsible for performing CQI reporting-related processing and signaling based on an appropriate AI / ML model (or algorithm).

[0041] Access network node (base station) Figure 3 is a block diagram showing the main components (or similar access network nodes) of the base station 5 shown in Figure 1. As shown, the base station 5 includes transceiver circuitry 51 capable of transmitting and receiving signals from connected UE3 via one or more antennas 53, and transmitting and receiving signals from other network nodes (directly or indirectly) via network interface 55. Network interface 55 typically includes appropriate base station-to-base station interfaces (e.g., X2 / Xn) and appropriate base station-to-core network interfaces (e.g., S1 / N1 / N2 / N3). Controller 57 controls the operation of base station 5 according to software stored in memory 59. The software may be pre-installed in memory 59 and / or downloaded, for example, via telecommunications network 1 or from a removable data storage device (RMD). The software includes, among other things, an operating system 61, a communication control module 63, and (optionally) an AI / ML module 100.

[0042] The communication control module 63 is responsible for processing (generating / transmitting / receiving) signaling between the base station 5 and other nodes such as the UE3 and core network nodes. The signaling may include control signaling (including UCI and DCI), particularly related to PUCCH and / or PDCCH and CSI feedback. The communication control module 63 is also responsible for determining the resource set and codebook for a particular channel.

[0043] If present, the AI / ML module 100 is responsible for performing CQI reporting-related processing and signaling based on an appropriate AI / ML model (or algorithm).

[0044] Core network function Figure 4 is a block diagram showing the main components of a typical core network function, such as the CPF10 or UPF11 shown in Figure 1. As illustrated, the core network function includes a transceiver circuit 71 capable of transmitting signals to and receiving signals from other nodes (including UE3, base station 5, and other core network nodes) via a network interface 75. A controller 77 controls the operation of the core network function according to software stored in memory 79. The software may be pre-installed in memory 79 and / or downloaded, for example, via the telecommunications network 1 or from a removable data storage device (RMD). The software includes, among other things, an operating system 81 and a communication control module 83.

[0045] The communication control module 83 is responsible for processing (generating / transmitting / receiving) signaling between the core network node and other nodes such as UE3, base station 5, and other core network nodes.

[0046] Detailed explanation To improve accuracy and rapid adaptation to channel conditions, a relatively short CSI reporting periodicity is configured for UE3. For example, periodic CSI (P-CSI) feedback from UE3 to the scheduler may be sent every few TTIs. However, in this system, the CSI feedback timing may autonomously adapt to the traffic arrival rate and channel conditions.

[0047] The first example (referred to as "Solution 1") proposes a non-AI / ML-based CSI feedback method. The second example (referred to as "Solution 2") proposes an AI / ML-based CSI feedback method for overhead reduction and CQI prediction.

[0048] Solution 1 In this case, UE3 (using the communication control module 43) receives information from the serving base station 5 (e.g., via the upper layer) to configure CQI reporting. This information may include appropriate configuration parameters for periodic CQI reporting (or quasi-periodic CQI reporting). Thus, effectively, UE3 is configured to provide the network with CQI reporting having the desired periodicity. It will be understood that the periodicity in this case may be relatively low to allow for high-speed link adaptation as needed.

[0049] Beneficially, UE3 is also configured to determine whether it can skip sending a CQI report (one or more CQI reports) in order to reduce signaling overhead. Specifically, if the measured CQI does not result in a CQI level update, the CQI report may not need to be sent to the scheduler (base station 5). For example, if UE3 is not operating or does not measure changes in channel conditions (or, for example, relatively small changes within relevant thresholds), UE3 may determine that the relevant CQI report is unlikely to result in a change in the link configuration used by base station 5. Accordingly, in accordance with this determination, UE3 may decide not to send a CQI report on a particular CQI reporting opportunity. In this case, if there is no current CQI report, base station 5 may be configured to continue using the previously received (most recent) CQI report (until UE3 sends an updated CQI) or to take appropriate action to predict and apply a new CQI for UE3 (without relying on the skipped CQI report). It will also be understood that base station 5 may trigger aperiodic CQI reports via DCI at appropriate times. For example, base station 5 may be configured to trigger aperiodic CQI reports at a different period than the period applied to periodic CQIs. Base station 5 may also be configured to trigger aperiodic CQI reports after failing to receive a certain number of CQI reports from UE3 (e.g., after a total number of skipped / failed CQIs, or after a given number of consecutive CQIs).

[0050] In summary, UE3 will only send periodic CQI measurement reports to base station 5 if there is an update to the CQI reported value. Beneficially, when the CQI does not change for a certain period, it is possible to employ a relatively low periodicity while avoiding the associated signaling overhead. At the same time, in this case, since UE3 will report the CQI to base station 5 according to the relevant configuration, the relatively low periodicity allows for fast link adaptation when a sudden CQI change occurs.

[0051] Furthermore, to prevent detection omissions, the following measures may be applied: 1) UE3 may be configured with a timer (e.g., a CQIValidityLength timer) that specifies a time window (from the last transmitted CQI report) in which the CQI may be considered valid. When the timer expires, even if there is no change in the measured CQI, UE3 may be configured to send a new CQI report (or send an aperiodic CQI report if requested by base station 5 when the timer expires). 2) Whenever UE3 reaches a specific location or moves a certain distance from a reference location (e.g., the location of the last CQI report), UE3 may be configured to update the CQI, and UE3 may also be configured to send a new CQI report even if there is no change in the measured CQI.

[0052] Solution 2: (AI / ML-based solution) In this solution, artificial intelligence (AI) / machine learning (ML) is employed to predict or indicate expected points of change in channel conditions, as opposed to entirely computational channel estimation. Therefore, this solution can contribute to reduced complexity and improved CQI accuracy. This AI / ML-based approach allows for adjustment of the CSI feedback rate / CSI reporting pattern based on the predicted CSI change points. For example, a UE may be configured to send CSI feedback only when it reaches a point of significant change (which may be determined by time, location, or distance). Beneficially, the CSI feedback to the base station includes the actual CQI value measured by the UE. This is quite different from other AI / ML-based solutions that predict CSI from data acquired from other UEs (which can be less accurate because CQI itself is a predicted value, not a measured value). Conventional periodic CSI reporting reports every few milliseconds, regardless of whether the CQI has changed or whether reporting is required at that time or location (e.g., no data traffic).

[0053] Figure 5 is a schematic diagram of a framework for CSI feedback based on an AI / ML model. The AI / ML model (or algorithm) may be provided via the AI / ML module 100.

[0054] More specifically, the data acquisition function 101 of the CSI feedback framework provides input data to the model training function 102 and the model inference function 103. Examples of input data may include one or more of the following: measurements from UE3 and / or other network entities, feedback from actor 104, and outputs from the AI / ML model. In this example, no preparation of AI / ML algorithm-specific data (e.g., data preprocessing and cleaning, formatting, and transformation) is performed in the data acquisition function 101.

[0055] In Figure 5, the term "training data" refers to the data required as input for the AI / ML model training function 102, and "inference data" refers to the data required as input for the AI / ML model inference function 103.

[0056] The model training function 102 performs ML model training, validation, and testing, which can generate model performance metrics as part of the model testing procedure. The model training function 102 also, if necessary, plays a role in data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the training data delivered by the data acquisition function 101.

[0057] It will be understood that various functions of the AI / ML-based CSI feedback framework may be implemented on different nodes. For example, some functions (at least one) may be provided by UE3, and other functions (at least one) may be provided by base station 5. It will also be understood that some functions may be implemented on two or more nodes (for example, both UE3 and base station 5 may have related data collection functions 101, or may be configured to input data into a common data collection function 101).

[0058] Beneficially, the AI / ML-based frameworks described above may be used to enhance CSI feedback (e.g., reducing CSI-related overhead, improving CSI accuracy, and / or predicting CSI). Below is a description of some scenarios in which the use of AI / ML-based frameworks may be beneficial, along with several exemplary methods for reporting / predicting CQIs.

[0059] It will be understood that UE3 may be periodic or repetitive / repeatable CQI fluctuations in a controlled environment such as a factory automation setup, e.g., a moving object (mechanical arm or vehicle with a fixed path). The relative stability and / or limitations of the UE / vehicle movement make it possible to predict future channel fluctuations.

[0060] The set of CQI reporting points may be calculated by base station 5 based on time and / or UE location. Following initial training using the AI / ML model shown in Figure 5, base station 5 may configure a timing set or a location set for each UE3 (or group of UEs).

[0061] More specifically, UE3 may be configured using a timing set {t0, t1, t2, t3, t4, ..., tn}, where the CSI feedback time tn can be configured within a period relatively longer than the fixed CQI reporting periodicity. This relatively longer period may be provided by appropriate information elements (e.g., timingSetValidityDuration information element) in higher-layer signaling (e.g., RRC). When configured using a timing set, UE3 reports CQIs within the feedback time provided by the set.

[0062] Similarly, UE3 may be composed of a location set {p0, p1, p2, p3, p4, ..., pn} or a distance set {d0, d1, d2, d3, d4, ..., dn}, and the CSI feedback is based on the UE's location pn after the time window, or the distance dn of the UE moved within the time window. The time windows for location and distance may be given by relevant information elements (e.g., locationSetValidityDuration information element and lengthOfDistance information element) in higher-level signaling (e.g., RRC). It will be understood that a significant location, or a significant change in location (distance moved), may represent a significant change in channel conditions. When composed of a location set and / or a distance set, UE3 reports CQI at the locations given by the sets.

[0063] A combination of time and position / distance sets may be used, and it will be understood that a particular area / position / distance may have an associated timing set (which may differ from the timing set applicable to another area / position / distance).

[0064] Base station 5 may derive applicable time / location / distance sets from model training (using its associated AI / ML module 100). UE3 knows its own location / travel distance and / or may flag / notify using AI-based positioning for CQI report updates when UE3 reaches each designated point / location or travels a distance corresponding to a given value in the configured set.

[0065] Beneficial in this regard, the aforementioned set can be used to predict or indicate points of change in channel conditions, and they can contribute to reducing complexity and improving CQI accuracy.

[0066] CSI Feedback Mode Configuration The CSI feedback mode used by UE3 may be configurable by RRC signaling (for example, using a properly formatted "RRCReconfiguration" message). For example, the RRC signaling may include informational elements to indicate the applicable CSI feedback modes {normal mode, AI-ML-Method1, gNBPredictionMode2, ...}.

[0067] Configuring the CSI feedback mode may include one or more of the following steps: 1) The base station 5 may send to the UE3 an index associated with a CQI timing set selected from a list of CQI update specific timing sets after CQI feedback model training. In this case, the list may be provided via appropriate information elements (e.g., cqiUpdateTimingInfoSets) or a table having an index associated with each CQI timing set. 2) The base station 5 may send to the UE3 an index associated with a CQI location set selected from a list of CQI update unique location sets after CQI feedback model training. In this case, the list may be provided via appropriate information elements (e.g., cqiUpdateLocationInfoSets) or a table having an index associated with each CQI location set. 3) Base station 5 may send to UE3 an index associated with a distance set selected from a list of CQI update specific distance sets after CQI feedback model training. In this case, the list may be provided via an appropriate information element (e.g., cqiUpdateDistanceSets) or a table having an index associated with each CQI distance set. 4) Base station 5 may configure / reconfigure the index of applicable CQI timing sets when updating the UE's location / distance and / or during peak / off-peak hours of traffic arrival in order to further optimize CQI reporting. 5) UE3 sends a reduced CQI report to base station 5 according to which index is configured for UE3.

[0068] Signaling to support reduced CSI feedback For the CQI timing set, UE3 may provide a periodic CSI reporting pattern for CQI reporting via an appropriate information element (e.g., cqi-reportingPattern). Each bit of the pattern corresponds to the normal CQI reporting periodicity, with a value of "0" or "1" indicating a skip of CQI reporting or normal CSI feedback during the validity period of the CQI reporting pattern, respectively.

[0069] For a CQI position / distance set, UE3 may provide a CQI reporting positioning pattern for CQI reporting via an appropriate information element (e.g., cqi-reportingPos). Each bit of the pattern corresponds to a pre-configured significant position / distance, with a value of "0" or "1" indicating "no change" or "update" of the periodic CQI reporting pattern / CQI periodicity, respectively.

[0070] Note that time / location / distance may be reset to "0" or the starting point after a configurable time, number of locations, or distance length, and CSI feedback may be skipped if no traffic is expected.

[0071] Signaling to support autonomous CSI feedback CQI reporting patterns may be updated autonomously. For example, a CSI feedback pattern may be updated autonomously when it reaches a point of significant variation (which may be determined by, for example, time, location, and / or distance) based on a trained / configured / reconfigurable location / distance set index and associated timing set index. Table 1 is an exemplary mapping table that may be used to determine appropriate timing set indexes for a given location / distance set index. [Table 1]

[0072] CQI reporting periodicity may be updated autonomously. For example, CQI reporting periodicity may be updated autonomously when it reaches a point of significant variation (determined by time, location, and / or distance) based on trained / configured / reconfigurable CQI periodicity and associated location / distance sets. Table 2 is an exemplary mapping table that may be used to determine an appropriate location / distance set for a given CQI periodicity. [Table 2]

[0073] Apply the concept to "spatial features" or "wireless fingerprints." It will be understood that the aforementioned concept of "location" may be extended to a generalized concept that includes the option of "spatial features" or "wireless fingerprint" instead of using UE3's physical location. In this case, "fingerprint" means that a physical location can be identified by the wireless conditions at that location.

[0074] These can be defined, for example, as learned representations of physical location based on reference signal received power (RSRP) or path loss measurements.

[0075] The concept of "distance" (highlighted in the previous slide) between two wireless fingerprints can also be defined in different ways. For example, "distance" could be the difference between RSRP, reference signal quality (RSRQ), SINR / CQI, or path loss measurements.

[0076] Note that if you need to compute wireless fingerprints in UE3, you will need to provide the inference model to UE3.

[0077] Corrections and replacements Detailed embodiments have been described above. As those skilled in the art will understand, several modifications and substitutions can be made in those embodiments while benefiting from the disclosures embodied in the above embodiments. Only a few of these substitutions and modifications are described here as examples.

[0078] It will be understood that Solution 1 may be implemented without AI / ML components. However, at least some aspects of Solution 1 (e.g., distance or location-based CQI effectiveness) may be combined with the AI / ML-based approach of Solution 2.

[0079] It will be understood that the above embodiment can be applied to both the new 5G wireless system and the LTE system (E-UTRAN).

[0080] For the sake of clarity, the above description assumes that the UE, access network nodes (base stations), and core network nodes have several separate modules (such as communication control modules). These modules may be provided in this way in certain applications, for example, where an existing system is modified to implement the present disclosure. However, in other applications, such as systems designed from the outset with the features of the present invention in mind, these modules may be integrated into the overall operating system or code, and therefore may not be identifiable as separate entities. These modules may be implemented in software, hardware, firmware, or a combination thereof.

[0081] Each controller may include, but is not limited to, one or more hardware-implemented computer processors, microprocessors, central processing units (CPUs), arithmetic logic units (ALUs), input / output (IO) circuits, internal memory / cache (programs and / or data), processing registers, communication buses (e.g., control buses, data buses and / or address buses), direct memory access (DMA) functions, hardware or software-implemented counters, pointers and / or timers, and any other suitable form of processing circuitry.

[0082] In the embodiments described above, several software modules have been explained. As those skilled in the art will understand, the software modules may be provided in compiled or uncompiled form and supplied to the UE, access network nodes (base stations), and core network nodes via a computer network or as signals on a recording medium. Furthermore, the functions performed by some or all of this software may be performed using one or more dedicated hardware circuits. However, the use of software modules is preferred because it facilitates the updating of the UE, access network nodes, and core network nodes to update their functions.

[0083] The functions of a base station (referred to as a “distributed” base station or gNB) may be divided between one or more distributed units (DUs) and a central unit (CU), where the CUs typically perform higher-level functions and communication with the next-generation core, and the DUs perform lower-level functions and communication via radio interfaces with neighboring UEs (i.e., cells operated by the gNB). A distributed gNB includes the following functional units: gNB Central Unit (gNB-CU): A logical node that controls the operation of one or more gNB-DUs and hosts the gNB's Radio Resource Control (RRC) layer, Service Data Adaptation Protocol (SDAP) layer, and Packet Data Convergence Protocol (PDCP) layer (or the en-gNB's RRC and PDCP layers). The gNB-CU terminates the so-called F1 interface connected to the gNB-DU. A gNB-DU is a logical node that hosts the Radio Link Control (RLC), Medium Access Control (MAC), and Physical (PHY) layers of a gNB or en-gNB, and its operation is partially controlled by a gNB-CU. A single gNB-DU supports one or more cells. A single cell is supported by only one gNB-DU. A gNB-DU terminates the F1 interface connected to the gNB-CU. gNB-CU-Control Plane (gNB-CU-CP): A logical node that hosts the control plane portion of the RRC and PDCP protocols for the gNB-CU for en-gNB or gNB. The gNB-CU-CP terminates the so-called E1 interface connected to gNB-CU-UP and the F1-C (F1 control plane) interface connected to gNB-DU. gNB-CU-UserPlane (gNB-CU-UP): A logical node that hosts the user plane portion of the PDCP protocol for gNB-CU for en-gNB, and the user plane portions of the PDCP protocol and SDAP protocol for gNB-CU for gNB. gNB-CU-UP terminates the E1 interface connected to gNB-CU-CP and the F1-U (F1 user plane) interface connected to gNB-DU.

[0084] When a distributed base station or a similar control-plane-user-plane (CP-UP) partition is employed, it will be understood that the base station may be divided into separate control-plane and user-plane entities, each of which may include the associated transceiver circuitry, antennas, network interfaces, controllers, memory, operating systems, and communication control modules. When the base station comprises a distributed base station, the network interface (reference number 55 in Figure 3) also includes E1 and F1 interfaces (F1-C for the control plane and F1-U for the user plane) for signal communication between the respective functions of the distributed base station. In this case, the communication control module also plays a role in communication between the control-plane portion and the user-plane portion of the base station (generating, transmitting, and receiving signaling messages). When a distributed base station is used, it will be understood that it is not necessary to include both the control-plane portion and the user-plane portion for communication resource preemption, as described in the exemplary embodiments above. It will be understood that preemption can be handled by the user-plane portion of the base station without going through the control-plane portion (and vice versa).

[0085] The above embodiments are also applicable to “non-mobile” or generally fixed user devices. The above-mentioned mobile devices may include MTC / IoT devices, etc. In this disclosure, user equipment (or "UE," "mobile station," "mobile device," or "wireless device") is an entity connected to a network via a wireless interface.

[0086] Please note that this disclosure is not limited to dedicated communication devices, but can be applied to any device having communication functions as described in the following paragraphs.

[0087] (When used in 3GPP) The terms “User Equipment” or “UE,” “Mobile Station,” “Mobile Device,” and “Radio Device” are generally intended to be synonymous with each other and include standalone mobile stations such as terminals, cell phones, smartphones, tablets, cellular IoT devices, IoT devices, and machines. It will be recognized that the terms “Mobile Station” and “Mobile Device” also include devices that remain stationary for extended periods.

[0088] UE may also be items of equipment for production or manufacturing and / or energy-related machinery (e.g., boilers; engines; turbines; solar panels; wind turbines; hydroelectric generators; thermoelectric generators; nuclear generators; batteries; nuclear systems and / or related equipment; heavy electrical machinery; pumps including vacuum pumps; compressors; fans; blowers; hydraulic equipment; pneumatic equipment; metalworking machinery; manipulators; robots and / or their application systems; tools; molds or dies; rolls; conveying equipment; elevators; material handling equipment; textile machinery; sewing equipment; printing and / or related machinery; paper conversion equipment; chemical machinery; mining machinery and / or construction machinery and / or related equipment; machinery and / or equipment for agriculture, forestry and / or fisheries; safety and / or environmental protection equipment; tractors; precision bearings; chains; gears; power transmission equipment; lubrication equipment; valves; pipe fittings; and / or application systems for any of the aforementioned equipment or machinery, etc.).

[0089] UE may be items such as transportation equipment (e.g., transportation equipment such as rolled materials; automobiles; motorcycles; bicycles; trains; buses; carts; human-powered vehicles; ships and other vessels; aircraft; rockets; satellites; drones; balloons, etc.).

[0090] UE may be, for example, an item of information and communication equipment (e.g., information and communication equipment such as electronic computers and related equipment; communication and related equipment; electronic components, etc.). UE may include, for example, refrigerators, refrigerator applications, items of goods and / or service industry equipment, vending machines, automated service machines, office equipment or machinery, consumer electronics and electronic devices (e.g., consumer electronics such as audio equipment; video equipment; speakers; radios; televisions; microwave ovens; rice cookers; coffee machines; dishwashers; washing machines; dryers; electronic fans or related equipment; vacuum cleaners, etc.).

[0091] The UE may be, for example, an electrical application system or device (e.g., an X-ray system; a particle accelerator; radioisotope equipment; sound wave equipment; electromagnetic application equipment; an electronic power application system or device).

[0092] UE may include, for example, electronic lamps, lighting fixtures, measuring instruments, analyzers, testers, or surveying or sensing equipment (e.g., surveying or sensing equipment such as smoke detectors; human presence sensors; motion sensors; wireless tags, etc.), wristwatches or clocks, inspection equipment, optical devices, medical equipment and / or systems, weapons, tableware items, hand tools, etc.

[0093] The UE may be, for example, a wireless-equipped portable information terminal or related equipment (e.g., a wireless card or module designed to be attached to or inserted into another electronic device (e.g., a personal computer, an electrical measuring instrument)).

[0094] The UE may be part of a device or system that uses various wired and / or wireless communication technologies to provide the applications, services, and solutions described below with respect to the Internet of Things (IoT).

[0095] Internet of Things (IoT) devices (or "Things") may be equipped with appropriate electronics, software, sensors, network connectivity, etc., that enable them to collect and exchange data with each other and with other communication devices. IoT devices may include automated devices that follow software instructions stored in internal memory. IoT devices may operate without requiring human supervision or interaction. IoT devices may also remain stationary and / or inactive for extended periods. IoT devices may be implemented (generally) as part of a stationary device. IoT devices may also be embedded in a non-stationary device (e.g., a vehicle) or attached to an animal or person being monitored / tracked.

[0096] It will be understood that IoT technology can be implemented on any communication device that can connect to a communication network to send / receive data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory.

[0097] It will be understood that IoT devices are sometimes called machine-type communication (MTC) communication devices or machine-to-machine (M2M) communication devices. It will be understood that a UE may support one or more IoT or MTC applications. Some examples of MTC applications are listed in the table below (source: 3GPP TS 22.368 V 13.1.0, Annex B, whose contents are incorporated herein by reference). This list is not exhaustive and is intended to illustrate some examples of machine-type communication applications. [Table 3]

[0098] Applications, services, and solutions may include Mobile Virtual Network Operator (MVNO) services, emergency radio communication systems, Private Branch Exchange (PBX) systems, PHS / digital cordless communication systems, Point of Sale (POS) systems, notification call systems, Multimedia Broadcast and Multicast Service (MBMS), Vehicle to Everything (V2X) systems, train radio systems, location-related services, disaster / emergency radio communication services, community services, video streaming services, femtocell application services, Voice over LTE (VoLTE) services, billing services, wireless on-demand services, roaming services, activity monitoring services, telecommunications carrier / communication network selection services, function restriction services, Proof of Concept (PoC) services, personal information management services, and ad-hoc network / delay-tolerant networking (DTN) services.

[0099] Furthermore, the aforementioned UE categories are merely examples of applications of the technical concepts and exemplary embodiments described in this document. Of course, these technical concepts and embodiments are not limited to the aforementioned UEs and can be modified in various ways.

[0100] This information may indicate a timing subset of all periodic channel quality reporting opportunities configured for the UE, in which case the method performed by the UE may further include transmitting a periodic channel quality report indicating the current channel quality value to the radio access network at a specific channel quality reporting opportunity determined by the timing subset.

[0101] The subset may be identified based on at least one index associated with each channel quality reporting timing value.

[0102] The information may indicate a specific location, in which case the method performed by the UE may further include transmitting a periodic channel quality report to the radio access network indicating the current channel quality value at that specific location.

[0103] A specific location may be identified based on an index associated with each channel quality reporting location within a set of channel quality reporting locations. A specific location may also be identified based on a spatial signature or radio fingerprint associated with the radio conditions at that location.

[0104] The information may also indicate distance, in which case the method performed by the UE may further include sending a periodic channel quality report to the radio access network indicating the current channel quality value when the difference between the UE's current location and the location at which it previously sent a channel quality report to the radio access network reaches or exceeds the distance. The distance may be indicated based on the index associated with each distance in a set of distances associated with periodic channel quality reports.

[0105] The distance may be determined based on at least one of the following at the current location of the UE and at the location when transmitting a previous channel quality report to the radio access network: the difference between each reference signal received power (RSRP) value, the difference between each reference signal received quality (RSRQ) value, the difference between each signal-to-interference noise ratio (SINR) value, the difference between each channel quality indicator (CQI) value, and the difference between each path loss measurement.

[0106] At least one of the following may be provided via an associated pattern: information indicating a timing subset, information indicating a specific location, and information indicating distance.

[0107] At least one of the timing subset, specific location, and distance may be determined using an artificial intelligence / machine learning model.

[0108] A timing subset, a specific location, a distance, and at least one of the conditions may be configured via higher-layer (e.g., wireless resource control) signaling.

[0109] The methods performed by a node in a wireless access network may further include receiving a periodic channel quality report from the UE at a particular channel quality reporting opportunity, indicating the current channel quality value at that reporting opportunity, provided that at least one condition is met.

[0110] The methods performed by a node in a wireless access network may further include receiving channel quality reports from the UE indicating each channel quality value associated with a channel, and, based on each channel quality value, using an artificial intelligence / machine learning model, updating at least one condition relating to at least one fluctuation point to trigger at least one further periodic channel quality report from the UE.

[0111] At least one point of variation in the channel quality value associated with a channel may be determined by at least one of the relevant time, relevant location, and relevant distance.

[0112] An artificial intelligence / machine learning model may include a data collection function configured to acquire information about channel quality and information about UE; a model training function configured to acquire training data from the data collection function and acquire model performance feedback; a model inference function configured to provide model performance feedback to the model training function and to provide output based on inference data acquired from the data collection function and further based on model deployment / update information acquired from the model training function; and an actor function configured to provide feedback to the data collection function based on the output from the model inference function.

[0113] A channel quality report may include at least one of the following: an implicit rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI).

[0114] Various other modifications are obvious to those skilled in the art and will not be described in further detail here.

[0115] For example, all or part of the exemplary embodiments disclosed above may also be described as follows, but are not limited to these. (Note 1) A method performed by a user equipment (UE) of a wireless access network regarding channel adaptation of a wireless interface between the UE and a node, comprising receiving information indicating at least one condition for triggering the transmission of a periodic channel quality report, The system includes, when at least one condition is met, transmitting a periodic channel quality report indicating the current channel quality value to the wireless access network at a particular channel quality reporting opportunity, Information, - Channel quality change conditions that indicate the required difference between the current channel quality value and the previous channel quality value transmitted to the wireless access network, - Travel conditions indicating the travel required by the UE to transmit periodic channel quality reports to the radio access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the required difference between the UE's current location and the location when transmitting the previous channel quality report to the radio access network, - Time conditions indicating the validity period of previous channel quality reports that had the same channel quality value as the current channel quality value. A method that demonstrates at least one of the following. (Note 2) The information shows a timing subset of all periodic channel quality reporting opportunities configured for the UE. The method further includes transmitting a periodic channel quality report indicating the current channel quality value to a wireless access network at a specific channel quality reporting opportunity determined by a timing subset. The method described in Appendix 1. (Note 3) The method described in Appendix 2, wherein the subset is identified based on at least one index associated with each channel quality reporting timing value. (Note 4) The information indicates a specific location. The method further includes transmitting a periodic channel quality report indicating the current channel quality value to a wireless access network at a specific location. The method described in any one of the appendices 1 to 3. (Note 5) The method described in Appendix 4, wherein a specific location is identified based on an index associated with each channel quality reporting location within a set of channel quality reporting locations. (Note 6) The method according to Appendix 4, wherein a specific location is identified based on a spatial signature or radio fingerprint associated with the radio conditions at that location. (Note 7) The information indicates distance. The method further includes transmitting a periodic channel quality report indicating the current channel quality value to the radio access network when the difference between the current location of the UE and the location at which a previous channel quality report was transmitted to the radio access network reaches or exceeds a certain distance. The method described in any one of the appendices 1 to 6. (Note 8) The method described in Appendix 7, wherein the distance is identified based on the index associated with each distance in a set of distances associated with periodic channel quality reports. (Note 9) The distance is such that the UE's current location and the location at which it transmits the previous channel quality report to the radio access network are as follows: The difference between the respective reference signal received power (RSRP) values, The difference between each reference signal reception quality (RSRQ) value and the difference between each signal-to-interference noise ratio (SINR) value, The difference between each Channel Quality Indicator (CQI) value, The difference between each path loss measurement and The method described in Appendix 7, determined based on at least one of the following. (Note 10) At least one of the following is provided via an associated pattern: information indicating a timing subset, information indicating a specific location, and information indicating distance. The method described in any one of the appendices 2 to 9. (Note 11) At least one of the following is determined using an artificial intelligence / machine learning model: a timing subset, a specific location, and distance. The method described in any one of the appendices 2 to 10. (Note 12) A timing subset, a specific location, a distance, and at least one of the conditions are configured via radio resource control signaling. The method described in any one of the appendices 1 to 11. (Note 13) A method performed by a node of a wireless access network regarding channel adaptation of a wireless interface between a user equipment (UE) and a node of the wireless access network, Sending information that indicates at least one condition to trigger the sending of periodic channel quality reports, This includes receiving a periodic channel quality report from the UE at a particular channel quality reporting opportunity, when at least one condition is met, which shows the current channel quality value. Information, - Channel quality change conditions that indicate the required difference between the current channel quality value and the previous channel quality value transmitted to the wireless access network, - Travel conditions indicating the travel required by the UE to transmit periodic channel quality reports to the radio access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the required difference between the UE's current location and the location when transmitting the previous channel quality report to the radio access network, - Time conditions indicating the validity period of previous channel quality reports that had the same channel quality value as the current channel quality value. A method that demonstrates at least one of the following. (Note 14) A method performed by a node of a wireless access network regarding channel adaptation of a wireless interface between a user equipment (UE) and a node of the wireless access network, Using artificial intelligence / machine learning models, predict at least one point of variation in the channel quality value associated with a channel, A method comprising sending information to the UE indicating at least one condition relating to at least one fluctuation point in order to trigger at least one periodic channel quality report by the UE to report an actual channel quality value associated with at least one fluctuation point. (Note 15) The method according to Appendix 14, further comprising receiving a periodic channel quality report from the UE at a particular channel quality reporting opportunity, indicating the current channel quality value at that reporting opportunity, provided that at least one condition is met. (Note 16) Receiving channel quality reports from the UE that show the channel quality value for each channel associated with the channel, The method according to Appendix 14 or 15, further comprising using an artificial intelligence / machine learning model to update at least one condition relating to at least one fluctuation point to trigger at least one further periodic channel quality report by the UE, based on each channel quality value. (Note 17) The method according to any one of the appendices 14 to 16, wherein at least one point of variation in the channel quality value associated with the channel is determined by at least one of the relevant time, relevant location, and relevant distance. (Note 18) Artificial intelligence / machine learning models, A data collection function configured to acquire information about channel quality and information about UE, A model training function is configured to acquire training data from a data collection function and obtain model performance feedback. A model inference function is configured to provide model performance feedback to the model training function, and to provide output based on inference data obtained from the data collection function, and further based on model deployment / update information obtained from the model training function. Actor functions configured to provide feedback to data collection functions based on the output from model inference functions, and The method described in any one of the appendices 14 to 17, including the method described in any one of the appendices 14 to 17. (Note 19) The method according to any one of Annexes 1 to 18, wherein the channel quality report includes at least one of an implicit rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI). (Note 20) A UE relating to channel adaptation of a wireless interface between user equipment (UE) and nodes in a wireless access network, A means for receiving information indicating at least one condition for triggering the transmission of periodic channel quality reports, The system includes means for transmitting a periodic channel quality report indicating the current channel quality value to a wireless access network at a particular channel quality reporting opportunity when at least one condition is met, Information, - Channel quality change conditions that indicate the required difference between the current channel quality value and the previous channel quality value transmitted to the wireless access network, - Travel conditions indicating the travel required by the UE to transmit periodic channel quality reports to the radio access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the required difference between the UE's current location and the location when transmitting the previous channel quality report to the radio access network, - Time conditions indicating the validity period of previous channel quality reports that had the same channel quality value as the current channel quality value. A UE that shows at least one of the following. (Note 21) A node in a wireless access network relating to channel adaptation of the wireless interface between user equipment (UE) and a node in a wireless access network, A means for transmitting information indicating at least one condition for triggering the transmission of a periodic channel quality report, The system includes means for receiving periodic channel quality reports from the UE, indicating the current channel quality value, at a particular channel quality reporting opportunity, when at least one condition is met, and the information is - Channel quality change conditions that indicate the required difference between the current channel quality value and the previous channel quality value transmitted to the wireless access network, - Travel conditions indicating the travel required by the UE to transmit periodic channel quality reports to the radio access network, -Location conditions indicating the location or area where periodic channel quality reports need to be transmitted to the wireless access network, -Location change conditions that indicate the required difference between the UE's current location and the location when transmitting the previous channel quality report to the radio access network, - Time conditions indicating the validity period of previous channel quality reports that had the same channel quality value as the current channel quality value. A node that represents at least one of the following. (Note 22) A node in a wireless access network relating to channel adaptation of the wireless interface between user equipment (UE) and a node in a wireless access network, A means for predicting at least one point of variation in the channel quality value associated with a channel using an artificial intelligence / machine learning model, A node comprising means for sending information indicating at least one condition relating to at least one fluctuation point to the UE, in order to trigger at least one periodic channel quality report by the UE to report an actual channel quality value associated with at least one fluctuation point.

[0116] This application is based on and claims the benefit of priority from UK Patent Application No. 2204740.1, filed on 31 March 2022, the disclosure thereof is incorporated herein by reference in its entirety. [Explanation of Symbols]

[0117] 1. Telecommunications Systems 3 Mobile devices 5 base station 7 Core Network 10. Control Plane Function (CPF) 11. User Plane Function (UPF) 31 Transceiver Circuit 33 Antennas 35 User Interface 37 Controllers 39 memory 41 Operating Systems 43 Communication control module 51 Transceiver Circuit 53 Antenna 55 Network Interfaces 57 Controllers 59 memory 61 Operating Systems 63 Communication control module 71 Transceiver Circuit 75 Network Interfaces 77 Controllers 79 memory 81 Operating Systems 83 Communication control module 100 AI / ML modules 101 Data Collection 102 Model Training 103 Model Inference 104 Actors

Claims

1. A User Equipment (UE) relating to channel adaptation of a wireless interface between the UE and an access network node, Means for transmitting information indicating the location of the UE or information indicating the current time to the access network node, A means for receiving reference information for determining whether the channel quality of the wireless interface has changed, which is derived at the access network node via an Artificial Intelligence / Machine Learning (AI / ML) model based on information indicating the location of the UE or information indicating the current time, Means for determining, based on the reference information, that the current channel quality value of the wireless interface has changed from a previous channel quality value transmitted to the access network node, In one of several channel quality reporting opportunities, means for transmitting a periodic channel quality report to the access network node, which includes information indicating the current channel quality value in accordance with the decision, A means for autonomously changing the periodicity of the channel quality report using the AI / ML model of the aforementioned UE, A UE equipped with

2. Means for receiving information to indicate a timing subset of all periodic channel quality reporting opportunities configured for the aforementioned UE, Means for transmitting the periodic channel quality report indicating the current channel quality value to the access network node at the channel quality reporting opportunities indicated by the timing subset, The UE according to claim 1, comprising:

3. Means for receiving information to indicate the specific location of a resource, Means for transmitting the periodic channel quality report indicating the current channel quality value at the specified location to the access network node, The UE according to claim 1, comprising:

4. The access network node relates to channel adaptation of the wireless interface between User Equipment (UE) and the access network node, Means for receiving information from the UE indicating the location of the UE or information indicating the current time, A means for transmitting reference information, derived at the access network node via an Artificial Intelligence / Machine Learning (AI / ML) model based on information indicating the location of the UE or information indicating the current time, for the UE to determine whether or not the channel quality of the wireless interface has changed. The system includes means for receiving a periodic channel quality report from the UE, which includes information indicating the current channel quality value, at one of a plurality of channel quality reporting opportunities, when the UE determines, based on the reference information, that the current channel quality value of the wireless interface has changed from a previous channel quality value transmitted to the access network node. The periodicity of the channel quality report is autonomously modified by the UE using the UE's AI / ML model. Access network node.

5. A method performed by User Equipment (UE) for channel adaptation of a wireless interface between the UE and an access network node, Transmitting information indicating the location of the UE or information indicating the current time to the access network node, Based on information indicating the location of the UE or information indicating the current time, the access network node receives reference information for determining whether the channel quality of the wireless interface has changed, which is derived via an Artificial Intelligence / Machine Learning (AI / ML) model. Determining, based on the reference information, that the current channel quality value of the wireless interface has changed from the previous channel quality value transmitted to the access network node, In one of several channel quality reporting opportunities, a periodic channel quality report including information indicating the current channel quality value in accordance with the decision is transmitted to the access network node. The AI / ML model of the aforementioned UE is used to autonomously change the periodicity of the channel quality report, Methods that include...

6. A method performed by an access network node for channel adaptation of a wireless interface between User Equipment (UE) and the access network node, Receiving information from the UE indicating the location of the UE or information indicating the current time, Based on information indicating the location of the UE or information indicating the current time, the UE transmits reference information derived at the access network node via an Artificial Intelligence / Machine Learning (AI / ML) model, which allows the UE to determine whether or not the channel quality of the wireless interface has changed. The UE determines, based on the reference information, that the current channel quality value of the wireless interface has changed from a previous channel quality value transmitted to the access network node, and in one of a plurality of channel quality reporting opportunities, it receives a periodic channel quality report from the UE that includes information indicating the current channel quality value. The periodicity of the channel quality report is autonomously modified by the UE using the UE's AI / ML model. method.

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