Artificial intelligence model delivery via radio control plane

Abstract

The radio access network node may determine to update an artificial intelligence machine learning model at the user equipment. The node may transmit the model in a radio resource control message comprising a primary portion and a secondary portion. The main portion may be used to send control information. The secondary portion may be used to send a model or other data. The control message may include a format indication indicating a code rate to be used by the user equipment to decode the main portion. The radio resource control message may include a secondary format indication indicating a code rate to be used to decode the secondary portion. The format indication may include a retransmission indication indicating that retransmission of one or more segments of one or both of the primary portion or the secondary portion is enabled.

Description

Cross Reference to Related Applications

[0001] This application claims priority to U.S. Non-Provisional Patent Application No. 18 / 296,979, filed April 6, 2023, entitled “ARTIFICIAL INTELLIGENCE MODEL DELIVERY VIA RADIO CONTROL PLANE,” the entirety of which is hereby incorporated by reference herein. BACKGROUND

[0002] The term “New Radio” (NR) associated with 5th-Generation mobile wireless communication systems (5G) refers to technical aspects used in a wireless Radio Access Network (RAN) including several Quality of Service classes (QoS), including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). The URLLC QoS class is associated with strict latency requirements (e.g., low latency or low signal / message delay) and high reliability radio performance, while traditional eMBB use cases can be associated with high-capacity wireless communication, which can allow less strict latency requirements (e.g., higher latency than URLLC) and less reliable radio performance than URLLC. Performance requirements for mMTC can be lower than eMBB use cases. Some use case applications involving mobile devices or mobile user equipment (such as smartphones, wireless tablets, smartwatches, etc.) can impose variations on a given RAN resource load or demand. SUMMARY

[0003] In order to provide a basic understanding of some of the various embodiments, a brief summary of the disclosed subject matter is presented. This summary is not an extensive overview of the various embodiments. It is neither intended to identify key or critical elements of the various embodiments nor to delineate the scope of the various embodiments. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0004] In an example embodiment, a method may include: a radio access network node including a processor transmitting control channel information corresponding to a communication session between the radio access network node and the user equipment according to a first encoding format; and the radio access network node transmitting artificial intelligence (AI) model information to the user equipment according to a second encoding format, wherein the first encoding format and the second encoding format are different. The first encoding format may correspond to or indicate a first rate, the second encoding format may correspond to or indicate a second rate, and the first rate is lower than the second rate. Therefore, due to the slower or lower rate, the control channel information can be transmitted with higher reliability, and larger AI model information can be delivered faster, but with lower reliability, which may result in the need to retransmit parts or segments of the AI ​​model information.

[0005] Control channel information and artificial intelligence model information can be transmitted in Radio Resource Control (RRC) signaling messages. The control channel information and artificial intelligence model information can be transmitted in control channel messages. Radio Resource Control signaling messages may include a Radio Resource Control signaling message format indication indicating a first encoding format. Radio Resource Control signaling messages may include a Radio Resource Control signaling message format indication indicating a second encoding format. In one embodiment, a Radio Resource Control signaling message may include separate format indications corresponding to control information or AI model information, respectively. In one embodiment, a single control channel message may include a format indication indicating a first rate for transmitting control information, and a single control channel message may also indicate a second rate for transmitting AI model information.

[0006] Control channel information may be transmitted via the main part of a radio resource control signaling message, and artificial intelligence model information may be transmitted via the secondary and tertiary parts of a radio resource control signaling message. Secondary and tertiary format indications may indicate the format, coding rate, or other parameter information used for transmitting or decoding information transmitted in the secondary or tertiary parts.

[0007] In one embodiment, control channel information may be transmitted via the main portion of a radio resource control signaling message, and artificial intelligence model information may be transmitted at least via a sub-portion of the radio resource control signaling message. The radio resource control signaling message may include a first radio resource format indication indicating a first number of control channel signaling segments for transmitting control channel information in the main portion of the radio resource control signaling message. The radio resource control signaling message may include a second radio resource format indication indicating a second control channel signaling segment for transmitting artificial intelligence model information in the sub-portion of the radio resource control signaling message.

[0008] In one embodiment, control channel information may be sent in a first radio resource control signaling message, and artificial intelligence model information may be sent in a second radio resource control signaling message. The first radio resource control signaling message may include a radio resource format indication indicating a control channel signaling segment for sending the artificial intelligence model information in the second radio resource control signaling message.

[0009] In one embodiment, control channel information and artificial intelligence model information may be transmitted in a radio resource control signaling message, wherein the control channel information is transmitted via the main portion of the radio resource control signaling message, and wherein the artificial intelligence model information is transmitted via a sub-portion of the radio resource control signaling message. The radio resource control signaling message may include a first radio resource format indication indicating a first group of one or more first control channel signaling segments for transmitting control channel information via the main portion of the radio resource control signaling message, and the first radio resource format indication may indicate a second group of one or more first sequence segment identifiers corresponding to the first group of one or more first control channel signaling segments, respectively. The radio resource control signaling message may include a second radio resource format indication indicating a third group of one or more second control channel signaling segments for transmitting artificial intelligence model information via the sub-portion of the radio resource control signaling message, and the second radio resource format indication may indicate a fourth group of one or more second sequence segment identifiers corresponding to the third group of one or more second control channel signaling segments, respectively. In one embodiment, the first first sequence segment identifier in the second group of one or more first sequence segment identifiers is the same as the first second sequence segment identifier in the fourth group of one or more second sequence segment identifiers. In other words, the segment corresponding to the main part of the radio resource control signaling message can be identified by an identifier that has the same sequence number as the segment of the second part of the radio resource control signaling message.

[0010] In another example embodiment, the radio access network node may include a processor configured to train a radio function AI learning model to generate a trained radio function AI learning model. The processor may be configured to send control channel information corresponding to a communication session between the radio access network node and the user equipment via the main portion of a radio resource control signaling message, and the processor may be configured to send the trained radio function AI learning model to the user equipment via a sub-portion of the radio resource control signaling message. Therefore, the trained radio function AI learning model can be sent to the user equipment via the same RRC signaling message in which the control channel information is transmitted.

[0011] In one embodiment, the main portion of the radio resource control signaling message may be transmitted according to a first encoding format corresponding to a first reliability, and the secondary portion of the radio resource control signaling message may be transmitted according to a second encoding format corresponding to a second reliability. In one embodiment, the second reliability is lower than the first reliability.

[0012] In one embodiment, the radio resource control signaling message may include a first radio resource format indication indicating a first control channel signaling segment for transmitting control channel information via the main part of the radio resource control signaling message, and the radio resource control signaling message may include a second radio resource format indication indicating a second control channel signaling segment for transmitting a radio function artificial intelligence learning model via the subpart of the radio resource control signaling message.

[0013] In one embodiment, a first radio resource format indication may include a first segment identifier corresponding to a first control channel signaling segment used to transmit control channel information via the main part of a radio resource control signaling message. A second radio resource format indication may include a second segment identifier corresponding to a second control channel signaling segment used to transmit a radio function artificial intelligence learning model via the subpart of a radio resource control signaling message. The radio resource control signaling message may include a retransmission enable indication to instruct the user equipment to request retransmission of either the first or second control channel signaling segment. Therefore, the user equipment may be configured to, via the retransmission enable indication, request retransmission of less than all control information transmitted in the main part of the RRC signaling message or less than all AI model information transmitted in the subpart of the RRC signaling message by sending a retransmission request to the radio access network node and a segment identifier corresponding to a segment that the user equipment can determine was not received without error.

[0014] In one embodiment, a non-transitory machine-readable medium may include executable instructions that, when executed by a processor of a radio access network node, cause the execution of operations including: receiving from at least one user equipment at least one radio performance metric corresponding to at least one radio performance parameter; and training a radio function learning model using the at least one radio performance metric to generate an updated radio function learning model to be used by the at least one user equipment. This operation may include transmitting control channel information to the at least one user equipment via a first portion of a radio resource control signaling message according to a first control channel coding scheme, and may also include transmitting the updated radio function learning model to the at least one user equipment via a second portion of a radio resource control signaling message according to a second control channel coding scheme. The first and second control channel coding schemes may be different. Thus, for example, the same RRC signaling message may be used to transmit control channel information using a reliable coding rate and to transmit the updated radio function learning model using a less reliable but faster coding rate.

[0015] In one embodiment, a first portion of the radio resource control signaling message may include one or more first control channel signaling segments, and a second portion of the radio resource control signaling message may include one or more second control channel signaling segments. The radio resource control signaling message may include a first radio resource format indication, which includes one or more first segment identifiers corresponding to the one or more first control channel signaling segments. The radio resource control signaling message includes a second radio resource format indication, which includes one or more second segment identifiers corresponding to the one or more second control channel signaling segments.

[0016] In one embodiment, the radio resource control signaling message may include a first retransmission enable instruction to instruct the user equipment to request retransmission of at least one of one or more first control channel signaling segments. The radio resource control signaling message may include a second retransmission enable instruction to instruct the user equipment to request retransmission of at least one of one or more second control channel signaling segments.

[0017] In another example, one method embodiment may include receiving a control channel message, comprising control channel information and artificial intelligence model information, from a radio access network node by a user equipment including a processor. The method may further include decoding the control channel information by the user equipment according to a first decoding format corresponding to a first encoding format to generate decoded control channel information, and the method may further include decoding the artificial intelligence model information by the user equipment according to a second decoding format corresponding to a second encoding format to generate decoded artificial intelligence model information. The method may further include updating a trained artificial intelligence learning model by the user equipment based on the artificial intelligence model information to generate an updated trained artificial intelligence learning model. The method may include operating radio functions by the user equipment according to the updated trained artificial intelligence learning model. In one embodiment, the first encoding format may correspond to a first rate, and the second encoding format may correspond to a second rate. In one embodiment, the first rate is lower than the second rate.

[0018] In one embodiment, the example method may further include operation by the user equipment based on the decoded control channel information before decoding the artificial intelligence model information according to the second decoding format.

[0019] In one embodiment, the control channel information and the artificial intelligence model information may be received from the radio access network node in a radio resource control signaling message. The radio resource control signaling message may include a radio resource control signaling message format indication indicating a first encoding format. The radio resource control signaling message may include a first portion for transmitting control channel information and a second portion for transmitting artificial intelligence model information. The radio resource control signaling message format indication may indicate at least one control channel signaling segment for transmitting control channel information in the first portion of the radio resource control signaling message. The radio resource control signaling message format indication may indicate that retransmission of at least one of the at least one control channel signaling segments is enabled by the radio access network node. The example method may also include the user equipment determining that at least one of the at least one control channel signaling segment has been decoded with an error. The method may also include the user equipment sending a retransmission request message to the radio access network node, the retransmission request message including a retransmission request for at least one of the at least one control channel signaling segment that has been decoded with an error. The method may further include: receiving a retransmission segment corresponding to at least one of the at least one control channel signaling segments that has been decoded with an error by the user equipment; and decoding the retransmission segment according to a first decoding format by the user equipment to generate a decoded retransmission segment. The retransmission request message may include at least one of the following: a process identifier corresponding to a first part of the radio resource control signaling message, or a segment identifier corresponding to at least one of the at least one control channel signaling segments that has been decoded with an error.

[0020] In one embodiment, a radio resource control signaling message may include a radio resource control signaling message format indication indicating a second encoding format. The radio resource control signaling message may include a first portion for transmitting control channel information and a second portion for transmitting artificial intelligence model information. The radio resource control signaling message format indication may indicate at least one control channel signaling segment for transmitting a trained artificial intelligence learning model in the second portion of the radio resource control signaling message. The radio resource control signaling message format indication may indicate that retransmission of at least one of the at least one control channel signaling segments is enabled by the radio access network node. The example method may also include having a user equipment determine that at least one of the at least one control channel signaling segment has been decoded with an error. The method may also include having the user equipment send a retransmission request message to the radio access network node, the retransmission request message including a retransmission request for at least one of the at least one control channel signaling segments that has been decoded with an error. The method may also include: having the user equipment receive a retransmission segment corresponding to the at least one of the at least one control channel signaling segments that has been decoded with an error; and decoding the retransmission segment according to the second decoding format to produce a decoded retransmission segment. The retransmission request message may include at least one of the following: a process identifier corresponding to the second part of a radio resource control signaling message, or a segment identifier corresponding to at least one of at least one control channel signaling segment that has been decoded with an error. The method may also include operation by the user equipment based on the decoded control channel information before decoding the retransmission segment according to the second decoding format.

[0021] In one embodiment, a radio resource control signaling message may include a first part for transmitting control channel information and a second part for transmitting artificial intelligence model information, and the first part may include a radio resource control signaling message format indication that indicates the second part.

[0022] In another example embodiment, a user equipment (UE) may include a processor configured to determine a radio performance parameter metric corresponding to operation of the UE relative to a radio access network (RAN) node, to generate the determined radio performance parameter metric. The processor may be configured to transmit the determined radio performance parameter metric to the RAN node for use in training a radio function artificial intelligence (RFI) learning model to generate a trained RFI learning model. The processor may also be configured to: receive control channel information corresponding to operation of the UE relative to the RAN node via a main portion of a radio resource control (RFC) signaling message; and receive the trained RFI learning model from the RAN node via a secondary portion of the RFC signaling message.

[0023] In one embodiment, the main portion of a Radio Resource Control (RRC) signaling message may be received according to a first decoding scheme corresponding to a first rate, and the secondary portion may be received according to a second decoding scheme corresponding to a second rate. In one embodiment, the first rate may be lower than the second rate. Therefore, the user equipment can adjust the decoding of the RRC signaling message to retrieve control channel information, and then adjust the decoding of the same RRC signaling message to different settings to retrieve a trained radio function artificial intelligence learning model.

[0024] In one embodiment, a radio resource control signaling message may include a first radio resource format indication, which may include one or more first segment identifiers, each indicating one or more first control channel signaling segments for transmitting control channel information via the main part of the radio resource control signaling message. The radio resource control signaling message may also include a second radio resource format indication, which may include one or more second segment identifiers, each indicating one or more second control channel signaling segments for transmitting a radio function artificial intelligence learning model via the subpart of the radio resource control signaling message.

[0025] Radio resource control signaling messages may include a retransmission enable instruction (e.g., by a radio access network node) to enable retransmission of one or more first control channel signaling segments or one or more second control channel signaling segments.

[0026] In one embodiment, the processor is further configured to determine that at least one of one or more first control channel signaling segments or one or more second control channel signaling segments has been erroneously received, to generate at least one segment that has been determined to have been erroneously received. The processor may also be configured to send a retransmission request message to a radio access network node requesting the retransmission of the at least one segment that has been determined to have been erroneously received.

[0027] In another example embodiment, a non-transitory machine-readable medium may include executable instructions that, when executed by a processor of a user equipment, cause the execution of an operation including: receiving control channel information from a radio access network node via a first portion of a radio resource control signaling message; and decoding the first portion of the radio resource control signaling message according to a first decoding rate. The operation may further include: receiving an updated radio function learning model from the radio access network node via a second portion of the radio resource control signaling message, wherein the updated radio function learning model includes updated learning model information based on operations of at least one of a group of user equipments, including the user equipment. The operation may further include decoding the second portion of the radio resource control signaling message according to a second decoding rate to generate a decoded updated radio function learning model. The operation may further include: receiving from a radio access network node a first portion indication including one or more first segment identifiers of a first group, the first group of one or more first segment identifiers indicating one or more segments corresponding to a second group of the first portion of the radio resource control signaling message; and receiving from a radio access network node a second portion indication including one or more second segment identifiers of a third group, the third group of one or more second segment identifiers indicating one or more segments corresponding to a fourth group of the second portion of the radio resource control signaling message. In one embodiment, the first decoding rate and the second decoding rate are different.

[0028] In one embodiment, the operation may further include operating the user equipment based on the decoded and updated radio function learning model.

[0029] In one embodiment, the radio resource control signaling message may include a first retransmission enable instruction to instruct a user equipment (UE) to request retransmission of a first segment of a first part of the radio resource control signaling message or a second segment of a second part of the radio resource control signaling message. The operation may also include determining that the first segment of the first part of the radio resource control signaling message or the second segment of the second part of the radio resource control signaling message contains an error or was received incorrectly, to generate a determined erroneous segment. The operation may further include: sending a retransmission request to a radio access network (RAN) node including a first segment identifier or a second segment identifier indicating the determined erroneous segment; and receiving from the RNA node, in response to the retransmission request, a first segment corresponding to the first segment identifier indicated in the retransmission request or a second segment corresponding to the second segment identifier indicated in the retransmission request. Attached Figure Description

[0030] Figure 1 The wireless communication system environment is shown.

[0031] Figure 2 An example environment with radio functionality implemented in conjunction with a corresponding learning model is shown.

[0032] Figure 3 An example radio resource control signaling message is shown, comprising different parts, each encoded and transmitted according to a different format.

[0033] Figure 4A An example radio resource control signaling message is shown, comprising different parts, each encoded and transmitted according to a different format, with each part comprising multiple segments.

[0034] Figure 4B An example radio resource control signaling message is shown, comprising different parts, one part of which includes an indication corresponding to another part.

[0035] Figure 5A An example radio resource control signaling message is shown, comprising different parts, each encoded and transmitted according to a different format, and each including its own different format indication containing a retransmission enable indication.

[0036] Figure 5B An example radio resource control signaling message is shown, comprising different segmented parts, wherein the different segmented parts are encoded and transmitted according to different formats, and each includes its own different format indication, with the decoding of one part taking precedence over the other.

[0037] Figure 5C An example radio resource control signaling message is shown, comprising different segmented parts, wherein the different segmented parts are encoded and transmitted according to different formats, and each includes a different format indication containing a retransmission enable indication, wherein decoding of one part takes precedence over another.

[0038] Figure 6 An example environment is shown where a user equipment learns model updates as it moves from the coverage of one radio access network to the coverage of another.

[0039] Figure 7 An example radio resource segment retransmission request is shown.

[0040] Figure 8 A timing diagram is shown for an example method of updating the learning model at the user equipment by a radio access network node.

[0041] Figure 9 A timing diagram is shown for an example method of updating a learning model by a radio access network node via control channel resources and using learning model update information received from the radio access network node via control channel resources.

[0042] Figure 10 A flowchart illustrating an example method for updating an artificial intelligence learning model via control plane resources is shown.

[0043] Figure 11 A block diagram of an example method is shown.

[0044] Figure 12 A block diagram of an example radio access network node is shown.

[0045] Figure 13 A block diagram of an example non-transitory machine-readable medium is shown.

[0046] Figure 14 A block diagram of an example method is shown.

[0047] Figure 15 A block diagram of an example user device is shown.

[0048] Figure 16 A block diagram of an example non-transitory machine-readable medium is shown.

[0049] Figure 17 An example computer environment is shown.

[0050] Figure 18 A block diagram of an example wireless UE is shown. Detailed Implementation

[0051] As a preliminary point, those skilled in the art will readily understand that this embodiment has broad utility and application. Many methods, embodiments, and adaptations of this application (in addition to those described herein), as well as many variations, modifications, and equivalent arrangements, will be apparent or reasonably indicated from the spirit or scope of the various embodiments of this application.

[0052] Accordingly, while this application has been described in detail with respect to various embodiments herein, it should be understood that this disclosure illustrates one or more concepts expressed by various exemplary embodiments and is made merely for the purpose of providing a complete disclosure. The following disclosure is neither intended nor construed as limiting this application or otherwise excluding any such other embodiments, adaptations, variations, modifications, and equivalent arrangements, and the embodiments described herein are limited only by the appended claims and their equivalents.

[0053] As used in this disclosure, in some embodiments, the terms "component," "system," etc., are intended to refer to or include computer-related entities or entities associated with operating means having one or more specific functions, wherein the entity may be hardware, a combination of hardware and software, software, or software in execution. As an example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable program, a thread of execution, computer-executable instructions, a program, and / or a computer. By way of illustration and not limitation, applications running on a server and the server itself can both be components.

[0054] One or more components may reside within a process and / or execution thread, and components may be located on a single computer and / or distributed across two or more computers. Furthermore, these components may execute from various computer-readable media on which various data structures are stored. Components may communicate via local and / or remote processes, such as according to signals having one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or with other systems across a network, such as the Internet). As another example, a component may be a device having specific functions provided by mechanical parts operated by electrical or electronic circuitry (operated by a software or firmware application executed by a processor), wherein the processor may be internal or external to the device and executes at least a portion of the software or firmware application. As yet another example, a component may be a device providing specific functions via electronic components without mechanical parts, the electronic components including a processor therein to execute software or firmware that at least partially endows the electronic components with the functions. While various components have been shown as separate components, it will be understood that multiple components may be implemented as a single component, or a single component may be implemented as multiple components, without departing from the exemplary embodiments.

[0055] As used herein, the term "cause" is used in the context of a system, device, or component "causing" one or more actions or operations relating to the nature of a complex computing environment in which multiple components and / or devices may be involved in some computational operations. Non-limiting examples of actions that may or may not involve multiple components and / or devices include sending or receiving data, establishing connections between devices, determining intermediate results toward obtaining a result, etc. In this regard, a computing device or component can cause an operation by playing any role in performing the operation. Therefore, when describing the operation of a component herein, it should be understood that, where an operation is described as being caused by a component, the operation may optionally be performed in cooperation with one or more other computing devices or components, such as, but not limited to, sensors, antennas, audio and / or visual output devices, other devices, etc.

[0056] Furthermore, various embodiments can be implemented as methods, apparatus, or articles of art using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer implementing the disclosed subject matter. The term "article of art" as used herein is intended to cover a computer program accessible from any computer-readable (or machine-readable) device or computer-readable (or machine-readable) storage / communication medium. For example, computer-readable storage media may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs)), smart cards, and flash memory devices (e.g., cards, sticks, flash drives). Of course, those skilled in the art will recognize that many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

[0057] For example, artificial intelligence (AI) and machine learning (ML) models can enable performance, operational capabilities, and improvements in 5G implementations, such as network automation, optimized signaling overhead, energy savings at equipment, and maximized traffic capacity. AI / ML model functionality can be implemented and constructed in many different forms, utilizing diverse vendor-specific designs. 5G radio access network nodes (RANs) of the network to which user equipment can attach or to which user equipment can register can manage or control the real-time performance of AI / ML models at various user equipment locations for a variety of radio functions.

[0058] As disclosed herein, several embodiments facilitate the dynamic management and updating of various AI / ML models deployed at different user equipment devices. The network RAN ​​can dynamically control the activation, deactivation, triggering, or updating of the learning model (which may be radio function-specific) based on the monitoring and analysis of defined real-time performance metrics corresponding to the learning model executed at the user equipment. It will be understood that even if the learning model can implement a specific radio function, the metric being monitored or analyzed may be a learning model metric, and not necessarily a radio function metric (e.g., mathematical / statistical metrics are not necessarily radio function metrics, such as signal strength).

[0059] Now turn to the attached image. Figure 1An example of a wireless communication system 100 supporting blind decoding of the PDCCH (Physical Downlink Control Channel) candidate or search space is illustrated according to various aspects of this disclosure. The wireless communication system 100 may include one or more base stations 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a Long Term Evolution (LTE) network, an Advanced LTE (LTE-A) network, an LTE-A Pro network, or a New Radio (NR) network. In some examples, the wireless communication system 100 may support enhanced broadband communication, ultra-reliable (e.g., mission-critical) communication, low-latency communication, communication with low-cost, low-complexity devices, or any combination thereof. As shown, examples of UE 115 may include smartphones, cars or other vehicles, or drones or other aircraft. Another example of a UE may be a virtual reality device 117, such as smart glasses, virtual reality headsets, augmented reality headsets, and other similar devices that can provide the wearer with images, video, audio, touch, taste, or smell. A UE, such as VR device 117, can transmit or receive wireless signals with RAN base station 105 via long-range wireless link 125, or the UE / VR device can receive or transmit wireless signals via short-range wireless link 137, which may include a wireless link with UE device 115, such as a Bluetooth link, Wi-Fi link, etc. A UE, such as device 117, can communicate simultaneously via multiple wireless links (such as on link 125 with base station 105 and on short-range wireless links). VR device 117 can also communicate with the wireless UE via cable or other wired connection. The RAN or its components can be referenced. Figure 12 To implement this using one or more computer components as described.

[0060] continue Figure 1 As discussed, base stations 105 can be distributed throughout a geographical area to form a wireless communication system 100, and can be devices of different forms or with different capabilities. Base stations 105 and UEs 115 can communicate wirelessly via one or more communication links 125. Each base station 105 can provide a coverage area 110 where UEs 115 and base stations 105 can establish one or more communication links 125. Coverage area 110 can be an example of a geographical area where base stations 105 and UEs 115 can support signal communication according to one or more radio access technologies.

[0061] UE 115 can be distributed throughout the entire coverage area 110 of the wireless communication system 100, and each UE 115 can be stationary, mobile, or both at different times. UE 115 can be devices of different forms or with different capabilities. Figure 1Some example UE 115s are shown in the document. The UE 115 described herein is capable of communicating with various types of devices, such as other UE 115s, base station 105, or network devices (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network devices). Figure 1 As shown.

[0062] Base station 105 may communicate with core network 130, communicate with each other, or both. For example, base station 105 may interface with core network 130 via one or more backhaul links 120 (e.g., via S1, N2, N3, or other interfaces). Base station 105 may communicate directly (e.g., directly between base stations 105) or indirectly (e.g., via core network 130) on backhaul links 120 (e.g., via X2, Xn, or other interfaces), or both. In some examples, backhaul link 120 may include one or more radio links.

[0063] One or more base stations 105 described herein may include, or may be referred to by those skilled in the art as, base station, radio base station, access point, radio transceiver, NodeB, eNodeB (eNB), next-generation NodeB or gigabit NodeB (any of which may be referred to as bNodeB or gNB), home NodeB, home eNodeB or other suitable terms.

[0064] UE 115 may include or be referred to as a mobile device, wireless device, remote device, handheld device, or subscriber device, or any other suitable term, wherein "device" may also be referred to as a unit, station, terminal, or client, etc. UE 115 may also include or be referred to as a personal electronic device, such as a cellular phone, personal digital assistant (PDA), tablet computer, laptop computer, personal computer, or router. In some examples, UE 115 may include or be referred to as a wireless local loop (WLL) station, Internet of Things (IoT) device, Internet of Everything (IoE) device, or machine-type communication (MTC) device, etc., which may be implemented in various objects such as machinery, vehicles, or smart meters.

[0065] UE 115 is capable of communicating with various types of devices, such as other UE 115s that may sometimes act as relays, as well as base station 105 and network devices, including examples such as macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations. Figure 1 As shown.

[0066] UE 115 and base station 105 can wirelessly communicate with each other via one or more communication links 125 on one or more carriers. The term "carrier" can refer to a set of radio frequency spectrum resources having a defined physical layer structure for supporting communication link 125. For example, a carrier for communication link 125 may include a portion (e.g., a bandwidth portion (BWP)) of a radio frequency spectrum band operating according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry acquisition signaling (e.g., synchronization signals, system information), control signaling coordinating carrier operation, user data, or other signaling. Wireless communication system 100 can use carrier aggregation or multi-carrier operation to support communication with UE 115. Depending on the carrier aggregation configuration, UE 115 can be configured to have multiple downlink component carriers and one or more uplink component carriers. Carrier aggregation can be used in conjunction with both frequency division duplex (FDD) component carriers and time division duplex (TDD) component carriers.

[0067] In some examples (e.g., in a carrier aggregation configuration), the carrier may also have acquisition signaling or control signaling to coordinate the operation of other carriers. The carrier may be associated with a frequency channel (e.g., an Evolved Universal Mobile Telecommunications System Terrestrial Radio Access (E-UTRA) Absolute Radio Frequency Channel Number (EARFCN)) and can be located according to a channel grating used for UE 115 discovery. The carrier can operate in offline mode, where initial acquisition and connection can be performed by the UE 115 via the carrier, or the carrier can operate in active mode, where the connection is anchored using different carriers (e.g., the same or different radio access technologies).

[0068] The communication link 125 shown in the wireless communication system 100 may include uplink transmission from UE 115 to base station 105, or downlink transmission from base station 105 to UE 115. The carrier may carry downlink or uplink communication (e.g., in FDD mode), or may be configured to carry both downlink and uplink communication (e.g., in TDD mode).

[0069] A carrier can be associated with a specific bandwidth of radio frequency spectrum, which in some examples may be referred to as the carrier or the “system bandwidth” of wireless communication system 100. For example, the carrier bandwidth may be one of several bandwidths determined for a specific radio access technology (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 MHz). Devices of wireless communication system 100 (e.g., base station 105, UE 115, or both) may have a hardware configuration that supports communication over a specific carrier bandwidth, or may be configured to support communication over one of a set of carrier bandwidths. In some examples, wireless communication system 100 may include a base station 105 or UE 115 that supports simultaneous communication via carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured to operate on a portion (e.g., a subband, BWP) or all of the carrier bandwidth.

[0070] The signal waveform transmitted on a carrier can consist of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques, such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform extended OFDM (DFT-S-OFDM)). In a system employing MCM, a resource element can consist of a symbol period (e.g., the duration of a modulation symbol) and a subcarrier, where the symbol period and subcarrier spacing are inversely proportional. The number of bits carried by each resource element can depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both). Therefore, the more resource elements the UE 115 receives and the higher the order of the modulation scheme, the higher the data rate of the UE. Wireless communication resources can refer to a combination of radio frequency spectrum resources, temporal resources (e.g., search space), or spatial resources (e.g., spatial layers or beams), and the use of multiple spatial layers can further increase the data rate or data integrity used for communication with the UE 115.

[0071] One or more basic parameter sets can be supported for a carrier, where the basic parameter sets may include subcarrier spacing (Δf) and cyclic prefix. A carrier can be divided into one or more BWPs with the same or different basic parameter sets. In some examples, UE 115 can be configured to have multiple BWPs. In some examples, a single BWP for a carrier can be active at a given time, and communication for UE 115 can be restricted to one or more active BWPs.

[0072] The time interval for base station 105 or UE 115 can be expressed as a multiple of a basic time unit. For example, the basic time unit could be T. s = 1 / (Δf max ·N f The sampling period is ) seconds, where Δf maxN can represent the maximum supported subcarrier spacing. f This can represent the maximum supported Discrete Fourier Transform (DFT) size. The time interval of the communication resources can be organized according to radio frames, where each radio frame has a specified duration (e.g., 10 milliseconds (ms)). Each radio frame can be identified by a System Frame Number (SFN) (e.g., ranging from 0 to 1023).

[0073] Each frame may include multiple consecutively numbered subframes or time slots, and each subframe or time slot may have the same duration. In some examples, a frame may (e.g., in the time domain) be divided into subframes, and each subframe may be further divided into multiple time slots. Alternatively, each frame may include a variable number of time slots, and the number of time slots may depend on the subcarrier spacing. For example, depending on the length of the cyclic prefix pre-added to each symbol period, each time slot may include multiple symbol periods. In some wireless communication systems 100, time slots may be further divided into multiple mini-time slots containing one or more symbols. In addition to the cyclic prefix, each symbol period may also contain one or more (e.g., N) symbols. f (Number) sampling periods. The duration of a symbol period can depend on the subcarrier spacing or the operating frequency band.

[0074] A subframe, time slot, mini-time slot, or symbol can be the smallest scheduling unit of the wireless communication system 100 (e.g., in the time domain) and can be referred to as a transmission time interval (TTI). In some examples, the duration of the TTI (e.g., the number of symbol periods in the TTI) can be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communication system 100 can be dynamically selected (e.g., in bursts of shortened time intervals (STTIs)).

[0075] Physical channels can be multiplexed on a carrier using various techniques. For example, one or more of Time Division Multiplexing (TDM), Frequency Division Multiplexing (FDM), or hybrid TDM-FDM techniques can be used to multiplex physical control channels and physical data channels on a downlink carrier. A control region (e.g., a control resource set (CORESET)) for a physical control channel can be defined by multiple symbol periods and can be extended across the system bandwidth or a subset of the system bandwidth of the carrier. One or more control regions (e.g., CORESETs) can be configured for a group of UEs 115. For example, one or more UEs 115 can monitor or search for control information in control regions or spaces according to one or more search space sets, and each search space set can include one or more control channel candidates under one or more aggregation levels arranged in a cascaded manner. The aggregation level for control channel candidates can refer to the number of control channel resources (e.g., control channel elements (CCEs)) associated with coded information in a control information format having a given payload size. The search space set can include a common search space set configured to send control information to multiple UEs 115, and a UE-specific search space set for sending control information to a specific UE 115. This paper discloses novel and unconventional alternative search spaces and configurations for monitoring and decoding them.

[0076] Base station 105 may provide communication coverage via one or more cells (e.g., macro cells, small cells, hotspots, or other types of cells, or any combination thereof). The term "cell" may refer to a logical communication entity used (e.g., on a carrier) to communicate with base station 105 and may be associated with an identifier used to distinguish neighboring cells (e.g., Physical Cell Identifier (PCID), Virtual Cell Identifier (VCID), or others). In some examples, a cell may also refer to a geographic coverage area 110 or a portion (e.g., a sector) of geographic coverage area 110 on which the logical communication entity operates. Depending on various factors, such as the capabilities of base station 105, the range of such cells can range from small areas (e.g., structures, subsets of structures) to large areas. For example, a cell may be or include examples such as buildings, subsets of buildings, or external spaces between or overlapping geographic coverage areas 110.

[0077] Macro cells typically cover a relatively large geographical area (e.g., a radius of several kilometers) and allow unrestricted access for UEs 115 that subscribe to services from network providers supporting macro cells. In contrast, small cells can be associated with lower-power base stations 105 and can operate in the same or different (e.g., licensed, unlicensed) frequency bands as macro cells. Small cells can provide unrestricted access to UEs 115 that subscribe to services from network providers, or they can provide restricted access to UEs 115 associated with small cells (e.g., UEs 115 in a Closed Subscriber Group (CSG), or UEs 115 associated with a user in a home or office). Base station 105 can support one or more cells and can also use one or more component carriers to support communication on one or more cells.

[0078] In some examples, a carrier can support multiple cells and can be configured with different cells based on different protocol types that can provide access for different types of devices (e.g., MTC, Narrowband IoT (NB-IoT), Enhanced Mobile Broadband (eMBB)).

[0079] In some examples, base station 105 may be mobile, thus providing communication coverage for mobile geographic coverage areas 110. In some examples, different geographic coverage areas 110 associated with different technologies may overlap, but the different geographic coverage areas 110 may be supported by the same base station 105. In other examples, overlapping geographic coverage areas 110 associated with different technologies may be supported by different base stations 105. The wireless communication system 100 may include, for example, a heterogeneous network, in which different types of base stations 105 use the same or different radio access technologies to provide coverage for various geographic coverage areas 110.

[0080] The wireless communication system 100 can support synchronous or asynchronous operation. For synchronous operation, base stations 105 can have similar frame timing, and transmissions from different base stations 105 can be approximately aligned in time. For asynchronous operation, base stations 105 can have different frame timings, and in some examples, transmissions from different base stations 105 can be misaligned in time. The techniques described herein can be used for both synchronous and asynchronous operation.

[0081] Some UE 115 devices (such as MTC or IoT devices) can be low-cost or low-complexity devices and can provide automated communication between machines (e.g., via machine-to-machine (M2M) communication). M2M communication or MTC can refer to data communication technologies that allow devices to communicate with each other or with base station 105 without human intervention. In some examples, M2M communication or MTC can include communication from devices that integrate sensors or instruments to measure or capture information and relay such information to a central server or application, which uses or presents this information to people interacting with the application. Some UE 115 devices can be designed to collect information or automate the behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security sensing, physical access control, and transaction-based billing.

[0082] Some UE 115s can be configured to operate in a power-saving mode, such as half-duplex communication (e.g., a mode that supports unidirectional communication via transmit or receive but not simultaneous transmit and receive). In some examples, half-duplex communication can be performed at a reduced peak rate. Other power-saving techniques for UE 115s include entering a power-saving deep sleep mode when not engaged in active communication, operating on limited bandwidth (e.g., according to narrowband communication), or a combination of these techniques. For example, some UE 115s can be configured to operate using a narrowband protocol type associated with a defined portion or range (e.g., a set of subcarriers or resource blocks (RBs)) within a carrier, within a carrier guard band, or outside a carrier.

[0083] Wireless communication system 100 can be configured to support ultra-reliable communication or low-latency communication, or various combinations thereof. For example, wireless communication system 100 can be configured to support ultra-reliable low-latency communication (URLLC) or mission-critical communication. UE 115 can be designed to support ultra-reliable, low-latency, or mission-critical functions (e.g., mission-critical functions). Ultra-reliable communication may include private or group communication and may be supported by one or more mission-critical services, such as mission-critical push-to-talk (MCPTT), mission-critical video (MCVideo), or mission-critical data (MCData). Support for mission-critical functions may include service prioritization, and mission-critical services may be used for public safety or general commercial applications. The terms “ultra-reliable,” “low-latency,” “mission-critical,” and “ultra-reliable low-latency” are used interchangeably herein.

[0084] In some examples, UE 115 may also communicate directly with other UE 115 on a device-to-device (D2D) communication link 135 (e.g., using a peer-to-peer (P2P) or D2D protocol). Communication link 135 may include a sidelink communication link. One or more UEs 115 utilizing D2D communication may be within the geographic coverage area 110 of base station 105. Other UEs 115 in the group may be outside the geographic coverage area 110 of base station 105, or may not be able to receive transmissions from base station 105. In some examples, multiple groups of UEs 115 communicating via D2D communication may utilize a one-to-many (1:M) system, where a UE transmits to each other UE in the group. In some examples, base station 105 facilitates the scheduling of resources for D2D communication. In other cases, D2D communication is performed between UEs 115 without involving base station 105.

[0085] In some systems, the D2D communication link 135 may be an example of a communication channel (such as a side-link communication channel) between vehicles (e.g., UE 115). In some examples, vehicles may communicate using vehicle-to-everything (V2X) communication, vehicle-to-vehicle (V2V) communication, or some combination thereof. Vehicles may send information related to traffic conditions, signaling, weather, safety, emergencies, or any other information related to the V2X system. In some examples, vehicles in a V2X system may communicate using vehicle-to-network (V2N) communication, via one or more RAN network nodes (e.g., base station 105) to roadside infrastructure (such as roadside units) or the network, or both.

[0086] Core network 130 can provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. Core network 130 can be an evolved packet core (EPC) or a 5G core (5GC), and can include at least one control plane entity (e.g., a mobility management entity (MME), access and mobility management function (AMF)) managing access and mobility, and at least one user plane entity (e.g., a serving gateway (S-GW), packet data network (PDN) gateway (P-GW), or user plane function (UPF)) routing packets or interconnects to external networks. The control plane entity can manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management, for UE 115 served by base station 105 associated with core network 130. User IP packets can be delivered through the user plane entity, which can provide IP address allocation and other functions. The user plane entity can connect to IP services 150 for one or more network operators. IP service 150 may include access to the Internet, intranet(s), IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0087] Some network devices, such as base station 105, may include sub-components such as access network entity 140, which may be an example of an access node controller (ANC). Each access network entity 140 may communicate with UE 115 through one or more other access network transport entities 145, which may be referred to as a radio headend, smart radio headend, or transmit / receive point (TRP). Each access network transport entity 145 may include one or more antenna panels. In some configurations, the various functions of each access network entity 140 or base station 105 may be distributed among various network devices (e.g., radio headends and ANCs) or combined into a single network device (e.g., base station 105).

[0088] Wireless communication system 100 can operate using one or more frequency bands (typically in the range of 300 MHz to 300 GHz). Generally speaking, the region from 300 MHz to 3 GHz is referred to as the ultra-high frequency (UHF) region or decimeter band because the wavelength range is from approximately 1 decimeter to 1 meter. UHF waves may be blocked or deflected by buildings and environmental features, but the waves can penetrate structures sufficiently to enable macrocells to provide service to UE 115 located indoors. Compared to transmissions using smaller frequencies and longer waves in the high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz, UHF wave transmission can be associated with smaller antennas and shorter distances (e.g., less than 100 km).

[0089] The wireless communication system 100 can also operate in the ultra-high frequency (SHF) region using a frequency band from 3 GHz to 30 GHz (also known as the centimeter band), or in the extremely high frequency (EHF) region of a spectrum (e.g., from 30 GHz to 300 GHz) (also known as the millimeter band). In some examples, the wireless communication system 100 can support millimeter-wave (mmW) communication between the UE 115 and the base station 105, and the EHF antennas of the corresponding devices can be smaller and more closely spaced than UHF antennas. In some examples, this can facilitate the use of an in-device antenna array. However, compared to SHF or UHF transmissions, EHF transmissions may experience greater atmospheric attenuation and shorter distances during propagation. The techniques disclosed herein can be employed between transmissions using one or more different frequency regions, and the designated use of frequency bands between these frequency regions may vary by country or regulatory body.

[0090] Wireless communication system 100 can utilize both licensed and unlicensed radio frequency spectrum bands. For example, wireless communication system 100 can employ licensed assisted access (LAA), unlicensed LTE (LTE-U) radio access technology, or NR technology in unlicensed frequency bands (such as the 5 GHz Industrial, Scientific and Medical (ISM) band). When operating in unlicensed radio frequency spectrum bands, devices such as base station 105 and UE 115 can employ carrier sensing for collision detection and avoidance. In some examples, operation in unlicensed frequency bands can be configured based on carrier aggregation in conjunction with component carriers operating in licensed frequency bands (e.g., LAA). Operation in unlicensed spectrum can include examples such as downlink transmission, uplink transmission, P2P transmission, or D2D transmission.

[0091] Base station 105 or UE 115 may be equipped with multiple antennas, which can be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of base station 105 or UE 115 may be located within one or more antenna arrays or antenna panels, which may support MIMO operation or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be located together at an antenna assembly (such as an antenna tower). In some examples, the antennas or antenna arrays associated with base station 105 may be located in diverse geographical locations. Base station 105 may have an antenna array with multiple rows and columns of antenna ports, which base station 105 can use to support beamforming for communication with UE 115. Similarly, UE 115 may have one or more antenna arrays, which may support various MIMO or beamforming operations. Additionally or alternatively, antenna panels may support radio frequency beamforming for signals transmitted via antenna ports.

[0092] Base station 105 or UE 115 can use MIMO communication to utilize multipath signal propagation and improve spectral efficiency by transmitting or receiving multiple signals via different spatial layers. This technique can be referred to as spatial multiplexing. For example, multiple signals can be transmitted by a transmitting device via different antennas or different combinations of antennas. Similarly, multiple signals can be received by a receiving device via different antennas or different combinations of antennas. Each of the multiple signals can be referred to as a separate spatial stream and can carry bits associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers can be associated with different antenna ports used for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO), where in single-user MIMO, multiple spatial layers are transmitted to the same receiving device, while in multi-user MIMO, multiple spatial layers are transmitted to multiple devices.

[0093] Beamforming (also known as spatial filtering, directional transmission, or directional reception) is a signal processing technique used at a transmitting or receiving device (e.g., base station 105, UE 115) to shape or manipulate an antenna beam (e.g., transmit beam, receive beam) along a spatial path between the transmitting and receiving devices. Beamforming can be achieved by combining signals transmitted via antenna elements of an antenna array such that some signals propagating at a specific azimuth relative to the antenna array experience constructive interference, while other signals experience destructive interference. Adjustments to the signals transmitted via the antenna elements can include the transmitting or receiving device applying amplitude offset, phase offset, or both to the signals carried via the antenna elements associated with that device. The adjustments associated with each antenna element can be defined by a beamforming weight set associated with a specific azimuth (e.g., relative to the antenna array of the transmitting or receiving device, or relative to some other azimuth).

[0094] Base station 105 or UE 115 may use beam scanning technology as part of beamforming operations. For example, base station 105 may use multiple antennas or antenna arrays (e.g., antenna panels) for beamforming operations to enable directional communication with UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signaling) may be transmitted multiple times by base station 105 in different directions. For example, base station 105 may transmit signals based on different beamforming weight sets associated with different transmission directions. Transmissions in different beam directions may be used (e.g., by a transmitting device such as base station 105 or by a receiving device such as UE 115) to identify beam directions for later transmission or reception by base station 105.

[0095] Some signals (such as data signals associated with a specific receiving device) may be transmitted by base station 105 in a single beam direction (e.g., the direction associated with a receiving device such as UE 115). In some examples, the beam direction associated with transmission along a single beam direction may be determined based on signals transmitted in one or more beam directions. For example, UE 115 may receive one or more signals transmitted by base station 105 in different directions and may report to the base station an indication that UE 115 received signals with the highest signal quality or other acceptable signal quality.

[0096] In some examples, transmissions by a device (e.g., base station 105 or UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or radio frequency beamforming to generate combined beams for transmissions (e.g., from base station 105 to UE 115). UE 115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to a configured number of beams across the system bandwidth or one or more subbands. Base station 105 may transmit reference signals (e.g., cell-specific reference signals (CRS), channel state information reference signals (CSI-RS)), which may be precoded or unprecoded. UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., multi-panel codebook, linear combination codebook, port selection codebook). Although these techniques are described with reference to signals transmitted by base station 105 in one or more directions, UE 115 may also employ similar techniques to transmit signals multiple times in different directions (e.g., to identify the beam direction for subsequent transmission or reception for UE 115) or to transmit signals in a single direction (e.g., to transmit data to a receiving device).

[0097] When receiving various signals (such as synchronization signals, reference signals, beam selection signals, or other control signaling) from base station 105, the receiving device (e.g., UE 115) can attempt multiple receiving configurations (e.g., directional listening). For example, the receiving device can attempt multiple receiving directions by: receiving via different antenna subarrays; processing the received signal according to different antenna subarrays; receiving according to different sets of receiving beamforming weights applied to signals received at multiple antenna elements of the antenna array (e.g., different directional listening weight sets); or processing the received signal according to different sets of receiving beamforming weights applied to signals received at multiple antenna elements of the antenna array, any of which can be referred to as “listening” according to different receiving configurations or receiving directions. In some examples, the receiving device can use a single receiving configuration to receive along a single beam direction (e.g., when receiving data signals). The single receiving configuration can be aligned based on beam directions determined by listening according to different receiving configuration directions (e.g., beam directions with the highest signal strength, highest signal-to-noise ratio (SNR), or other acceptable signal quality determined based on listening to multiple beam directions).

[0098] The wireless communication system 100 can be a packet-based network operating according to a layered protocol stack. In the user plane, communication at the bearer or Packet Data Convergence Protocol (PDCP) layer can be IP-based. The Radio Link Control (RLC) layer can perform packet segmentation and reassembly for communication on logical channels. The Medium Access Control (MAC) layer can perform priority processing and multiplexing logical channels into transport channels. The MAC layer can also use error detection, error correction, or both to support retransmissions at the MAC layer to improve link efficiency. In the control plane, the Radio Resource Control (RRC) protocol layer can provide the establishment, configuration, and maintenance of RRC connections between the UE 115 and the base station 105 or core network 130 that supports radio bearers for user plane data. At the physical layer, transport channels can be mapped to physical channels.

[0099] UE 115 and base station 105 can support data retransmission to increase the likelihood of successful data reception. Hybrid Automatic Repeat Request (HARQ) feedback is a technique used to increase the likelihood of correctly receiving data on communication link 125. HARQ can include a combination of error detection (e.g., using Cyclic Redundancy Check (CRC)), forward error correction (FEC), and retransmission (e.g., Automatic Repeat Request (ARQ)). Under poor radio conditions (e.g., low signal-to-noise ratio conditions), HARQ can improve throughput at the MAC layer. In some examples, the device can support HARQ feedback within the same time slot, where the device can provide HARQ feedback in a specific time slot for data received in the preceding symbol of that time slot. In other cases, the device can provide HARQ feedback in a subsequent time slot or according to some other time interval.

[0100] Traditional rule-based models can be implemented in user equipment to perform various radio frequency (RF) functions or signal processing functions, such as beamforming, channel estimation, demodulation, and decoding, and can be based on well-established system models. Such models can produce satisfactory performance, provided they closely follow the actual behavior of the radio network system in which the user equipment is operating. However, the performance of traditional models may not be optimal. AI / ML-based models generally outperform their traditional counterparts; unlike traditional rule-based models, AI / ML-based models can be based on data rather than predetermined rules. Therefore, the output or outcome of a traditional rule-based model can be considered "deterministic" because the input is applied to static rules that produce a "deterministic" output, while the output or outcome of an AI / ML model can be considered probabilistic because the learned model typically infers possible outputs based on coefficients, factors, functions, or other variables that may have already been reached based on the model's previous inputs.

[0101] Using AI / ML models can drive improvements in user equipment performance compared to traditional rule-based models. Several AI / ML-driven use cases can include AI / ML channel state information (CSI) acquisition / prediction, AI / ML radio localization, and AI / ML beam management. While AI / ML-based models trained using data from actual real-world operations may potentially outperform traditional rule-based models, they can be less robust in situations where the radio system / environment has already undergone changes that were not experienced or "seen" during the model's training, thus providing less than ideal results. Therefore, in situations where the learning model is "unknown," it may infer less desirable outputs compared to static rule-based models. This problematic situation can be caused by, for example, specific network / user equipment conditions or configurations, the architecture of the AI / ML learning model, or a combination thereof. Therefore, ideally, the implementation process should allow the network RAN ​​to update the AI / ML learning model.

[0102] For an AI / ML learning model implementation of the radio function at a user equipment (UE), the UE or gNB / RAN can predict the modulation and coding scheme (MCS) and can report the timing for this using a given amount of channel state information. The modulation and coding scheme can be referred to as a format. A format or scheme can be associated with quality of service. Channel conditions or interference conditions absent during training or modeling may systematically lead to suboptimal MCS selection, which may consequently result in a violation of minimum equipment performance objectives.

[0103] Since the RAN has better processing power than the UE to train or further modify the learning model, it is ideal to train the AI / ML model at the radio access network node and then send the model or the trained / updated model to the user equipment. Therefore, AI / ML model transfer and delivery from the RAN to the UE via the radio link is ideal for utilizing AI / ML processing-intensive model training performed separately by the RAN and UE, where the UE actively runs the model to perform radio functions based on inferences from the AI / ML model. For example, the AI / ML model can be trained at the RAN node and delivered or transferred as a trained model to the user equipment device on the downlink radio interface to perform AI / ML-driven beam failure detection and recovery operations. Depending on the model's complexity and purpose, the size of the AI / ML model can range from small (e.g., one kilobyte or less) to large (e.g., hundreds of megabytes).

[0104] Artificial intelligence machine learning models can be delivered to user equipment via control channels or control channel resources, which can be scheduled control channel resources. However, existing control channel signaling has traditionally been designed with coding schemes or formats that carry small amounts of control information at low coding rates, resulting in high reliability for the delivery of control channel information. Since conventional control channel signaling messages tend to include small amounts of information, the slow rate associated with low coding rates is an acceptable trade-off for high reliability, prompting the delivery of control channel information required for user equipment operations. Compared to conventional control channel information messages, AI / ML model information can include a large amount of information or data, and sending AI / ML models or AI / ML model updates according to conventional control channel coding schemes or formats can lead to decreased spectral efficiency (e.g., control channel resources carrying AI / ML model information may "take over" a large portion of bandwidth available for other uses, and may result in a scarcity of resources that could otherwise be allocated or scheduled for data transmission). Other problems that may arise from using control channel signaling messages sent according to conventional highly reliable control channel schemes due to the large AI / ML model control payload compared to typical control channel information sizes may include increased control channel decoding latency. The size of AI / ML models can overwhelm the capacity of control channels, thereby "squeezing out" the use of control channel resources for important non-AI / ML model control information.

[0105] Accordingly, the embodiments disclosed herein can be applied to RAN nodes or user equipment to enable novel transmission / encoding techniques or novel reception / decoding techniques, respectively, that facilitate the efficient delivery of large AI / ML model information via control channel resources. In a two-level Radio Resource Control (RRC) signaling embodiment, the first RRC signaling portion can carry non-AI-critical control information, and one or more second RRC signaling portions can be dynamically scheduled and can transmit larger AI / ML model payloads. Although the two RRC signaling portions are transmitted as parts of a single aggregated signal via the same RRC channel resources, the two RRC signaling portions can be independently encoded and segmented at the RAN node according to different encoding schemes or formats. Such aggregated or multi-level control channel signaling messages enable user equipment to independently decode each control signaling portion, and the decoding or decoding failure of one portion does not affect the decoding performance of the other portion. Therefore, for critical non-AI / ML control information, control channel reliability and latency performance are maintained even when a large amount of AI / ML model data is transmitted on the same control channel but in different portions. Instead of making the delivery of control channel information less reliable and taking longer to decode due to sending large AI / ML models or model updates, by using a two- or multi-level RRC signaling message design as disclosed herein with independent partial decoding and segmentation, decoding errors or increased control channel decoding delays corresponding to the decoding of the RRC control channel signaling message portion carrying AI / ML model information will not be propagated to the RRC control channel signaling message portion carrying critical non-AI / ML control information.

[0106] In addition to two- or multi-level control channel signaling messages, the embodiments disclosed herein also facilitate the retransmission of RRC portions of RRC control channel signaling messages. Unlike existing RRC procedures that can retransmit the entire RRC payload in the event of RRC signaling decoding failure, the embodiments disclosed herein facilitate partial RRC signaling retransmission by configuring the user equipment to report portions or segments of the RRC control channel signaling message that have not been successfully received or decoded, or to request retransmission of such erroneously received portions or segments. Therefore, for RRC control channel signaling message portions carrying AI / ML model segments that failed to be decoded by the user equipment, only the undecoded segments can be retransmitted, rather than the entire AI / ML model. Unlike existing semi-static RRC signaling update procedures, the embodiments disclosed herein can include dynamic user equipment actions and RRC signaling to facilitate the decoding of multiple portions or multi-level RRC control signaling messages.

[0107] In a sense, existing control channel resources are traditionally used to carry the minimum possible amount of control information to provide control information to user equipment. Various existing control signaling processes (including downlink control information (DCI) signaling and radio resource control signaling) are semi-static. 5G control channels are small to maximize stringent latency and reliability requirements—without reliable and fast control channel transmission, reception, and decoding, acceptable performance for applications with stringent performance requirements (such as URLLC / XR use cases) may not be achievable, fulfilled, or otherwise met. Strict control channel radio performance targets make traditional control channel signaling message capacity inefficient due to, for example, the low coding rate corresponding to high reliability (e.g., the lower the coding rate, the more likely the user equipment is to decode control channel signaling messages, even if the radio link with the RAN is subjected to network congestion or interference). Consequently, transmitting a small amount of control information on a highly reliable and fast control channel uses a large amount of resources (e.g., a large amount of bandwidth) relative to the amount of information conveyed by the control messages.

[0108] Transmitting or delivering AI / ML model information from radio access network nodes to user equipment devices via control channel signaling on the radio control channel can lead to large AI / ML models consuming or overwhelming the overall radio resource capacity of the network, resulting in significantly fewer resources available for useful data transmission, such as supporting certain applications. (From the network operator's perspective, consuming resources that could be used for data transmission via control channel messaging is also undesirable, as the operator may only be able to charge users for data transmission, not for the transmission of control channel information.) Furthermore, because control channels are generally designed to carry as little information as possible, transmitting large AI / ML model information on existing control channels, and potentially taking over large amounts of available radio network frequency and timing resources, typically leads to spectral efficiency in terms of radio resource utilization. Another disadvantage of using control channel messages to transmit AI / ML model information is that essential control information critical to the operation of user equipment may be blocked or squeezed out of delivery by large blocks of AI / ML model information transmitted on the same control channel signaling.

[0109] Traditionally, control channel messages are discarded if they are not successfully decoded, and unlike data transmission, there are no provisions for retransmission or acknowledgment feedback. This lack of provisions for control channel message retransmission is partly due to the fact that traditional control channels are conservative in terms of coding rate (e.g., conservative for low coding rates), highly reliable, and typically used to carry small amounts of control information. Because of the low coding rate and small message size, traditional control channel messages are usually successfully decoded, or if unsuccessful, partial retransmission is unnecessary, as the small size of the control information means that retransmitting the complete control payload will not significantly impact network resource usage.

[0110] However, as AI / ML model information (especially large amounts, such as megabytes) is transmitted in the same format used for transmitting important non-AI-related control signaling, the delivery of conventional control channel information can degrade drastically in terms of reliability and latency (transmitting larger blocks of information incurs significant reception and decoding time). Therefore, for example, when a user equipment experiences poor RF conditions and the control channel configured for the user equipment cannot be successfully received, the entire control signaling block (including both AI-related and non-AI-related information, if transmitted according to conventional coding rates and formats) may be retransmitted, thus extending the delay before the user equipment can receive and decode the important control information. In handover scenarios (e.g., a UE moving from optimal service by a first RAN to nested service by a second RAN), if non-AI-related control information regarding the handover is delayed due to longer control signaling transmission delays caused by control channel transmission of the payload corresponding to the AI ​​model information, the UE may exhibit radio failure.

[0111] Accordingly, the embodiments disclosed herein separate existing critical control information from AI / ML model-related information and apply different encoding schemes, profiles, or formats to different types of information, even if control information and AI / ML information may be contained in a single scheduled control channel message timing or transport block. The embodiments disclosed herein facilitate the efficient transmission of AI / ML model information as part of RRC signaling without affecting the radio performance or delivery corresponding to critical control channel information.

[0112] Now go to Figure 2 , Figure 2The system 200, including RAN node 105, communicates with user equipment 115 via radio link 125. UE 115 can perform various radio functions 205A-205n, which can be prompted by corresponding machine learning models 215A-215n. During UE 115 radio operation and communication with RAN 105, UE can send parameter measurement reports 220A-220n, which may include one or more learning model parameter measurements corresponding to 215A-215n respectively. Reports 220A-220n may include one or more control action requests, such as requesting deactivation or retraining of one or more of models 215A-215n. RAN 105 can send radio resource control messages 225 to UE 115 corresponding to learning model information 215.

[0113] AI / ML learning models (such as those deployed at UE device 115) Figure 2 Model 215 shown may be implementation-specific (e.g., a vendor-proprietary learning model). (Examples of vendors that can provide proprietary learning models may include user equipment manufacturers or providers of applications for user equipment, network equipment providers or providers of applications for network equipment, or mobile network operators or providers of applications for mobile network operators.) The network RAN ​​may determine the overall performance of the learning model deployed at the UE to meet minimum device performance requirements. As disclosed herein, a dynamic reporting process may prompt the user equipment device to compile and report indications reflecting or indicating the model performance of the corresponding learning model, which may be configured or pre-configured.

[0114] A specific user equipment (UE) device can employ several different AI / ML learning model implementations to operate, perform, or otherwise enable different radio functions. Different learning model parameter metrics can indicate the performance of different learning models. The UE can compile and report one or more different learning model performance indicator parameter metrics or indications for each learning model. Different learning model metrics can be associated with their respective different filtering or time resolution configurations. Therefore, such customized metric reporting for a given learning model can facilitate optimized tracking and reporting for each active learning model for each UE device 115 served by RAN 105, such as... Figure 1 or Figure 2As shown. Accordingly, network RAN ​​105 can obtain and use the real-time performance of each learning model active at UE 115 to optimize the performance of the learning model and the inferences it may generate. Furthermore, several reporting variants can be customized to suit various AI / ML learning model implementations or purposes, such as precisely absolute, precisely relative, quantitative, or time-based (e.g., historical) metric reports. Network node RAN 105 can dynamically trade AI / ML learning model reporting overhead for accuracy in obtaining AI / ML model performance metrics.

[0115] For the performance of AI / ML learning models, depending on the nature of the problem being solved and the corresponding learning model function (e.g., regression or classification), or the radio function performed or prompted by the learning model, various parameters and corresponding metrics can be considered, analyzed, or evaluated. For example, for radio functions (such as channel estimation or channel state information (CSI) compression), regression functions can be used in the learning model to potentially evaluate the following parameters or corresponding metrics: mean squared error (MSE); root mean square error (RMSE); normalized mean squared error (NMSE); absolute mean error (MAE); R-squared; generalized cosine similarity (GCS); or squared generalized cosine similarity (SGCS). Table 1 shows example functions defining the corresponding learning model parameters, and the metrics corresponding to the corresponding learning model parameters can be monitored and evaluated as listed above. Table 1

[0116] For classification problems (such as beam index prediction), accuracy metrics can be analyzed to determine the performance of the learning model that is driving beam index prediction. Other example learning model parameter metrics that can indicate the performance of a learning model solving a classification problem may include, but are not limited to: the absolute number of true negatives, true positives, false negatives, and false positives; precision and recall; or the F1 score. The F1 score can include an evaluation metric used to characterize the performance of a machine learning model or classifier and provide combined information about the precision and recall of the learning model. A high F1 score typically indicates high values ​​for both recall and precision metrics.

[0117] The implementation of AI / ML learning models at different devices can be vendor-specific as described above and can be transparent to network nodes (e.g., the RAN serving the UE may not have access to the specific functionality and programming of a given learning model deployed in the UE that enables radio functions). To manage and enable UE devices to achieve performance targets, RAN nodes can be made aware of the capabilities of the UE devices and the overall performance of the AI / ML learning models. Therefore, when an active UE device first connects to the serving network RAN, it can send device-specific AI / ML capability information including the following information elements (IEs): the types of algorithms supported by AI / ML, including supervised learning, unsupervised learning, and reinforcement learning; a list of radio functions supported by AI / ML; a list of supported AI / ML model-specific metrics to be estimated and reported; the model library size for each radio function, e.g., the number of models that can be stored for each radio function; or an indication of model classification (small / medium / large), which can prompt the network RAN ​​to define or determine the dataset to be used by the learning model. For example, for a large number of neurons (e.g., nodes in a neural network of the learning model), determining a commensurate number of information samples can be used to avoid overfitting of the learning model. AI / ML capability information elements can be part of device capability signaling based on subsequent Radio Resource Control (RRC) signaling or on dynamically scheduled uplink control information (UCI) transmissions. Accordingly, the network RAN ​​can determine updates to one or more learning models and deliver the updated models or their corresponding coefficients to the user equipment.

[0118] Delivery of dynamic AI models on the radio control plane.

[0119] In one embodiment, two- or more (may be more than two) levels of RRC signaling messages may include two or more phases, shares, portions, subsets, or other divisions. These phases, shares, portions, subsets, or other divisions are independently encoded and segmented, and therefore treated differently depending on the content of each level / part. In one embodiment, segment-aware retransmission may be implemented only for sub-RRC signaling subsets or portions carrying AI / ML or other data, such that the RAN avoids retransmitting the entire AI / ML model data when the user equipment fails to receive a single segment or a portion containing fewer segments than the entire portion.

[0120] On the network / RAN node side.

[0121] In one embodiment, a basic radio resource control (RRC) signaling message can be transmitted as a two-level message comprising a main part and a secondary part. The main RRC signaling part may include conventional critical control information (e.g., for handover, random access, measurement, etc.) and may be encoded according to the main format, while the secondary RRC signaling part may be encoded according to the secondary format and may include an AI / ML model payload. It will be understood that other data besides AI / ML information or different from AI / ML information may also be transmitted in the secondary RRC signaling message part. In addition to the secondary part, the RRC signaling message may also include a third part or additional part encoded according to the secondary format or according to different third, fourth, etc., encoding and transmission formats. Large AI / ML model information may be separated from basic non-AI control information and may be treated differently depending on the RF conditions corresponding to the user equipment receiving the RRC signaling message or depending on the size of the AI / ML model information. To enable user equipment (UE) to receive and decode different portions of RRC control signaling messages based on different encoding parameters or other format parameters, the RAN can transmit novel RRC format indicators as part of an RRC signaling message that signals to the receiving UE to indicate the RRC procedure identifier associated with the main or one or more secondary, tertiary, etc., RRC signaling message portions. Based on the information contained in the format indicator, UE can adaptively receive and decode various AI and non-AI control information from the same control signaling message using different radio configurations. For basic and small control configurations of the main RRC signaling message format, retransmission of segments corresponding to the main portion may not be necessary in the event of decoding failure. However, for large AI / ML model information transmitted in secondary RRC message portions, partial retransmission can be enabled if indicated by the format indicator, to enable reception of large AI / ML control information at a rate that may correspond to a lower reliability than the rate available for transmitting control information.

[0122] In one embodiment, when the control information and AI / ML model information payloads are segmented into multiple parts (segmentation can be used to facilitate the transmission of large models), retransmitting only segments that may have been lost or incorrectly received by the user equipment can enhance control channel capacity. Furthermore, since there are often large differences between existing control information and AI / ML model payloads, different segmentation steps or different segment sizes can be used in the embodiments disclosed herein. For example, control channel information in the main RRC signaling message portion can be transmitted using segments of a first size, and AI / ML information can be transmitted using segments of a second size.

[0123] On the equipment side.

[0124] To prevent the transmission of AI / ML model payload RRC signaling from interfering with the transmission of control information to the UE, independent segmentation and decoding can be supported at the UE, ensuring that the failure to decode one RRC signaling portion or its segment does not significantly affect another part of the RRC signaling. Such independent transmission cannot be achieved using a single encoding and segmentation, where the UE must correctly receive all segments of the RRC signaling message to decode it. With large AI / ML model data transmitted as part of the RRC signaling message, it is highly likely that at least one segment will not be successfully decoded, rendering the entire received RRC information useless if conventional single encoding were used. Using the embodiments described herein, each of the main RRC signaling portion and one or more secondary RRC signaling portions can be independently segmented and encoded, enabling the UE to rapidly decode the critical non-AI control information of the main RRC upon full reception, without waiting for full reception of all segments corresponding to much larger (multiple) secondary RRC signaling portions. Therefore, even under poor RF conditions where one or more segments of the subpart of an RRC message carrying AI / ML model information segments are not received error-free, the user equipment device can still receive and decode important non-AI control information and avoid the effective blocking of control information reception.

[0125] Unlike existing RRC and general control channel procedures, the embodiments disclosed herein can also prompt a UE device to request the retransmission of one or more segments of a secondary RRC signaling message carrying an AI / ML model if decoding is unsuccessful, wherein, according to existing RRC and general control channel procedures, if the payload is not successfully received, the entire RRC signaling is retransmitted.

[0126] like Figure 3As shown, a network / RAN node sends a Radio Resource Control (RRC) signaling message 300 to a user equipment (UE) with AI / ML capabilities. The RRC signaling message 300 is shown divided into two parts—a primary RRC signaling part 315 and a secondary RRC signaling part 325. Each of the primary part 315 and the secondary part 325 can be encoded and segmented independently or differently from each other. The RRC signaling primary part 315 and the secondary part 325 may include segments with overlapping segment numbers. (Overlapping means that the segment number of the secondary part 325 can be the same as the starting segment after the primary RRC segment has been received and decoded, or it can be reset to start with the same number as that starting segment.) Furthermore, the segment size of each RRC primary part 315 and RRC secondary part 325 can be different. In one embodiment, the segmentation of the primary part 315 or the secondary part 325 can be proportional to the total size of the information carried by the respective part. User equipment receiving RRC signaling message 300 can attempt to decode the main part 315 and the secondary part 325 independently without the failure of decoding one part affecting the decoding of the other part.

[0127] To facilitate independent decoding, the RAN node sending the RRC signaling message 300 can add a new Radio Resource Control (RRC) signaling message format indication 310 to the RRC signaling information. The RRC signaling message format indication 310 can indicate an RRC procedure identifier to the user equipment (UE), which can indicate either a main section 315 or a secondary RRC signaling section 325. The main format indication 310 can prompt the UE to determine how to receive and decode the subsequent or secondary section 325. The format indication 310 can indicate the decoding rate to be used for decoding section 315. The format indication 310 can also indicate a format indication 320. The format indication 310 can indicate the number of segments and the corresponding segment sizes for section 315. The format indication 320 can also indicate the number of segments and the corresponding segment sizes for section 325. In the event that the user equipment fails to decode a segment, and with RRC retransmission enabled, the user equipment may refer to instruction 310 or instruction 320 to determine the segment and RRC procedure identifier, and report a specific retransmission request to the RAN node (in one example, the user equipment may request a retransmission of segment 2 of sub-RRC part 325).

[0128] In the example embodiment, Figure 4AThe diagram illustrates the independent segmentation of each of the main RRC signaling portion 415 and the secondary RRC signaling portion 425 of RRC signaling message 405. This ability to implement independent segmentation allows RAN nodes to dynamically determine and configure different segment sizes and numbers for segments 415-0, ..., 415-n of the main RRC signaling portion 415 or segments 425-0, ..., 425-n of the secondary RRC signaling portion 425. Therefore, large AI / ML model information embedded in the secondary portion 425 of RRC signaling message 405 can be segmented using larger segment sizes to reduce the number of segments while still maintaining reasonable AI / ML model reception reliability. Format indication 410 can indicate the number of segments 415-0, ..., 415-n of portion 415 and their corresponding segment sizes. Format indication 420 can indicate the number of segments 425-0, ..., 425-n of portion 425 and their corresponding segment sizes. Format instruction 410 can indicate the decoding rate to be used for decoding segments 415-0, ..., 415-n. Format instruction 420 can indicate the decoding rate to be used for decoding segments 425-0, ..., 425-n. Format instruction 410 can indicate the rate and resources used for decoding instruction 420. It will be understood that... Figure 3 The number of segments in the main part 315 or the secondary part 325 is indicated by indicators 310 or 320 respectively, or as shown in the figure. Figure 4A The number of segments of the main part 415 or the secondary part 425 is indicated by indicators 410 or 420 respectively, which can enable the user equipment to avoid blind decoding of the main part or the secondary part.

[0129] exist Figure 4B In the example embodiment shown, the first RRC signaling 435 may include conventional control channel information. RRC signaling 435 may include indication 440 to indicate a second RRC signaling 445. Indication 440 may include a Media Access Control (MAC) element (CE) for configuring and scheduling the user equipment to receive and decode the second RRC signaling 445, which may include large-scale AI / ML model information. Figure 4B The illustrated embodiment has the advantage of deviating less from traditional standardization processes. For example, adding a standard MAC CE as an indication 440 to RRC signaling 435 configured and transmitted according to existing RRC signaling prompts both the first signaling 435 and the second signaling 445 to be decoded according to the format configured for RRC signaling, without using format indications such as in Figure 4A The format indication 410 or format indication 420 is shown and described with reference to Figure 4. Figure 4BThe illustrated embodiment can be adapted for use with user equipment reporting fairly satisfactory received signal strength / coverage from the serving RAN / cell, making the probability of decoding the first RRC signaling 435 very high. As an example, in Figure 4B If the first RRC signaling 435 shown is not successfully received or decoded by the user equipment, the user equipment will not be able to recognize the existence of the second RRC signaling 445, which is scheduled to carry AI / ML model information. Therefore, when the signal strength at the user equipment is poor, Figure 4B The illustrated embodiment may not be suitable, although it does not use novel format indications corresponding to multiple parts, thus imposing a minor deviation from current radio access network deployments.

[0130] Now go to Figure 5A As mentioned earlier, control signaling traditionally does not support retransmission due to the small size and high reliability of the control information associated with transmission. However, partial retransmission of control channel information can be beneficial by utilizing AI / ML model information transmitted via control channel resources. Therefore, as... Figure 5A As shown in embodiment 505, as part of the multi-level RRC signaling embodiment disclosed herein, the RAN node may add a retransmission request indication 514. The retransmission indication 514 may include one or more bits added to a format indication 512, which indicates a format corresponding to the main portion 515. Similarly, the RAN node may add one or more retransmission request indication bits 524 to a format indication 522, which may correspond to one or more segments of a sub-portion 525.

[0131] A RAN node can selectively activate control information retransmission for one RRC signaling portion or share, while using a conventional control channel transmission configuration (e.g., control channel retransmission is not supported) for another RRC portion or share. For example, if a secondary RRC signaling portion includes a large AI / ML model, the user equipment may not decode the secondary RRC signaling portion if a single segment is dropped or not successfully decoded. Sending the complete secondary RRC portion 525 carrying the entire AI / ML model may be inefficient. Therefore, enabling retransmission can prompt the user equipment to report only one or more segment identifiers corresponding to one or more segments of the secondary RRC signaling portion to be retransmitted.

[0132] like Figure 5B and Figure 5C As shown, example embodiments 545 and 575 can respectively prompt the user equipment to decode and report partial reception status of the RRC control channel. Figure 5BAs shown, the independent encoding and segmentation of the main RRC message 515 and the secondary RRC message 525 enable the user equipment to independently decode each RRC signaling part based on the information contained in format indications 512 or 522. Therefore, the decoding failure of one RRC signaling part does not negatively affect the decoding performance of another RRC signaling part. For example, if the user equipment successfully decodes the control information contained in part 515 according to the format information indicated by indication 5512 (to be used for decoding segments 515-0, ..., 515-n), the user equipment can continue operating using the control information contained in part 515 while simultaneously decoding segments 525-0, ..., 525-n of part 525 according to the format indicated in indication 522. Furthermore, since the decoding of control information is overwhelmed by the large AI model payload, the reception latency problem of blocking or increasing the decoding of important non-AI control information is avoided.

[0133] like Figure 5C As shown, an RRC segment-aware retransmission embodiment 575 is illustrated. Depending on whether retransmission is enabled for any part, or depending on the decoding conditions corresponding to the part of the transmission that may not have been successfully completed, the user equipment can determine and send separate and independent control channel retransmission requests for each RRC signaling main part 510 and RRC signaling sub-part 520. Therefore, the user equipment can receive important non-AI control information via retransmission based on retransmission indication 514 without waiting for decoding or retransmission and decoding of information contained in sub-part 520. The user equipment can send an RRC retransmission request to request retransmission of RRC signaling main part segments 515-0, ..., 515-n, while still receiving large sub-RRC signaling parts 525, thereby prompting operation based on the control information contained in the main part 510 without waiting for reception and decoding of sub-part 520.

[0134] Figure 6The diagram illustrates an environment 600 where UE 115 moves out of coverage area 610 corresponding to RAN 105A and into coverage area 615 corresponding to RAN 105B in direction 605, while receiving updated AI / ML information 225. As shown, the first segment 225-1 (indicated by the shaded block of AI / ML model information 225) was delivered to UE 115 before the UE moved out of range 610. UE 115 can send a retransmission request 620 to RAN 105A to request the retransmission of the unreceived segment 225-2. Accordingly, since UE 115 has switched from being served by RAN 105A to being served by RAN 105B, RAN 105A can send segment 225-2 to RAN 105B via the backhaul link in response to receiving request 620. Then, RAN 105B can send segment 225-2 to UE 115 via radio link 125, wherein RAN 105B serves UE 115 better than RAN 105A (e.g., the signal received from RAN 105B is stronger than the signal received from RAN 105A).

[0135] Now go to Figure 7 , Figure 7 An example RRC retransmission request 700 sent by a user equipment (UE) with AI / ML capabilities to a serving RAN node is shown. In the event of a failure to decode a primary or secondary RRC segment, the UE can send a retransmission request 700, which may include: an RRC master procedure identifier 715 indicating the primary part of the RRC signaling; an RRC segment identifier 720 indicating the segment to be retransmitted corresponding to the primary part indicated by identifier 715; an RRC secondary procedure identifier 725 indicating the secondary part of the RRC signaling; or an RRC segment identifier 730 indicating the segment to be retransmitted corresponding to the secondary part indicated by identifier 725.

[0136] Now go to Figure 8 , Figure 8 A timing diagram of example method 800 is shown. In action 805, RAN node 105 can obtain data from the core network (such as...) Figure 1 The core network 130 shown receives the coverage threshold and the maximum allowed time period for sending Radio Resource Control (RRC) signaling carrying AI model information. Upon receiving a coverage level report sent by WTRU / UE 115 in action 810, indicating that the coverage at WTRU / UE meets the configured coverage threshold within the indicated time period, RAN node 105 sends the AI ​​model information as part of the RRC reconfiguration signaling in action 815.

[0137] However, if RAN node 105 receives a coverage level report from WTRU / UE 115, indicating a violation of the configured coverage threshold within the indicated time period, or indicating that the configured coverage threshold is met but not within the configured time period, RAN node 105 can use two independent segmentation procedures to segment the RRC message. One segmentation procedure is used for the main RRC signaling portion, and the other segmentation procedure is used for AI model delivery via the secondary RRC signaling portion. In action 825, RAN 105 can generate a multi-bit segmentation-aware RRC reconfiguration format indication indicating the RRC segmentation procedure identifier, and in action 830, this RRC reconfiguration format indication can be sent together with the RRC reconfiguration signaling. In action 835, upon detecting or receiving from WTRU / UE 115 a link failure indication or coverage level report triggering inter-RAN node handover, RAN 105 may prioritize retransmission (via radio interface) or forwarding (via backhaul link) of the remaining RRC segments corresponding to the main RRC procedure identifier sent in action 830, relative to the transmission of the sub-AI model RRC procedure. In action 840, RAN node 105 may retransmit (via radio interface link) or forward (via backhaul link) to another RAN the remaining RRC segments or untransmitted RRC segments corresponding to the main RRC procedure indicated in action 830 (e.g., segments that were not successfully transmitted to WTRU / UE 115 or not successfully received by WTRU / UE 115 before receiving the link failure or poor coverage report in action 835). Upon completion of the retransmission (via radio interface) or forwarding (via backhaul link) of the remaining RRC reconfiguration segment corresponding to the primary RRC procedure indicated in action 830, in action 845, RAN node 105 may send (via radio interface) or forward (via backhaul link) to another RAN node the remaining RRC reconfiguration segment (e.g., including segments of the AI / ML model) corresponding to the secondary RRC procedure to be sent by the other RAN node to WTRU / UE 115.

[0138] Now go to Figure 9 , Figure 9A timing diagram of method 900 is shown. In action 905, the AI-capable WTRU / UE 115 can receive and decode RRC reconfiguration signaling from the serving RAN node 105. The RRC signaling may include one or more format indications and one or more RRC segment-aware retransmission enable indications. In action 910, the WTRU / UE 115 can determine the RRC format indication received in action 905 via signaling. Upon receiving the RRC signaling main part format indication, the WTRU / UE 115 can receive the RRC signaling segment corresponding to the indicated main RRC procedure / part in action 915. In action 920, the WTRU / UE 115 can combine and decode the RRC segments indicated in the format indication as corresponding to the main RRC procedure / part. The UE 115 can combine / aggregate the received segments corresponding to the main procedure / part or secondary procedure / part for decoding that procedure / part, and if the user equipment receives all segments corresponding to that procedure / part, the user equipment can decode that procedure / part. In the context of RRC segmented retransmission, UE 115 may fail to receive one or more segments corresponding to the main procedure / part or sub-procedure / part. UE 115 may send a negative acknowledgment to RAN 105 indicating that the segment was not correctly decoded. However, UE 115 may not "discard" the received failed segments, as a segment that was not correctly decoded may still contain useful information energy, even if the segment was not correctly decoded. Therefore, UE 115 can combine or superimpose the first failed transmission of a segment with a second retransmission or another additional retransmission of that segment, so that different transmissions of the same segment add energy to each other, thereby enhancing decoding capability due to the combined gain.

[0139] If decoding of an RRC segment corresponding to the indicated main RRC procedure fails, and if an RRC main part retransmission enable instruction has been received, in action 925, WTRU / UE 115 may send an RRC segment retransmission request to RAN 105, indicating the main RRC procedure identifier and the segment identifier indicating the main part segment to be retransmitted. In action 930, if the AI ​​model is delivered using the secondary RRC procedure indicated by the format instruction, WTRU / UE 115 may receive the RRC signaling segment corresponding to the secondary RRC part. In action 935, WTRU / UE 115 may combine and decode the RRC segments corresponding to the secondary RRC part. If decoding of an RRC segment corresponding to the secondary RRC procedure fails, and if an RRC secondary part retransmission instruction has been received, WTRU / UE 115 may send an RRC segment retransmission request in action 940, indicating the secondary RRC procedure identifier and the segment identifier corresponding to one or more secondary part segments to be retransmitted.

[0140] Now go to Figure 10 , Figure 10 A flowchart of example method 1000 is shown. Method 1000 begins with action 1005. In action 1010, the radio access network node can access the network from the core network (e.g., Figure 1 The core network 130 shown receives configuration information transmitted through the control channel. Figure 10 In action 1015, the user equipment can send a coverage report to a radio access network node, reporting the coverage or signal strength corresponding to the radio access network node and measured by the user equipment. The configuration received in action 1010 may include coverage criteria or coverage thresholds. If the signal strength reported by the user equipment in action 1015 meets the coverage or signal strength criteria, for example, if the reported signal strength is greater than the criterion threshold, then method 1000 proceeds to action 1025, and can transmit artificial intelligence machine learning information in the control channel resources using a reliable coding rate before proceeding to action 1080 and terminating.

[0141] Returning to the description of action 1020, if the criteria received in the configuration of action 1010 are not met, method 1000 proceeds to action 1030. In action 1030, the radio access network node may transmit control information in the main part of a radio resource control signaling message encoded at a first rate, and transmit artificial intelligence machine learning information in the secondary part of a radio resource control signaling message encoded at a second rate. In action 1035, the user equipment may receive the radio resource control signaling message having the main part and the secondary part. The radio resource control signaling message may include one or more format indicators, which may indicate the main format and the secondary format that can be applied respectively to the reception and decoding of the main part and the secondary part of the radio resource control signaling message.

[0142] In action 1040, the user equipment may decode or attempt to decode the control information received in the radio resource control signaling message transmitted in action 1030 according to the coding rate or decoding rate indicated in the primary format indication described in reference 1035. In action 1045, the user equipment may determine whether the control information decoded in action 1040 was successfully decoded. If the control information was successfully decoded, method 1000 proceeds to action 1050. In action 1050, the user equipment may decode or attempt to decode the artificial intelligence machine learning information according to the second coding rate or second decoding rate indicated by the secondary format indication received in the RC signaling message in 1035. In action 1055, the user equipment may determine whether the artificial intelligence machine learning information was successfully decoded. If the artificial intelligence machine learning information was successfully decoded, method 1000 proceeds to action 1080 and terminates.

[0143] Returning to the description of action 1045, if it is determined that the control information was not successfully decoded, method 1000 proceeds to action 1060. In action 1060, based on a retransmission instruction that can be part of the radio resource control signaling message received in action 1035, the user equipment may request retransmission of one or more segments of the main part of the radio resource control signaling message received in action 1035. In action 1065, the radio access network node may receive the retransmission request sent to it in 1060 and may determine whether the user equipment is moving, for example, moving out of the coverage corresponding to the radio access network node and moving into the coverage of a different radio access network node that can provide better communication services to the user equipment. The radio access network node may determine in action 1065 that the user equipment is moving, and method 1000 proceeds to action 1070.

[0144] In action 1070, a radio access network node can receive from the user equipment (UE) an indication of a pending or pending-decoding segment of the main or secondary portion of the radio resource control signaling message received in action 1035, and the RAN can transmit the pending / decoding segment to a different radio access network node that can provide better communication services to the UE via a radio link or backhaul link. If a radio access network node has already transmitted the pending (UE) portion of the radio resource control signaling message received in action 1035 to a different radio access network node, then the different or new radio access network node becomes the serving radio access network node serving the UE, and can transmit the pending main or secondary portion of the radio resource signaling message in action 1030, and method 1000 continues with respect to the retransmissions described above.

[0145] Returning to the description of action 1065, if it is determined that the user equipment is not moving out of the coverage of the radio access node and into better coverage corresponding to a different radio access network node, the radio access node returns to action 1030 and retransmits one or more segments of the main or secondary part of the radio resource control signaling message previously transmitted in action 1030 that are still pending reception.

[0146] Returning to the description of action 1055, if the user equipment determines that the AI ​​machine learning information decoded in action 1050 was not fully decoded, or was not successfully decoded without errors, then method 1000 proceeds to action 1075, and the user equipment requests retransmission of one or more segments corresponding to the AI ​​machine learning information that was not received or correctly decoded. Method 1000 proceeds from action 1075 to action 1065, and continues as previously described.

[0147] Now go to Figure 11 ,Figure 11 An example embodiment of method 1100 is illustrated, comprising: at block 1105, a radio access network node including a processor transmitting control channel information corresponding to a communication session between the radio access network node and the user equipment according to a first encoding format to a user equipment; at block 1110, the radio access network node transmitting artificial intelligence model information to the user equipment according to a second encoding format; at block 1115, wherein the first encoding format and the second encoding format are different; at block 1120, wherein the control channel information and the artificial intelligence model information are transmitted in a radio resource control signaling message; and at block 1125, wherein the control channel information is transmitted via a main portion of the radio resource control signaling message, and wherein the artificial intelligence model information is transmitted at least via a sub-portion of the radio resource control signaling message.

[0148] Now go to Figure 12 , Figure 12 A radio access network node 1200 is shown, comprising a processor configured to: in block 1205, train a radio function artificial intelligence learning model to generate a trained radio function artificial intelligence learning model; in block 1210, transmit control channel information corresponding to a communication session between the radio access network node and the user equipment via the main portion of a radio resource control signaling message; and in block 1215, transmit the trained radio function artificial intelligence learning model to the user equipment via the secondary portion of a radio resource control signaling message.

[0149] Now go to Figure 13 , Figure 13 A non-transitory machine-readable medium 1300 is shown, comprising executable instructions that, when executed by a processor of a radio access network node, cause the execution of operations including: at block 1305, receiving from at least one user equipment at least one radio performance metric corresponding to at least one radio performance parameter; at block 1310, training a radio function learning model using the at least one radio performance metric to generate an updated radio function learning model to be used by the at least one user equipment; at block 1315, transmitting control channel information to the at least one user equipment via a first portion of a radio resource control signaling message according to a first control channel coding scheme; at block 1320, transmitting the updated radio function learning model to the at least one user equipment via a second portion of a radio resource control signaling message according to a second control channel coding scheme; and at block 1325, wherein the first control channel coding scheme and the second control channel coding scheme are different.

[0150] Now go to Figure 14 , Figure 14An example embodiment of method 1400 is illustrated, comprising: at block 1405, a user equipment including a processor receiving a control channel message including control channel information and artificial intelligence model information from a radio access network node; at block 1410, the user equipment decoding the control channel information according to a first decoding format corresponding to a first encoding format to generate decoded control channel information; at block 1415, the user equipment decoding the artificial intelligence model information according to a second decoding format corresponding to a second encoding format to generate decoded artificial intelligence model information; at block 1420, the user equipment updating a trained artificial intelligence learning model based on the artificial intelligence model information to generate an updated trained artificial intelligence learning model; at block 1425, the user equipment operating radio functions according to the updated trained artificial intelligence learning model; and at block 1430, wherein the control channel information and the artificial intelligence model information are received from the radio access network node in a radio resource control signaling message.

[0151] Now go to Figure 15 , Figure 15 An example user equipment 1500 is shown, including a processor configured to: in block 1505, determine a radio performance parameter metric corresponding to the operation of the user equipment relative to a radio access network node, to generate the determined radio performance parameter metric; in block 1510, send the determined radio performance parameter metric to the radio access network node for use in training a radio function artificial intelligence learning model, to generate a trained radio function artificial intelligence learning model; in block 1515, receive control channel information corresponding to the operation of the user equipment relative to the radio access network node from the radio access network node via a main portion of a radio resource control signaling message; in block 1520, receive the trained radio function artificial intelligence learning model from the radio access network node via a secondary portion of a radio resource control signaling message; and in block 1525, wherein the main portion of the radio resource control signaling message is received according to a first decoding scheme corresponding to a first rate, wherein the secondary portion is received according to a second decoding scheme corresponding to a second rate, and wherein the first rate is lower than the second rate.

[0152] Now go to Figure 16 , Figure 16A non-transitory machine-readable medium 1600 is shown, comprising executable instructions that, when executed by a processor of a user equipment, cause the execution of operations including: in block 1605, receiving control channel information from a radio access network node via a first portion of a radio resource control signaling message; in block 1610, decoding the first portion of the radio resource control signaling message according to a first decoding rate; in block 1615, receiving an updated radio function learning model from the radio access network node via a second portion of the radio resource control signaling message, wherein the updated radio function learning model includes updated learning model information based on operations of at least one of a group of user equipments, including the user equipment; in block 1620, according to a second decoding rate... The code rate is used to decode the second part of the radio resource control signaling message to generate a decoded updated radio function learning model; in block 1625, a first part indication including one or more first segment identifiers of a first group is received from a radio access network node, the first group of one or more first segment identifiers indicating one or more segments corresponding to the second group of the first part of the radio resource control signaling message; in block 1630, a second part indication including one or more second segment identifiers of a third group is received from a radio access network node, the third group of one or more second segment identifiers indicating one or more segments corresponding to the fourth group of the second part of the radio resource control signaling message; and in block 1635, the first decoding rate and the second decoding rate are different.

[0153] To provide additional context for the various embodiments described herein, Figure 17 The following discussion is intended to provide a brief overview of a suitable computing environment 1700 in which various embodiments of the embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in combination with other program modules and / or as a combination of hardware and software.

[0154] Generally speaking, program modules include routines, programs, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will understand that the method can be practiced using other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, IoT devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, each of which can be operatively coupled to one or more associated devices.

[0155] The embodiments illustrated herein can also be practiced in a distributed computing environment, where certain tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can reside either in local or remote memory storage devices.

[0156] Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and / or communication media, these two terms being used interchangeably herein as follows. A computer-readable storage medium or a machine-readable storage medium can be any available storage medium accessible by a computer, and includes both volatile and non-volatile media, as well as removable and non-removable media. By way of example and not limitation, a computer-readable storage medium or a machine-readable storage medium can be implemented in conjunction with any method or technique used for storing information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0157] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-transitory media that can be used to store desired information. In this regard, when applied herein to storage, memory, or computer-readable media, the terms “tangible” or “non-transitory” should be understood to exclude only the propagation of a transient signal itself as a modifier, without waiving the rights to all standard storage, memory, or computer-readable media that do not only propagate transient signals themselves.

[0158] Computer-readable storage media can be accessed by one or more local or remote computing devices (e.g., via access requests, queries, or other data retrieval protocols) to perform various operations with respect to the information stored on the media.

[0159] Communication media typically embody computer-readable instructions, data structures, program modules, or other structured or unstructured data in data signals such as modulated data signals (e.g., carrier waves or other transmission mechanisms), and include any information delivery or transmission medium. The term "one or more modulated data signals" refers to signals whose one or more characteristics are set or altered in a manner that encodes information in one or more signals. By way of example and not limitation, communication media include wired media (such as wired networks or direct wired connections) and wireless media (such as acoustic media, RF media, infrared media, and other wireless media).

[0160] Refer again Figure 17 Example environment 1700 for implementing various embodiments of the aspects described herein includes a computer 1702, which includes a processing unit 1704, system memory 1706, and a system bus 1708. The system bus 1708 couples system components, including but not limited to system memory 1706, to the processing unit 1704. The processing unit 1704 can be any of a variety of commercial processors and may include cache memory. Dual microprocessors and other multiprocessor architectures may also be used as the processing unit 1704.

[0161] System bus 1708 can be any of several types of bus architectures, which can further interconnect to memory buses (with or without memory controllers), peripheral buses, and local buses using any of a variety of commercial bus architectures. System memory 1706 includes ROM 1710 and RAM 1712. The Basic Input / Output System (BIOS) can be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), or EEPROM, containing basic routines that facilitate the transfer of information between components within computer 1702, such as during startup. RAM 1712 may also include high-speed RAM, such as static RAM for caching data.

[0162] Computer 1702 also includes an internal hard disk drive (HDD) 1714 (e.g., EIDE, SATA), one or more external storage devices 1716 (e.g., floppy disk drive (FDD) 1716, memory stick or flash drive reader, memory card reader, etc.), and an optical disc drive 1720 (e.g., capable of reading from or writing to CD-ROMs, DVDs, BDs, etc.). Although the internal HDD 1714 is shown as residing within computer 1702, it can also be configured for external use within a suitable chassis (not shown). Additionally, although not shown in environment 1700, a solid-state drive (SSD) may be used in addition to, or in place of, HDD 1714. HDD 1714, external storage devices(s) 1716, and optical disc drive 1720 can be connected to system bus 1708 via HDD interface 1724, external storage interface 1726, and optical disc drive interface 1728, respectively. The interface 1724 for the external driver implementation may include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external driver connection technologies are also contemplated in the embodiments described herein.

[0163] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, etc. For computer 1702, the drive and storage medium are adapted to store any data in a suitable digital format. Although the above description of computer-readable storage media refers to various types of storage devices, those skilled in the art will understand that other types of computer-readable storage media (whether currently existing or developed in the future) may also be used in the example operating environment. Furthermore, any such storage medium may contain computer-executable instructions for performing the methods described herein.

[0164] Multiple program modules can be stored in the drive and RAM 1712, including the operating system 1730, one or more application programs 1732, other program modules 1734, and program data 1736. All or part of the operating system, applications, modules, and / or data can also be cached in RAM 1712. The systems and methods described herein can be implemented using various commercial operating systems or combinations of operating systems.

[0165] Computer 1702 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment used for operating system 1730, and the emulated hardware may optionally be different from... Figure 17 The hardware is shown. In such an embodiment, the operating system 1730 may include one of a plurality of virtual machines (VMs) hosted on the computer 1702. Further, the operating system 1730 may provide a runtime environment for the application 1732, such as the Java Runtime Environment or the .NET Framework. A runtime environment is a consistent execution environment that allows the application 1732 to run on any operating system that includes a runtime environment. Similarly, the operating system 1730 may support containers, and the application 1732 may be in the form of a container as a lightweight offline executable package, which includes, for example, code, runtime, system tools, system libraries, and settings for the application.

[0166] Furthermore, computer 1702 may include a security module, such as a Trusted Processing Module (TPM). For example, before loading the next boot component, the boot component uses the TPM to hash the next boot component over time and wait for the result to match a security value. This process can occur at any level of the computer 1702's code execution stack (e.g., applied to the application execution level or the operating system (OS) kernel level) to achieve security at any code execution level.

[0167] Users can input commands and information to computer 1702 through one or more wired / wireless input devices, such as keyboard 1738, touchscreen 1740, and pointing devices such as mouse 1742. Other input devices (not shown) may include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls or other remote controls, joysticks, virtual reality controllers and / or virtual reality headsets, game controllers, styluses, image input devices (e.g., cameras), gesture sensor input devices, visual motion sensor input devices, emotion or face detection devices, biometric input devices (e.g., fingerprint or iris scanners), etc. These and other input devices are typically connected to processing unit 1704 via input device interface 1744 (which may be coupled to system bus 1708), but may also be connected via other interfaces such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, Bluetooth® interfaces, etc.

[0168] The monitor 1746 or other types of display devices can also be connected to the system bus 1708 via an interface such as the video adapter 1748. In addition to the monitor 1746, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0169] Computer 1702 can operate in a networked environment via logical connections to one or more remote computers (such as remote computers 1750, etc.) via wired and / or wireless communications. Remote computers 1750 can be workstations, server computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer-to-peer devices, or other common network nodes, and typically include many or all of the elements described relative to computer 1702, although for the sake of brevity only memory / storage device 1752 is shown. The depicted logical connections include wired / wireless connections to a local area network (LAN) 1754 and / or a larger network (e.g., a wide area network (WAN) 1756). Such LAN and WAN networking environments are common in offices and companies and enable enterprise-wide computer networks (such as intranets), all of which can connect to global communication networks (e.g., the Internet).

[0170] When used in a LAN networking environment, computer 1702 can connect to local network 1754 via a wired and / or wireless communication network interface or adapter 1758. Adapter 1758 enables wired or wireless communication with LAN 1754, which may also include a wireless access point (AP) configured thereon for communicating with adapter 1758 in wireless mode.

[0171] When used in a WAN networking environment, computer 1702 may include modem 1760, or may be connected to a communication server on WAN 1756 via other means (such as via the Internet) for establishing communication on WAN 1756. Modem 1760 may be an internal or external wired or wireless device and may be connected to system bus 1708 via input device interface 1744. In a networking environment, program modules or portions thereof depicted with respect to computer 1702 may be stored in remote memory / storage device 1752. It will be understood that the network connection shown is an example, and other means of establishing communication links between computers may be used.

[0172] When used in a LAN or WAN networking environment, in addition to, or instead of, the external storage device 1716 described above, computer 1702 can also access cloud storage systems or other network-based storage systems. Generally, the connection between computer 1702 and the cloud storage system can be established, for example, on LAN 1754 or WAN 1756 via adapter 1758 or modem 1760, respectively. After connecting computer 1702 to the associated cloud storage system, external storage interface 1726 can manage the storage provided by the cloud storage system with the help of adapter 1758 and / or modem 1760, just as it manages other types of external storage. For example, external storage interface 1726 can be configured to provide access to cloud storage sources as if these sources were physically connected to computer 1702.

[0173] Computer 1702 is operable to communicate with any wireless device or entity operably configured for wireless communication (e.g., printer, scanner, desktop and / or portable computer, portable data assistant, communications satellite, any device or location associated with a wirelessly detectable tag (e.g., phone booth, newsstand, store shelf, etc.), and telephone). This can include Wi-Fi and Bluetooth® wireless technologies. Therefore, communication can be a predefined structure like a traditional network, or simply self-organizing communication between at least two devices.

[0174] Go to Figure 18 , Figure 18A block diagram of example UE 1860 is shown. UE 1860 may include a smartphone, wireless tablet, wirelessly capable laptop computer, wearable device, machine device enabling vehicle telematics, tracking device, remote sensing device, etc. UE 1860 includes a first processor 1830, a second processor 1832, and shared memory 1834. UE 1860 includes radio front-end circuitry 1862, which may be referred to herein as a transceiver, but is understood to typically include transceiver circuitry, separate filters, and a separate antenna to enable communication over a wireless link (such as...). Figure 1 Transceiver 1862 transmits and receives signals on one or more wireless links 125, 135, and 137 shown. Furthermore, transceiver 1862 may include multiple circuits or may be tunable to accommodate different frequency ranges, different modulation schemes, or different communication protocols, thereby enabling long-range wireless links (such as device-to-device links, such as link 135) and short-range wireless links (such as link 137).

[0175] continue Figure 18 According to the description, UE 1860 may also include features for facilitating communication with... Figure 1 The SIM 1864 or SIM profile for wireless communication of the RAN 105 or core network 130 shown may include information stored in memory (memory 1834 or a separate memory section). Figure 18 While the SIM 1864 is presented as a single component in the shape of a traditional SIM card, it will be understood that the SIM 1864 can represent multiple SIM cards, multiple SIM profiles, or multiple eSIMs, some or all of which can be implemented in hardware or software. It will be understood that a SIM profile may include information such as security credentials (e.g., encryption keys, values ​​that can be used to generate encryption keys, or shared values ​​shared between the SIM 1864 and another device, which could be...). Figure 1 (Components of RAN 105 or core network 130 shown). For example, SIM profile 1864 may also include unique identification information for the SIM or SIM profile, such as International Mobile Subscriber Identity (IMSI) or information that may constitute an IMSI.

[0176] SIM 1864 is shown coupled to both the first processor portion 1830 and the second processor portion 1832. This implementation offers the advantage that the first processor portion 1830 may not need to request or receive information or data that the second processor 1832 might request from SIM 1864, thereby eliminating the use of the first processor as a "man-in-the-middle" when the second processor uses information from the SIM in performing its functions and executing applications. The first processor 1830 may be a modem processor or a baseband processor, shown as smaller than processor 1832. Processor 1832 may be a more sophisticated application processor to visually indicate the relative sophistication (i.e., processing power and performance) level and the corresponding relative operating power consumption level between the two processor portions. Keeping the second processor section 1832 in a sleep / inactive / low-power state when the UE 1860 does not need the second processor section 1832 to execute applications and process application-related data provides the following advantages: power consumption is reduced when the UE only needs to use the first processor section 1830 to monitor routine configuration bearer management and mobility management / maintenance processes while in listening mode, or to monitor the search space that has been configured for monitoring by the UE while the second processor section remains inactive / sleep.

[0177] For example, UE 1860 may also include sensors 1866 that can provide signals to the first processor 1830 or the second processor 1832, such as temperature sensors, accelerometers, gyroscopes, barometers, humidity sensors, etc. Output devices 1868 may include, for example, one or more visual displays (e.g., computer monitors, VR devices, etc.), acoustic transducers (such as speakers or microphones), vibration components, etc. Output devices 1868 may include software that interfaces with output devices external to UE 1860 (e.g., visual displays, speakers, microphones, tactile devices, olfactory or gustatory devices, etc.).

[0178] The following glossary of terms given in Table 2 may be used to describe one or more embodiments disclosed herein. Table 2

[0179] The above description includes non-limiting examples of various embodiments. It is certainly not possible to describe every conceivable combination of components or methods for the purpose of describing the disclosed subject matter, and those skilled in the art will recognize that other combinations and arrangements of various embodiments are possible. The disclosed subject matter is intended to encompass all such changes, modifications, and variations falling within the spirit and scope of the appended claims.

[0180] Regarding the various functions performed by the aforementioned components, devices, circuits, systems, etc., unless otherwise indicated, the terminology used to describe such components (including references to "means") is intended to also include any (e.g., functionally equivalent) structures(s) that perform the specified functions of said components, even if they are not structurally equivalent to the disclosed structures. Furthermore, while specific features of the disclosed subject matter may be disclosed only with respect to one of several embodiments, such features may be combined with one or more other features of other embodiments, which may be desirable and advantageous for any given or particular application.

[0181] The terms “exemplary” and / or “indicative” or variations thereof, as may be used herein, are intended to mean as examples, instances, or illustrations. For the avoidance of doubt, the subject matter disclosed herein is not limited to such examples. Furthermore, any aspect or design described herein as “exemplary” and / or “indicative” is not necessarily to be construed as being more preferred or advantageous than other aspects or designs, nor does it imply the exclusion of equivalent structures and techniques known to those skilled in the art. Further, to the extent to which the terms “comprising,” “having,” “including,” and other similar words are used in the detailed description or claims, such terms are intended to be included in a manner similar to the term “comprising” as an open transitional term, without excluding any additional or other elements.

[0182] As used herein, the term “or” is intended to mean inclusive “or” rather than exclusive “or.” For example, the phrase “A or B” is intended to include instances A and B, as well as both A and B. Additionally, the articles “a” and “an” used in this application and the appended claims should generally be interpreted as meaning “one or more” unless otherwise specified or clearly indicated from the context to be in the singular form.

[0183] The term "set" as used herein excludes the empty set, i.e., a set containing no elements. Therefore, "set" in this disclosure includes one or more elements or entities. Similarly, the term "group" used herein refers to a collection of one or more entities.

[0184] Unless the context clearly indicates otherwise, the terms “first,” “second,” “third,” etc., used in the claims are for clarity only and do not indicate or imply any temporal order. For example, “first determination,” “second determination,” and “third determination” do not indicate or imply that the first determination will precede the second determination, and vice versa.

[0185] The description of the embodiments shown in this disclosure (including those described in the abstract) provided herein is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples have been described herein for illustrative purposes, various modifications are possible within the scope of such embodiments and examples, as will be recognized by those skilled in the art. In this regard, although the subject matter has been described herein in conjunction with various embodiments and corresponding drawings, it should be understood where applicable that other similar embodiments may be used, or modifications and additions may be made to the described embodiments to perform the same, similar, alternative, or substitute functions of the disclosed subject matter without departing from it. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but should be interpreted in accordance with the breadth and scope of the appended claims.

Claims

1. A method comprising: A user equipment including a processor receives a control channel message from a radio access network node, the control channel message including control channel information and artificial intelligence model information; The user equipment decodes the control channel information according to a first decoding format corresponding to the first encoding format to generate decoded control channel information; The user equipment decodes the artificial intelligence model information according to a second decoding format corresponding to the second encoding format to generate decoded artificial intelligence model information; The user equipment updates the trained artificial intelligence learning model based on the artificial intelligence model information to generate an updated trained artificial intelligence learning model. as well as The user equipment operates the radio functions based on the updated, trained artificial intelligence learning model.

2. The method of claim 1, wherein the first encoding format corresponds to a first rate, wherein the second encoding format corresponds to a second rate, and wherein the first rate is lower than the second rate.

3. The method according to claim 1, further comprising: The user equipment operates based on the decoded control channel information before decoding the artificial intelligence model information according to the second decoding format.

4. The method according to claim 1, wherein the control channel information and the artificial intelligence model information are received from the radio access network node in a radio resource control signaling message.

5. The method of claim 4, wherein the radio resource control signaling message includes a radio resource control signaling message format indication indicating the first encoding format.

6. The method of claim 5, wherein the radio resource control signaling message includes a first portion for transmitting the control channel information and a second portion for transmitting the artificial intelligence model information, wherein the radio resource control signaling message format indicates at least one control channel signaling segment for transmitting the control channel information in the first portion of the radio resource control signaling message, wherein the radio resource control signaling message format indicates that retransmission of at least one of the at least one control channel signaling segment by the radio access network node is enabled, and wherein the method further comprises: The user equipment determines that at least one of the at least one control channel signaling segments has been decoded with an error; The user equipment sends a retransmission request message to the radio access network node, the retransmission request message including a request for retransmission of the at least one of the at least one control channel signaling segments that has been decoded and has the error; The user equipment receives a retransmission segment corresponding to the at least one of the at least one control channel signaling segments that has been decoded and has the error; as well as The user equipment decodes the retransmission segment according to the first decoding format to generate the decoded retransmission segment.

7. The method of claim 6, wherein the retransmission request message includes at least one of the following: a process identifier corresponding to the first portion of the radio resource control signaling message, or a segment identifier corresponding to at least one of the at least one control channel signaling segment that has been decoded and has the error.

8. The method of claim 4, wherein the radio resource control signaling message includes a radio resource control signaling message format indication indicating the second encoding format.

9. The method of claim 8, wherein the radio resource control signaling message includes a first portion for transmitting the control channel information and a second portion for transmitting the artificial intelligence model information, wherein the radio resource control signaling message format indicates that at least one control channel signaling segment for transmitting the trained artificial intelligence learning model in the second portion of the radio resource control signaling message, wherein the radio resource control signaling message format indicates that retransmission of at least one of the at least one control channel signaling segment by the radio access network node is enabled, and wherein the method further comprises: The user equipment determines that at least one of the at least one control channel signaling segments has been decoded with an error; The user equipment sends a retransmission request message to the radio access network node, the retransmission request message including a request for retransmission of the at least one of the at least one control channel signaling segments that has been decoded and has the error; The user equipment receives a retransmission segment corresponding to the at least one of the at least one control channel signaling segments that has been decoded and has the error; as well as The retransmission segment is decoded according to the second decoding format to generate the decoded retransmission segment.

10. The method of claim 9, wherein the retransmission request message includes at least one of the following: a process identifier corresponding to the second part of the radio resource control signaling message, or a segment identifier corresponding to the at least one of the at least one control channel signaling segments that has been decoded with the error.

11. The method of claim 9, further comprising: The user equipment operates based on the decoded control channel information before decoding the retransmission segment according to the second decoding format.

12. The method of claim 4, wherein the radio resource control signaling message includes a first portion for transmitting the control channel information and a second portion for transmitting the artificial intelligence model information, and wherein the first portion includes a radio resource control signaling message format indication indicating the second portion.

13. A user equipment, comprising: The processor is configured as follows: Determine the radio performance parameter measure corresponding to the operation of the user equipment relative to the radio access network node, so as to generate the determined radio performance parameter measure; The determined radio performance parameter metrics are sent to the radio access network node for use in training the radio function AI learning model to generate a trained radio function AI learning model. The user equipment receives control channel information from the radio access network node via the main part of the radio resource control signaling message, which corresponds to the operation of the user equipment relative to the radio access network node. as well as The trained radio function artificial intelligence learning model is received from the radio access network node via a sub-part of the radio resource control signaling message.

14. The user equipment of claim 13, wherein the main portion of the radio resource control signaling message is received according to a first decoding scheme corresponding to a first rate, wherein the secondary portion is received according to a second decoding scheme corresponding to a second rate, and wherein the first rate is lower than the second rate.

15. The user equipment of claim 13, wherein the radio resource control signaling message includes a first radio resource format indication, the first radio resource format indication including one or more first segment identifiers, the one or more first segment identifiers respectively indicating one or more first control channel signaling segments for transmitting the control channel information via the main portion of the radio resource control signaling message, and wherein the radio resource control signaling message includes a second radio resource format indication, the second radio resource format indication including one or more second segment identifiers, the one or more second segment identifiers respectively indicating one or more second control channel signaling segments for transmitting the radio function artificial intelligence learning model via the secondary portion of the radio resource control signaling message.

16. The user equipment of claim 15, wherein the radio resource control signaling message includes a retransmission enable indication, the retransmission enable indication indicating that a request for retransmission of the one or more first control channel signaling segments or the one or more second control channel signaling segments is enabled.

17. The user equipment of claim 16, wherein the processor is further configured to: Determining that at least one of the one or more first control channel signaling segments or the one or more second control channel signaling segments is erroneously received, to generate at least one segment that has been determined to have been erroneously received; and A retransmission request message is sent to the radio access network node, the retransmission request message requesting a retransmission of at least one segment that was determined to have been erroneously received.

18. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processor of a user equipment, cause the execution of an operation, the operation comprising: Control channel information is received from the radio access network node via the first part of the radio resource control signaling message; The first portion of the radio resource control signaling message is decoded according to the first decoding rate; The radio access network node receives an updated radio function learning model via the second part of the radio resource control signaling message, wherein the updated radio function learning model includes updated learning model information based on the operation of at least one user equipment from a group of user equipment including the user equipment. The second part of the radio resource control signaling message is decoded according to the second decoding rate to generate a decoded and updated radio function learning model. Receive from the radio access network node a first part indication including a first group of one or more first segment identifiers, wherein the first group of one or more first segment identifiers indicates a second group of one or more segments corresponding to the first part of the radio resource control signaling message; as well as The radio access network node receives a second part indication including one or more second segment identifiers in a third group, wherein the third group of one or more second segment identifiers indicates one or more segments corresponding to a fourth group of the second part of the radio resource control signaling message. The first decoding rate and the second decoding rate are different.

19. The non-transitory machine-readable medium of claim 18, further comprising: The user equipment is operated according to the decoded and updated radio function learning model.

20. The non-transitory machine-readable medium of claim 18, wherein the radio resource control signaling message includes a first retransmission enable indication to indicate to the user equipment that the user equipment requests retransmission of a first segment of a first portion of the radio resource control signaling message or retransmission of a second segment of a second portion of the radio resource control signaling message is enabled, the operation further comprising: Determining that the first segment of the first part of the radio resource control signaling message or the second segment of the second part of the radio resource control signaling message contains an error, to generate a determined error segment; A retransmission request is sent to the radio access network node, the retransmission request including a first segment identifier or a second segment identifier indicating the determined erroneous segment; as well as In response to the retransmission request, the radio access network node receives either the first segment corresponding to the first segment identifier indicated in the retransmission request or the second segment corresponding to the second segment identifier indicated in the retransmission request.

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