Artificial intelligence model training for idle mode assistance

Abstract

One or more radio access network nodes may determine learning model configuration information for training a learning model corresponding to a user equipment in an idle mode. The node may broadcast a training configuration resource indication in an information block indicating a resource available for broadcasting the learning model training configuration or indicating a resource available for broadcasting the training result. While idle, the user equipment may decode the training configuration according to the training configuration resource indication, and perform a training action indicated in the training configuration. The learning model may be trained based on the training action while the user device is idle. While idle, the user equipment may estimate radio parameters using a model trained while the user equipment is idle, and transmit the estimated radio parameters to the node for establishing a connection with the node.

Description

Related Applications

[0001] This application claims priority to U.S. Non-Provisional Patent Application No. 18 / 303,362, filed April 19, 2023, and entitled “Artificial Intelligence Model Training for Idle Mode Assistance,” the entirety of which is hereby incorporated by reference herein. BACKGROUND

[0002] The “New Radio” (NR) terminology associated with the fifth generation mobile wireless communication system (“5G”) refers to technical aspects used in a wireless radio access network (“RAN”) that includes several quality of service classes (QoS), including ultra-reliable and 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 of radio performance, while traditional eMBB use cases can be associated with high-capacity wireless communication that can allow for less strict latency requirements (e.g., higher latency than URLLC) and less reliable radio performance compared to URLLC. The performance requirements of mMTC can be lower than eMBB use cases. Some use case applications involving mobile devices or mobile user equipment (such as smartphones, wireless tablet computers, smart watches, etc.) can impose variations on a given RAN resource load or demand. SUMMARY

[0003] The following presents a simplified summary of the disclosed subject matter to provide a basic understanding of some of the embodiments of various aspects. This summary is not an extensive overview of the various aspects. It is neither intended to identify key or critical elements of the various aspects nor to delineate the scope of the various aspects. 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 can include a method comprising broadcasting, by a radio access network node comprising a processor, a learning model configuration information block message, the message comprising a training configuration information indication indicating a training configuration resource, the training configuration resource usable for broadcasting a learning model training configuration by the radio access network node, and broadcasting, by the radio access network node, the learning model training configuration according to the training configuration resource. The learning model training configuration can comprise a training action indication indicating a training action performable by an idle user equipment.

[0005] In embodiments, the learning model training configuration can include a training action indication indicating a training action, where the training action indication can correspond to a radio function learning model or a radio function learning model feature. Performing the training action can result in a determined radio function parameter value corresponding to the radio function learning model / feature. Examples of training actions can include transmitting a timing advance preamble by the user equipment, receiving a reference signal identifier for determining a best beam with respect to the user equipment, or transmitting a sounding reference signal group identifier. The learning model configuration information block message can also include a training result resource indication indicating a training result resource usable for transmitting the determined radio function parameter value. The example method can also include transmitting, by the radio access network node, the determined radio function parameter value to the idle user equipment in accordance with the training result resource.

[0006] The training action can correspond to a radio function learning model, where performing the training action is to result in a determined radio function parameter value corresponding to the radio function learning model. The example method can also include receiving, by the radio access network node from the idle user equipment, a radio resource control signal message including the determined radio function parameter value. The example method can also include establishing, by the radio access network node, a connection with the idle user equipment using the determined radio function parameter value received from the idle user equipment in the radio resource control signal message, as a result of which the idle user equipment becomes a connected user equipment. In embodiments, the determined radio function parameter value can include a timing advance value corresponding to a corresponding timing advance of the radio access network node with respect to the idle user equipment. In embodiments, the determined radio function parameter value can include a best serving beam indication corresponding to a beam associated with the radio access network node, the best serving beam having a higher signal strength with respect to the user equipment than other signal strengths associated with other beams corresponding to the radio access network node other than the beam.

[0007] In embodiments, the learning model training configuration can include a training resource indication that indicates, to the idle user equipment, training resources available for performing training actions by the idle user equipment. In embodiments, the radio access network node can be a first radio access network node, and the example method can further include receiving, by the first radio access network node from a second radio access network node, a non-training resource indication, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, the non-training resource indication indicating, to the first radio access network node, non-training resources to be reserved by the second radio access network node and available for non-training operations by the second radio access network node. The example method can further include scheduling, by the first radio access network node, the training resources to avoid overlapping the training resources corresponding to the first radio access network node with the non-training resources corresponding to the second radio access network node.

[0008] In embodiments of the example method, the radio access network node can be a first radio access network node, and the learning model training configuration can include a training action indication that indicates training actions to be performed by at least one idle user equipment with respect to the first radio access network node to produce first determined learning model parameter values. The example method can further include receiving, by the first radio access network node from the at least one idle user equipment, the first determined learning model parameter values. The example method can further include receiving, by the first radio access network node from a node of a second radio access network, second determined learning model parameter values, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, wherein the training actions are performed by at least one of the at least one idle user equipment with respect to the second radio access network to produce the second determined learning model parameter values. The example method can further include determining, by the first radio access network node, a composite determined learning model parameter value based on the first determined learning model parameter values and based on the second determined learning model parameter values, and broadcasting, by the first radio access network node, the composite determined learning model parameter value to the at least one idle user equipment via a composite result information block message. The composite determined learning model parameter value can be based on information sent by a user equipment to the first radio access network node and information sent by that user equipment or by another user equipment to the second radio access network node. The composite determined learning model parameter value can be based on information sent by a user equipment or by another user equipment to the first radio access network node and information sent by another user equipment to the second radio access network node.

[0009] In an embodiment, the training action can correspond to a radio function learning model, and the composite determined learning model parameter values can be used by the at least one idle user equipment to train the radio function learning model to produce a trained learning model at the at least one idle user equipment. The example method can further include receiving, by the first radio access network node from the at least one idle user equipment, a connection request message including a performance indicator estimated by the at least one idle user equipment using the trained learning model to produce an estimated performance indicator. Based on the estimated performance indicator, the method can further include establishing a connection with the idle user equipment, whereby the idle user equipment becomes a connected user equipment with respect to the first radio access network node.

[0010] In an embodiment, the learning model configuration information block message can be a system information block message. In another embodiment, the learning model configuration information block message can be a master information block message. Thus, the idle mode user equipment can obtain the configuration of the result resource information from an information block that the user equipment is configured to decode when idle.

[0011] In another example embodiment, the first radio access network node can include a processor configured to receive, from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node, a non-training resource indication that indicates to the first radio access network node non-training resources that will be used by the second radio access network node for non-training operations, and schedule training resources that will be used by the at least one idle mode user equipment for performing training actions with respect to the first radio access network node, whereby the training resources and the non-training resources do not overlap. The processor can be further configured to broadcast a master information block message that includes a training configuration resource indication that indicates training configuration resources that will be used by the first radio access network node to broadcast learning model training configurations. The processor can be further configured to broadcast the learning model training configurations in accordance with the training configuration resources.

[0012] The learning model training configurations can include a training resource indication that indicates to the at least one idle mode user equipment training resources that will be used by the at least one idle mode user equipment to perform the training actions. The master information block message can further include a training result resource indication that indicates training result resources that will be used by the at least one idle mode user equipment to receive, from the first radio access network node, training results produced by the at least one idle mode user equipment performing the training actions.

[0013] In an embodiment, the processor can be further configured to determine a training result resulting from the execution of the training action by the at least one idle mode user equipment; and establish a connection with the at least one idle mode user equipment based on the training result, whereby the at least one idle mode user equipment becomes at least one connected mode user equipment.

[0014] In another example embodiment, a non-transitory machine readable medium can include executable instructions that, when executed by a processor of a first radio access network node, facilitate performance of operations including broadcasting a first information block message that includes a training configuration resource indication indicating a training configuration resource. The operations can further include broadcasting a learning model training configuration according to the training configuration resource, where the learning model training configuration includes a training action indication indicating a training action to be performed by a first idle user equipment of at least one idle user equipment with respect to the first radio access network node to produce a first determined learning model parameter value. The operations can further include receiving the first determined learning model parameter value from the first idle user equipment of the at least one idle user equipment, and receiving a second determined learning model parameter value from a second radio access network node, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, where a training action is performed by at least a second idle user equipment of the at least one idle user equipment with respect to the second radio access network node to produce the second determined learning model parameter value. The operations can further include determining an updated learning model based on the first determined learning model parameter value and based on the second determined learning model parameter value, and broadcasting the updated learning model to the first idle user equipment of the at least one idle user equipment via a second information block message. In an embodiment, the training action can have been performed by the first idle user equipment of the at least one idle user equipment with respect to the second radio access network node to produce the second determined learning model parameter value. In an embodiment, the operations can further include sending the updated learning model to the second radio access network node via a backhaul link.

[0015] In another example embodiment, a method can include receiving, by a user equipment including a processor, a learning model configuration information block message from a first radio access network node, the message including a training configuration resource indication indicating a training configuration resource available for broadcasting a learning model training configuration by the first radio access network node. The method can further include receiving, by the user equipment, the learning model training configuration according to the training configuration resource. The method can further include decoding, by the user equipment, the learning model training configuration. The decoding of the learning model training configuration includes blind decoding. The learning model training configuration can include a training action indication indicating a training action to be performed by the user equipment.

[0016] In an embodiment, the learning model training configuration can comprise at least one timing advance preamble corresponding to a second radio access network node, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, and the training action can comprise transmitting one of the at least one timing advance preamble to the second radio access network node. The method can further comprise transmitting, by the user equipment, one of the at least one timing advance preamble corresponding to the second radio access network node to the second radio access network node, wherein the one of the at least one timing advance preamble corresponding to the second radio access network node can be used by the second radio access network node to produce at least one updated timing advance learning model parameter corresponding to the timing advance learning model.

[0017] The learning model configuration information block message can comprise a training result resource indication indicating a training result resource usable for receiving, by the user equipment, the at least one updated timing advance learning model parameter. The method can further comprise receiving, by the user equipment, the at least one updated timing advance learning model parameter via the training result resource. Based on the at least one updated timing advance learning model parameter, the method can further comprise updating, by the user equipment, the timing advance learning model to produce an updated timing advance learning model.

[0018] In an embodiment, based on the updated timing advance learning model, the method can further comprise determining, by the user equipment, a timing advance with respect to the user equipment and the first radio access network node. The method can further comprise transmitting, by the user equipment, a connection establishment request message comprising the timing advance to the first radio access network node, and based on the connection establishment request information, establishing, by the user equipment, a communication connection with the first radio access network node, thus the user equipment is in a connected mode or becomes connected with respect to the first radio access network node.

[0019] In an embodiment, based on the updated timing advance learning model, the method can further comprise determining, by the user equipment, a timing advance with respect to the user equipment and the second radio access network node. The method can further comprise transmitting, by the user equipment, a connection establishment request message comprising the timing advance to the second radio access network node. Based on the connection establishment request message, the method can further comprise establishing, by the user equipment, a communication connection with the second radio access network node, thus the user equipment is in a connected mode with respect to the second radio access network node.

[0020] In example embodiments, a user equipment can include a processor configured to receive a learning model configuration information block message from a radio access network node, where the learning model configuration information block message can include a training configuration resource indication indicating a training configuration resource available for receiving a learning model training configuration from the radio access network node. The processor can also be configured to receive the learning model training configuration according to the training configuration resource and decode the learning model training configuration, where the learning model training configuration includes a training action indication indicating a training action to be performed by the user equipment. The processor can also be configured to perform the training action to produce a training action result and send the training action result to the radio access network node. In embodiments, the training action includes generating a sounding reference signal, such that the training action result is the generated sounding reference signal, and where the generated sounding reference signal is sent to the radio access network node, the generated sounding reference signal is available for training, by the radio access network node, of an uplink resource grant learning model to produce a trained uplink resource grant learning model.

[0021] In embodiments, the processor can also be configured to establish a communication connection with the radio access network node, where the communication connection includes at least one uplink resource granted by the radio access network node based on the trained uplink resource grant learning model. The grant of the at least one uplink resource by the radio access network node can be based on an exclusion of a transmission of the sounding reference signal by the user equipment after the transmission of the generated sounding reference signal by the user equipment. In other words, the at least one uplink resource can be granted by the radio access network node based on a prospectively transmitted sounding reference signal transmitted by the user equipment when the user equipment is idle.

[0022] In embodiments, the radio access network node can be a first radio access network node, the user equipment can perform the training action with respect to the first radio access network node, such that the training action result is a first training action result, and the user equipment can send the first training action result to the first radio access network node. The processor can also be configured to perform the training action with respect to a second radio access network node to produce a second training action result, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node. The process can also be configured to send the second training action result to the second radio access network node.

[0023] In yet another example embodiment, a non-transitory machine readable medium can include executable instructions that, when executed by a processor of a user equipment, facilitate performance of operations including receiving, from a first radio access network node while the user equipment is idle, a learning model configuration information block message, the message including a training result resource, or including an indication of a training result resource, usable by the user equipment to receive training results from the first radio access network node, and receiving, from the first radio access network node while the user equipment is idle, a learning model training configuration, the learning model training configuration including a training action indication indicating a training action performable by the user equipment with respect to the first radio access network node. The operations can also include performing, while the user equipment is idle, the training action with respect to the first radio access network node to produce a first training action result.

[0024] In embodiments, the operations can also include receiving the first training action result from the first radio access network node. The first training action result can be received in accordance with the training result resource indicated in the learning model configuration information block message.

[0025] In embodiments, the operations can also include performing, while the user equipment is idle, a training action with respect to a second radio access network node to produce a second training action result, wherein the second radio access network node is a neighboring radio access network node with respect to the first radio access network node, and receiving the second training action result from the second radio access network node.

[0026] In embodiments, the first radio access network node can use the first training action result to update the learning model.

[0027] In embodiments, the first training action result can be used by the user equipment to update the learning model to produce an updated learning model to be used by the user equipment. The learning model is a beam selection learning model, and wherein the updated learning model is an updated beam selection learning model.

[0028] In embodiments, the operations can further include determining, while the user equipment is idle, a determined preferred serving beam corresponding to the first radio access network node to be used during connection setup with the first radio access network node using the updated beam selection learning model. The operations can further include sending, while the user equipment is idle, a connection setup message to the first radio access network node, the connection setup message including a preferred serving beam indication indicating to the first radio access network node that the determined preferred serving beam is to be used to establish a connection with the first radio access network node. The operations can further include establishing a connection with the first radio access network node, wherein the connection includes the determined preferred serving beam, and wherein the establishment of the connection with the first radio access network node excludes a beam sweep to determine a best beam corresponding to the user equipment.

[0029] In embodiments, the learning model can include a timing advance learning model. The updated learning model can include an updated timing advance learning model. BRIEF DESCRIPTION OF DRAWINGS

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

[0031] Figure 2 An example environment with radio functions implemented in conjunction with corresponding learning models is shown.

[0032] Figure 3 An example environment in which artificial intelligence machine learning model information is exchanged between radio access network nodes and used to update artificial intelligence machine learning models at idle mode user equipment is shown.

[0033] Figure 4 An example radio resource control signal learning model configuration information block message is shown.

[0034] Figure 5A An example learning model training configuration for training a learning model at idle mode user equipment is shown.

[0035] Figure 5B An example learning model training configuration for training a timing advance learning model at idle mode user equipment is shown.

[0036] Figure 5C An example learning model training configuration for training a channel state information learning model at idle mode user equipment is shown.

[0037] Figure 5D An example learning model training configuration for training an uplink beam selection model learning model at a radio access network node is shown.

[0038] Figure 6An example training result information block for sending results of a training action is shown.

[0039] Figure 7 An example training result information block for sending timing advance results is shown.

[0040] Figure 8 An example radio resource control signal message connection setup message is shown that includes a preferred beam determined by a user equipment.

[0041] Figure 9 A timing diagram of an example method of training a user equipment while in idle mode is shown.

[0042] Figure 10 A flowchart of an example method of training a user equipment while in idle mode is shown.

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

[0044] Figure 12 A block diagram of an example first 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 equipment 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 user equipment is shown. DETAILED DESCRIPTION

[0051] First, those skilled in the art will readily appreciate that the present embodiments are well adapted to carry out the objects and to attain the ends and advantages mentioned above. Numerous embodiments of the present application will be readily apparent to those skilled in the art from the foregoing description. The methods, embodiments and adaptations of the present application above have been presented by way of example only and are not intended to limit the scope of the application. Numerous modifications and adaptations of the methods, embodiments and adaptations of this application are possible and apparent to those skilled in the art in light of the foregoing description.

[0052] Accordingly, although the present application has been described herein in detail based upon various embodiments, it is to be understood that the disclosure is illustrative of one or more concepts of the present application and the scope of the application should not be limited to the exact details shown and described herein. The following disclosure is not intended or to be construed to limit the present application or otherwise to exclude any such other embodiments, adaptations, variations, modifications and equivalent arrangements, the present embodiments being merely illustrative of the present application as defined by the appended claims and their equivalents.

[0053] As used in this disclosure, in some embodiments, the terms "component," "system" and the like are intended to refer to or comprise a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be a component.

[0054] One or more components can reside within a process and / or thread of execution and a component can be localized on one computer and / or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software application or firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts; the electronic components can include a processor therein to execute software or firmware that confer(s), at least in part, the functionality of the electronic components. While various components have been shown as separate components, it will be understood that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments.

[0055] The term "facilitating," as used herein in the context of a system, device, or component "facilitating" one or more actions or operations, relates to the nature of complex computing environments in which multiple components and / or multiple devices can be involved in some computing operation. Non-limiting examples of actions that can or can not involve multiple components and / or multiple devices include sending or receiving data, establishing a connection between devices, determining intermediate results used to obtain a result, etc. In this regard, a computing device or component can facilitate an operation by playing any role in completing the operation. When operations of a component are described herein, it is therefore to be understood that where an operation is described as being facilitated by a component, the operation can optionally be completed under the coordination of 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 a method, apparatus, or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer-readable (or machine-readable) device or computer- readable (or machine-readable) storage / communication media. A computer-readable storage medium can include, but is not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical storage devices (e.g., compact disk (CD), digital versatile disk (DVD)), smart cards, and flash memory devices (e.g., card, stick, key drive). Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

[0057] Artificial intelligence ("AI") and machine learning ("ML") models can facilitate performance and operational functionality and improvements of 5G implementations, such as network automation, optimization of signaling overhead, device energy saving, and traffic capacity maximization. Artificial intelligence machine learning model ("AI / ML model") functionality can be implemented and built in many different forms and different vendor proprietary designs. A 5G radio access network node ("RAN") of a network to which a user device can attach or to which a user device can register can manage or control real-time AI / ML model performance at different user device apparatuses for various radio functions.

[0058] The network RAN can dynamically control the activation, deactivation, triggering (which can be radio function specific) or update of the learning model depending on the monitoring and analysis of defined real-time performance metrics corresponding to the learning model executed at the user equipment. It should be appreciated that even if the learning model can be implementing a specific radio function, the monitored or analyzed metrics can be learning model metrics and not necessarily radio function metrics (e.g., mathematical / statistical metrics are not necessarily radio function metrics, e.g., signal strength).

[0059] Turning now to the drawings, Figure 1 An example of a wireless communications system 100 that supports blind decoding of PDCCH candidates or search spaces according to the present disclosure is shown. The wireless communications system 100 can include one or more base stations 105, one or more UEs 115, and a core network 130. In some examples, the wireless communications system 100 can be a Long Term Evolution (LTE) network, an LTE-Advanced (LTE-A) network, an LTE-A Pro network, or a New Radio (NR) network. In some examples, the wireless communications system 100 can support enhanced broadband communications, ultra-reliable (e.g., mission critical) communications, low latency communications, communications with low-cost and low-complexity devices, or any combination thereof. As illustrated, examples of UEs 115 can include a smartphone, a car or other vehicle, or a drone or other flying aircraft. Another example of a UE can be a virtual reality (VR) appliance 117, such as smart glasses, a VR headset, an augmented reality headset, and other similar devices that can provide images, video, audio, haptics, taste, or smell to a wearer. A UE, such as the VR appliance 117, can transmit or receive wireless signals with RAN base stations 105 via long-range wireless links 125, or the UE / VR appliance 100 can receive or transmit wireless signals via short-range wireless links 137, which can include a wireless link with the UE device 115, such as a Bluetooth link, a Wi-Fi link, and the like. A UE, such as the appliance 117, can communicate simultaneously via multiple wireless links, such as through a link 125 with a base station 105 and through a short-range wireless link. The VR appliance 117 can also communicate with a wireless UE via a cable or other wired connection. The RAN or components thereof can be implemented by a base station 105, which can be a device that communicates with UEs 115 and can include or operate as an access point, a network access device, a base station, a Node-B, an eNode-B, a gNode-B, a gNB, a Home Node-B, a Home eNode-B, a relay, a carrier, a base transceiver station, a site controller, an access point (AP), a wireless Fidelity (Wi-Fi) AP, or the like. The base station 105 can also be more generally referred to as a network node or a network access node. The base station 105 can be located on the ground (ground base station, outdoor base station) or on a roof (rooftop base station, rooftop Figure 12 One or more computer components described are implemented.

[0060] Continuing Figure 1The base stations 105 can be dispersed throughout the geographic area 100 and can be associated with one or more cellular service providers. The base stations 105 can be macro base stations and / or small cell base stations. The base stations 105 can provide communication coverage for a respective geographic area 110 via one or more base station antennas and / or antenna arrays. The geographic area 110 can be divided into sectors 112 making up a portion of the geographic area 110, and each sector 112 can be served by a respective base station 105. The base stations 105 can also be referred to as gNBs, NodeBs, eNodeBs, or other similar terminology.

[0061] The UEs 115 can be dispersed throughout the areas 110, and each UE 115 can be stationary or mobile. A UE 115 can also be referred to as a mobile device, a wireless device, a remote device, an industrial device, or other suitable terminology. Figure 1 Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein can be able to communicate with various types of devices, such as other UEs 115, base stations 105, or network equipment (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network equipment). Figure 1 As illustrated, UEs 115 can include one or more transceivers 120, one or more processors 122, memory 123, and one or more components (e.g., communication component 124, processing component 126, or other components 128). In some examples, some or all of the components of UE 115 can be interconnected or operate using one or more components of the transceivers 120 and / or processors 122. In some examples, the transceivers 120 and / or processors 122 can be on the same chip as the components of UE 115, while in other examples, the transceivers 120 and / or processors 122 can be on different chips.

[0062] The base stations 105 can be dispersed throughout the geographic area 100 and can be associated with one or more cellular service providers. The base stations 105 can be macro base stations and / or small cell base stations. The base stations 105 can provide communication coverage for a respective geographic area 110 via one or more base station antennas and / or antenna arrays. The geographic area 110 can be divided into sectors 112 making up a portion of the geographic area 110, and each sector 112 can be served by a respective base station 105. The base stations 105 can also be referred to as gNBs, NodeBs, eNodeBs, or other similar terminology.

[0063] One or more of the base stations 105 described herein can include or can be referred to as a base transceiver station, a radio base station, an access point, a radio transceiver, a NodeB, an eNodeB (eNB), a next-generation NodeB or giga-NodeB (either of which can be referred to as a gNB), a Home NodeB, a Home eNodeB, or other suitable terminology.

[0064] A UE 115 can include or can be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology, where the “device” can also be referred to as a unit, a station, a terminal, or a client, among other examples. A UE 115 can include or can be referred to as a personal electronic device such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, a personal computer, or a router, among other examples. In some examples, a UE 115 can include or be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communications (MTC) device, among other examples, which can be implemented in various objects such as appliances, vehicles, or smart meters, among other examples.

[0065] A UE 115 can be able to communicate with various types of devices, such as other UEs 115 that can sometimes act as relays for the Figure 1 wireless communications system 100, base stations 105, and network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or customer premises equipment (CPE), among other examples, as shown in FIG. 1.

[0066] The UEs 115 and the base stations 105 can wirelessly communicate with one another via one or more communication links 125 over one or more carriers. The term “carrier” can refer to a set of radio frequency spectrum resources (e.g., frequency channels or physical resource blocks (PRBs)) that can be utilized by a base station 105 to communicate with UEs 115. The set of spectrum resources can be understood to be “aggregated” carriers and / or can be referred to as a “component carrier” (CC). In some examples, the base station 105 can communicate with the UE 115 using multiple CCs. In other examples, the base station 105 can communicate with the UE 115 using a single CC. Different combinations of CCs can be used for communicating with different UEs 115. Examples of symmetry in CCs can be found in the disclosure of U.S. Patent Application No. 16 / 209,395, filed December 3, 2018, which is incorporated by reference herein.

[0067] In some examples (e.g., in carrier aggregation configurations), a carrier can also have acquisition signaling or control signaling that coordinates operations of other carriers. A carrier can be associated with a frequency channel (e.g., an evolved universal mobile telecommunication system terrestrial radio access (e-UTRA) absolute radio frequency channel number (EARFCN)) and can be positioned based on a channel raster for discovery by the UEs 115. Carriers can operate in an independent mode, where initial acquisition and connection can be achieved via the carriers by the UEs 115, or carriers can operate in a non-independent mode, where connection is anchored by a different carrier (e.g., of the same or a different radio access technology).

[0068] The communication links 125 shown in wireless communication system 100 can include uplink transmissions from a UE 115 to a base station 105, or downlink transmissions from a base station 105 to a UE 115. Carriers can carry downlink or uplink communications (e.g., in FDD mode), or can be configured to carry downlink and uplink communications (e.g., in TDD mode).

[0069] A carrier can be associated with a particular bandwidth of the radio frequency spectrum, and in some examples the carrier bandwidth can be referred to as a “system bandwidth” of the carrier or wireless communications system 100. For example, the carrier bandwidth can be one of a number of determined bandwidths (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 megahertz (MHz)) of a particular radio access technology. Devices of wireless communications system 100 (e.g., base stations 105, UEs 115, or both) can have hardware configurations that support communications over a particular carrier bandwidth, or can be configurable to support communications over one of a set of carrier bandwidths. In some examples, wireless communications system 100 can include base stations 105 or UEs 115 that support simultaneous communications via multiple carriers associated with different carrier bandwidths. In some examples, each served UE 115 can be configured to operate over portions (e.g., sub-bands, BWPs) or all of the carrier bandwidth.

[0070] Signal waveforms transmitted over a carrier can be composed of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system employing MCM techniques, a resource element can consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier, where the symbol period and subcarrier spacing are inversely related. 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). Thus, the more resource elements that a UE 115 receives and the higher the order of the modulation scheme, the higher the data rates can be for the UE. A wireless communications resource can refer to a combination of a radio frequency spectrum resource, a time resource, (e.g., search space) or a spatial resource (e.g., spatial layers or beams), and the use of multiple spatial layers can further increase the data rate for communications with a UE 115.

[0071] One or more numbers can be supported for a carrier, where a number can include a subcarrier spacing (Af) and a cyclic prefix. A carrier can be partitioned into one or more BWPs with the same or different numbers. In some examples, a UE 115 can be configured with multiple BWPs. In some examples, a single BWP of a carrier 110 can be active at a given time and communications by the UE 115 can be restricted to the one or more active BWPs.

[0072] Time intervals for the base stations 105 or the UEs 115 can be expressed in multiples of a basic time unit which may, for example, be a sampling period of Ts= 1 / (Afmax) seconds, where Afmaxmay be a maximum supported subcarrier spacing. Time intervals of a communications resource can be expressed in multiples of a basic time unit (e.g., four basic time units can be a slot). Time intervals of a communications resource can also be expressed in multiples of a slot (e.g., a subframe can be two slots). The basic time unit variable, Ts, can be used in expressions for computing time resources and communication parameters. max •N f ) seconds, where Af max may represent a maximum supported subcarrier spacing, and N f may represent a maximum supported discrete fourier transform (DFT) size. Time intervals of a communications resource can be organized as radio frames, each

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

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

[0075] According to various techniques, physical channels can be multiplexed on a carrier. Physical control channels and physical data channels can be multiplexed on a downlink carrier, for example, using one or more of time division multiplexing (TDM) techniques, frequency division multiplexing (FDM) techniques, or hybrid TDM-FDM techniques. A control region (e.g., a control resource set (CORESET)) for a physical control channel can be defined by a number of symbol periods and can extend across the system bandwidth or a subset of the system bandwidth of a carrier. One or more control regions (e.g., CORESETs) can be configured for a set of UEs 115. For example, one or more of the UEs 115 can monitor or search control regions or spaces for control information according to one or more search space sets, and each search space set can include one or more control channel candidates in one or more aggregation levels arranged in a cascaded manner. An aggregation level of a control channel candidate can refer to a number of control channel resources (e.g., control channel elements (CCEs)) associated with an encoded information of a control information format having a given payload size. Search space sets can include common search space sets configured for sending control information to multiple UEs 115 and UE-specific search space sets used for sending control information to a specific UE 115. Other search spaces and configurations for monitoring and decoding them are disclosed herein that are novel and not conventional.

[0076] The base stations 105 can provide communication coverage for a respective geographic area by exchanging communications with the UEs 115 dispersed throughout the geographic area via one or more base stations 105, such as macro cells, small cells, hot spots, or other like types of wireless communication access points and / or grid cells. The term "cell" can refer to a logical communication entity used for communication with a base station 105 (e.g., through a carrier) and can be associated with a identifier, such as a physical cell identifier (PCID), a virtual cell identifier (VCID), or otherwise. In some examples, a cell can not exist independently, but can be tied to a geographic area (e.g., a geographic coverage area 110) associated with a base station 105 or to a geographic coverage area 110 associated with a base station 105. For example, a cell can be associated with a geographic coverage area 110 that a base station 105 operates on, or otherwise covers, or a geographic coverage area 110 that a base station 105 is otherwise related to or tied to. The geographic coverage area 110 for a base station 105 can be divided into components, such as sectors or cells, by various techniques, each of which can be referred to as a cell. A component can also be referred to as a cell, and / or can provide communication coverage for a particular area or geographic coverage area 110. In some examples, the term "cell" and / or "component" and / or the like can be a logical communication entity corresponding to a geographical coverage area 110 associated with a base station 105 and can be used in this context. For example, "cells" are not limited to particular geographic coverage areas 110, but rather the term "cell" can be used to describe or otherwise refer to a base station 105 and / or a geographic coverage area 110 associated with a base station 105, depending on context.

[0077] Macro cells can generally cover relatively large geographic areas (e.g., 5s of kilometers in radius) and can allow unrestricted access by UEs 115 with service subscriptions with the network provider. Small cells can be associated with a lower-powered base station 105, as compared with the macro cell, and can include base stations 105 that operate in a licensed, unlicensed, or shared frequency spectrum. A small cell can provide full service to UEs 115 in a local area (e.g., a home) or can function as a range extender to facilitate wireless access by UEs 115 in an area that is not covered by a macro cell. Small cells can also improve network capacity (e.g., by handling a large number of accesses by UEs 115) and / or coverage (e.g., by supporting coverage overlap).

[0078] In some examples, a carrier can support multiple cells, and different cells of the carrier can be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)). Cars can be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)).

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

[0080] The wireless communications system 100 can support synchronous or asynchronous operation. For synchronous operation, the base stations 105 can have similar frame timings, and transmissions from different base stations 105 can be approximately aligned in time. For asynchronous operation, the base stations 105 can have different frame timings, and transmissions from different base stations 105 can not be aligned in time. The techniques described herein can be used for either synchronous or asynchronous operations.

[0081] Some UEs 115, such as MTC or IoT devices, can be low cost or low complexity devices, and can provide for 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 one another or a base station 105 without the need for human intervention. In some examples, M2M communication or MTC can include communications from devices that integrate sensors or meters to measure or capture information and relay such information to a central server or application program that makes use of the information or presents the information to humans in an intuitive manner. Some UEs 115 can be designed to collect information or enable automated behavior of machines or other devices. 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 surveillance, physical access control, and transaction-based business charging.

[0082] Some UEs 115 can be configured to employ operating modes that reduce power consumption, such as a half-duplex communications mode that supports one -way communication via transmission or reception, but not simultaneous transmission and reception. In some examples, a half-duplex communications mode can be performed at a reduced peak rate. Other power conservation techniques for UEs 115 include entering a power saving deep sleep mode when not engaging in active communications, operating over a limited bandwidth (e.g., according to a narrowband

[0083] The wireless communications system 100 can be configured to support ultra-reliable communications or low-latency communications, or various combinations thereof. For example, the wireless communications system 100 can be configured to support ultra-reliable low-latency communications (URLLC) or mission critical communications. UEs 115 can be designed to support ultra-reliable, low-latency, or mission critical functions (e.g., mission critical function). Ultra-reliable communications can include private communication or group communication, and can 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 can include prioritization of services, and mission critical services can be used for public safety or general commercial applications. The terms ultra-reliable, low-latency, mission critical, and ultra-reliable low- latency can be used interchangeably herein.

[0084] In some examples, UEs 115 can also be able to communicate directly with other UEs 115 using a device-to-device (D2D) communication link 135 (e.g., using a peer-to-peer (P2P) or D2D protocol). The communication link 135 can include a sidelink communication link. One or more UEs 115 utilizing D2D communications can be within the geographic coverage area 110 of a base station 105. Other UEs 115 in such a group can be outside the geographic coverage area 110 of a base station 105 or be otherwise unable to receive transmissions from a base station 105. In some examples, groups of the UEs 115 communicating via D2D communications can utilize a one-to-many (1 :M) system in which each UE 115 transmits to other UEs 115 in the group. In some examples, a base station 105 facilitates the D2D communications by scheduling resources and receiving D2D communications transmissions. D2D communications can be through a direct optical, acoustic, RF, or wireless communication link 135 between UEs 115.

[0085] In some systems, the D2D communication link 135 can be an example of a communication channel, such as a sidelink communication channel, between vehicles (e.g., UEs 115). In some examples, vehicles can communicate using vehicle-to-everything (V2X) communications, vehicle-to-vehicle (V2V) communications, or some combination of these. A vehicle can signal information related to traffic conditions, signal scheduling, weather, safety, emergencies, or any other information relevant to a V2X system. In some examples, a vehicle in a V2X system can communicate with a network via one or more RAN network nodes (e.g., base stations 105) using vehicle-to-network (V2N) communications, with road side infrastructure, such as road side units, using vehicle-to-infrastructure (V2I) communications, or with both.

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

[0087] Some of the network devices, such as base stations 105, can include subcomponents such as an access network entity 140, which can be an example of an access node controller (ANC). Each access network entity 140 can communicate with UEs 115 through one or more other access network transmission entities 145, which can be referred to as radio heads, smart radio heads, or transmission / reception points (TRPs). Each access network transmission entity 145 can include one or more antenna panels. In some configurations, various functions of each access network entity 140 or base station 105 can be distributed across various network devices (e.g., radio heads and ANCs) or consolidated into a single network device (e.g., a base station 105).

[0088] The wireless communications system 100 can operate using one or more frequency bands, often in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally, the region from 300 MHz to 3 GHz is known as the ultra-high frequency (UHF) region or decimeter band, since the wavelengths range from approximately one decimeter to one meter in length. UHF waves can be blocked or redirected by buildings and environmental features, but the waves can penetrate structures sufficiently for a macro cell to provide service to UEs 115 located indoors. Transmission of UHF waves may

[0089] The wireless communications system 100 can operate in an ultra high frequency (SHF) region of the frequency spectrum, such as between 3 GHz and 30 GHz, also known as centimeter band, or in an extremely high frequency (EHF) region of the frequency spectrum, such as between 30 GHz and 300 GHz, also known as millimeter band. In some examples, the wireless communications system 100 can support millimeter wave (mmW) communications between the UEs 115 and the base stations 105, and EHF antennas of the respective devices can be smaller and more closely spaced than UHF antennas. In some examples, this can facilitate use of antenna arrays within a device. However, the propagation of EHF transmissions can be subject to even greater atmospheric attenuation than SHF or UHF transmissions, and EHF transmissions can therefore have a shorter range than other transmissions. Techniques disclosed herein can be employed across transmissions that use one or more different frequency regions, and designated use of bands across these frequency regions can differ by country or regulatory regime.

[0090] The wireless communications system 100 can utilize both licensed and unlicensed radio frequency spectrum bands. For example, the wireless communications system 100 can employ License Assisted Access (LAA), LTE-Unlicensed (LTE-U) radio access technology, or NR technology in an unlicensed frequency spectrum band, such as the 5 GHz industrial, scientific and medical (ISM) band. When operating in unlicensed frequency spectrum bands, access points and UEs such as the base stations 105 and the UEs 115 can employ carrier sensing for collision detection and avoidance. In some examples, operations in unlicensed bands can be based on a carrier aggregation configuration in combination with a component carrier operating in a licensed band (e.g., LAA). Operations in unlicensed spectrum can include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, among other examples.

[0091] The base stations 105 or UEs 115 can be equipped with multiple antennas, which can be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. The antennas of a base station 105 or a UE 115 can be co-located or separated by a relatively small distance, such as within a metro area. For example, one or more base station antennas or antenna arrays can be located within antenna assemblies, such as antenna towers or masts, that can be co-located with a base station 105. In some examples, the antennas associated with a base station 105 can be located in different geographic locations. A base station 105 can have an antenna array with a number of rows and columns of antenna ports that can be used to support beamforming of communications signals to UEs 115. Likewise, a UE 115 can have one or more antenna arrays that can support various MIMO or beamforming operations. Additionally, or alternatively, antenna panels can support radio frequency beamforming of 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), where multiple spatial layers are transmitted to the same receiving device, and multi-user MIMO (MU-MIMO), where 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 that can be used at a transmitting or receiving device (e.g., base station 105, UE 115) to shape or control antenna beams (e.g., transmit beam, receive beam) along a spatial path between the transmitting and receiving devices. Beamforming can be achieved by combining signals communicating via antenna elements of an antenna array, such that some signals propagating in a specific orientation relative to the antenna array experience constructed interference, while others experience destructive interference. Adjustment of signals communicating via antenna elements can include the transmitting or receiving device applying amplitude shift, phase shift, or both to signals carried via antenna elements associated with that device. The adjustment associated with each antenna element can be defined by a beamforming weight set associated with a specific orientation (e.g., relative to the antenna array of the transmitting or receiving device, or relative to some other orientation).

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

[0095] Some signals, such as data signals associated with a particular receiving device, can be transmitted by a base station 105 in a single beam direction (e.g., a direction associated with the receiving device such as a UE 115). In some examples, signals transmitted in one or more beam directions can be used by a UE 115 to identify a beam direction for subsequent transmission or reception by the base station 105. For example, a UE 115 can receive one or more of the signals transmitted by the base station 105 in different directions and report to the base station an indication of the signal the UE 115 received with a highest signal quality or an otherwise acceptable signal quality.

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

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

[0098] Wireless communications system 100 can be a packet-based network that operates according to a layered protocol stack. In the user plane, communications at the bearer or Packet Data Convergence Protocol (PDCP) layer can be IP -based. A Radio Link Control (RLC) layer can perform packet segmentation and reassembly to communicate over logical channels. A Medium Access Control (MAC) layer can perform priority handling and multiplexing of logical channels into transport channels. The MAC layer can also use error detection techniques, error correction techniques, or both, to support retransmissions at the MAC layer, e.g., using hybrid automatic repeat request (HARQ), to improve link efficiency. In the control plane, the Radio Resource Control (RRC) protocol layer can provide establishment, configuration, and maintenance of an RRC connection between a UE 115 and a base station 105 or core network 130, supporting user plane data. At the physical layer, transport channels can be mapped to physical channels.

[0099] The UEs 115 and the base stations 105 can support retransmissions of data to increase the likelihood that data is received successfully. Hybrid automatic repeat request (HARQ) feedback is one technique used to increase the likelihood that data is received successfully. HARQ can include a combination of error detection (e.g., using a cyclic redundancy check (CRC)), forward error correction (FEC), and retransmission (e.g., request-based ARQ). HARQ can improve throughput at the MAC layer in poor radio conditions (e.g., low signal-to-noise conditions). In some examples, a device can support same-slot HARQ feedback, where the device can provide HARQ feedback in a specific time slot for data received in a previous time slot. In other cases, the device can provide HARQ feedback in a subsequent time slot, or according to some other time interval.

[0100] Conventional rule-based models can be implemented in user equipment to perform various radio frequency (“RF”) or signal processing functions, such as beamforming, channel estimation, demodulation, and decoding, and can be based on well-refined system models. Such models can produce satisfactory performance as long as the model closely follows the actual behavior of the radio network system in which the user equipment is operating. However, the performance of conventional models can be suboptimal. AI / ML-based models are generally superior to their conventional analogs; unlike conventional rule-based models, AI / ML-based models can be based on data rather than pre-determined rules of conventional models. Thus, the output or result of a conventional rule-based model can be considered “deterministic” in that the input is applied to static rules, resulting in a “determined” output, whereas the output or result of an AI / ML model can be considered probabilistic in that the learned model generally infers a likely output based on coefficients, factors, functions, or other variables that can be derived based on previous inputs to the model.

[0101] Using AI / ML models can facilitate improved user equipment performance compared to using traditional rule-based models. Multiple AI / ML driven use cases can include AI / ML timing advance acquisition / prediction, AI / ML channel state information (“CSI”) acquisition / prediction, AI / ML radio positioning, and AI / ML beam management. While AI / ML based models trained using data from actual real-world operations can outperform traditional rule-based models, learning models can not be as robust and thus provide less desirable results in situations where the radio system / environment can experience changes that can not have been experienced or “seen” during training of the learning model, and thus the learning model can infer less satisfactory outputs compared to static rule-based models in such situations that are “unknown” to the learning model. Such problematic situations can be caused by, for example, particular network / user equipment conditions or configurations, or by the architecture of the AI / ML learning model, or a combination thereof. Thus, it is desirable to implement a procedure where the network RAN can update the AI / ML learning model.

[0102] For AI / ML learning model implementation of radio functions at user equipment, the user equipment or gNB / RAN can predict a modulation and coding scheme (“MCS”), and can use a given amount of channel state information reporting occasion for this. The modulation and coding scheme can be referred to as a format. The format or scheme can be associated with a quality of service. Channel conditions or interference conditions that were not present during training or the model can systematically result in less than ideal MCS selection, which can correspondingly result in violation of minimum device performance targets.

[0103] Turning now to Figure 2 the figure illustrates a system 200 including a RAN node 105 in communication with a user equipment 115 via a wireless link 125. The UE 115 can perform various radio functions 205A through 205n, which can be facilitated by corresponding machine learning models 215A through 215n, respectively. During UE 115 wireless operation and communication with the RAN 105, the UE can transmit parameter metric reports 220A through 220n, which can include one or more learning model parameter metrics corresponding to 215A through 215n, respectively. The reports 220A through 220n can include one or more control action requests, e.g., requesting deactivation or retraining of one or more of the models 215A through 215n. The RAN 105 can transmit radio resource control messages 225 corresponding to the learning model information 215 to the UE 115.

[0104] AI / ML learning models, such as deployed at UE devices 115 Figure 2The illustrated model 215 can be implementation specific (e.g., vendor proprietary learning model). (Examples of vendors that can provide proprietary learning models can include a user equipment manufacturer or a provider of applications for user equipment, a network equipment provider or a provider of applications for network equipment, or a mobile network operator or a provider of applications for a mobile network operator.) The network RAN can determine the overall performance of the learning model deployed at the UE to facilitate minimum device performance requirements. The dynamic reporting procedure can facilitate user equipment devices compiling and reporting indications that can be configured or preconfigured to reflect or indicate model performance of the corresponding learning model.

[0105] A particular user equipment device can employ several different AI / ML learning model implementations for running, executing, or otherwise facilitating different radio functions. Different learning model parameter metrics can indicate performance of the different learning models. The user equipment can compile and report one or more different learning model performances, each learning model indicating parameter metrics or indications. Different learning model metrics can be associated with different corresponding filtering or time resolution configurations. Thus, as Figure 1 or Figure 2 illustrated, such calibration metric reporting for a given learning model can facilitate optimized tracking and reporting for each active learning model for each user equipment device 115 that can be served by the RAN 105. Thus, the network RAN 105 can obtain and use real-time performance of each learning model active at the UE 115 to facilitate optimal performance of the learning model and inferences that it can generate. Moreover, several reporting variants can be tailored to suit various AI / ML learning model implementations or purposes, e.g., precise absolute, precise relative, quantized, or time (e.g., historical) metric reporting. The network node RAN 105 can dynamically trade-off AI / ML learning model reporting overhead to obtain accuracy of the AI / ML model performance metrics.

[0106] For AI / ML learning model performance, various parameters and metrics corresponding thereto can be considered, analyzed, or evaluated depending on the nature of the problem being solved and the corresponding learning model function (e.g., regression or classification), or radio function that is executed or facilitated by the learning model. For example, for radio functions such as channel estimation or channel state information (“CSI”) compression, the following parameters or metrics corresponding thereto can be potentially evaluated in the learning model: mean squared error (“MSE”); root mean squared error (“RMSE”); normalized mean squared error (“NMSE”); mean absolute error (“MAE”); R-squared; generalized cosine similarity (“GCS”); or squared generalized cosine similarity (“SGCS”). Table 1 shows example functions defining corresponding learning model parameters, metrics corresponding thereto, as listed above, can be monitored and evaluated. Table 1

[0107] For classification problems such as beam index prediction, accuracy parameter metrics can be analyzed to determine the performance of learning models that facilitate beam index prediction. Other example learning model parameter metrics that can indicate the performance of a learning model to solve a classification problem can include, but are not limited to, the absolute number of true negatives, true positives, false negatives, and false positives; precision and recall; or F1 score. The F1 score can include an evaluation metric that is used to represent the performance of a machine learning model or classifier and provides combined information about the precision and recall of a learning model. A high F1 score metric generally indicates high values for both the recall and precision metrics.

[0108] AI / ML learning model implementations at different devices can be vendor specific as described above and can be transparent to network nodes (e.g., a RAN serving a UE can not have access to the specific functionality and programming of a given learning model deployed in a UE that facilitates radio functionality). To manage and facilitate UE device implementation performance goals, a RAN node can be aware of the capabilities of a UE device and overall AI / ML learning model performance. Thus, upon first connecting to a serving network RAN, an active UE device can send device specific AI / ML capability information including the following information elements (“IE”): types of AI / ML supported algorithms including supervised learning, unsupervised learning, and reinforcement learning; a list of AI / ML supported radio functions; a list of AI / ML model specific metrics supported for estimation and reporting; a model library size per radio function, e.g., the number of models that can be stored for each radio function; or an indication of model categorization (small / medium / large) that can facilitate a network RAN to define or determine data sets to be used by a learning model. For example, for a large number of neurons (e.g., nodes of a learning model neural network), a determination can be made using a comparable number of information samples that can avoid overfitting of a learning model. The AI / ML capability information elements can be part of device capability signaling sent based on subsequent radio resource control (“RRC”) signaling or based on dynamic scheduling uplink control information (“UCI”). Thus, a network RAN can determine updates to one or more learning models and can deliver updated models or coefficients corresponding thereto to a user device.

[0109] The prior art refines, updates, and delivers AI / ML models, model information, and model parameter values only for connected mode devices (e.g., user equipment devices that have established a communication connection with a serving radio access network node). Using conventional techniques, channel conditions, control channels, data channels, reference signals, AI / ML functions, and supported AI / ML radio features corresponding to connected mode user equipment are identified and established. However, conventional techniques are not conducive to AI / ML training and assistance support for idle mode user equipment devices. Radio access network nodes do not “know” idle mode user equipment devices because the user equipment is not connected to the node. As a result, AI / ML capabilities, supported AI / MI driven features, and locations of user equipment are unknown to the network.

[0110] AI / ML model training facilitates efficient deployment of AI / ML algorithms within a cellular wireless communication network. Trained AI / ML models can provide predictive outputs based on actual data that provide results sufficient to support satisfactory performance. Without training, AI / ML models used at user equipment devices or RAN nodes can output erroneous or inaccurate results that can result in inappropriate actions or predictive estimates. Models trained with large and diverse training sample sets can produce AI / ML models that identify various radio conditions and can responsively refine the output of the model based on detected radio conditions. However, AI / ML model training is a time consuming, signaling overhead intensive, and processing heavy operation that is typically tailored to the needs of a particular user equipment, AI / ML model capabilities of the user equipment, or radio functions corresponding to the model being trained. For connected mode user equipment devices that are “known” at the network and have established channel conditions, data channels, control channels, serving radio access network nodes, or AI / ML capabilities, AI / ML model training can adequately adapt to the needs and radio conditions of each connected mode device. For connected mode user equipment, AI / ML model training information can be communicated or delivered via device specific or device generic established control channels or data channels. Training of AI / ML learning models with respect to user equipment in connected mode can be accomplished using conventional techniques by communicating or downloading model information to connected user equipment to perform using real-time data. As a result, for connected mode devices, communication of learning model information can occur via control change exchanges or via data channel delivery because for connected mode devices, data and control channels have been identified, adapted to device channel conditions, and otherwise established.

[0111] However, during typical usage conditions, user equipment devices are in an idle state (e.g., the UE has no connection established with a radio access network node) for a significant portion of the time the device is powered on. While idle, no data channel, control channel, and serving RAN are established, not even known. Therefore, AI / ML model training using conventional techniques for idle mode user equipment is not performed.

[0112] Excluding AI / ML model training support for idle mode devices significantly constrains or limits the true potential of AI / ML deployment, as most cellular user equipment devices are in an idle state for a significant portion of the time the user equipment is powered on. Moreover, training an AI / ML model using current radio condition information as one or more inputs to the learning model during an extended idle mode period, during which radio conditions can be changing (e.g., the user equipment is moving, or in idle mode), can facilitate faster and more reliable transitioning to an active connected state when the device attempts to establish a network session if the user equipment uses an AI / ML model that is not trained based on current conditions. For example, when a user equipment transitions to a connected mode with a radio access network node, a user equipment with a well-trained AI / ML model driving downlink beam prediction functionality during idle mode time (e.g., a model trained using current radio condition information) can use the learning model trained based on recent radio conditions to proactively determine a predicted fine downlink beam and identify to the radio access network node the predicted fine downlink beam that the user equipment device expects to get sufficient coverage. Therefore, by using a beam selection learning model to proactively train the learning model (e.g., train the model while the UE is idle) to determine, predict, or estimate the best beam on a user equipment device connected to a radio access network node, the node can avoid or exclude sending reference signals that will be used to refine the downlink beam determination corresponding to the device, thus potentially reducing access latency and signaling overhead.

[0113] Embodiments disclosed herein facilitate AI / ML model assistance and training for multiple radio functions for idle mode user equipment devices. Examples include AI / ML assisted timing advance (“TA”) acquisition, AI / ML assisted downlink reference signal and beam estimation, or AI / ML assisted uplink reference signal estimation. Embodiments disclosed herein facilitate training of AI / ML models that implement various radio functions when the user equipment is in idle mode, idle state, inactive mode, or inactive state (which can be collectively referred to as “idle” or “idle mode”). Training AI / ML models while the user equipment is idle can facilitate reducing the time to transition to connected mode, and can facilitate a more reliable and efficient transition from idle to connected than if the AI / MI were trained after the user equipment connects to a serving radio access network node. For example, when an idle mode device is sufficiently trained for TA acquisition and beam estimation while idle, the user equipment can proactively and prospectively efficiently perform TA estimation (while the user equipment is in idle mode) and predict / estimate one or more radio function parameter values, and transmit the one or more parameter values to the RAN node that the user equipment is attempting to connect to (e.g., to establish a data / call session). Thus, the RAN node that the UE is attempting to connect to can avoid performing a process to determine or obtain radio parameter values, as the parameter values can be proactively provided by the UE using a trained AI / ML model (trained while the UE is in idle mode), or obtained prospectively by the RAN node, thus enabling a faster and more efficient transition to connected mode and establishment of a data / call session than if such parameter values were determined after the UE begins establishing a connection.

[0114] Embodiments disclosed herein can implement inter-cell / inter-RAN coordination procedures via backhaul links to exchange coordinated AI / ML assisted training data, parameter values, updated models, samples, or other model information to be provided to idle mode user equipment devices. Embodiments described herein can implement multiple signaling procedures to deliver AI / ML assisted idle mode configuration and result feedback for both idle mode devices and coordinating RAN nodes to achieve a desired AI / ML model training accuracy corresponding to radio functions. In embodiments, a new radio application programming interface (“API”) can facilitate AI / ML capable devices reporting back AI / ML model predicted radio key performance indicators (“KPIs”) based on estimated parameter values generated by AI / ML models trained while the UE is in idle mode, or values derived therefrom, when transitioning from idle mode to connected mode. Thus, not only can AI / ML models at user equipment be trained while the user equipment is in idle mode; idle mode user equipment devices can provide proactive / prospective predictive intelligence using AI / ML training moments for various radio functions when attempting to connect to a radio access network node (e.g., when transitioning to connected mode).

[0115] Dynamic AI / ML training assistance for idle mode devices.

[0116] Turning now to Figure 3 , the actions shown by environment 300 facilitate idle mode AI / ML training assistance. At action 1, a coordinating group of RAN nodes that can include RAN nodes of neighboring RAN nodes / cells 105A and 105B can coordinate via backhaul link 320 with respect to idle mode AI / ML training assistance, including with respect to supporting radio function AI / ML training while user equipment is in idle mode. RAN nodes 105A and 105B can coordinate expected actions of user equipment devices 115A or 115B, as well as coordinate actions of the RAN nodes (e.g., the RAN nodes can coordinate user equipment to transmit reference signals or to transmit a preamble or reference signal), and associated with each RAN node AI / ML training resources (e.g., the RAN nodes coordinate to avoid resource scheduling for training purposes to overlap with resource scheduling for non-AI idle mode operations such as paging). At action 2A or 2B, coordinating RAN node 105A or 105B can deliver AI / ML configuration information block signal messages 500A or 500B, respectively, which can be detected by idle mode user equipment devices 115A or 115B within coverage of the coordinating RAN node, e.g., via broadcast signaling. Messages 500A or 500B can include or indicate configuration information corresponding to currently available AI / ML learning model idle mode training assistance and supported idle mode radio functions (e.g., learning models that facilitate radio functions that can be trained while the UE is in idle mode).

[0117] At action 3A or 3B, the idle mode user equipment device apparatus 115A or 115B with AI / ML capability can perform the AI / ML training actions (e.g., measure AI / ML training reference signals transmitted from the RAN 105A or RAN 105B, transmit uplink preambles and / or reference signals to the RAN 105A or RAN 105B, etc.) in or indicated by the configuration message 500A or 500B for the radio functions that can be supported or can be of interest (e.g., of interest to the RAN or UE). At action 4, in case the training results need to be fed back to the AI / ML model of all idle mode training or a subset thereof of the user equipment apparatus, the coordinating RAN nodes 105A and 105B can exchange AI / ML training results 340A or 340B (e.g., the results exchanged at action 4 can include, for example, uplink coverage received from UE device transmitted reference signals, estimated timing advance received from UE device, etc.) via the backhaul interface link 320, for example, to facilitate reconciliation of the learning model corresponding to the radio function at one RAN with the same learning model at the other RAN. The AI / ML training assistance result information block, as broadcast signal message 350A or 350B, respectively, contains the result(s) of inter-cell AI / ML training collected from neighboring cells / RAN nodes of the same AI / ML coordination group that can include 105A or 105B.

[0118] In embodiments, upon establishing a connection to a RAN 105A or 105B, the new radio signaling API can facilitate a user equipment device 115A or 115B indicating to a RAN node 105A or 105B a proactively determined radio access key performance indicator (“KPI”) that can be estimated based on an AI / ML model corresponding to a radio function associated with the KPI when the UE 115A or 115B is in idle mode. Thus, the user equipment device 115A or 115B can train an AI / ML model during an idle mode period, run various radio functions based on the trained model to estimate radio function information / KPIs, and subsequently provide such estimated / predicted intelligence to the RAN node via the new radio API during connection establishment with the RAN 105A or 105B, resulting in faster and more reliable network access. For example, in a user equipment device actively providing an anticipated (e.g., determined with a learned model trained while the UE is idle) best set of downlink beams to a RAN node when the user equipment device is establishing a connection to the RAN node, the RAN node can minimize the delay and use of overhead resources for transmitting reference signals to the user equipment device for the UE to use to determine a determined best downlink beam for the UE by using the proactively determined best beam. Because the UE has proactively provided such KPI intelligence, access delay and use of downlink / uplink signaling overhead can be reduced as compared to what can otherwise occur if no indication of a best beam predicted by an AI / ML learned model trained while the UE is idle is transmitted to the RAN during connection establishment.

[0119] Turning now to Figure 4 , the figure illustrates a learning model configuration information block message 410, which can include a training configuration resource indication 405 including or indicating a training configuration resource to be used by a radio access network node for broadcasting or available for broadcasting learning model training configurations, such as can be sent in a message 500A or message 500B sent by actions 2A or 2B described with reference to Figure 3 Figure 4 ​The training resource configuration resource indication 405 shown may include a training result resource indication, which may include or indicate training result resources that can be used to transmit determined radio function parameter values, which may be determined by the RAN that has already transmitted information block message 410. The training configuration resource indication 405 may indicate training configuration resources that will be used for broadcasting or can be used to broadcast AI / ML training configuration information block message 415, which may include or indicate a learning model training configuration. In an embodiment, the configuration information block message 415 may indicate one or more resources that will be used for transmission, or can be used by a radio access network node to transmit a learning model training configuration.

[0120] The training configuration resource indication 405 may indicate one or more training result resources that will be used for broadcasting or can be used to broadcast AI / ML training result information block messages 420. These AI / ML training result information block messages 420 may indicate training results of the learning model, or may indicate resources to be used for transmission, or may be used by radio access network nodes to transmit or broadcast training results of the learning model, such as in... Figure 3 Action 5 shown sends either message 350A or 350B as a result. It should be understood that message 350A may not include the same information as message 350B.

[0121] continue Figure 4 As part of the Broadcast Master Information Block (“MIB”) 410 or Basic System Information Block (“SIB”) 410 (information block 410 can be either a MIB or a SIB), the RAN can indicate the presence of the AI / ML Idle Model Training Auxiliary Configuration Information Block 415 or the AI / ML Result Information Block 420 via indication 405. The RAN can send a novel presence indication 405, as part of the MIB or SIB, to indicate the availability of the novel system information block 415 or its corresponding resource, which may carry or indicate a resource that can carry an idle mode AI / ML training auxiliary configuration, which may be embodied in the reference... Figure 5A , Figure 5B , Figure 5C or Figure 5D In one or more example configurations described. Instruction 405 may indicate a resource that can carry novel result system information block 420, which may carry or may indicate a resource that can carry idle mode AI / ML training auxiliary sampling results, which may be reflected in the reference. Figure 6 or Figure 7In the described example. When the idle mode user equipment device determines via indication 405 that there is an AI / ML SIB and corresponding resource(s), the user equipment can decode (which can include blind decoding) one or more AI / ML configuration blocks 415 or one or more AI / ML result blocks 420, which can include information corresponding to one or more idle mode indicated radio functions, such as learning model training configuration or determined radio function parameter values.

[0122] Now turning to Figure 5A , an idle mode AI / ML configuration 500 SIB is shown. Configuration 500 can be contained in the information block 415 described with reference to Figure 4 , or can be indicated by an indication included in information block 415 (e.g., information block 415 can include configuration 500, or can indicate resources that can be used by the user equipment to receive configuration 500). Configuration 500 can include an identifier field 502 indicating an AI-ML learning model, a radio function that can be implemented by the AI / ML learning model, or an AI / ML learning model characteristic. One or more coordinating RAN node identifiers in field 504 can be associated in configuration 500 with each defined radio characteristic 502A to 502n, which can be indicated by a defined or determined characteristic identifier in field 502. Configuration 500 can associate a training action 506 for an uplink or downlink direction with a training characteristic 502 and an associated coordinating RAN of a coordinating group of RANs. Configuration 500 can associate training resources 508 that can be used or will be used to perform one or more corresponding training actions 506 with corresponding RANs 504 with respect to characteristic 502. Thus, configuration 500 can be used to indicate to an idle mode user equipment one or more AI / ML models or characteristics that can be trained for one or more radio functions. Configuration 500 can not necessarily indicate or command the user equipment to perform training actions 506 for characteristic 502 with respect to RANs indicated in RAN file 504.

[0123] In Figure 5BIn the illustrated example, the example AI / ML configuration included or indicated in the configuration information block 510 can facilitate random access channel (“RACH”) timing advance (“TA”) training of a TA learning model or TA learning model feature by a feature corresponding to the feature identifier included in the feature identifier field 512. Adjacent RAN nodes in a RAN coordination group (which can include two or more RAN nodes that can be adjacent or proximate) can coordinate RACH TA training resources 518A, 518B, 518n that a user device can use to transmit a corresponding defined TA preamble. The TA preamble can be contained in or part of a defined preamble group indicated in the preamble group field 516. The user device can transmit the preamble indicated in the field 516 in the uplink direction to a coordinating RAN identified in the corresponding RAN field 514. The preamble group can include a defined preamble group that has been configured to be associated with a given RAN 514, such that reception by the RAN via resources 518 corresponding to the preamble group 516 corresponding to the given RAN can indicate to the receiving RAN that the RAN is to determine a TA with respect to a user device that can have transmitted the preamble. In response to receiving a preamble of a TA preamble group or TA preamble pool, the receiving RAN can train or update a learning model or learning model feature corresponding to TA acquisition. The trained / updated results of the RAN can be transmitted to user devices via a results SIB. Thus, upon decoding the AI / ML configuration SIB 510, an idle mode device, if interested or supporting AI / ML driven TA acquisition 512, can transmit the indicated uplink TA preamble, which can be randomly selected by the idle mode user device from the configured TA preamble group 516B via corresponding resources 518B to the associated target RAN node 514B. Adjacent RAN nodes can coordinate with one another such that the resources indicated in the field 518 to be used for resources used by the corresponding RAN identified in the field 514 are not to overlap with resources that can be used by the adjacent RAN for non-AI / ML training purposes (e.g., with respect to time or frequency).

[0124] In Figure 5CIn another embodiment shown, for example, the AI / ML configuration information block 520 can facilitate training of channel state information (“CSI”) features 522. The coordinating RAN nodes indicated in field 524 can transmit a set of CSI beams indicated in field 526 via resources indicated in field 528. An idle mode user equipment can receive and detect a received coverage level corresponding to the beams via corresponding resources indicated in field 528, and can subsequently train a CSI AI / ML model at the user equipment. The user equipment can transmit a determined best beam result indication corresponding to the RAN identified in field 524 via small data transmission (e.g., according to existing techniques that facilitate fast transmission of small payloads by idle mode devices), or the idle mode user equipment device can transition to connected mode and transmit the best beam indication as a data transmission before returning to idle mode.

[0125] In Figure 5D In another embodiment shown, the example AI / ML configuration block 530 can facilitate training of a sounding reference signal (“SRS”) learning model feature 532 at a user equipment. The training action indication 536 in configuration 530 can include an indication by the used equipment to transmit a configured SRS reference signal pattern to RAN nodes of an AI / ML RAN coordination group via resources 538 while idle, as indicated in field 534. SRS training can facilitate RAN and user equipment optimization of uplink decoding performance. Training of RAN nodes by a user equipment while the user equipment is idle on uplink channel conditions can facilitate providing dynamic uplink transmission configuration to the user equipment almost immediately when the user equipment connects to the RAN node and establishes an uplink session. By training the SRS learning model feature while the user equipment is in idle mode, the RAN node can avoid requesting additional SRS transmissions from a transitioning user equipment. Thus, based on the SRS model feature 532 trained while the user equipment is idle, less time is spent refining uplink channel quality determinations before an uplink session is established, which can result in faster uplink session establishment.

[0126] Turning now to Figure 6 , an example SIB 600 is shown for carrying AI / ML result samples. The SIB 600 can be transmitted by a RAN node to a user equipment, for example, in response to an indication 405 shown. The indication 405 can indicate (e.g., the SIB 600 can be the same as) the SIB 420 shown. In embodiments, the SIB 600 shown can include an indication of a training result resource that can be or will be used for transmission of UE AI / ML model training information / results by the RAN, such as determined radio function parameter values. Figure 4 Figure 4 Figure 6

[0127] ​​​In an example, corresponding training results 604A corresponding to the identified learning model features 602 can be transmitted or broadcast to user equipment via the SIB 600. For example, one or more RAN nodes belonging to an AI / ML training coordination group can transmit or broadcast a SIB 600 including AI / ML training samples, which can have been determined at the RAN node and can be beneficially used by idle mode user equipment. Thus, for radio features IDs corresponding to AI / ML models 602 trained at the RAN, the RAN can transmit training results or training sample 604 to user equipment via the SIB 600.

[0128] In Figure 7 In the illustrated embodiment, the AI / ML training results SIB 700 can facilitate training of a RACH TA learning model or training of RACH TA learning model features 702. For RACH TA learning model features, multiple information objects 720 can be indicated as corresponding to radio access network nodes, such as Figure 3 The illustrated RAN 105A or RAN 105B can belong to or participate in an AI / ML coordination group. Thus, estimated TA levels or TA parameter values can be reported to UEs by RANs via the results SIB 700 corresponding to preamble indicated by indication 720. For example, if Figure 3 The illustrated UE transmits a preamble selected from Figure 5B The illustrated preamble set 516A (e.g., assuming RAN 105A is identified in field 514A of configuration 510 Figure 5B The illustrated RAN 105A), then RAN 105A can be identified in field 720 of SIB 700. RAN 105A can return TA results 720A-1 to the UE indicating TA produced by RAN 105A based on receiving a preamble yl from the UE while the UE is in idle mode performing AI / ML training operations. If the UE transmitted preambles y2 or y i , respectively, while in idle mode, RAN 105A can also return results 720A-2 or 720A-ii, respectively. Thus, AI / ML capable user equipment devices can receive and decode the results SIB 700 while in idle mode and according to resources indicated by, for example, indication 405 of MIB 410 Figure 4 illustrated) and extract from therein TA levels corresponding to one or more RAN nodes to which the UE can have transmitted one or more preambles 516 Figure 5B illustrated).

[0129] Turning now to Figure 8The radio API signaling message environment 800 is shown. The message 820 can carry in the novel message portion 825 the proactively / deterministically determined AI / ML model output radio estimates from the user equipment device 115 to the RAN node 105. For example, upon the user equipment device 115 transitioning from idle mode to connected mode with the RAN 105, the user equipment can proactively provide one or more radio KPI estimates to the RAN node. The one or more KPIs can be generated by an AI / ML model trained by the user equipment 115 while idle. The trained model can be a model that implements one or more radio functions. In Figure 8 In the example shown, the user equipment device 115 can indicate to the RAN 105 via the novel API portion 825 of the RRC message 820 the best downlink CSI beam with beam index 802 corresponding to the trained beam feature 801 via which the user equipment expects (e.g., based on the trained AI / ML model) to receive the best coverage level with respect to other downlink beams corresponding to the RAN 105. The UE 115 can also send in the novel API portion 825 the expected / predicted uplink timing advance value 806 corresponding to the trained feature 805. The indication of the best beam index 802 can correspond to the predicted beam prediction accuracy 804 (determined by the AI / ML model trained while the UE was idle), and the indication of the TA 806 can correspond to the predicted TA accuracy 808 (determined by the AI / ML model trained while the UE was idle).

[0130] Accordingly, the session establishment or connection establishment between the UE 115 and the RAN 105 can be based on information and KPI elements that are determined proactively / forward looking (e.g., determined while the UE is idle), and thus, the RAN 105 can avoid triggering CSI beam refinement procedures or uplink timing advance acquisition procedures during session establishment, as the UE device has proactively provided the predicted KPIs to the RAN. As part of the AI KPI information block 825 that can be transmitted during the RRC connection request signaling 820, the UE 115 can only signal the achievable prediction accuracy 804 or 808 corresponding to the AI / ML model that generated the respective predicted KPI 802 or 806. Because the AI / ML model can have been trained at the device while in idle mode, during which the RAN 105 is typically unaware of the performance of the AI / ML model, the RAN can use the accuracy prediction 804 or 808, respectively, to determine whether to use the estimated KPI 802 or 806. Accordingly, the novel KPI information elements 802 and 806, and the corresponding accuracy information elements 804 and 808, can facilitate the RAN to determine whether the AI model at the UE 115 (which can be used to generate the predicted KPI information elements) is operating satisfactorily. If the reported prediction accuracy level 804 or 806 is low (e.g., below a threshold configured at the RAN 105), the RAN can ignore the corresponding KPI 802 or 806 received from the UE 115, and instead trigger conventional radio procedures for establishing device connection (e.g., determine beam selection via beam sweeping or TA acquisition during RRC connection establishment). As an example, if the respective accuracy 804-1 or 804-2 does not satisfy a configured threshold, the RAN 105 can override and ignore the signaled best beam indication 802-1 or 802-2, and traditionally trigger a beam refinement procedure during connection establishment.

[0131] Turning now to Figure 9 the figure illustrates a timing diagram for an example method 900. At action 905, neighboring RAN nodes 105A and 105B can exchange supported AI / ML model training features via backhaul / Xn interfaces that can be trained while the UE 115 is idle. The information exchanged at action 905 can include specific reception and transmission signal information corresponding to each coordinating RAN 105A or 105B. The information exchanged at action 905 can include timing, frequency, and periodicity information corresponding to the RAN 105A or 105B. At action 910A or 905B, the RAN node 105A or 105B, respectively, can transmit a novel AI / ML configuration system information block scheduling indication (e.g., the indication 405) as part of a synchronization signal block (SSB) and / or as part of a master information block (e.g., as a master information block (MIB) or a system information block (SIB)) to the UE 115. Figure 4 the indication 405) as part of a synchronization signal block (SSB) and / or as part of a master information block (e.g., as a master information block (MIB) or a system information block (SIB)) to the UE 115.Figure 4 The new indication sent at action 910A or 910B can include an idle mode inter-cell training configuration indication. In Figure 9 At actions 915A and 915B, RAN nodes 105A and 105B can send an AI / ML idle mode training configuration to UE 115 via the first scheduled configuration AI / ML SIB resources (e.g., corresponding to information block 415 shown in Figure 4 At actions 915A and 915B, RAN nodes 105A and 105B can send an AI / ML idle mode training configuration to UE 115 via the first scheduled configuration AI / ML SIB resources (e.g., corresponding to information block 415 shown in Figure 5A At actions 920A or 920B, UE 115 can receive, decode, or train an AI / ML model according to the configuration received at actions 915A or 915B.

[0132] On a condition that a configured AI / ML idle mode training period for a certain AI / ML feature being trained expires during an idle mode of UE 115, RAN node 105A or 105B can receive one or more AI / ML idle mode training reports from each other via a backhaul link at action 925. At actions 930A or 930B, RAN node 105A or 105B can send one or more novel AI / ML results system information block scheduling indications to UE 115 as part of a synchronization signal block and / or a master information block, which can indicate resources scheduled to send AI / ML idle mode inter-cell training result samples (e.g., information exchanged at action 925) to UE 115. The indications referenced at actions 930A or 930B can correspond to or can be Figure 4 The new indication sent at action 910A or 910B can include an idle mode inter-cell training configuration indication. In Figure 9 At actions 935A or 935B, RAN node 105A or 105B can send one or more AI / ML training result samples via the second scheduled AI / ML training result sampling SIB resources (e.g., corresponding to training result information block 420 shown in Figure 4 At actions 935A or 935B, RAN node 105A or 105B can send one or more AI / ML training result samples via the second scheduled AI / ML training result sampling SIB resources (e.g., corresponding to training result information block 420 shown in

[0133] Turning now to Figure 10The diagram illustrates a flowchart of example method 1000. Method 1000 begins with action 1005. In action 1010, a radio access network node may determine learning model configuration information, or a group of adjacent radio access network nodes may cooperate or coordinate with each other and exchange learning model configuration information. The determined or exchanged learning model configuration information may include indications of radio functions that may be implemented or facilitated by one or more learning models at the radio access network node or user equipment. The determined or exchanged learning model configuration information may correspond to resources that the radio access network node can use to broadcast learning model configurations or learning model results for reception by user equipment in idle mode. The determined or exchanged learning model configuration information may correspond to a radio function learning model, which can be trained using information generated by training actions performed when the user equipment is idle. Training actions may include one or more actions that can be performed by the user equipment when the user equipment is idle. Training actions may include one or more actions that can be performed by the radio access network node when the user equipment is idle. Examples of training actions may include sending references. Figure 5B Description of preamble 516, receive reference Figure 5C The described CSI reference signal identifier 526, or the sent reference Figure 5D The SRS group identifier 536 is described.

[0134] continue Figure 10 The description states that in action 1015, a radio access network node that determines configuration information or coordinates with another radio access network node to determine configuration information at action 1010 may broadcast a training configuration information block message resource indication in MIB 410, for example, referring to... Figure 4 The description indicates instruction 405. Continue. Figure 10 As described in action 1020, the radio access network node can, via the resource indication indicated in indication 405, in the training configuration information block (e.g., reference...). Figure 4 The broadcast learning model configuration in the described information block 415 (e.g., reference) Figure 5A The configuration described is 500).

[0135] continue Figure 10 As described in action 1025, the user equipment can configure resources according to the training settings indicated in action 1015, for example, according to the reference... Figure 4 The description refers to the training configuration resources indicated in instruction 405, and the receiving and detection configuration information blocks. Continue. Figure 10In the description of Action 1035, the learning model configuration information block or the learning model configuration contained therein can include an indication of one or more training actions corresponding to one or more learning models, or a learning model feature corresponding to a radio function that can be implemented by a learning model. At Action 1035, the user equipment can decide not to perform any of the training actions indicated in the learning model configuration, to perform one training action, multiple training actions, or all of the training actions. At Action 1040, based on or in response to the user equipment performing the training action(s) indicated in the learning model configuration, the radio access network node that transmitted the learning model configuration, or another radio access network node that can cooperate with the radio access network node that broadcast the learning model configuration at Action 1010, can determine a training result.

[0136] At Action 1045, the radio access network node that determined the training result at Action 1040 can determine whether the determined training result should be broadcast, transmitted, or otherwise provided to the user equipment that performed the training action at Action 1035. The determination made at Action 1045 can include a determination of whether the training result determined at Action 1040 should be broadcast, transmitted, or otherwise provided to user equipment other than the user equipment that performed the training action at Action 1035. If it is determined at Action 1045 that the training result determined at 1040 need not be provided to the user equipment, the method 1000 can proceed to Action 1070. At Action 1070, the radio access network node and the user equipment that performed the training action at Action 1035 can establish a communication connection in accordance with a learning model that can be trained based on the training action performed by the user equipment at Action 1033. It will be appreciated that the user equipment can be in an idle mode, or can otherwise not have a connection established with the radio access network node during at least the performance of Action 1015 to the performance of Action 1045, and the establishment of a connection with the radio access network node can be initiated at Action 1070. Accordingly, the establishment of a connection at Action 1070 can include the transmission and reception of radio resource control signaling messages. After the connection between the user equipment that performed the training action at Action 1035 and the radio access network node in an idle mode is established at Action 1070, the method 1000 proceeds to Action 1075 and ends.

[0137] Returning to the description of action 1045, if the radio access network node that determined the training result in 1040 determines that the user equipment may need to receive the training result determined in action 1040, then method 1000 proceeds to action 1050. At action 1050, the radio access network node that determined the training result in action 1040 can broadcast the training result determined in action 1045, which will be received or detected by the user equipment performing the training action in action 1035, based on the training result resources that can be indicated in the training configuration information block message broadcast in action 1015. Therefore, in addition to the training model configuration information block message resource indication sent or broadcast in action 1015 specifying resources that can be used to send or broadcast the configuration information block in action 1020 (e.g., besides referencing...), Figure 4 The described instruction 405 can be used via reference Figure 4 The training configuration information block 415 described herein is used to send resources other than those described in Figure 5 (referring to the sending configuration 500 resource). In action 1015, the training configuration information block message resource indication is sent, for example, referring to... Figure 4 The described instruction 405 may also include an indication of training resources available or to be used by the radio access network node for determining the training results in action 1040 to broadcast the training results in 1050. In action 1055, the user equipment may receive one or more training results, based on training result resources already indicated in the training configuration information block message resource indication broadcast in 1015, for example, based on reference... Figure 4 The resources described in instruction 405 may be broadcast at action 1050. The user equipment may use the training results received at 1055 to update or train the learning model or learning model features, which correspond to the training action performed at action 1035.

[0138] At action 1060, in embodiments, the user equipment can determine to use an updated or trained learning model that can be trained or updated based on, in accordance with, or in response to the performance of the training action at action 1035, or that can be trained or updated based on, in accordance with, or in response to the training results received at action 1055, to prospectively determine one or more key performance indicators. For example, the training action performed at action 1035 can include the user equipment sending a preamble to be used by the radio access network node for training a timing advance machine learning model or a timing advance machine learning model feature. The results of training the machine learning model or machine learning model feature associated with determining timing advance can cause the model parameter results determined by the radio access network node at action 1045 to be provided to the user equipment at action 1050. The user equipment, after updating the timing advance machine learning model or timing advance machine learning model feature, can use the updated / trained timing advance machine learning model or updated / trained timing advance machine learning model feature to prospectively determine timing advance with respect to the radio access network that can have determined or can have cooperatively determined the learning model configuration information at action 1010 at action 1063. The term “prospectively” (the term “proactively” can be used interchangeably herein) is used in the sense that the user equipment prospectively determining timing advance can be in idle mode and can determine the timing advance prior to initiating establishment or attempting to establish a connection with the radio access network node that can have determined or cooperatively determined the machine learning model configuration information at action 1010.

[0139] At action 1065, the user equipment prospectively determining one or more key performance indicators at action 1063 can send the one or more prospectively determined KPIs to the radio access network node prior to or during the connection establishment procedure performed by the user equipment at action 1070, thus avoiding determining the one or more key performance indicators, such as timing advance or best beam selection, during the connection establishment at action 1070. After establishing the connection at action 1070, the method 1000 proceeds to action 1075 and ends.

[0140] Now turning to Figure 11The figure illustrates an example embodiment method 1100, which includes broadcasting, by a radio access network node comprising a processor, a learning model configuration information block message comprising a training configuration resource indication indicating training configuration resources available for broadcasting learning model training configurations by the radio access network node, at block 1105; broadcasting, by the radio access network node, a learning model training configuration according to the training configuration resources, at block 1110; wherein the learning model training configuration comprises a training action indication indicating a training action performable by an idle user equipment, at block 1115; wherein the training action corresponds to a radio function learning model, wherein performing the training action results in a determined radio function parameter value corresponding to the radio function learning model, the method further comprising: receiving, by the radio access network node, a radio resource control signal message comprising the determined radio function parameter value from the idle user equipment; and establishing, by the radio access network node, a connection with the idle user equipment using the determined radio function parameter value received from the idle user equipment in the radio resource control signal message, at block 1125, thus the idle user equipment becomes a connected user equipment.

[0141] Turning now to Figure 12 The figure illustrates a first radio access network node 1200, at block 1205, the node 1200 comprises a processor configured to receive, from a second radio access network node, a non-training resource indication, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, indicating to the first radio access network node non-training resources to be used by the second radio access network node for non-training operations; schedule, at block 1210, training resources to be used by at least one idle mode user equipment for performing a training action with respect to the first radio access network node, such that the training resources and the non-training resources do not overlap; broadcast, at block 1215, a master information block message comprising a training configuration resource indication indicating training configuration resources to be used by the first radio access network node for broadcasting learning model training configurations; and broadcast, at block 1220, a learning model training configuration according to the training configuration resources.

[0142] Turning now to Figure 13The figure illustrates a non-transitory machine-readable medium 1300 including executable instructions that, when executed by a processor of a first radio access network node, facilitate performance of operations including broadcasting a first information block message including a training configuration resource indication indicating a training configuration resource; 1310 broadcasting a learning model training configuration according to the training configuration resource, wherein the training model training configuration includes a training action indication indicating a training action to be performed by a first idle user equipment of the at least one idle user equipment with respect to the first radio access network node to produce a first determined learning model parameter value; at block 1315, receiving the first determined learning model parameter value from the first idle user equipment of the at least one idle user equipment; at block 1320, receiving a second determined learning model parameter value from a second radio access network node, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, wherein the training action is performed by at least a second idle user equipment of the at least one idle user equipment with respect to the second radio access network node to produce the second determined learning model parameter value; at block 1325, determining an updated learning model based on the first determined learning model parameter value and based on the second determined learning model parameter value; and at block 1330, broadcasting the updated learning model to the first idle user equipment of the at least one idle user equipment via a second information block message.

[0143] Turning now to Figure 14 The figure illustrates an example embodiment method 1400 including receiving, by a user equipment including a processor, from a first radio access network node, at block 1405, a learning model configuration information block message including a training configuration resource indication indicating a training configuration resource available for broadcasting a learning model training configuration by the first radio access network node; receiving, by the user equipment, at block 1410, the learning model training configuration according to the training configuration resource; decoding, by the user equipment, at block 1415, the learning model training configuration; at block 1420, wherein the decoding of the learning model training configuration includes blind decoding; and at block 1425, wherein the learning model training configuration includes a training action indication indicating a training action to be performed by the user equipment.

[0144] Turning now to Figure 15The figure illustrates an example user equipment 1500 that includes a processor configured to receive, from a radio access network node, a learning model configuration information block message, where the learning model configuration information block message includes a training configuration resource indication that indicates a training configuration resource available for receiving a learning model training configuration from the radio access network node; receive, at block 1510, the learning model training configuration according to the training configuration resource; decode, at block 1515, the learning model training configuration, where the learning model training configuration includes a training action indication that indicates a training action to be performed by the user equipment; perform, at block 1520, the training action to produce a training action result; send, at block 1525, the training action result to the radio access network node; where the training action includes generating a sounding reference signal, at block 1530, such that the training action result is the generated sounding reference signal, and where the generated sounding reference signal is sent to the radio access network node, the generated sounding reference signal is usable by the radio access network node to train an uplink resource grant learning model to produce a trained uplink resource grant learning model; establish, at block 1535, a communication connection with the radio access network node, where the communication connection includes at least one uplink resource granted by the radio access network node based on the trained uplink resource grant learning model; and where the radio access network node’s grant of the at least one uplink resource is based on the user equipment excluding transmission of the sounding reference signal after the user equipment sends the generated sounding reference signal, at block 1540.

[0145] Turning now to Figure 16 The figure illustrates a non-transitory machine-readable medium 1600 that includes executable instructions that, when executed by a processor of a user equipment, facilitate performance of operations including receiving, from a first radio access network node while the user equipment is idle, a learning model configuration information block message that includes a training result resource available for receiving, by the user equipment, a training result from the first radio access network node; receiving, at block 1610, from the first radio access network node while the user equipment is idle, a learning model training configuration that includes a training action indication that indicates a training action performable by the user equipment with respect to at least the first radio access network node; performing, at block 1615, while the user equipment is idle, the training action with respect to the first radio access network node to produce a first training action result; performing, at block 1620, while the user equipment is idle, the training action with respect to a second radio access network node to produce a second training action result, where the second radio access network node is a neighboring radio access network node with respect to the first radio access network node; and receiving, at block 1625, the second training action result from the second radio access network node.

[0146] To provide additional context for various embodiments described herein, Figure 17 The following discussion and the accompanying drawings aim to provide a brief, general description of a suitable computing environment 1700 in which the various embodiments of the application described herein can be implemented. While the above description has been 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 also can be implemented in combination with other program modules and / or as a combination of hardware and software.

[0147] Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, IoT devices, distributed computing systems, and comparable devices, each of which can be operated to execute the methods described herein, and each of which can be operatively coupled to one or more associated devices.

[0148] The embodiments illustrated herein can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0149] Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and / or communications media, each of which can be distinct from each other and each of which can be present in a computing device. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, program data, structured data, or unstructured data.

[0150] Computer-readable storage media can include without limitation random access memories (RAM), read only memories (ROM), electrically erasable programmable read only memories (EEPROMs), flash memories, or other memory technologies, compact disc read only memories (CD-ROMs), digital versatile discs (DVDs), Blu-Ray discs (BDs), or other disc storage, cassette tapes, punch- cards, paper, optical marks, or other computing device storage devices, and / or any other tangible and / or non-transitory media that can be used to store desired information. In this context, the terms “tangible” or “non-transitory,” as applied to a storage, memory, or computer-readable medium, are to be understood as merely excluding a propagating transitory signal per se as modifiers and do not relinquish a right to all standards- of-memory that are not only propagating transitory signals per se.

[0151] 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, for various operations with respect to information stored by the media.

[0152] Communication media typically embodies computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal carrier wave or other transport mechanism and includes any information delivery or transport media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media.

[0153] Referring again to Figure 17 , an example environment 1700 for implementing various embodiments of aspects described herein includes a computer 1702, which includes a processing unit 1704, a system memory 1706, and a system bus 1708. The system bus 1708 couples system components including, but not limited to, the system memory 1706 to the processing unit 1704. The processing unit 1704 can be any of various commercially available processors, and can include dual microprocessors and other multi-processor architectures. The system memory 1706 can include read-only memory (ROM) and random access memory (RAM) and can include a cache or buffers as well.

[0154] The system bus 1708 can be any of several types of bus structures including a memory bus with memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The 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), EEPROM, flash memory or the like, which BIOS contains the basic routines that help to transfer information between elements within the computer 1702, such as during startup. The RAM 1712 can also include high-speed RAM such as static RAM for caching data.

[0155] The 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 drive 1720 (e.g., which can read or write from CD-ROM disks, DVD, BD, etc.). While the internal HDD 1714 is shown as being within the computer 1702, the internal HDD 1714 can also be configured for external use in an appropriate chassis (not shown). Moreover, while not shown in the environment 1700, a solid state drive (SSD) can be used in addition to, or in place of, the HDD 1710. The HDD 1714, external storage devices 1716, and disk drive 1720 can be connected to the system bus 1708 by an HDD interface 1724, an external storage interface 1726, and a disk drive interface 1728, respectively. The interface 1724 for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technology. Other external drive connection technology is within the contemplation of the embodiments described herein.

[0156] The drives and their associated computer-readable storage media, provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth for the computer 1702. For the computer 1702, the drives and storage media accommodate the storage of any data in a suitable digital format. While the description of computer-readable storage media above refers to a type of storage device, one skilled in the art will appreciate that other types of storage media that are

[0157] Numerous 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.

[0158] Computer 1702 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment of operating system 1730, and the emulated hardware may optionally be compatible with... Figure 17 The hardware shown differs. In this embodiment, the operating system 1730 may include one of multiple virtual machines (VMs) hosted on the computer 1702. Furthermore, the operating system 1730 may provide a runtime environment, such as the Java Runtime Environment or the .NET Framework, for use with the application 1732. The 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, which is a lightweight, standalone executable software package that includes, for example, code, runtime, system tools, system libraries, and application settings.

[0159] Furthermore, computer 1702 may include security modules, such as a Trusted Processing Module (TPM). For example, using a TPM, the boot component mixes the next live boot component and waits for the result to match a security value before loading the next boot component. This process can occur at any layer of computer 1702's code execution stack, for example, applied to the application execution level or the operating system (OS) kernel level, thereby achieving security at any code execution level.

[0160] A user can enter commands and information into the computer 1702 through one or more wire / wireless input devices, e.g., a keyboard 1738, a touch screen 1740, and a pointing device, such as a mouse 1742. Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and / or virtual reality headset, a gamepad, a stylus, an image input device, such as a camera, a gesture sensor input device, a visual movement sensor input device, an emotional or facial detection device, a biometric input device, such as a fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit 1704 through an input device interface 1744 that can be coupled to the system bus 1708, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc.

[0161] A display 1746 or other type of display device can also be connected to the system bus 1708 via an interface, such as a video adapter 1748. In addition to the display 1746, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0162] The computer 1702 can operate in a networked environment using logical connections to one or more remote computers, such as a remote computer(s) 1750. The remote computer(s) 1750 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1702, although, for purposes of brevity, only a memory / storage device 1752 is illustrated. The logical connections depicted include wire / wireless connectivity to a local area network (LAN) 1754 and / or larger networks, e.g., a wide area network (WAN) 1756. Such LAN and WAN networking environments are commonplace in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

[0163] When used in a LAN networking environment, the computer 1702 can be connected to the local network 1754 through a wire / wireless communication network interface or adapter 1758. The adapter 1758 can facilitate wire or wireless communication to the LAN 1754, which can also include a wireless access point (AP) disposed thereon for communicating with the adapter 1758 in wireless mode.

[0164] When used in a LAN or WAN networking environment, the computer 1702 can be connected to the LAN 1754 through a network adapter 1758. When used in a WAN networking environment, the computer 1702 can include a modem 1760 or other means for establishing communications over the WAN 1756, such as by telephone

[0165] When used in a LAN or WAN networking environment, the computer 1702 can access cloud storage systems or other network-based storage systems in addition to or instead of external storage devices 1716 as described above. Generally, a connection between the computer 1702 and a cloud storage system can be established through the LAN 1754 or WAN 1756, e.g., through the adapter 1758 or modem 1760, respectively. In connecting the computer 1702 to an associated cloud storage system, the external storage interface 1726 can manage storage provided by the cloud storage system with the aid of the adapter 1758 and / or modem 1760, just as it manages other types of external storage. For example, the external storage interface 1726 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 1702.

[0166] The computer 1702 can operate in a networked environment using logical connections to one or more other computers, such as a host 1788. The host 1788 can be located in a remote location, such as a server computer in a data center, or can be located in a local location, such as a server computer in a local area network (LAN) or WAN. The host 1788 can be connected to the LAN 1754 through a network adapter 1790, or can be connected to the WAN 1756 through a modem 1792, or other means for establishing communications over the WAN 1756, such as by telephone. The host 1788 can be a personal computer, a server, a router, a network PC, a workstation, a microprocessor, a microprocessing system, a minicomputer, a mainframe, a supercomputer, a network appliance, a web appliance, or other computing device connected to an external agent and external storage as described above. The host 1788 can also be or include any wireless device or entity operatively disposed in wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant, a communications satellite, any equipment or location associated with a wireless detectable tag (e.g., a kiosk, a newsstand, a shelf, etc.), and a telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technology. Thus, communication can be between two devices, or a self-organizing communication, or a pre-defined structure as in a traditional network.

[0167] Turning to Figure 18The figure illustrates a block diagram of example UE 1860. UE 1860 may include a smartphone, a wireless tablet computer, a wirelessly capable laptop computer, a wearable device, a machine that can facilitate vehicle telematics, a tracking device, a remote sensing device, etc. UE 1860 includes a first processor 1830, a second processor 1832, and shared memory 1834. UE 1860 includes a radio front-end circuitry 1862, which may be referred to herein as a transceiver, but should be understood to generally include transceiver circuitry, separate filters, and separate antennas for facilitating the transmission and reception of signals over a wireless link, such as... Figure 1 One or more wireless links 125, 135, and 137 are shown. Furthermore, transceiver 1862 may include multiple sets of circuitry, or may be tunable to accommodate different frequency ranges, different modulation schemes, or different communication protocols to facilitate long-range wireless links such as link 125, device-to-device links such as link 135, and short-range wireless links such as link 137.

[0168] continue Figure 18 As described above, UE 1860 may also include SIM 1864 or SIM profile, which may include information stored in memory (memory 34 or a separate memory portion) for facilitating communication with... Figure 1 The wireless communication of RAN 105 or core network 130 shown. Figure 18 The SIM 1864 is presented as a single component in the shape of a traditional SIM card; however, it should 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 should 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). SIM profile 1864 may also include SIM or SIM profile-specific identification information, such as International Mobile Subscriber Identity (“IMSI”) or information that may constitute an IMSI.

[0169] The illustrated SIM 1864 is coupled to both the first processor portion 1830 and the second processor portion 1832. Such an implementation can provide the advantage that the first processor portion 30 can not need to request or receive information or data from the SIM 1864 that the second processor 1832 can request, thus eliminating the use of the first processor as an "intermediary" when the second processor uses information from the SIM in performing its functions and executing applications. The first processor 1830, which can be a modem processor or a baseband processor, is shown as being less than the processor 1832, which can be a more complex application processor, to intuitively indicate the relative level of complexity (i.e., processing power and performance) and the corresponding relative level of operational power consumption between the two processor portions. When the UE 1860 does not need the second processor portion 1832 to execute an application and process data related to the application, keeping it in a dormant / inactive / low-power consumption state provides the advantage of reducing power consumption when the UE only needs to use the first processor portion 1830 in a listening mode for monitoring routine configured bearer management and mobility management / maintenance procedures, or for monitoring search spaces that the UE is configured to monitor while the second processor portion remains inactive / dormant.

[0170] The UE 1860 can also include sensors 1866 that can provide signals to the first processor 1830 or the second processor 1832, such as a temperature sensor, an accelerometer, a gyroscope, a barometer, a humidity sensor, and the like. The output devices 1868 can include, for example, one or more visual displays (e.g., computer displays, VR devices, etc.), acoustic sensors such as speakers or microphones, vibration components, and the like. The output devices 1868 can include software that interfaces with output devices (e.g., visual displays external to the UE 1860, speakers, microphones, haptic devices, smell or taste devices, etc.).

[0171] The following glossary of terms as presented in Table 2 can apply to one or more descriptions of the embodiments disclosed herein. Table 2

[0172] The above description includes non-limiting examples of various embodiments. Of course, it is impossible to describe every conceivable combination of components or methodologies for describing the disclosed subject matter, and one skilled in the art will recognize that further combinations and permutations of various embodiments are possible. The disclosed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims.

[0173] With respect to various functions performed by the above-described components, devices, circuits, systems, and the like, unless otherwise indicated, the terms describing such components (including references to a "means") are intended to also include any structure for performing the stated component's function, either alone or in combination with other structure, even though such structure is not explicitly described herein. Further, although specific features of the disclosed subject matter can be disclosed with reference to one or more implementations, such features can be combined with other features of other implementations as appropriate, unless explicitly stated otherwise.

[0174] The terms "exemplary" and / or "illustrative," as can be used herein, are intended to mean serving as examples, instances, or illustrations. For the avoidance of doubt, the subject matter disclosed herein is not limited to the examples disclosed herein and is not necessarily limited to any particular implementations. In addition, any aspect or design described herein as "exemplary" and / or "illustrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent structure and operations. Furthermore, the term "comprising" (as well as variations such as "comprise," "has," "including," and so on) as used herein is intended to mean that the composition or process include the recited elements, but not excluding others. The term "coupled" as used herein is intended to mean the joining of two members that are coupled directly to each other or that, while not directly coupled to each other, are coupled to the same member or entity.

[0175] The term "or," as used herein, is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless specified otherwise, or clear from context, "X employs A or B" is intended to mean any of the natural inclusive permutations. That is, if X employs A; or if X employs B; or if X employs both A and B, then "X employs A or B" is satisfied. Moreover, the articles "a" and "an" as used in the application and the appended claims should generally be construed to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form. The term "set," as employed herein does not include an empty set, i.e., a set that has no elements. Accordingly, a "set" in the present disclosure includes one or more elements or entities. Likewise, the term "group," as utilized herein, refers to a set of one or more entities.

[0176] The term "set," as employed herein does not include an empty set, i.e., a set that has no elements. Accordingly, a "set" in the present disclosure includes one or more elements or entities. Likewise, the term "group," as utilized herein, refers to a set of one or more entities.

[0177] The terms "first," "second," "third," etc. as used in the claims are used for clarity and do not otherwise reflect a chronological order. For example, "a first determination," "a second determination," and "a third determination" do not indicate or imply that the first determination is to be made before the second determination, or that the second determination is to be made before the third determination, and so on.

[0178] The description of the illustrated embodiments of the disclosure provided herein, including the description in the Abstract, is not intended to be exhaustive or to be assumed to be the only embodiments of the disclosure. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible within the scope of the embodiments and examples contemplated herein as those skilled in the art will recognize. In this regard, while the subject matter has been described in connection with various embodiments and corresponding drawings, it will be understood that other similar embodiments or variations can be used in place of, or in addition to, the described embodiments to perform the same, similar, alternative, or equivalent functionality of the disclosed subject matter without deviating from the disclosed subject matter. The disclosed subject matter is thus not limited to any single embodiment described herein, but instead has wide applicability with respect to the general subject matter. The disclosure should therefore be interpreted in the broadest and most liberal manner so as to not limit the scope of the disclosed subject matter.

Claims

1. A method comprising: A learning model configuration information block message is broadcast by a radio access network node including a processor. The learning model configuration information block message includes a training configuration resource indication that indicates training configuration resources that can be used by the radio access network node to broadcast the learning model training configuration. as well as The learning model training configuration is broadcast by the radio access network node according to the training configuration resources.

2. The method of claim 1, wherein the learning model training configuration includes a training action indication indicating training actions that can be performed by an idle user device.

3. The method of claim 1, wherein the learning model training configuration includes a training action indication indicating a training action, wherein the training action corresponds to a radio function learning model, wherein executing the training action generates determined radio function parameter values ​​corresponding to the radio function learning model, and wherein the learning model configuration information block message further includes a training result resource indication, the training result resource indication specifying training result resources that can be used to send the determined radio function parameter values, the method further comprising: The radio access network node sends the determined radio function parameter values ​​to the idle user equipment based on the training result resources.

4. The method of claim 2, wherein the training action corresponds to a radio function learning model, wherein performing the training action generates determined radio function parameter values ​​corresponding to the radio function learning model, the method further comprising: The radio access network node receives a radio resource control signal message from the idle user equipment, including the determined radio function parameter values; as well as The radio access network node establishes a connection with the idle user equipment using the determined radio function parameter values ​​received from the idle user equipment in the radio resource control signal message, thereby making the idle user equipment a connected user equipment.

5. The method of claim 4, wherein the determined radio function parameter value includes a timing advance value, the timing advance value corresponding to the timing advance of the radio access network node relative to the idle user equipment.

6. The method of claim 4, wherein the determined radio function parameter value includes an indication of the best serving beam corresponding to a beam associated with the radio access network node, the beam having a higher signal strength than other signal strengths associated with other beams corresponding to the radio access network node other than the beam.

7. The method of claim 2, wherein the learning model training configuration includes a training resource indication that indicates to the idle user equipment training resources available for the idle user equipment to perform the training action.

8. The method of claim 7, wherein the radio access network node is a first radio access network node, and the method further comprises: The first radio access network node receives a non-training resource indication from a second radio access network node, the second radio access network node being a neighboring radio access network node to the first radio access network node. The non-training resource indication indicates to the first radio access network node non-training resources that will be reserved by the second radio access network node and can be used for non-training operations by the second radio access network. as well as The training resources are scheduled by the first radio access network node to avoid overlap between the training resources corresponding to the first radio access network node and the non-training resources corresponding to the second radio access network node.

9. The method of claim 1, wherein the radio access network node is a first radio access network node, wherein the learning model training configuration includes a training action instruction indicating a training action to be performed by at least one idle user equipment with respect to the first radio access network node to generate a first determined learning model parameter value, the method further comprising: The first radio access network node receives the first determined learning model parameter value from the at least one idle user equipment. The first radio access network node receives a second determined learning model parameter value from a second radio access network node, the second radio access network node being a neighboring radio access network node with respect to the first radio access network node, wherein the training action is performed by at least one of the at least one idle user equipment with respect to the second radio access network node to generate the second determined learning model parameter value; The first radio access network node determines the composite determined learning model parameter value based on the first determined learning model parameter value and the second determined learning model parameter value; as well as The first radio access network node broadcasts the composite determined learning model parameter values ​​to the at least one idle user equipment via a composite result information block message.

10. The method of claim 9, wherein the training action corresponds to a radio function learning model, and wherein the composite determined learning model parameter values ​​can be used to train the radio function learning model by the at least one idle user equipment to generate a trained learning model at the at least one idle user equipment.

11. The method of claim 10, further comprising: The first radio access network node receives a connection request message from the at least one idle user equipment, the connection request message including a performance metric estimated by the at least one idle user equipment using the trained learning model to generate the estimated performance metric; as well as Based on the estimated performance metrics, a connection is established with the idle user equipment, thereby making the idle user equipment a user equipment connected to the first radio access network node.

12. The method according to claim 1, wherein the learning model configuration information block message is a system information block message.

13. The method according to claim 1, wherein the learning model configuration information block message is a main information block message.

14. A first radio access network node, comprising: The processor is configured as follows: A non-training resource indication is received from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node. The non-training resource indication indicates to the first radio access network node non-training resources that will be used by the second radio access network node for non-training operations. Training resources are scheduled, and these training resources will be used by at least one idle mode user equipment to perform training actions with respect to the first radio access network node, so that the training resources and the non-training resources do not overlap. Broadcast main information block message, the main information block message including training configuration resource indication, the training configuration resource indication indicating that the first radio access network node will use the training configuration resources for broadcasting the learning model training configuration; as well as The training configuration of the learning model is broadcast according to the training configuration resources.

15. The first radio access network node of claim 14, wherein the learning model training configuration includes a training resource indication that indicates to the at least one idle mode user equipment the training resources to be used by the at least one idle mode user equipment to perform the training action.

16. The first radio access network node of claim 14, wherein the main information block message further includes a training result resource indication, the training result resource indication indicating a training result resource to be used by the at least one idle mode user equipment to receive training results from the first radio access network node, the training results being generated by the at least one idle mode user equipment performing the training action.

17. The first wireless access network node of claim 14, wherein the processor is further configured to: Determine the training result generated by the training action performed by the at least one idle mode user device; and Based on the training results, a connection is established with the at least one idle mode user equipment, thereby making the idle mode user equipment at least one connected mode user equipment.

18. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processor of a first wireless access network node, facilitate the execution of an operation, including: Broadcast a first information block message, the first information block message including a training configuration resource indication that indicates training configuration resources; According to the training configuration resource broadcast learning model training configuration, wherein the learning model training configuration includes training action instructions, the training action instructions indicating that a training action to be performed by a first idle user equipment in at least one idle user equipment with respect to the first radio access network node to generate a first determined learning model parameter value. Receive the first determined learning model parameter value from the first idle user equipment among the at least one idle user equipment; A second determined learning model parameter value is received from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node, wherein the training action is performed by at least the second idle user equipment among the at least one idle user equipment with respect to the second radio access network node to generate the second determined learning model parameter value; Based on the first determined learning model parameter values ​​and based on the second determined learning model parameter values, the updated learning model is determined; as well as The updated learning model is broadcast to the first idle user equipment among the at least one idle user equipment via a second information block message.

19. The non-transitory machine-readable medium of claim 18, wherein the training action is performed by the first idle user equipment of the at least one idle user equipment with respect to the second radio access network node to generate the second determined learning model parameter values.

20. The non-transitory machine-readable medium of claim 18, further comprising: The updated learning model is sent to the second radio access network node via the backhaul link.

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