Machine learning feedback between network entities

Abstract

Various aspects of the present disclosure generally relate to wireless communications. In some aspects, a source network entity may send a message to a receiver network entity including a request for machine learning feedback associated with an artificial intelligence / machine learning action triggered by the source network entity. Thus, the source network entity may receive the machine learning feedback in response to the request. In some aspects, the machine learning feedback is included in a data type-independent Class 2 message from the receiver network entity. Numerous other aspects are described.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This patent application claims priority to commonly assigned Indian Provisional Patent Application No. 202321007830 entitled "MACHINE LEARNING FEEDBACK BETWEEN NETWORK ENTITIES," filed on February 7, 2023. The disclosure of the prior application is considered part of, and incorporated by reference into, this patent application.

[0002] Aspects of the present disclosure relate generally to wireless communications and to techniques and apparatus for transmitting and receiving machine learning feedback between network entities. [Background technology]

[0003] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple-access technologies include Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, Orthogonal Frequency Division Multiple Access (OFDMA) systems, Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems, Time Division Synchronous Code Division Multiple Access (TD-SCDMA) systems, and Long Term Evolution (LTE). LTE / LTE-Advanced is a set of extensions to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0004] A wireless network may include one or more network nodes that support communication for wireless communication devices, such as a user equipment (UE) or multiple UEs. A UE may communicate with a network node via downlink and uplink communications. The "downlink" (or "DL") refers to the communication link from the network node to the UE, and the "uplink" (or "UL") refers to the communication link from the UE to the network node. Some wireless networks may support device-to-device communications via local links (e.g., sidelink (SL), wireless local area network (WLAN) links, and / or wireless personal area network (WPAN) links, among other examples).

[0005] The above multiple access technologies have been adopted in various telecommunications standards to provide common protocols that allow various UEs to communicate at city, national, regional, and / or global levels. New Radio (NR), sometimes referred to as 5G, is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by improving spectral efficiency, lowering costs, improving service, utilizing new spectrum, and by using Orthogonal Frequency Division Multiplexing (OFDM) with Cyclic Prefix (CP) (CP-OFDM) on the downlink and CP-OFDM and / or Single-Carrier Frequency Division Multiplexing (SC-FDM) (also known as Discrete Fourier Transform Spread OFDM, DFT-s-OFDM) on the uplink, better integrating with other open standards, and supporting beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation. As demand for mobile broadband access continues to grow, further improvements in LTE, NR, and other radio access technologies remain useful. Summary of the Invention

[0006] Some aspects described herein relate to an apparatus for communication at a source network entity. The apparatus may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be individually or collectively configured to send a message to a receiver network entity including a request for machine learning feedback associated with an artificial intelligence / machine learning (AI / ML) action triggered by the source network entity. The one or more processors may be individually or collectively configured to receive the machine learning feedback in response to the request.

[0007] Some aspects described herein relate to an apparatus for communication at a receiver network entity. The apparatus may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be individually or collectively configured to receive, from the source network entity, a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity. The one or more processors may be individually or collectively configured to transmit the machine learning feedback in response to the request.

[0008] Some aspects described herein relate to an apparatus for communication at a source network entity. The apparatus may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be individually or collectively configured to send a request for machine learning feedback to a receiver network entity. The one or more processors may be individually or collectively configured to receive, upon request, a Class 2 message from the receiver network entity, independent of data type, including the machine learning feedback.

[0009] Some aspects described herein relate to an apparatus for communication at a receiver network entity. The apparatus may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be individually or collectively configured to receive a request for machine learning feedback from a source network entity. The one or more processors may be individually or collectively configured to transmit, upon request, a data type-independent Class 2 message including the machine learning feedback to the source network entity.

[0010] Some aspects described herein relate to a method of communication performed by a source network entity. The method may include transmitting a message to a receiver network entity, the message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity. The method may include receiving the machine learning feedback in response to the request.

[0011] Some aspects described herein relate to a method of communication performed by a receiver network entity. The method may include receiving a message from a source network entity including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity. The method may include transmitting the machine learning feedback in response to the request.

[0012] Some aspects described herein relate to a method of communication performed by a source network entity. The method may include transmitting a request for machine learning feedback to a receiver network entity. The method may include receiving, in response to the request, a Class 2 message from the receiver network entity that is data type independent and includes the machine learning feedback.

[0013] Some aspects described herein relate to a method of communication performed by a receiver network entity. The method may include receiving a request for machine learning feedback from a source network entity. In response to the request, the method may include transmitting a Class 2 message to the source network entity, the message being data type independent and including the machine learning feedback.

[0014] Some aspects described herein relate to a non-transitory computer-readable medium storing a set of instructions for communication by a source network entity. The set of instructions, when executed by one or more processors of the source network entity, may cause the source network entity to send a message to a receiver network entity including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity. The set of instructions, when executed by one or more processors of the source network entity, may cause the source network entity to receive the machine learning feedback upon the request.

[0015] Some aspects described herein relate to a non-transitory computer-readable medium storing a set of instructions for communication by a receiver network entity. The set of instructions, when executed by one or more processors of the receiver network entity, may cause the receiver network entity to receive a message from a source network entity that includes a request for machine learning feedback associated with an AI / ML action triggered by the source network entity. The set of instructions, when executed by the one or more processors of the receiver network entity, may cause the receiver network entity to transmit the machine learning feedback in response to the request.

[0016] Some aspects described herein relate to a non-transitory computer-readable medium storing a set of instructions for communication by a source network entity. The set of instructions, when executed by one or more processors of the source network entity, may cause the source network entity to send a request for machine learning feedback to a receiver network entity. The set of instructions, when executed by one or more processors of the source network entity, may cause the source network entity to receive, upon request, a Class 2 message from the receiver network entity that is data type independent and includes the machine learning feedback.

[0017] Some aspects described herein relate to a non-transitory computer-readable medium storing a set of instructions for communication by a receiver network entity. The set of instructions, when executed by one or more processors of the receiver network entity, may cause the receiver network entity to receive a request for machine learning feedback from a source network entity. The set of instructions, when executed by the one or more processors of the receiver network entity, may cause the receiver network entity to transmit a Class 2 message, independent of data type, including the machine learning feedback to the source network entity upon request.

[0018] Some aspects described herein relate to an apparatus for communication. The apparatus may include means for transmitting a message to a receiver network entity including a request for machine learning feedback associated with an AI / ML action triggered by the apparatus. The apparatus may include means for receiving the machine learning feedback in response to the request.

[0019] Some aspects described herein relate to an apparatus for communication. The apparatus may include means for receiving, from a source network entity, a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity. The apparatus may include means for transmitting the machine learning feedback in response to the request.

[0020] Some aspects described herein relate to an apparatus for communication. The apparatus may include means for transmitting a request for machine learning feedback to a receiver network entity. The apparatus may include means for receiving, in response to the request, a Class 2 message from the receiver network entity that is data type independent and includes the machine learning feedback.

[0021] Some aspects described herein relate to an apparatus for communication. The apparatus may include means for receiving a request for machine learning feedback from a source network entity. The apparatus may include means for transmitting, in response to the request, a Class 2 message to the source network entity, the Class 2 message being data type independent and including the machine learning feedback.

[0022] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, network entities, network nodes, wireless communication devices, and / or processing systems substantially as described herein with reference to and as illustrated in the drawings and this specification.

[0023] The foregoing has outlined rather broadly the features and technical advantages of embodiments according to the present disclosure in order that the following Detailed Description may be better understood. Additional features and advantages will be described hereinafter. The concepts and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent structures do not depart from the scope of the appended claims. The nature of the concepts disclosed herein, both their organization and method of operation, together with associated advantages, will be better understood by considering the following description in conjunction with the accompanying figures. Each of the figures is provided for the purpose of illustration and description, and not as a definition of the limits of the claims.

[0024] Although aspects are described in this disclosure by way of example with respect to some examples, those skilled in the art will understand that such aspects can be implemented in many different configurations and scenarios. The techniques described herein may be implemented using a variety of platform types, devices, systems, shapes, sizes, and / or packaging configurations. For example, some aspects may be implemented via integrated chip embodiments or other non-modular component-based devices (e.g., end-user devices, vehicles, communications devices, computing devices, industrial equipment, retail / purchasing devices, medical devices, and / or artificial intelligence devices). Aspects may be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and / or system-level components. Devices incorporating the described aspects and features may include additional components and features for implementing and practicing the claimed and described aspects. For example, the transmission and reception of wireless signals may include one or more components (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, summers, and / or analog summers) for analog and digital purposes. It is contemplated that aspects described herein may be practiced in a wide variety of devices, components, systems, distributed configurations, and / or end-user devices of various sizes, shapes, and configurations.

[0025] In order that the above-listed features of the present disclosure may be understood in detail, a more detailed description, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the accompanying drawings. It should be noted, however, that the accompanying drawings illustrate only certain exemplary embodiments of the present disclosure, and therefore should not be considered as limiting the scope of the present disclosure, as the description may be incorporated into other equally effective embodiments. The same reference numbers in different drawings may identify the same or similar elements. [Brief explanation of the drawings]

[0026] [Figure 1] FIG. 1 illustrates an example of a wireless network according to the present disclosure. [Figure 2] FIG. 1 illustrates an example of a network node in communication with a user equipment (UE) in a wireless network, according to the present disclosure. [Figure 3] FIG. 1 illustrates an exemplary disaggregated base station architecture in accordance with the present disclosure. [Figure 4] FIG. 1 illustrates an example of requesting and sending artificial intelligence / machine learning information according to the present disclosure. [Figure 5] FIG. 1 illustrates an example associated with a request for machine learning feedback according to the present disclosure. [Figure 6] FIG. 1 illustrates an example associated with a request for machine learning feedback according to the present disclosure. [Figure 7] FIG. 1 illustrates an example associated with sending machine learning feedback in accordance with the present disclosure. [Figure 8] FIG. 1 illustrates an exemplary process associated with sending and receiving machine learning feedback in accordance with the present disclosure. [Figure 9] FIG. 1 illustrates an exemplary process associated with sending and receiving machine learning feedback in accordance with the present disclosure. [Figure 10] FIG. 1 illustrates an exemplary process associated with sending and receiving machine learning feedback in accordance with the present disclosure. [Figure 11] FIG. 1 illustrates an exemplary process associated with sending and receiving machine learning feedback in accordance with the present disclosure. [Figure 12] FIG. 1 is a diagram of an exemplary apparatus for wireless communication according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0027] Various aspects relate generally to wireless communications, and more particularly to artificial intelligence / machine learning (AI / ML) actions. Some aspects relate, more specifically, to including a request for machine learning feedback in an AI / ML action execution message. For example, a source network entity may send an AI / ML action execution message based on output from a computer algorithm that triggers the AI / ML action. Accordingly, the source network entity may request machine learning feedback to validate the AI / ML action. Additionally, some aspects relate, more specifically, to using a data-type-independent Class 2 message to provide machine learning feedback. For example, a receiver network entity may send a Class 2 message in response to a request for machine learning feedback.

[0028] Certain aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages: For example, a source network entity may improve a computer algorithm (e.g., via retraining) based on machine learning feedback to optimize future decisions. As a result, the improved computer algorithm may trigger future AI / ML actions that save power, save processing resources, reduce latency, increase throughput, and / or improve the quality and reliability of wireless communications.

[0029] Various aspects of the present disclosure will now be described more fully with reference to the accompanying drawings. However, the present disclosure may be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Those skilled in the art will appreciate that the scope of the present disclosure is intended to encompass all aspects of the present disclosure disclosed herein, whether implemented independently or in combination with any other aspects of the present disclosure. For example, a device can be implemented or a method can be practiced using any number of the aspects described herein. Furthermore, the scope of the present disclosure is intended to encompass such devices or methods practiced using other structure, functions, or structure and functions in addition to or other than the various aspects of the present disclosure described herein. It should be understood that any aspect of the present disclosure disclosed herein can be embodied by one or more elements of a claim.

[0030] Several aspects of telecommunications systems will now be presented with reference to various apparatus and techniques. These apparatus and techniques are described in the following Detailed Description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be realized using hardware, software, or a combination thereof. Whether such elements are implemented as hardware or software depends on the particular application and design constraints imposed on the overall system.

[0031] Although aspects may be described herein using terminology commonly associated with 5G or New Radio (NR) radio access technology (RAT), aspects of the present disclosure may also be applied to other RATs, such as 3G RAT, 4G RAT, and / or RATs subsequent to 5G (e.g., 6G).

[0032] 1 illustrates one embodiment of a wireless network 100 in accordance with the present disclosure. Wireless network 100 may be, or may include elements of, a 5G (e.g., NR) network and / or a 4G (e.g., Long Term Evolution (LTE)) network, among other examples. Wireless network 100 may include one or more network nodes 110 (shown as network node 110a, network node 110b, network node 110c, and network node 110d), user equipment (UE) 120 or more UEs 120 (shown as UE 120a, UE 120b, UE 120c, UE 120d, and UE 120e), and / or other entities. Network node 110 is a network node that communicates with UE 120. As shown, network node 110 may include one or more network nodes. For example, network node 110 may be an aggregated network node, meaning that the aggregated network node is configured to utilize a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node (e.g., within a single device or unit). As another example, network node 110 may be a disaggregated network node (sometimes referred to as a disaggregated base station), meaning that network node 110 is configured to utilize a protocol stack that is physically or logically distributed among two or more nodes (e.g., one or more central units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)).

[0033] In some embodiments, the network node 110 is or includes a network node, such as a RU, that communicates with the UE 120 over a radio access link. In some embodiments, the network node 110 is or includes a network node, such as a DU, that communicates with other network nodes 110 over a fronthaul link or a midhaul link. In some embodiments, the network node 110 is or includes a network node, such as a CU, that communicates with other network nodes 110 over a midhaul link or with a core network over a backhaul link. In some embodiments, the network node 110 (e.g., an aggregated network node 110 or a disaggregated network node 110) may include multiple network nodes, such as one or more RUs, one or more CUs, and / or one or more DUs. The network nodes 110 may include, for example, NR base stations, LTE base stations, Node Bs, eNBs (e.g., in 4G), gNBs (e.g., in 5G), access points, transmission reception points (TRPs), DUs, RUs, CUs, network mobility elements, core network nodes, network elements, network equipment, RAN nodes, or combinations thereof. In some embodiments, the network nodes 110 may interconnect to each other or to one or more other network nodes 110 within the wireless network 100 through various types of fronthaul, midhaul, and / or backhaul interfaces, such as direct physical connections, air interfaces, or virtual networks, using any suitable transport network.

[0034] In some embodiments, a network node 110 may provide communication coverage for a particular geographic area. In the Third Generation Partnership Project (3GPP), the term “cell” can refer to the coverage area of ​​the network node 110 and / or a network node subsystem serving that coverage area, depending on the context in which the term is used. The network node 110 may provide communication coverage for a macrocell, a picocell, a femtocell, and / or another type of cell. A macrocell may cover a relatively large geographic area (e.g., a few kilometers in radius) and may allow unrestricted access by UEs 120 with service subscriptions. A picocell may cover a relatively small geographic area and may allow unrestricted access by UEs 120 with service subscriptions. A femtocell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs 120 having an association with the femtocell (e.g., UEs 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a pico cell may be referred to as a pico network node. A network node 110 for a femto cell may be referred to as a femto network node or a home network node. In the embodiment shown in FIG. 1 , network node 110a may be a macro network node for macro cell 102a, network node 110b may be a pico network node for pico cell 102b, and network node 110c may be a femto network node for femto cell 102c. A network node may support one or multiple (e.g., three) cells. In some embodiments, a cell may not necessarily be fixed, and the geographic area of ​​a cell may move according to the location of a mobile network node 110 (e.g., a mobile network node).

[0035] In some aspects, the term “base station” or “network node” may refer to an aggregated base station, a non-aggregated base station, an integrated access and backhaul (IAB) node, a relay node, or one or more components thereof. For example, in some aspects, a “base station” or “network node” may refer to a CU, DU, RU, a Near-Real Time (RT) RAN Intelligent Controller (RIC), or a Non-Real Time (Non-RT) RIC, or a combination thereof. In some aspects, the term “base station” or “network node” may refer to one device configured to perform one or more functions, such as those described herein in connection with network node 110. In some aspects, the term “base station” or “network node” may refer to multiple devices configured to perform one or more functions. For example, in some distributed systems, multiple different devices (which may be located in the same geographic location or different geographic locations) may each be configured to perform at least a portion of the functions or to replicate the performance of at least a portion of the functions, and the term "base station" or "network node" may refer to any one or more of those different devices. In some aspects, the term "base station" or "network node" may refer to one or more virtual base stations or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the term "base station" or "network node" may refer to one of the base station functions and not another base station function. In this manner, a single device may include two or more base stations.

[0036] The wireless network 100 may include one or more relay stations. A relay station is a network node that can receive a data transmission from an upstream node (e.g., a network node 110 or a UE 120) and forward the data transmission to a downstream node (e.g., a UE 120 or a network node 110). A relay station may also be a UE 120 that can relay transmissions for other UEs 120. In the embodiment shown in FIG. 1, network node 110d (e.g., a relay network node) may communicate with network node 110a (e.g., a macro network node) and UE 120d to facilitate communications between network node 110a (e.g., a macro network node) and UE 120d. A network node 110 that relays communications may also be referred to as a relay station, a relay base station, a relay network node, a relay node, a repeater, etc.

[0037] The wireless network 100 may be a heterogeneous network that includes different types of network nodes 110, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, etc. These different types of network nodes 110 may have different transmit power levels, different coverage areas, and / or different susceptibility to interference within the wireless network 100. For example, the macro network nodes may have high transmit power levels (e.g., 5-40 watts), while the pico network nodes, femto network nodes, and relay network nodes may have lower transmit power levels (e.g., 0.1-2 watts).

[0038] A network controller 130 may be coupled to or in communication with a set of network nodes 110 and may provide coordination and control for these network nodes 110. The network controller 130 may communicate with the network nodes 110 via backhaul or midhaul communication links. The network nodes 110 may communicate with each other directly or indirectly via wireless or wired backhaul communication links. In some aspects, the network controller 130 may be or may include a CU or a core network device.

[0039] The UEs 120 may be dispersed throughout the wireless network 100, and each UE 120 may be fixed or mobile. The UEs 120 may include, for example, access terminals, terminals, mobile stations, and / or subscriber units. The UE 120 may be a cellular phone (e.g., a smartphone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, smart wristband, smart jewelry (e.g., a smart ring or smart bracelet)), an entertainment device (e.g., a music device, a video device, and / or satellite radio), a vehicle component or sensor, a smart meter / sensor, industrial manufacturing equipment, a global positioning system device, a UE function of a network node, and / or any other suitable device configured to communicate over a wireless or wired medium.

[0040] Some UEs 120 may be considered machine-type communication (MTC) UEs or evolved or enhanced machine-type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags that may communicate with a network node, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered Internet-of-Things (IoT) devices and / or may be implemented as narrowband IoT (NB-IoT) devices. Some UEs 120 may be considered customer premises equipment. The UE 120 may include a housing that houses components of the UE 120, such as a processor component and / or a memory component. In some embodiments, the processor component and the memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., memory) may be operatively coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0041] In general, any number of wireless networks 100 may be deployed within a given geographic area. Each wireless network 100 may support a particular RAT and may operate on one or more frequencies. A RAT may be referred to as a radio technology, air interface, etc. A frequency may be referred to as a carrier, frequency channel, etc. To avoid interference between wireless networks of different RATs, each frequency may support a single RAT within a given geographic area. In some cases, NR or 5G RAT networks may be deployed.

[0042] In some embodiments, two or more UEs 120 (e.g., those shown as UE 120a and UE 120e) may communicate directly (e.g., without using network node 110 as an intermediary to communicate with each other) using one or more sidelink channels. For example, the UEs 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, a vehicle-to-everything (V2X) protocol (which may include, e.g., a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, or a vehicle-to-pedestrian (V2P) protocol), and / or a mesh network. In such embodiments, the UEs 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by the network node 110.

[0043] Devices of wireless network 100 may communicate using an electromagnetic spectrum, which may be subdivided by frequency or wavelength into various classes, bands, channels, etc. For example, devices of wireless network 100 may communicate using one or more operating bands. In 5G NR, two initial operating bands have been identified, designated by the frequency ranges FR1 (410 MHz to 7.125 GHz) and FR2 (24.25 GHz to 52.6 GHz). It should be understood that, although a portion of FR1 is above 6 GHz, FR1 is often referred to (interchangeably) as the “sub-6 GHz” band in various documents and papers. Similar nomenclature issues may arise with respect to FR2, which is often referred to (interchangeably) as the “millimeter wave” band in documents and papers, even though it is different from the extremely high frequency (EHF) band (30 GHz to 300 GHz), which is identified by the International Telecommunications Union (ITU) as the “millimeter wave” band.

[0044] Frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified operating bands for these mid-band frequencies as the frequency range designated FR3 (7.125 GHz to 24.25 GHz). Frequency bands included within FR3 may inherit FR1 and / or FR2 characteristics, and thus may effectively extend the characteristics of FR1 and / or FR2 to the mid-band frequencies. Higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as the frequency ranges designated FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands is included within the EHF band.

[0045] With the above examples in mind, it should be understood that, unless otherwise specified, terms such as "sub-6 GHz," as used herein, may broadly refer to frequencies that may be below 6 GHz, frequencies that may be in the FR1 range, or frequencies that may include mid-band frequencies. Furthermore, unless otherwise specified, it should be understood that terms such as "millimeter wave," as used herein, may broadly refer to frequencies that may include mid-band frequencies, frequencies that may be in the FR2, FR4, FR4-a, or FR4-1, and / or FR5 ranges, or frequencies that may be in the EHF band. It is contemplated that frequencies included within these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0046] In some aspects, network controller 130 may include communications manager 140. As described in more detail elsewhere herein, communications manager 140 may send a message (e.g., to a receiver network entity) including a request for machine learning feedback associated with an AI / ML action triggered by network controller 130 and may receive the machine learning feedback in response to the request. The machine learning feedback may be included in a Class 2 message (e.g., from a receiver network entity) that is independent of the data type. Alternatively, as described in more detail elsewhere herein, communications manager 140 may receive a message (e.g., from a source network entity) including a request for machine learning feedback associated with an AI / ML action (e.g., triggered by a source network entity) and may send the machine learning feedback in response to the request. The machine learning feedback may be included in a Class 2 message (e.g., to a source network entity) that is independent of the data type. Additionally or alternatively, communications manager 140 may perform one or more other operations described herein.

[0047] In some aspects, network node 110 may include a communications manager 150. As described in more detail elsewhere herein, communications manager 150 may send a message (e.g., to a receiver network entity) including a request for machine learning feedback associated with an AI / ML action triggered by network node 110, and may receive the machine learning feedback in response to the request. The machine learning feedback may be included in a Class 2 message (e.g., from a receiver network entity) that is independent of the data type. Alternatively, as described in more detail elsewhere herein, communications manager 150 may receive a message (e.g., from a source network entity) including a request for machine learning feedback associated with an AI / ML action (e.g., triggered by a source network entity), and may send the machine learning feedback in response to the request. The machine learning feedback may be included in a Class 2 message (e.g., to a source network entity) that is independent of the data type. Additionally or alternatively, communications manager 150 may perform one or more other operations described herein.

[0048] As noted above, Figure 1 is provided as an example. Other examples may differ from those described with respect to Figure 1.

[0049] 2 illustrates an example embodiment 200 of a network node 110 in communication with a UE 120 in a wireless network 100 in accordance with the present disclosure. The network node 110 may be equipped with a set of antennas 234a through 234t, such as T antennas (T≧1). The UE 120 may be equipped with a set of antennas 252a through 252r, such as R antennas (R≧1). The network node 110 of example 200 includes one or more radio frequency components, such as the antennas 234 and a modem 232. In some embodiments, the network node 110 may include an interface, a communication component, or another component that facilitates communication with the UE 120 or another network node. Some network nodes 110 may not include a radio frequency component that facilitates direct communication with the UE 120, such as one or more CUs or one or more DUs.

[0050] At the network node 110, a transmit processor 220 may receive data destined for a UE 120 (or set of UEs 120) from a data source 212. The transmit processor 220 may select one or more modulation and coding schemes (MCSs) for the UE 120 based at least in part on one or more channel quality indicators (CQIs) received from the UE 120. The network node 110 may process (e.g., encode and modulate) data for the UE 120 and provide data symbols to the UE 120 based at least in part on the MCS(es) selected for the UE 120. The transmit processor 220 may process system information (e.g., related to semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, and / or higher layer signaling) and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for a reference signal (e.g., a cell-specific reference signal (CRS) or a demodulation reference signal (DMRS)) and a synchronization signal (e.g., a primary synchronization signal (PSS) or a secondary synchronization signal (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​the data symbols, control symbols, overhead symbols, and / or reference symbols, if applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems), depicted as modems 232a through 232t.For example, each output symbol stream may be provided to a modulator component (denoted as MOD) of modem 232. Each modem 232 may use a corresponding modulator component to process (e.g., for OFDM) the corresponding output symbol stream to obtain an output sample stream. Each modem 232 may further use a corresponding modulator component to process (e.g., convert to analog, amplify, filter, and / or upconvert) the output sample stream to obtain a downlink signal. Modems 232a through 232t may transmit a set of downlink signals (e.g., T downlink signals) via a corresponding set of antennas 234 (e.g., T antennas), denoted as antennas 234a through 234t.

[0051] At the UE 120, a set of antennas 252 (depicted as antennas 252a through 252r) may receive downlink signals from the network node 110 and / or other network nodes 110 and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) depicted as modems 254a through 254r. For example, each received signal may be provided to a demodulator component (depicted as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may further process the input samples (e.g., for OFDM) using the demodulator component to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modems 254, perform MIMO detection on the received symbols, if applicable, and provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for the UE 120 to a data sink 260, and provide decoded control and system information to a controller / processor 280. The term “controller / processor” may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other examples. In some embodiments, one or more components of the UE 120 may be included within a housing 284.

[0052] The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 may include, for example, one or more devices in a core network. The network controller 130 may communicate with the network node 110 via the communication unit 294.

[0053] One or more antennas (e.g., antennas 234a-t and / or antennas 252a-r) may include or be contained within one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and / or one or more antenna arrays, among other examples. An antenna panel, antenna group, set of antenna elements, and / or antenna array may include one or more antenna elements (in a single housing or multiple housings), a set of coplanar antenna elements, a set of non-coplanar antenna elements, and / or one or more antenna elements coupled to one or more transmitting and / or receiving components, such as one or more components of FIG. 2.

[0054] On the uplink, at the UE 120, the transmit processor 264 may receive and process data from the data source 262 and control information from the controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by the TX MIMO processor 266, if applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted to the network node 110. In some embodiments, the modem 254 of the UE 120 may include a modulator and a demodulator. In some embodiments, the UE 120 includes a transceiver. The transceiver may include any combination of the antenna(s) 252, the modem(s) 254, the MIMO detector 256, the receive processor 258, the transmit processor 264, and / or the TX MIMO processor 266. The transceiver may be used by a processor (e.g., controller / processor 280) and memory 282 to implement aspects of any of the methods described herein (e.g., with reference to Figures 5-12).

[0055] At the network node 110, uplink signals from the UE 120 and / or other UEs may be received by an antenna 234, processed by a modem 232 (e.g., a demodulator component of the modem 232, denoted as DEMOD), detected by a MIMO detector 236, if applicable, and further processed by a receive processor 238 to obtain decoded data and control information sent by the UE 120. The receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to a controller / processor 240. The network node 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The network node 110 may include a scheduler 246 for scheduling one or more UEs 120 for downlink and / or uplink communications. In some embodiments, the modem 232 of the network node 110 may include a modulator and a demodulator. In some embodiments, the network node 110 includes a transceiver. The transceiver may include any combination of antenna(s) 234, modem(s) 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to implement aspects of any of the methods described herein (e.g., with reference to FIGS. 5-12).

[0056] As described in more detail elsewhere herein, the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or any other component(s) of FIG. 2 may perform one or more techniques associated with sending and receiving machine learning feedback between network entities. For example, the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or any other component(s) of FIG. 2 may perform or direct the operations of, for example, process 800 of FIG. 8, process 900 of FIG. 9, process 1000 of FIG. 10, process 1100 of FIG. 11, and / or other processes described herein. The memory 242 and the memory 282 may store data and program codes for the network node 110 and the UE 120, respectively. In some embodiments, the memory 242 and / or the memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed by one or more processors of network node 110 and / or UE 120 (e.g., directly or after being compiled, translated, and / or interpreted), may cause the one or more processors, UE 120, and / or network node 110 to perform or direct the operations of, e.g., process 800 of FIG. 8 , process 900 of FIG. 9 , process 1000 of FIG. 10 , process 1100 of FIG. 11 , and / or other processes as described herein. In some embodiments, executing the instructions may include running the instructions, translating the instructions, compiling the instructions, and / or interpreting the instructions, among other examples. In some aspects, a source network entity described herein is network node 110, is included in network node 110, includes one or more components of network node 110 shown in FIG. 2, is network controller 130, is included in network controller 130, or includes one or more components of network controller 130 shown in FIG. 2.Similarly, a receiver network entity as described herein may be a network node 110, be included in a network node 110, include one or more components of the network node 110 shown in FIG. 2, be a network controller 130, be included in a network controller 130, or include one or more components of the network controller 130 shown in FIG. 2.

[0057] In some aspects, a source network entity (e.g., network node 110, network controller 130, and / or device 1200 of FIG. 12) may include means for transmitting (e.g., to a receiver network entity) a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity, and / or means for receiving the machine learning feedback in response to the request. Additionally or alternatively, the source network entity may include means for transmitting (e.g., to a receiver network entity) a request for machine learning feedback, and / or means for receiving (e.g., from a receiver network entity) a Class 2 message including the machine learning feedback in response to the request, independent of data type. In some aspects, the means for the source network entity to perform the operations described herein may include, for example, one or more of communications manager 150, transmit processor 220, TX MIMO processor 230, modem 232, antenna 234, MIMO detector 236, receive processor 238, controller / processor 240, memory 242, or scheduler 246. Alternatively, the means by which the source network entity performs the operations described herein may include, for example, one or more of the communications manager 140, the controller / processor 290, the memory 292, or the communications unit 294.

[0058] In some aspects, a receiver network entity (e.g., network node 110, network controller 130, and / or device 1200 of FIG. 12) may include means for receiving (e.g., from the source network entity) a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity, and / or means for transmitting the machine learning feedback in response to the request. Additionally or alternatively, the receiver network entity may include means for receiving (e.g., from the source network entity) a request for machine learning feedback, and / or means for transmitting (e.g., to the source network entity) a Class 2 message including the machine learning feedback in response to the request, independent of data type. In some aspects, the means for the receiver network entity to perform the operations described herein may include, for example, one or more of communications manager 150, transmit processor 220, TX MIMO processor 230, modem 232, antenna 234, MIMO detector 236, receive processor 238, controller / processor 240, memory 242, or scheduler 246. Alternatively, the means by which the receiver network entity performs the operations described herein may include one or more of the communications manager 140, the controller / processor 290, the memory 292, or the communications unit 294, for example.

[0059] In some aspects, an individual processor may perform all of the functions described as being performed by one or more processors. In some aspects, one or more processors may collectively perform a set of functions. For example, a first set of processor(s) of one or more processors may perform a first function described as being performed by one or more processors, and a second set of processor(s) of one or more processors may perform a second function described as being performed by one or more processors. The first set of processors and the second set of processors may be the same set of processors or different sets of processors. References to "one or more processors" should be understood to refer to any one or more of the processors described in connection with FIG. 2. References to "one or more memories" should be understood to refer to any one or more memories of a corresponding device, such as the memories described in connection with FIG. 2. For example, functions described as being performed by one or more memories may be performed by the same subset of one or more memories or different subsets of one or more memories.

[0060] 2 are shown as separate components, the functionality described above with respect to these blocks may be implemented in a single hardware, software, or combined component, or in various combinations of components. For example, the functionality described with respect to transmit processor 264, receive processor 258, and / or TX MIMO processor 266 may be performed by or under the control of controller / processor 280.

[0061] As noted above, Figure 2 is provided as an example. Other implementations may differ from those described with respect to Figure 2.

[0062] The deployment of a communication system, such as a 5G NR system, can be configured in multiple ways with various components or parts. In a 5G NR system or network, a network node, network entity, network mobility element, RAN node, core network node, network element, base station, or network equipment can be implemented in a centralized or disaggregated architecture. For example, a base station (e.g., a Node B (NB), evolved NB (eNB), NR base station, 5G NB, access point (AP), TRP, or cell, among other examples), or one or more units (or one or more components) performing base station functionality, may be implemented as a centralized base station (also known as a standalone base station or monolithic base station) or a disaggregated base station. A "network entity" or a "network node" may refer to a disaggregated base station or may refer to one or more units (e.g., one or more CUs, one or more DUs, one or more RUs, or a combination thereof) of a disaggregated base station.

[0063] An aggregated base station (e.g., an aggregated network node) may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node (e.g., within a single device or unit). A disaggregated base station (e.g., a disaggregated network node) may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (e.g., one or more CUs, one or more DUs, or one or more RUs). In some embodiments, a CU may be implemented within a network node, and one or more DUs may be co-located with the CU or, alternatively, may be geographically or virtually distributed throughout one or more other network nodes. A DU may be implemented to communicate with one or more RUs. Each of the CU, DU, and RU may also be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), among other examples.

[0064] The operation or network design of a base station type may take into account the aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an IAB network, an open radio access network (O-RAN, such as a network configuration supported by the O-RAN Alliance), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)) to facilitate scaling of a communication system by separating base station functionality into one or more units that can be deployed independently. A disaggregated base station may include functionality implemented across two or more units in various physical locations as well as functionality implemented virtually in at least one unit, which may allow flexibility in network design. Various units of a disaggregated base station may be configured for wired or wireless communication with at least one other unit of the disaggregated base station.

[0065] 3 illustrates an example disaggregated base station architecture 300 according to the present disclosure. The disaggregated base station architecture 300 may include a CU 310 that can communicate directly with a core network 320 via a backhaul link or indirectly with the core network 320 via one or more disaggregated control units (e.g., a quasi-RT RIC 325 via an E2 link, or a non-RT RIC 315 associated with a Service Management and Orchestration (SMO) framework 305, or both). The CU 310 can communicate with one or more DUs 330 via respective midhaul links, e.g., through an F1 interface. Each of the DUs 330 can communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 can communicate with one or more UEs 120 via respective radio frequency (RF) access links. In some implementations, a UE 120 may be served by multiple RUs 340 simultaneously.

[0066] Each of the units, including the CU 310, DU 330, RU 340, and quasi-RT RIC 325, non-RT RIC 315, and SMO framework 305, may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) over a wired or wireless transmission medium. Each of the units, or an associated processor or controller that provides instructions to the corresponding unit's one or more communication interfaces, may be configured to communicate with one or more of the other units over a transmission medium. In some embodiments, each of the units may include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units, and a wireless interface, which may include a receiver, transmitter, or transceiver (e.g., an RF transceiver), configured to receive, transmit, or transmit and receive signals over a wireless transmission medium to one or more of the other units.

[0067] In some aspects, the CU 310 can host one or more higher-layer control functions. Such control functions may include a radio resource control (RRC) function, a packet data convergence protocol (PDCP) function, or a service data adaptation protocol (SDAP) function, among other examples. Each control function may implement an interface configured to communicate signals with other control functions hosted by the CU 310. The CU 310 may be configured to handle user plane functionality (e.g., Central Unit-User Plane (CU-UP) functionality), control plane functionality (e.g., Central Unit-Control Plane (CU-CP) functionality), or a combination thereof. In some implementations, the CU 310 may be logically divided into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may bidirectionally communicate with the CU-CP unit via an interface, such as an E1 interface. The CU 310 may be implemented to communicate with the DU 330 as needed for network control and signaling.

[0068] Each DU 330 may correspond to a logical unit including one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more upper physical (PHY) layers, at least in part according to a functional division such as that defined by 3GPP. In some aspects, the one or more upper PHY layers may be realized by one or more modules for forward error correction (FEC) encoding and decoding, scrambling, and modulation and demodulation, among other examples. In some aspects, the DU 330 may further host one or more lower PHY layers, such as implemented by one or more modules for fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, or physical random access channel (PRACH) extraction and filtering, among other examples. Each layer (which may also be referred to as a module) may be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0069] Each RU 340 may implement lower layer functionality. In some deployments, the RU 340 controlled by the DU 330 may correspond to a logical node hosting RF processing functions or lower PHY layer functions, such as performing FFT, performing iFFT, digital beamforming, or PRACH extraction and filtering, among other examples, based on a functional partition such as a lower layer functional partition (e.g., a functional partition defined by 3GPP). In such an architecture, each RU 340 may be operated to handle over-the-air (OTA) communications with one or more UEs 120. In some implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU(s) 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable each DU 330 and CU 310 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0070] The SMO framework 305 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support deployment of dedicated physical resources related to RAN coverage requirements, which may be managed via an operation and maintenance interface (e.g., an O1 interface). For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform (e.g., an open cloud (O-Cloud) platform 390) to perform lifecycle management of the network element (e.g., to instantiate virtualized network elements) via a cloud computing platform interface (e.g., an O2 interface). Such virtualized network elements may include, but are not limited to, the CU 310, the DU 330, the RU 340, the non-RT RIC 315, and the quasi-RT RIC 325. In some implementations, the SMO framework 305 may communicate with hardware aspects of a 4G RAN, such as the open eNB (O-eNB) 311, via the O1 interface. Additionally, in some implementations, the SMO framework 305 can communicate directly with each of the one or more RUs 340 via a corresponding O1 interface. The SMO framework 305 can also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305.

[0071] The non-RT RIC 315 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, AI / ML workflows including model training and updates, or policy-based guidance of applications / features in the quasi-RT RIC 325. The non-RT RIC 315 may be coupled to or may communicate with the quasi-RT RIC 325 (e.g., via an A1 interface). The quasi-RT RIC 325 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources through data collection and action via interfaces (e.g., via an E2 interface) that connect one or more CUs 310, one or more DUs 330, or both, and the O-eNB to the quasi-RT RIC 325.

[0072] In some implementations, the non-RT RIC 315 can receive parameters or external enrichment information from an external server to generate AI / ML models to be deployed in the quasi-RT RIC 325. Such information can be utilized by the quasi-RT RIC 325 and can be received at the SMO framework 305 or non-RT RIC 315 from non-network data sources or from network functions. In some embodiments, the non-RT RIC 315 or quasi-RT RIC 325 can be configured to adjust RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns in performance and use the AI / ML models to take corrective action through the SMO framework 305 (e.g., reconfiguration via the O1 interface) or through the creation of RAN management policies (e.g., A1 interface policies).

[0073] As noted above, Figure 3 is provided as an example. Other examples may differ from those described with respect to Figure 3.

[0074] 4 illustrates an example embodiment 400 of requesting and transmitting AI / ML information according to the present disclosure. As shown in FIG. 4, a source network entity 401 and a receiver network entity 403 may communicate with each other (e.g., over wireless or wired backhaul). The source network entity 401 may include a network node (e.g., a Next Generation (NG) RAN (NG-RAN) node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320). Similarly, the receiver network entity 403 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320).

[0075] As indicated by reference numeral 405, the source network entity 401 may send, and the receiver network entity 403 may receive, an AI / ML information request (e.g., a data collection request as defined in 3GPP specifications). As used herein, "artificial intelligence / machine learning" or "AI / ML" refers to automated decision-making techniques and includes computer algorithms configured to automatically improve performance without explicit programming, such as supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms, among other examples. The AI / ML information request may indicate one or more types of measurements being requested (e.g., CQI, precoding matrix indicator (PMI), layer indicator (LI), rank indicator (RI), RSRP, RSSI, and / or another type of measurement). The source network entity 401 may send the AI / ML information request based on the type of measurement to be used as input to the computer algorithm (e.g., during algorithm training or algorithm deployment).

[0076] An AI / ML information request may be a Class 1 message. As used herein, "Class 1" refers to a message within a procedure that has a corresponding response (e.g., an acknowledgment indicating success or failure), while "Class 2" refers to a message within a procedure that does not have a corresponding response.

[0077] As indicated by reference numeral 410, the receiver network entity 403 may send, and the source network entity 401 may receive, an AI / ML information response (e.g., a data collection response as defined in the 3GPP specifications). For example, the AI / ML information response may include one or more measurements for the type(s) of measurement requested in the AI / ML information request. Thus, the source network entity 401 may use the measurement(s) to train a computer algorithm and / or apply a computer algorithm to make a decision.

[0078] In some aspects, the source network entity 401 may send an AI / ML information request to receive measurement(s) one time. Alternatively, the source network entity 401 may send an AI / ML information request to receive measurement(s) in response to an event (e.g., event A1, event A2, event A3, event A4, event A5, event A6, event B1, or event B2, among other examples, as defined in the 3GPP specifications). Additionally or alternatively, the source network entity 401 may send an AI / ML information request to receive measurement(s) periodically. Thus, as indicated by reference numeral 415, the receiver network entity 403 may send, and the source network entity 401 may receive, AI / ML information updates (e.g., data collection updates as defined in the 3GPP specifications) based on the triggering event and / or periodicity indicated in the AI / ML information request. The receiver network entity 403 may continue to send additional AI / ML information updates (e.g., based on a triggering event and / or periodicity) until the source network entity 401 sends an additional AI / ML information request that triggers the receiver network entity 403 to stop sending AI / ML information updates.

[0079] As mentioned above, the AI / ML information response may be a class 1 message, while the AI / ML information update may be a class 2 message.

[0080] After training and / or deployment of the computer algorithm, the source network entity may decide to validate the output from the computer algorithm (e.g., actions triggered by the computer algorithm). For example, the computer algorithm may decide to perform a handover (e.g., of a UE), perform an RRC release (e.g., of a UE), shut down or deactivate a cell, add or activate a cell, adjust the steering of a wireless beam, add or remove a carrier (e.g., when using carrier aggregation (CA)), add or remove a secondary node, and / or modify mobility parameters, among other examples. Without validating the actions triggered by the computer algorithm, the source network entity cannot improve the computer algorithm (e.g., via retraining). As a result, the source network entity may continue to make suboptimal decisions using the computer algorithm that waste power, waste processing resources, increase latency, reduce throughput, and / or reduce the quality and reliability of wireless communications.

[0081] Some techniques and apparatuses described herein enable a source network entity (e.g., a network node 110, such as an NG-RAN node) to include a request for machine learning feedback in an AI / ML action execution message. Thus, the source network entity may validate the action triggered by the computer algorithm based on the machine learning feedback. As a result, the source network entity may improve the computer algorithm (e.g., via retraining) to optimize future decisions and thus save power, save processing resources, reduce latency, increase throughput, and / or improve the quality and reliability of wireless communications. In addition, some techniques and apparatuses described herein enable a receiver network entity (e.g., a network node 110, such as an NG-RAN node) to send a data-type-independent Class 2 message using machine learning feedback. As a result, the source network entity may use the machine learning feedback to improve the computer algorithm (e.g., via retraining) to optimize future decisions and thus save power, save processing resources, reduce latency, increase throughput, and / or improve the quality and reliability of wireless communications.

[0082] As noted above, Figure 4 is provided as an example. Other examples may differ from those described with respect to Figure 4.

[0083] 5 illustrates an example embodiment 500 associated with a request for machine learning feedback in accordance with the present disclosure. As shown in FIG. 5, a source network entity 401 and a receiver network entity 403 may communicate with each other (e.g., over wireless or wired backhaul). The source network entity 401 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320). Similarly, the receiver network entity 403 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320).

[0084] As indicated by reference numeral 505, the source network entity 401 may send, and the receiver network entity 403 may receive, an AI / ML action execution message. For example, the source network entity 401 may receive an output from a computer algorithm that triggers an AI / ML action. The AI / ML action may include, among other examples, a handover of a UE (e.g., UE 120), an RRC release of the UE (e.g., UE 120), a shutdown or deactivation of a cell (e.g., including and / or controlled by the receiver network entity 403), an addition or activation of a cell (e.g., including and / or controlled by the receiver network entity 403), an adjustment of a beam (e.g., transmitted by and / or controlled by the receiver network entity 403), an addition or removal of a carrier (e.g., when CA is used), an addition or removal of a secondary node (e.g., including and / or controlled by the receiver network entity 403), and / or a modification of a mobility parameter (e.g., associated with the UE).

[0085] In some implementations, the AI / ML action take message may include a cause value (e.g., an integer associated with a cause category). Thus, the AI / ML action take message may include a cause value that indicates that the AI / ML action was triggered by machine learning. For example, the source network entity 401 may select a code point for the cause value based on a computer algorithm that triggers the AI / ML action. The source network entity 401 may select the code point based on a data structure stored in the memory of the source network entity 401 that associates cause value code points with cause categories (e.g., according to 3GPP specifications and / or another standard).

[0086] Additionally or alternatively, the AI / ML action take message may include a request for machine learning feedback associated with the AI / ML action. For example, the machine learning feedback may include at least one UE-related metric (e.g., average packet delay, average downlink (DL) throughput, average uplink (UL) throughput, and / or average packet error rate, among other examples) and / or at least one cell-related metric (e.g., resource status, cell performance data, and / or energy efficiency data of a neighboring NG-RAN node, such as the receiver network entity 403 or an entity communicating with the receiver network entity 403, among other examples). The request may be an information element (IE) in the AI / ML action take message (e.g., an AI / ML measurement ID IE as defined in the 3GPP specifications). The IE may indicate which metric(s) are requested. Additionally, the IE may indicate whether the machine learning feedback should be one-shot, periodic, or event-driven.

[0087] The receiver network entity 403 may send, and the source network entity 401 may receive, an acknowledgment of the AI / ML action take message, as indicated by reference numeral 510. The AI / ML action take message may include an Xn (or X2) message, such that the acknowledgment is an Xn (or X2) acknowledgment signal. Based on the AI / ML action take message, the source network entity 401 and the receiver network entity 403 may perform an AI / ML action, as indicated by reference numeral 515. For example, the source network entity 401 and the receiver network entity 403 may exchange one or more messages to perform the AI / ML action, as described above.

[0088] As indicated by reference numeral 520, the receiver network entity 403 may transmit, and the source network entity 401 may receive, machine learning feedback. For example, the receiver network entity 403 may transmit, after an AI / ML action, machine learning feedback based on a request in an AI / ML action take message. The machine learning feedback may be included in a Class 2 message (e.g., a Data Collection Response or a Data Collection Update, as defined in the 3GPP specifications). In some aspects, the Class 2 message may be data-independent. Thus, the receiver network entity 403 may encode the machine learning feedback into the message regardless of which UE-related metrics and / or cell-related metrics were requested by the source network entity 401.

[0089] Although example embodiment 500 illustrates one-shot feedback, other examples may include the receiver network entity 403 sending the machine learning feedback based on a triggering event and / or periodicity indicated in the request, and the source network entity 401 receiving the machine learning feedback. For example, the receiver network entity 403 may continue to send the machine learning feedback (e.g., based on a triggering event and / or periodicity) until the source network entity 401 sends an indication that triggers the receiver network entity 403 to stop sending the machine learning feedback.

[0090] 5, the source network entity 401 may improve its computer algorithms (e.g., via retraining) based on machine learning feedback to optimize future decisions. As a result, the source network entity 401 may use the improved computer algorithms to save power, save processing resources, reduce latency, increase throughput, and / or improve the quality and reliability of wireless communications.

[0091] As mentioned above, Figure 5 is given as one example. Other examples may differ from those described with respect to Figure 5.

[0092] 6 illustrates an example embodiment 600 associated with a request for machine learning feedback according to the present disclosure. As shown in FIG. 6, a source network entity 401 and a receiver network entity 403 may communicate with each other (e.g., over wireless or wired backhaul). The source network entity 401 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320). Similarly, the receiver network entity 403 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320).

[0093] As indicated by reference numeral 605, the source network entity 401 may send and the receiver network entity 403 may receive an AI / ML information request. As described in connection with reference numeral 405 of FIG. 4, the AI / ML information request may be a Class 1 message. The AI / ML information request may include one or more measurement identifications (IDs) associated with one or more types of measurements being requested. The measurement IDs may be alphanumeric identifiers associated with the corresponding measurement types.

[0094] In some aspects, the AI / ML information request may further include an indication that the measurement configuration is associated with an AI / ML action. For example, the AI / ML information request may include an explicit indication, such as an IE (e.g., to be defined in the 3GPP specification and / or another standard) associating the measurement ID(s) with a deactivated state. Alternatively, the AI / ML information request may include an implicit indication (e.g., because some measurement IDs are associated with AI / ML actions and other measurement IDs are not). Thus, the receiver network entity 403 refrains from sending one or more measurements based on the measurement ID(s) while the measurement ID(s) are associated with a deactivated state (e.g., because the measurement ID(s) are associated with feedback for the AI / ML action). Furthermore, the receiver network entity 403 may send machine learning feedback in response to an AI / ML action execution message (e.g., after the AI / ML information request), as described below.

[0095] As indicated by reference numeral 610, the receiver network entity 403 may send, and the source network entity 401 may receive, an AI / ML information response (e.g., a data collection response as defined in the 3GPP specifications). In some aspects, the AI / ML information response may include one or more measurements for the type(s) of measurement requested in the AI / ML information request. Thus, the source network entity 401 may use the measurement(s) to train a computer algorithm and / or apply a computer algorithm to make a decision.

[0096] If the type(s) of measurements corresponding to the measurement ID(s) are associated with a deactivated state, the AI / ML information response may acknowledge the AI / ML information request without including any measurements. The AI / ML information request may include an Xn (or X2) message such that the acknowledgement is an Xn (or X2) acknowledgement signal. In some aspects, the AI / ML information request may indicate that one or more first measurement types are associated with a deactivated state and that one or more second measurement types are now being requested. Thus, the AI / ML information response may include one or more measurements for the second measurement type(s) rather than measurements for the first measurement type(s).

[0097] In some aspects, as indicated by reference numeral 615, the receiver network entity 403 may send, and the source network entity 401 may receive, an AI / ML information update (e.g., a data collection update as defined in the 3GPP specifications). The AI / ML information update may be sent based on a triggering event and / or periodicity (e.g., as indicated in the AI / ML information request). In some aspects, the receiver network entity 403 may continue to send additional AI / ML information updates (e.g., based on a triggering event and / or periodicity) until the source network entity 401 sends an additional AI / ML information request that triggers the receiver network entity 403 to stop sending the AI / ML information updates.

[0098] As indicated by reference numeral 620, the source network entity 401 may send, and the receiver network entity 403 may receive, an AI / ML action execution message. For example, the source network entity 401 may receive an output from a computer algorithm that triggers an AI / ML action. The AI / ML action may include, among other examples, a handover of a UE (e.g., UE 120), an RRC release of the UE (e.g., UE 120), a shutdown or deactivation of a cell (e.g., including and / or controlled by the receiver network entity 403), an addition or activation of a cell (e.g., including and / or controlled by the receiver network entity 403), an adjustment of a beam (e.g., transmitted by and / or controlled by the receiver network entity 403), an addition or removal of a carrier (e.g., when CA is used), an addition or removal of a secondary node (e.g., including and / or controlled by the receiver network entity 403), and / or a modification of a mobility parameter (e.g., associated with the UE). In some implementations, as described in connection with reference numeral 505 in FIG. 5, the AI / ML action execution message may include a cause value (e.g., an integer associated with a cause category) indicating that the AI / ML action was triggered by machine learning.

[0099] Additionally or alternatively, the AI / ML action take message may include a request for machine learning feedback associated with the AI / ML action. The request may be an IE (e.g., an AI / ML measurement ID IE as defined in the 3GPP specifications) in the AI / ML action take message. The IE may include one or more measurement IDs previously indicated by the source network entity 401 (e.g., in an AI / ML information request, as described in connection with reference numeral 605). Additionally, in some aspects, the AI / ML action take message may associate the measurement ID(s) with an activation state. Thus, the receiver network entity 403 may initiate one or more measurements corresponding to the measurement ID(s) based on the measurement ID(s) being associated with an activation state.

[0100] The receiver network entity 403 may send, and the source network entity 401 may receive, an acknowledgment of the AI / ML action take message, as indicated by reference numeral 625. The AI / ML action take message may include an Xn (or X2) message, such that the acknowledgment is an Xn (or X2) acknowledgment signal. Based on the AI / ML action take message, the source network entity 401 and the receiver network entity 403 may perform an AI / ML action, as indicated by reference numeral 630. For example, the source network entity 401 and the receiver network entity 403 may exchange one or more messages to perform the AI / ML action, as described above.

[0101] As indicated by reference numeral 635, the receiver network entity 403 may transmit, and the source network entity 401 may receive, machine learning feedback. For example, the receiver network entity 403 may transmit, after an AI / ML action, machine learning feedback based on a request in an AI / ML action take message. The machine learning feedback may be included in a Class 2 message (e.g., a Data Collection Response or a Data Collection Update, as defined in the 3GPP specifications). In some aspects, the Class 2 message may be data-independent. Thus, the receiver network entity 403 may encode the machine learning feedback into the message regardless of which UE-related metrics and / or cell-related metrics were requested by the source network entity 401.

[0102] Although example embodiment 600 illustrates one-shot feedback, other examples may include the receiver network entity 403 sending the machine learning feedback based on a triggering event and / or periodicity indicated in the request, and the source network entity 401 receiving the machine learning feedback. For example, the receiver network entity 403 may continue to send the machine learning feedback (e.g., based on a triggering event and / or periodicity) until the source network entity 401 sends an indication that triggers the receiver network entity 403 to stop sending the machine learning feedback.

[0103] 6, the source network entity 401 may improve its computer algorithms (e.g., via retraining) based on machine learning feedback to optimize future decisions. As a result, the source network entity 401 may use the improved computer algorithms to save power, save processing resources, reduce latency, increase throughput, and / or improve the quality and reliability of wireless communications.

[0104] As mentioned above, Figure 6 is given as one example. Other examples may differ from those described with respect to Figure 6.

[0105] 7 illustrates an example embodiment 700 associated with transmitting machine learning feedback in accordance with the present disclosure. As shown in FIG. 7, a source network entity 401 and a receiver network entity 403 may communicate with each other (e.g., over wireless or wired backhaul). The source network entity 401 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320). Similarly, the receiver network entity 403 may include a network node (e.g., an NG-RAN node), an RU (e.g., RU 340), a DU (e.g., DU 330), a CU (e.g., CU 310), and / or a portion of a core network (e.g., core network 320).

[0106] As indicated by reference numeral 705, the source network entity 401 may send, and the receiver network entity 403 may receive, an AI / ML action execution message. For example, the source network entity 401 may receive an output from a computer algorithm that triggers an AI / ML action. The AI / ML action may include, among other examples, a handover of a UE (e.g., UE 120), an RRC release of the UE (e.g., UE 120), a shutdown or deactivation of a cell (e.g., including and / or controlled by the receiver network entity 403), an addition or activation of a cell (e.g., including and / or controlled by the receiver network entity 403), an adjustment of a beam (e.g., transmitted by and / or controlled by the receiver network entity 403), an addition or removal of a carrier (e.g., when CA is used), an addition or removal of a secondary node (e.g., including and / or controlled by the receiver network entity 403), and / or a modification of a mobility parameter (e.g., associated with the UE).

[0107] The receiver network entity 403 may send, and the source network entity 401 may receive, an acknowledgment of the AI / ML action take message, as indicated by reference numeral 710. The AI / ML action take message may include an Xn (or X2) message, such that the acknowledgment is an Xn (or X2) acknowledgment signal. Based on the AI / ML action take message, the source network entity 401 and the receiver network entity 403 may perform an AI / ML action, as indicated by reference numeral 715. For example, the source network entity 401 and the receiver network entity 403 may exchange one or more messages to perform the AI / ML action, as described above.

[0108] As indicated by reference numeral 720, the source network entity 401 may transmit, and the receiver network entity 403 may receive, an AI / ML information request. As described in connection with reference numeral 405 of FIG. 4, the AI / ML information request may be a Class 1 message. The AI / ML information request may further include a request for machine learning feedback associated with the AI / ML action. For example, the machine learning feedback may include at least one UE-related metric (e.g., average packet delay, average DL throughput, average UL throughput, and / or average packet error rate, among other examples) and / or at least one cell-related metric (e.g., resource status, cell performance data, and / or energy efficiency data of a neighboring NG-RAN node, such as the receiver network entity 403 or an entity communicating with the receiver network entity 403, among other examples). The request may be an IE within the AI / ML information request (e.g., an AI / ML feedback IE to be defined in the 3GPP specifications and / or another standard). The IE may indicate which metric(s) are requested. Additionally, the IE may indicate whether the machine learning feedback should be one-shot, periodic, or event-driven.

[0109] Alternatively, the request for machine learning feedback may be sent in a separate message, for example, the source network entity 401 may send and the receiver network entity 403 may receive a Class 1 message (e.g., to be defined in the 3GPP specifications and / or another standard) to request machine learning feedback.

[0110] As indicated by reference numeral 725, the receiver network entity 403 may send, and the source network entity 401 may receive, an AI / ML information response (e.g., a data collection response or a data collection update as defined in the 3GPP specifications). In some aspects, the AI / ML information response may include one or more measurements for the type(s) of measurement requested in the AI / ML information request. Thus, the source network entity 401 may use the measurement(s) to train a computer algorithm and / or apply a computer algorithm to make a decision.

[0111] As indicated by reference numeral 730, the receiver network entity 403 may transmit, and the source network entity 401 may receive, machine learning feedback. For example, the receiver network entity 403 may transmit the machine learning feedback based on a request in the AI / ML information request. The machine learning feedback may be included in a Class 2 message (e.g., a Data Collection Response or a Data Collection Update, as defined in the 3GPP specifications). In some aspects, the Class 2 message may be data-independent. Thus, the receiver network entity 403 may encode the machine learning feedback into the message regardless of which UE-related metrics and / or cell-related metrics were requested by the source network entity 401.

[0112] Although example embodiment 700 illustrates one-shot feedback, other examples may include the receiver network entity 403 sending the machine learning feedback based on a triggering event and / or periodicity indicated in the request, and the source network entity 401 receiving the machine learning feedback. For example, the receiver network entity 403 may continue to send the machine learning feedback (e.g., based on a triggering event and / or periodicity) until the source network entity 401 sends an indication that triggers the receiver network entity 403 to stop sending the machine learning feedback.

[0113] 7, the source network entity 401 may improve its computer algorithms (e.g., via retraining) based on machine learning feedback to optimize future decisions. As a result, the source network entity 401 may use the improved computer algorithms to save power, save processing resources, reduce latency, increase throughput, and / or improve the quality and reliability of wireless communications.

[0114] As mentioned above, Figure 7 is given as one example. Other examples may differ from those described with respect to Figure 7.

[0115] 8 illustrates an example process 800 performed, for example, by a source network entity, in accordance with the present disclosure. Example process 800 is an example of a source network entity (e.g., source network entity 401 and / or device 1200 of FIG. 12) performing operations associated with sending and receiving machine learning feedback.

[0116] 8, in some aspects, process 800 may include transmitting (e.g., to receiver network entity 403) a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity (block 810). For example, the source network entity may transmit (e.g., using transmitting component 1204 and / or communications manager 1206 shown in FIG. 12) a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity, as described herein.

[0117] 8, in some aspects, process 800 may include receiving the machine learning feedback upon request (block 820). For example, the source network entity (e.g., using the receiving component 1202 and / or the communications manager 1206 shown in FIG. 12) may receive the machine learning feedback upon request as described herein.

[0118] Process 800 may include additional aspects, such as any single aspect or any combination of aspects, described below and / or in connection with one or more other processes described elsewhere herein.

[0119] In a first aspect, the source network entity includes a RAN node.

[0120] In a second aspect, alone or in combination with the first aspect, the source network entity includes a CU.

[0121] In a third aspect, alone or in combination with one or more of the first and second aspects, the source network entity includes a DU.

[0122] In a fourth aspect, alone or in combination with one or more of the first through third aspects, the message includes an AI / ML action execution message for an AI / ML action.

[0123] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the request includes an IE in the AI / ML action execution message indicating the type of machine learning feedback being requested.

[0124] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the request includes an indication of a measurement ID previously indicated by the source network entity.

[0125] In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, the process 800 includes sending (e.g., using the sending component 1204 and / or the communications manager 1206) an AI / ML information request including an explicit or implicit indication that the measurement configuration is associated with an AI / ML action, the AI / ML action execution message activates reporting, and machine learning feedback is received in response to the AI / ML action execution message.

[0126] In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, the AI / ML action execution message includes a cause value indicating that the AI / ML action was triggered by machine learning.

[0127] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the AI / ML action includes a handover of the UE or cell activation.

[0128] 8 illustrates example blocks of process 800, in some aspects process 800 may include additional, fewer, different, or differently arranged blocks compared to the blocks illustrated in FIG 8. Additionally, or alternatively, two or more of the blocks of process 800 may be performed in parallel.

[0129] 9 illustrates an example process 900 performed, for example, by a receiver network entity, in accordance with the present disclosure. The example process 900 is an example of a receiver network entity (e.g., receiver network entity 403 and / or device 1200 of FIG. 12) performing operations associated with sending and receiving machine learning feedback.

[0130] 9, in some aspects, process 900 may include receiving (e.g., from source network entity 401) a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity (block 910). For example, a receiver network entity may receive (e.g., using receiving component 1202 and / or communications manager 1206 shown in FIG. 12) a message including a request for machine learning feedback associated with an AI / ML action triggered by the source network entity, as described herein.

[0131] 9, in some aspects, the process 900 may include transmitting the machine learning feedback upon request (block 920). For example, the receiver network entity (e.g., using the transmitting component 1204 and / or the communications manager 1206 shown in FIG. 12) may transmit the machine learning feedback upon request as described herein.

[0132] Process 900 may include additional aspects, such as any single aspect or any combination of aspects, described below and / or in connection with one or more other processes described elsewhere herein.

[0133] In a first aspect, the receiver network entity comprises a RAN node.

[0134] In a second aspect, alone or in combination with the first aspect, the receiver network entity includes a CU.

[0135] In a third aspect, alone or in combination with one or more of the first and second aspects, the receiver network entity includes a DU.

[0136] In a fourth aspect, alone or in combination with one or more of the first through third aspects, the message includes an AI / ML action execution message for an AI / ML action.

[0137] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the request includes an IE in the AI / ML action execution message indicating the type of machine learning feedback being requested.

[0138] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the request includes an indication of a measurement IE that was previously indicated to the receiver network entity.

[0139] In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, the process 900 includes receiving (e.g., using the receiving component 1202 and / or the communications manager 1206) an AI / ML information request including an explicit or implicit indication that the measurement configuration is associated with an AI / ML action, wherein the AI / ML action execution message activates reporting and the machine learning feedback is transmitted in response to the AI / ML action execution message.

[0140] In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, the AI / ML action execution message includes a cause value indicating that the AI / ML action was triggered by machine learning.

[0141] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the AI / ML action includes a handover of the UE or cell activation.

[0142] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the machine learning feedback is transmitted according to a periodicity.

[0143] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, machine learning feedback is sent in response to an event.

[0144] 9 illustrates example blocks of process 900, in some aspects process 900 may include additional, fewer, different, or differently arranged blocks compared to the blocks illustrated in FIG 9. Additionally or alternatively, two or more of the blocks of process 900 may be performed in parallel.

[0145] 10 illustrates an example process 1000 performed, for example, by a source network entity, in accordance with the present disclosure. Example process 1000 is an example of a source network entity (e.g., source network entity 401 and / or device 1200 of FIG. 12) performing operations associated with sending and receiving machine learning feedback.

[0146] 10, in some aspects, process 1000 may include transmitting (e.g., to receiver network entity 403) a request for machine learning feedback (block 1010). For example, the source network entity may transmit the request for machine learning feedback (e.g., using sending component 1204 and / or communications manager 1206 shown in FIG. 12) as described herein.

[0147] 10, in some aspects, process 1000 may include receiving, upon request, a Class 2 message (e.g., from receiver network entity 403) that is data type independent and includes machine learning feedback (block 1020). For example, the source network entity (e.g., using receiving component 1202 and / or communications manager 1206 shown in FIG. 12) may receive, upon request, a Class 2 message that is data type independent and includes machine learning feedback, as described herein.

[0148] Process 1000 may include additional aspects, such as any single aspect or any combination of aspects, described below and / or in connection with one or more other processes described elsewhere herein.

[0149] In a first aspect, the request for machine learning feedback is included in a Class 1 message for requesting measurements for training.

[0150] In a second aspect, alone or in combination with the first aspect, the request is included in an AI / ML action execution message associated with an AI / ML action triggered by the source network entity.

[0151] In a third aspect, alone or in combination with one or more of the first and second aspects, class 2 messages are transmitted according to a periodicity.

[0152] In a fourth aspect, either alone or in combination with one or more of the first to third aspects, a class 2 message is sent in response to an event.

[0153] 10 illustrates example blocks of process 1000, in some aspects process 1000 may include additional, fewer, different, or differently arranged blocks compared to the blocks illustrated in FIG 10. Additionally or alternatively, two or more of the blocks of process 1000 may be performed in parallel.

[0154] 11 illustrates an example process 1100 performed, for example, by a receiver network entity, in accordance with the present disclosure. Example process 1100 is an example of a receiver network entity (e.g., receiver network entity 403 and / or device 1200 of FIG. 12) performing operations associated with sending and receiving machine learning feedback.

[0155] 11, in some aspects, process 1100 may include receiving (e.g., from source network entity 401) a request for machine learning feedback (block 1110). For example, a receiver network entity may receive the request for machine learning feedback (e.g., using receiving component 1202 and / or communications manager 1206 shown in FIG. 12) as described herein.

[0156] 11, in some aspects, process 1100 may include transmitting (e.g., to source network entity 401) a Class 2 message that is data type independent and includes machine learning feedback upon request (block 1120). For example, the receiver network entity may transmit (e.g., using transmitting component 1204 and / or communications manager 1206 shown in FIG. 12) a Class 2 message that is data type independent and includes machine learning feedback upon request, as described herein.

[0157] Process 1100 may include additional aspects, such as any single aspect or any combination of aspects, described below and / or in connection with one or more other processes described elsewhere herein.

[0158] In a first aspect, the request for machine learning feedback is included in a Class 1 message for requesting measurements for training.

[0159] In a second aspect, alone or in combination with the first aspect, the request is included in an AI / ML action execution message associated with an AI / ML action triggered by the source network entity.

[0160] In a third aspect, alone or in combination with one or more of the first and second aspects, class 2 messages are transmitted according to a periodicity.

[0161] In a fourth aspect, either alone or in combination with one or more of the first to third aspects, a class 2 message is sent in response to an event.

[0162] 11 illustrates example blocks of process 1100, in some aspects process 1100 may include additional, fewer, different, or differently arranged blocks than those illustrated in FIG 11. Additionally or alternatively, two or more of the blocks of process 1100 may be performed in parallel.

[0163] 12 is a diagram of an example apparatus 1200 for wireless communication in accordance with the present disclosure. Apparatus 1200 may be a network entity, or a network entity may include apparatus 1200. In some aspects, apparatus 1200 includes a receiving component 1202, a transmitting component 1204, and / or a communications manager 1206, which may communicate with one another (e.g., via one or more buses and / or one or more other components). In some aspects, communications manager 1206 is communications manager 140 or communications manager 150 described in connection with FIG. 1. As shown, apparatus 1200 may communicate with another apparatus 1208, such as a UE or a network node (e.g., a CU, DU, RU, or base station), using receiving component 1202 and transmitting component 1204.

[0164] In some aspects, device 1200 may be configured to perform one or more operations described herein in conjunction with FIGS. 5-7. Additionally or alternatively, device 1200 may be configured to perform one or more processes described herein, such as process 800 of FIG. 8, process 900 of FIG. 9, process 1000 of FIG. 10, process 1100 of FIG. 11, or a combination thereof. In some aspects, device 1200 and / or one or more components illustrated in FIG. 12 may include one or more components of a network entity described in conjunction with FIG. 2. Additionally or alternatively, one or more components illustrated in FIG. 12 may be implemented within one or more components described in conjunction with FIG. 2. Additionally or alternatively, one or more components of a set of components may be implemented at least in part as software stored in memory. For example, a component (or a portion of a component) may be realized as instructions or code stored in a non-transitory computer-readable medium and executable by a controller or processor to perform the function or operation of the component.

[0165] Receiving component 1202 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from device 1208. Receiving component 1202 may provide the received communications to one or more other components of device 1200. In some aspects, receiving component 1202 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, among other examples) on the received communications and provide the processed signals to one or more other components of device 1200. In some aspects, receiving component 1202 may include one or more antennas, a modem, a demodulator, a MIMO detector, a receive processor, a controller / processor, a memory, or a combination thereof of the network entities described in connection with FIG.

[0166] The transmitting component 1204 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the device 1208. In some aspects, one or more other components of the device 1200 may generate communications and provide the generated communications to the transmitting component 1204 for transmission to the device 1208. In some aspects, the transmitting component 1204 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples) on the generated communications and transmit the processed signals to the device 1208. In some aspects, the transmitting component 1204 may include one or more antennas, a modem, a modulator, a transmit MIMO processor, a transmit processor, a controller / processor, a memory, or a combination thereof, of the network entities described in connection with FIG. 2 . In some aspects, the transmitting component 1204 may be collocated with the receiving component 1202 within a transceiver.

[0167] The communications manager 1206 may support the operation of the receiving component 1202 and / or the transmitting component 1204. For example, the communications manager 1206 may receive information associated with configuring the reception of communications by the receiving component 1202 and / or the transmission of communications by the transmitting component 1204. Additionally or alternatively, the communications manager 1206 may generate and / or provide control information to the receiving component 1202 and / or the transmitting component 1204 to control the reception and / or transmission of communications.

[0168] In some aspects, the apparatus 1200 may include or be included in a source network entity. Thus, the sending component 1204 may send (e.g., to the apparatus 1200) a message including a request for machine learning feedback associated with an AI / ML action triggered by the apparatus 1208. The receiving component 1202 may receive (e.g., from the apparatus 1208) the machine learning feedback in response to the request. In some aspects, the machine learning feedback may be included in a Class 2 message that is independent of the data type.

[0169] In some aspects, the sending component 1204 can further send an AI / ML information request that includes an explicit or implicit indication that the measurement configuration is associated with an AI / ML action. Thus, the AI / ML action execution message can activate reporting, and the receiving component 1202 can receive machine learning feedback in response to the AI / ML action execution message.

[0170] In some aspects, the apparatus 1200 may include or be included in a receiver network entity. Thus, the receiving component 1202 may receive (e.g., from the apparatus 1208) a message including a request for machine learning feedback associated with an AI / ML action triggered by the apparatus 1208. The transmitting component 1204 may transmit (e.g., to the apparatus 1208) the machine learning feedback in response to the request. In some aspects, the machine learning feedback may be included in a Class 2 message that is independent of the data type.

[0171] In some aspects, the receiving component 1202 can further receive an AI / ML information request that includes an explicit or implicit indication that the measurement configuration is associated with an AI / ML action. Accordingly, the AI / ML action execution message can activate reporting, and the transmitting component 1204 can transmit machine learning feedback in response to the AI / ML action execution message.

[0172] The number and arrangement of components shown in Figure 12 are provided as one example. In practice, there may be additional, fewer, different, or differently organized components than those shown in Figure 12. Furthermore, two or more of the components shown in Figure 12 may be implemented within a single component, or a single component shown in Figure 12 may be implemented as multiple distributed components. Additionally or alternatively, a set of components shown in Figure 12 may perform one or more functions that are described as being performed by another set of components shown in Figure 12.

[0173] The following provides a summary of several aspects of the disclosure.

[0174] Aspect 1: A method of communication performed by a source network entity, comprising: sending a message to a receiver network entity including a request for machine learning feedback associated with an artificial intelligence / machine learning (AI / ML) action triggered by the source network entity; and receiving the machine learning feedback in response to the request.

[0175] Aspect 2: The method of aspect 1, wherein the source network entity includes a radio access network node.

[0176] Aspect 3: The method of aspect 1, wherein the source network entity comprises a central unit.

[0177] Aspect 4: The method of aspect 1, wherein the source network entity comprises a distribution unit.

[0178] Aspect 5: The method of any one of aspects 1 to 4, wherein the message includes an AI / ML action execution message for an AI / ML action.

[0179] Aspect 6: The method of aspect 5, wherein the request includes an information element in the AI / ML action execution message indicating the type of machine learning feedback being requested.

[0180] Aspect 7: The method of aspect 5, wherein the request includes an indication of a measurement identity (ID) previously indicated by the source network entity.

[0181] Aspect 8: The method of aspect 7, further comprising sending an AI / ML information request including an explicit or implicit indication that the measurement configuration is associated with an AI / ML action, wherein the AI / ML action execution message further activates reporting, and wherein machine learning feedback is received in response to the AI / ML action execution message.

[0182] Aspect 9: The method of any of aspects 5 to 8, wherein the AI / ML action execution message includes a cause value indicating that the AI / ML action was triggered by machine learning.

[0183] Aspect 10: The method of any one of aspects 1 to 9, wherein the AI / ML action includes a handover of the user equipment or activation of a cell that includes the receiver network entity.

[0184] Aspect 11: A method of communication performed by a receiver network entity, the method including: receiving a message from a source network entity including a request for machine learning feedback associated with an artificial intelligence / machine learning (AI / ML) action triggered by the source network entity; and transmitting the machine learning feedback in response to the request.

[0185] Aspect 12: The method of aspect 11, wherein the receiver network entity includes a radio access network node.

[0186] Aspect 13: The method of aspect 11, wherein the receiver network entity comprises a central unit.

[0187] Aspect 14: The method of aspect 11, wherein the receiver network entity comprises a distribution unit.

[0188] Aspect 15: The method of any one of aspects 11 to 14, wherein the message includes an AI / ML action execution message for an AI / ML action.

[0189] Aspect 16: The method of aspect 15, wherein the request includes an information element in the AI / ML action execution message indicating the type of machine learning feedback being requested.

[0190] Aspect 17: The method of aspect 15, wherein the request includes an indication of a measurement identity (ID) previously indicated to the receiver network entity.

[0191] Aspect 18: The method of aspect 17, further comprising receiving an AI / ML information request associating the measurement ID with a deactivated state, wherein the AI / ML action execution message further associates the measurement ID with an activated state, and the machine learning feedback is sent based on the activated state.

[0192] Aspect 19: The method of any of aspects 15 to 18, wherein the AI / ML action execution message includes a cause value indicating that the AI / ML action was triggered by machine learning.

[0193] Aspect 20: The method of any of aspects 11 to 19, wherein the AI / ML action includes a handover of the user equipment or activation of a cell including the receiver network entity.

[0194] Aspect 21: The method of any of aspects 11 to 20, wherein the machine learning feedback is transmitted according to a periodicity.

[0195] Aspect 22: The method of any of aspects 11 to 20, wherein the machine learning feedback is sent in response to an event.

[0196] Aspect 23: A method of communication performed by a source network entity, the method including: sending a request for machine learning feedback to a receiver network entity; and receiving, in response to the request, a Class 2 message from the receiver network entity, independent of data type, including the machine learning feedback.

[0197] Aspect 24: The method of aspect 23, wherein the request for machine learning feedback is included in a class 1 message for requesting measurements for training.

[0198] Aspect 25: The method described in aspect 23, wherein the request is included in an artificial intelligence / machine learning (AI / ML) action execution message associated with an AI / ML action triggered by the source network entity.

[0199] Aspect 26: The method of any of aspects 23 to 25, wherein the class 2 messages are transmitted according to a periodicity.

[0200] Aspect 27: The method of any one of aspects 23 to 25, wherein the class 2 message is sent in response to an event.

[0201] Aspect 28: A method of communication performed by a receiver network entity, the method including: receiving a request for machine learning feedback from a source network entity; and, in response to the request, transmitting a Class 2 message to the source network entity, independent of data type, including the machine learning feedback.

[0202] Aspect 29: The method of aspect 28, wherein the request for machine learning feedback is included in a class 1 message for requesting measurements for training.

[0203] Aspect 30: The method described in aspect 28, wherein the request is included in an artificial intelligence / machine learning (AI / ML) action execution message associated with an AI / ML action triggered by the source network entity.

[0204] Aspect 31: The method of any of aspects 28 to 30, wherein the class 2 messages are transmitted according to a periodicity.

[0205] Aspect 32: The method of any of aspects 28 to 30, wherein the class 2 message is sent in response to an event.

[0206] Aspect 33: An apparatus for communication in a device, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor, the instructions causing the apparatus to perform one or more of the methods of aspects 1 to 32.

[0207] Aspect 34: A device for communication, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to perform one or more methods of aspects 1 to 32.

[0208] Aspect 35: An apparatus for communication, the apparatus comprising at least one means for performing one or more of the methods of aspects 1 to 32.

[0209] Aspect 36: A non-transitory computer-readable medium storing code for communication, the code including instructions executable by a processor, the instructions performing one or more of the methods of aspects 1 to 32.

[0210] Aspect 37: A non-transitory computer-readable medium storing a set of instructions for communication, the set of instructions including one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more methods of aspects 1 to 32.

[0211] The above disclosure provides illustration and description, but is not intended to be exhaustive or to limit the embodiments to the precise form disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the embodiments.

[0212] As used herein, the term "component" is intended to be broadly construed as hardware and / or combinations of hardware and software. "Software" is intended to be broadly construed to mean, among other examples, instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, and / or functions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. As used herein, a "processor" is implemented in hardware and / or a combination of hardware and software. It will be apparent that the systems and / or methods described herein can be realized in various forms of hardware and / or combinations of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, as it is understood that those skilled in the art can design software and hardware to perform the systems and / or methods based at least in part on the description herein.

[0213] As used herein, "meeting a threshold" can refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, etc., depending on the context.

[0214] Although particular combinations of features are recited in the claims and / or disclosed herein, those combinations are not intended to limit the disclosure of various aspects. Many of these features can be combined in ways not specifically recited in the claims and / or disclosed herein. The disclosure of various aspects includes each dependent claim in combination with every other claim in the claim set. As used herein, a phrase referring to "at least one of" a list of items refers to any combination of those items, including single members. As an example, "at least one of a, b, or c" is intended to encompass a, b, c, a+b, a+c, b+c, and a+b+c, as well as any combination having multiple identical elements (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b, a+c+c, b+b, b+b+b, b+b+c, c+c, and c+c+c, or any other permutation of a, b, and c).

[0215] No element, act, or instruction used herein should be construed as essential or required unless expressly described as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." Furthermore, as used herein, the article "the" is intended to include one or more items referred to in connection with the article "the" and may be used interchangeably with "one or more." Furthermore, as used herein, the terms "set" and "group" are intended to include one or more items and may be used interchangeably with "one or more." Where only one item is intended, the phrase "only one" or similar language is used. Also, as used herein, terms such as "has," "have," and "having" are intended to be open-ended terms that do not limit the elements they modify (e.g., an element that "has" A can also have B). Furthermore, the phrase "based on" is intended to mean "based at least in part on," unless expressly stated otherwise. As used herein, the term "or" is also intended to be inclusive when used in a series, and may be used interchangeably with "and / or," except where expressly stated otherwise (e.g., when used in combination with "either" or "only one of").

Claims

1. 1. An apparatus for communication in a source network entity, comprising: one or more memories; one or more processors coupled to the one or more memories, wherein the one or more processors: sending a message to a receiver network entity including a request for machine learning feedback associated with an artificial intelligence / machine learning (AI / ML) action triggered by the source network entity; the devices, individually or collectively configured to receive the machine learning feedback in response to the request.

2. The apparatus of claim 1 , wherein the source network entity comprises a radio access network node.

3. The apparatus of claim 1 , wherein the source network entity comprises a central unit.

4. The apparatus of claim 1 , wherein the source network entity comprises a distribution unit.

5. The apparatus of claim 1 , wherein the message comprises an AI / ML action execution message for the AI / ML action.

6. The apparatus of claim 5 , wherein the request includes an information element in the AI / ML action take message indicating the type of machine learning feedback being requested.

7. The apparatus of claim 5 , wherein the request includes an indication of a measurement identity (ID) previously indicated by the source network entity.

8. the one or more processors: individually or collectively configured to transmit an AI / ML information request including an explicit or implicit indication that a measurement configuration is associated with said AI / ML action; The apparatus of claim 7 , wherein the AI / ML action take message further activates reporting, and the machine learning feedback is received in response to the AI / ML action take message.

9. The apparatus of claim 5 , wherein the AI / ML action execution message includes a cause value indicating that the AI / ML action was triggered by machine learning.

10. The apparatus of claim 1 , wherein the AI / ML action comprises a handover of a user equipment or an activation of a cell that includes the receiver network entity.

11. 1. An apparatus for communication at a receiver network entity, comprising: one or more memories; one or more processors coupled to the one or more memories, wherein the one or more processors: receiving a message from a source network entity including a request for machine learning feedback associated with an artificial intelligence / machine learning (AI / ML) action triggered by the source network entity; the devices, individually or collectively configured to: transmit the machine learning feedback in response to the request.

12. The apparatus of claim 11 , wherein the receiver network entity comprises a radio access network node.

13. The apparatus of claim 11 , wherein the receiver network entity comprises a central unit.

14. The apparatus of claim 11 , wherein the receiver network entity comprises a distribution unit.

15. The apparatus of claim 11 , wherein the message comprises an AI / ML action execution message for the AI / ML action.

16. The apparatus of claim 15 , wherein the request includes an information element in the AI / ML action take message indicating the type of machine learning feedback being requested.

17. 16. The apparatus of claim 15, wherein the request includes an indication of a measurement identity (ID) previously indicated to the receiver network entity.

18. The apparatus of claim 11 , wherein the AI / ML action comprises a handover of a user equipment or an activation of a cell that includes the receiver network entity.

19. The apparatus of claim 11 , wherein the machine learning feedback is transmitted according to a periodicity.

20. The apparatus of claim 11 , wherein the machine learning feedback is sent in response to an event.

21. 1. An apparatus for communication in a source network entity, comprising: one or more memories; one or more processors coupled to the one or more memories, wherein the one or more processors: Sending a request for machine learning feedback to a receiver network entity; the devices, individually or collectively configured to: in response to the request, receive from the receiver network entity a data type-independent Class 2 message including the machine learning feedback.

22. 22. The apparatus of claim 21, wherein the request for machine learning feedback is included in a class 1 message for requesting measurements for training.

23. 22. The apparatus of claim 21, wherein the request is included in an artificial intelligence / machine learning (AI / ML) action execution message associated with an AI / ML action triggered by the source network entity.

24. 22. The apparatus of claim 21, wherein the class 2 messages are transmitted according to a periodicity.

25. 22. The apparatus of claim 21, wherein the class 2 message is sent in response to an event.

26. 1. An apparatus for communication at a receiver network entity, comprising: one or more memories; one or more processors coupled to the one or more memories, wherein the one or more processors: receiving a request for machine learning feedback from a source network entity; the devices, individually or collectively configured to: in response to the request, send a data type-independent Class 2 message to the source network entity, the message including the machine learning feedback.

27. 27. The apparatus of claim 26, wherein the request for machine learning feedback is included in a class 1 message for requesting measurements for training.

28. 27. The apparatus of claim 26, wherein the request is included in an artificial intelligence / machine learning (AI / ML) action execution message associated with an AI / ML action triggered by the source network entity.

29. 27. The apparatus of claim 26, wherein the class 2 messages are transmitted according to a periodicity.

30. 27. The apparatus of claim 26, wherein the class 2 message is sent in response to an event.

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