Network configured training process

Abstract

Various aspects of the disclosure generally relate to wireless communication and training models. In some aspects, a user equipment (UE) can receive, from a base station, configuration information for performing a training procedure of a BS configuration associated with optimizing network parameters. The UE can perform the training procedure of the BS configuration based at least in part on the configuration information. Numerous other aspects are provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This Patent Application claims priority to U.S. Nonprovisional Patent Application No. 17 / 247,574, filed December 16, 2020, entitled “NETWORK-CONFIGURED TRAINING PROCEDURE,” which is hereby expressly incorporated by reference herein in its entirety. TECHNICAL FIELD

[0003] Aspects of the disclosure relate generally to wireless communication, and to techniques and apparatus associated with network-configured training procedure. BACKGROUND

[0004] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems can employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, and / or the like). 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 enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3 GPP).

[0005] A wireless network can include a number of base stations (BSs) that can support communication for a number of user equipment (UEs). A user equipment (UE) can communicate with a base station (BS) via the downlink and uplink. The downlink (or forward link) refers to the communication from the BS to the UE, and the uplink (or reverse link) refers to the communication from the UE to the BS. As will be described in more detail herein, a BS can be referred to as a Node B, a gNB, an access point (AP), a radio head, a transmit receive point (TRP), a new radio (NR) BS, a 5G Node B, and / or the like.

[0006] The above multiple access technologies have been adopted in various telecommunication standards to provide common protocols that enable different wireless devices to communicate on a municipal, national, regional, and even global level. New Radio (NR), which can also be referred to as 5G, is a set of enhancements to the LTE mobile standard promulgated by the Third Generation Partnership Project (3GPP). NR is designed to better support mobile broadband Internet access by improving spectral efficiency, lowering costs, improving services, making use of new spectrum, and better integrating with other open standards using orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) (CP-OFDM) on the downlink (DL), using CP- OFDM and / or SC-FDM (e.g., also known as discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM)) on the uplink (UL), as well as supporting beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation. However, as the demand for mobile broadband access continues to increase, there exists a need for further improvements in LTE, NR, and other radio access technologies. SUMMARY

[0007] In some aspects, a method of wireless communication performed by a user equipment (UE) includes receiving, from a base station, configuration information for performing a training procedure of a BS configuration associated with optimizing a network parameter; and performing the training procedure of the BS configuration based at least in part on the configuration information.

[0008] In some aspects, a method of wireless communication performed by a base station includes receiving, from a server, a request for performing a training procedure of a server request associated with optimizing a network parameter; and performing the training procedure of the server request based at least in part on receiving the request.

[0009] In some aspects, a UE for training a model includes a memory and one or more processors operatively coupled to the memory, the memory and the one or more processors configured to: receive, from a base station, configuration information for performing a training procedure of a BS configuration associated with optimizing a network parameter; and perform the training procedure of the BS configuration based at least in part on the configuration information.

[0010] In some aspects, a base station for wireless communication includes a memory and one or more processors operatively coupled to the memory, the memory and the one or more processors configured to: receive, from a server, a request for performing a training procedure of a server request associated with optimizing a network parameter; and perform the training procedure of the server request based at least in part on receiving the request.

[0011] In some aspects, a non-transitory computer-readable medium storing a set of instructions for training a model includes one or more instructions that, when executed by one or more processors of a UE, cause the UE to: receive, from a base station, configuration information for performing a training procedure of a BS configuration associated with optimizing a network parameter; and perform the training procedure of the BS configuration based at least in part on the configuration information.

[0012] In some aspects, a non-transitory computer-readable medium storing a set of instructions for wireless communication includes one or more instructions that, when executed by one or more processors of a base station, cause the base station to: receive, from a server, a request for performing a training procedure of a server-requested associated with optimizing a network parameter; and perform the training procedure of the server-requested based at least in part on receiving the request.

[0013] In some aspects, an apparatus for training a model includes means for receiving, from a base station, configuration information for performing a training procedure of a BS configuration associated with optimizing a network parameter; and means for performing the training procedure of the BS configuration based at least in part on the configuration information.

[0014] In some aspects, an apparatus for wireless communication includes means for receiving, from a server, a request for performing a training procedure of a server-requested associated with optimizing a network parameter; and means for performing the training procedure of the server-requested based at least in part on receiving the request.

[0015] Aspects generally include a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, transmitter, wireless communication device, and / or processing system, as substantially described herein with reference to and as illustrated by the accompanying drawings and specification.

[0016] So that the manner in which the above-recited features and advantages of the examples in accordance with the present disclosure can be understood in detail, a brief description of a specific implementation is summarized above. Additional features and advantages will be described hereinafter. The disclosed BRIEF DESCRIPTION OF DRAWINGS

[0017] To gain a full understanding of the foregoing features of this disclosure, a more specific description of the invention, briefly summarized above, can be obtained by referring to various aspects, some of which are illustrated in the accompanying drawings. However, it should be noted that the drawings illustrate only certain typical aspects of this disclosure and are therefore not intended to limit the scope of the disclosure, as other equally valid aspects are permissible under this description. The same reference numerals in different drawings may identify the same or similar elements.

[0018] Figure 1 This is a diagram illustrating examples of wireless networks according to various aspects of this disclosure.

[0019] Figure 2 This is a diagram illustrating an example of communication between a base station and a UE in a wireless network according to various aspects of this disclosure.

[0020] Figure 3 This is a diagram illustrating an example of a training process associated with network configuration according to various aspects of this disclosure.

[0021] Figure 4 and Figure 5 This is a diagram illustrating an example process associated with the training process of network configuration according to various aspects of this disclosure.

[0022] Figure 6 and 7 This is a diagram of an example apparatus associated with a training process for network configuration based on various aspects of this disclosure. Detailed Implementation

[0023] The various aspects of this disclosure are described more fully below with reference to the accompanying drawings. However, this disclosure may be embodied in many different forms and should not be construed as limited to any particular 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 this disclosure to those skilled in the art. Based on the teachings herein, those skilled in the art will understand that the scope of this disclosure is intended to cover any aspect of this disclosure disclosed herein, whether implemented independently of or in combination with any other aspect of this disclosure. For example, an apparatus or a method may be implemented using any number of the aspects set forth herein. Furthermore, the scope of this disclosure is intended to cover such apparatuses or methods implemented using structures, functions, or structures and functions other than or different from the aspects of this disclosure set forth herein. It should be understood that any aspect of this disclosure disclosed herein may be embodied by one or more elements of the claims.

[0024] Several aspects of telecommunication systems will now be presented with reference to various apparatus and techniques. These apparatus and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, and / or the like (collectively referred to as “elements”). These elements can be implemented using hardware, software, or combinations thereof. Whether such elements are implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. It should be noted that while aspects can be described herein using terminology commonly associated with a 5G or NR radio access technology (RAT), aspects of the present disclosure can be applied to other RATs, such as a 3G RAT, a 4G RAT, and / or a RAT subsequent to 5G (e.g., 6G).

[0025] It should be noted that while aspects can be described herein using terminology commonly associated with a 5G or NR radio access technology (RAT), aspects of the present disclosure can be applied to other RATs, such as a 3G RAT, a 4G RAT, and / or a RAT subsequent to 5G (e.g., 6G).

[0026] Figure 1 FIG. 1 is a diagram illustrating an example of a wireless network 100, in accordance with various aspects of the present disclosure. The wireless network 100 can be or can include elements of a 5G (NR) network and / or an LTE network, among other examples. The wireless network 100 can include a number of base stations 110 (shown as BS 110a, BS 110b, BS 110c, and BS 1 lOd) and other network entities. A base station (BS) is an entity that communicates with user equipment (UE) and can also be referred to as an NR BS, a Node B, a gNB, a 5G node B (NB), an access point, a transmit receive point (TRP), and / or the like. Each BS can provide communication coverage for a particular geographic area. In 3GPP, the term “cell” can refer to a coverage area of a BS and / or a BS subsystem serving the coverage area, depending on the context in which the term is used.

[0027] BSs can provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell can cover a relatively large geographic area (e.g., 5 kilometers in radius) and can allow unrestricted access by UEs with service subscriptions. A pico cell can cover a relatively small geographic area (e.g., a sports stadium) and can allow unrestricted access by UEs with service subscriptions. A femto cell can cover a relatively small geographic area (e.g., a home) and can allow restricted access by UEs with service subscription(s). A BS for a macro cell can be referred to as a macro BS. A BS for a pico cell can be referred to as a pico BS. A BS for a femto cell can be referred to as a femto BS or a home BS. In the example Figure 1In the illustrated example, the BS 110a can be a macro BS for the macro cell 102a, the BS 110b can be a pico BS for the pico cell 102b, and the BS 110c can be a femto BS for the femto cell 102c. A BS can support one or multiple (e.g., three) cells. The terms “eNB,” “base station,” “NR BS,” “gNB,” “TRP,” “AP,” “Node B,” “5G NB,” and “cell” can be used interchangeably herein.

[0028] In some aspects, a cell can not necessarily be stationary, and the geographic area of the cell can move according to the location of a mobile BS. In some aspects, a BS can be interconnected to one or more other BSs or network nodes (not shown) in the wireless network 100 by various types of backhaul interfaces (e.g., direct physical connections, virtual network, etc.) using any appropriate transport network.

[0029] Wireless network 100 can also include relay stations. A relay station is an entity that can receive a transmission of data from an upstream station (e.g., a BS or a UE) and send a transmission of the data to a downstream station (e.g., a UE or a BS). A relay station can also be a UE that can relay transmissions for other UEs. In Figure 1 In the example shown in FIG. 1, a relay BS 1 lOd can communicate with macro BS 110a and a UE 120d in order to facilitate communication between BS 110a and UE 120d. A relay BS can also be referred to as a relay station, a relay base station, a relay, or the like.

[0030] Wireless network 100 can be a heterogeneous network that includes BSs of different types, e.g., macro BSs, pico BSs, femto BSs, relay BSs, etc. These different types of BSs can have different transmit power levels, different coverage areas, and different impacts on interference in wireless network 100. For example, macro BSs can have a high transmit power level (e.g., 5 to 40 Watts), whereas pico BSs, femto BSs, and relay BSs can have lower transmit power levels (e.g., 0.1 to 2 Watts).

[0031] A network controller 130 can couple to a set of BSs and can provide coordination and control for these BSs. Network controller 130 can be in communication with the BSs via a backhaul. The BSs can also communicate with one another, e.g., directly or indirectly via wireless or wireline backhaul.

[0032] The UEs 120 (e.g., 120a, 120b, 120c) can be dispersed throughout the wireless network 100, and each UE can be stationary or mobile. A UE can also be referred to as an access terminal, a terminal, a mobile station, a subscriber unit, a station, etc. A UE can be a cellular phone (e.g., a smart phone), 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 or equipment, a biometric sensor / device, a wearable device such as a smart watch, smart clothing, smart glasses, a smart wrist band, a smart jewel (e.g., a smart ring, a smart bracelet), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicular component or sensor, a smart meter / sensor, industrial manufacturing equipment, a global positioning system device, or any other suitable device that is configured to communicate via a wireless or wired medium.

[0033] Some UEs can be considered machine-type communication (MTC) or evolved or enhanced machine-type communication (eMTC) UEs. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, etc., that can communicate with a base station, another device (e.g., remote device), or some other entity. A wireless node can provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communication link. Some UEs can be considered Intemet-of-Things (IoT) devices, and / or can be implemented as NB-IoT (narrowband

[0034] In general, any number of wireless networks can be deployed in a given geographic area. Each wireless network can support a particular RAT and can operate on one or more frequencies. A RAT can also be referred to as a radio technology, an air interface, etc. A frequency can also be referred to as a carrier, a frequency channel, etc. Each frequency can support a single RAT in a given geographic area in order to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks can be deployed.

[0035] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) can communicate directly using one or more sidelink channels (e.g., without using a base station 110 as an intermediary to communicate with one another). For example, UEs 120 can communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, vehicle-to-everything (V2X) protocols (which can include vehicle-to- vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, etc.), and / or netw orked communications. In this case, UE 120 can perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by base stations 110.

[0036] Devices of wireless network 100 can communicate using the electromagnetic spectrum, which can be subdivided, based on frequency or wavelength, into various classes, bands, channels, and / or the like. For example, devices of wireless network 100 can communicate using an operating band having a first frequency range (FR1), which can span, for example, from 410 MHz to 7.125 GHz, and / or can communicate using an operating band having a second frequency range (FR2), which can span, for example, from 24.25 GHz to 52.6 GHz. Frequencies between FR1 and FR2 are sometimes referred to as mid- frequencies. Although a portion of FR1 is greater than 6 GHz, FR1 is often referred to as a “sub-6 GHz” frequency band. Similarly, FR2 is often referred to as a “millimeter wave” frequency band, despite being different from the extremely high frequency (EHF) frequency band, which is identified, by the International Telecommunications Union (ITU) as spanning from 30 GHz to 300 GHz. Thus, unless specifically stated otherwise, the term “sub-6 GHz” or like terminology used herein can broadly represent frequencies less than 6 GHz, frequencies within FR1, and / or mid-frequencies (e.g., greater than 7.125 GHz). Similarly, unless specifically stated otherwise, the term “millimeter wave” or like terminology used herein can broadly represent frequencies within the EHF band, frequencies within FR2, and / or mid-frequencies (e.g., less than 24.25 GHz). It is contemplated that frequencies included in FR1 and FR2 can be modified, and techniques described herein are applicable to those modified frequency ranges.

[0037] As indicated above, Figure 1 are provided as examples. Other examples can differ from what is described with respect to at least one of the Figure 1 described examples.

[0038] Figure 2is a diagram illustrating an example 200 of a base station 110 in communication with a UE 120 in a wireless network 100, in accordance with various aspects of the present disclosure. Base stations 110 can be equipped with T antennas 234a through 234t, and UEs 120 can be equipped with R antennas 252a through 252r, where in general T > 1 and R > 1.

[0039] At base station 110, a transmit processor 220 can receive data from a data source 212 for one or more UEs, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQIs) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS selected for the UE, and provide data symbols for all UEs. Transmit processor 220 can also process system information (e.g., for semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling) and provide overhead symbols and control symbols. Transmit processor 220 can also generate reference symbols for reference signals (e.g., a cell-specific reference signal (CRS) or a demodulation reference signal (DMRS)) and synchronization signals (e.g., a primary synchronization signal (PSS) or a secondary synchronization signal (SSS)). A transmit (TX) multiple-input multiple-output (MIMO) processor 230 can perform spatial processing (e.g., precoding) on the data symbols, the control symbols, the overhead symbols, and / or the reference symbols, if applicable, and provide T output symbol streams to T modulators (MODs) 232a through 232t. Each modulator 232 can process a respective output symbol stream (e.g., for OFDM) to obtain an output sample stream. Each modulator 232 can further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink signal. T downlink signals from modulators 232a through 232t can be transmitted via T antennas 234a through 234t, respectively.

[0040] At the UE 120, the antennas 252a through 252r can receive the downlink signals from the base station 110 and / or other base stations and can provide received signals to the demodulators (DEMODs) 254a through 254r, respectively. Each demodulator 254 can condition (e.g., filter, amplify, downconvert, and digitize) a received signal to obtain input samples. Each demodulator 254 can further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 can obtain received symbols from all R demodulators 254a through 254r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor 258 can 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 information and system information to a controller / processor 280. The term “controller / processor” can refer to one or more controllers, one or more processors, or combinations thereof. A channel processor can determine reference signal received power (RSRP) parameters, received signal strength indicator (RSSI) parameters, reference signal received quality (RSRQ) parameters, and / or channel quality indicator (CQI) parameters, among other examples. In some aspects, one or more components of UE 120 can be included in a housing 284.

[0041] The network controller 130 can include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 can include, for example, one or more devices in a core network. The network controller 130 can communicate with the base station 110 via the communication unit 294.

[0042] Antennas (e.g., antennas 234a through 234t and / or antennas 252a through 252r) can include or be included within one or more antenna panels, antenna groups, antenna element sets, and / or antenna arrays, among other examples. An antenna panel, antenna group, antenna element set, and / or antenna array can include one or more antenna elements. An antenna panel, antenna group, antenna element set, and / or antenna array can include a set of co-planar antenna elements and / or a set of non-co-planar antenna elements. An antenna panel, antenna group, antenna element set, and / or antenna array can include antenna elements within a single housing and / or antenna elements within multiple housings. An antenna panel, antenna group, antenna element set, and / or antenna array can include one or more antenna elements coupled to one or more transmit and / or receive components (such as one or more components of the transceiver 288 and / or the transceiver 266) within a housing. Figure 2

[0043] ​On the uplink, at UE 120, a transmit processor 264 can receive and process data from a data source 262 and control information (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI) from controller / processor 280. Transmit processor 264 can also generate reference symbols for one or more reference signals. The symbols from transmit processor 264 can be precoded by a TX MIMO processor 266 if applicable, further processed by modulators 254a through 254r (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted to base station 110. In some aspects, a modulator and a demodulator (e.g., modulator / demodulator 254) of UE 120 can be included in a modem of UE 120. In some aspects, UE 120 includes a transceiver. The transceiver can include any combination of antenna 252, modulators and / or demodulators 254, MIMO detector 256, receive processor 258, transmit processor 264, and / or TX MIMO processor 266. The transceiver can be used by a processor (e.g., controller / processor 280) and memory 282 to perform any of the methods described herein (for example, as described with reference to Figures 3-7 FIGS. 15 and 16).

[0044] At base station 110, the uplink signals from UE 120 and other UEs can be received by antennas 234, processed by demodulators 232, 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 UE 120. Receive processor 238 can provide the decoded data to a data sink 239 and the decoded control information to controller / processor 240. Base station 110 can include communication unit 244 and communicate to network controller 130 via communication unit 244. Base station 110 can include scheduler 246 to schedule UEs 120 for downlink and / or uplink communications. In some aspects, a modulator and a demodulator (e.g., modulator / demodulator 232) of base station 110 can be included in a modem of base station 110. In some aspects, base station 110 includes a transceiver. The transceiver can include any combination of antenna 234, modulators and / or demodulators 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver can be used by a processor (e.g., controller / processor 240) and memory 242 to perform any of the methods described herein (for example, as described with reference to Figures 3-7 FIGS. 15 and 16).

[0045] The controller / processor 240 of base station 110, the controller / processor 280 of UE 120 and / or Figure 2 Any other components may perform one or more techniques associated with the training process of the network configuration, as described in more detail elsewhere herein. For example, the controller / processor 240 of base station 110, the controller / processor 280 of UE 120, and / or Figure 2 Any other component can perform or direct, for example Figure 4 Process 400 Figure 5 The operation of process 500 and / or other processes as described herein. Memory 242 and 282 may store data and program code for base station 110 and UE 120, respectively. In some aspects, memory 242 and / or 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, one or more instructions, when executed by one or more processors of base station 110 and / or UE 120 (e.g., directly, or after compilation, translation, and / or interpretation), may cause one or more processors, UE 120, and / or base station 110 to perform or instruct, for example... Figure 4 Process 400 Figure 5 The operation of process 500 and / or other processes as described herein. In some aspects, the execution instructions may include run instructions, transform instructions, compile instructions, and / or interpret instructions, as well as other examples.

[0046] In some aspects, the UE (e.g., UE 120) includes: a unit for receiving configuration information from a base station for performing a training process of BS configuration associated with optimizing network parameters; and a unit for performing the training process of BS configuration at least in part based on the configuration information. The unit for the UE to perform the operations described herein may include one or more of, for example, an antenna 252, a demodulator 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, a TXMIMO processor 266, a modulator 254, a controller / processor 280, or a memory 282.

[0047] In some aspects, the UE includes: a unit for providing a report to the base station in connection with the training process of performing BS configuration, based at least in part on receiving a trigger or request for providing a report.

[0048] In some aspects, a base station (e.g., BS 110) includes means for receiving, from a server, a request for performing a training procedure associated with a server request to optimize network parameters and means for performing the training procedure requested by the server based at least in part on receiving the request. In some aspects, means for a base station to perform operations described herein can include, for example, one or more of transmit processor 220, TX MIMO processor 230, modulator 232, antenna 234, demodulator 232, MIMO detector 236, receive processor 238, controller / processor 240, memory 242, or scheduler 246.

[0049] In some aspects, a base station includes means for transmitting, to a server, a result associated with performing a training procedure requested by the server.

[0050] In some aspects, a base station includes means for transmitting, to a UE, configuration information associated with performing a training procedure configured by the BS based at least in part on a capability of the UE to perform the training procedure configured by the BS.

[0051] In some aspects, a base station includes means for transmitting, to one or more UEs, respective configuration information associated with performing respective training procedures configured by the BS or means for receiving, from one or more UEs, respective reports associated with performing respective training procedures configured by the BS.

[0052] Although Figure 2 The blocks in FIG. 13 are illustrated as distinct components, but the functionality described above with respect to these blocks can 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 can be performed by or under the control of controller / processor 280.

[0053] As indicated above, the Figure 2 are provided as examples. Other examples can differ from what is described with respect to the Figure 2 described examples.

[0054] A wireless network, such as an LTE network or a 5G / NR network (e.g., a network), can include a plurality of base stations in data communication with a plurality of UEs. A network provider can manage the network by managing the operation, administration, and maintenance (OAM) of the network. The network provider can manage the network and improve network performance with an OAM server. The OAM server can manage the network by, for example, managing coverage metrics provided in the network, managing handover procedures, updating network procedures, introducing new services, troubleshooting reported issues, and the like. To improve network performance, the OAM server can collect data from the plurality of UEs and the plurality of base stations. The OAM server can process the collected data to identify areas for improvement and / or issues and implement solutions that improve network performance.

[0055] Collecting (e.g., receiving) data from each of the plurality of UEs and each of the plurality of base stations can be burdensome because the collection can utilize network resources that could otherwise be used for other network operations. For example, collecting data from each of the plurality of UEs and the plurality of base stations can utilize network bandwidth (e.g., frequency and / or time resources) that could otherwise be used for communications in the network. Further, the amount of data collected can be substantial and can consume OAM server resources (e.g., memory storage, processing power, etc.) that could be used to perform other OAM tasks. Thus, collecting data can be infeasible. Further, the collected data can include private information associated with users of the plurality of UEs. Such private information can have to be collected and / or stored in a secure manner, making the collection and processing of the data expensive. Thus, it can be infeasible and expensive for the OAM server to collect and process data to improve network performance.

[0056] Various aspects of the technology and apparatus described herein are associated with a network-configured training procedure that can enable convenient and cost-efficient processing of data associated with a plurality of UEs in data communication with a plurality of base stations in a network. In some aspects, the network-configured training procedure can enable distributed processing of data by the plurality of UEs and the plurality of base stations. For example, an OAM server can request a base station among the plurality of base stations to perform a server-requested training procedure and provide results to be utilized by the OAM server to improve network performance. In turn, the base station can request one or more UEs among the plurality of UEs to perform a corresponding base station-configured training procedure and provide corresponding results that the base station can use to perform the server-requested training procedure. As a result, the network-configured training procedure can yield results that the OAM server can use to improve network performance without the OAM server having to perform infeasible and expensive data collection. In this way, the network-configured training procedure can enable a convenient and cost-efficient way to improve network performance.

[0057] In some aspects, a UE (e.g., UE 120) can receive, from a base station (e.g., BS 110), configuration information for performing a training procedure of a BS configuration associated with optimizing network parameters; and can perform the training procedure of the BS configuration based at least in part on the configuration information. In some aspects, a base station can receive, from a server, a request for performing a training procedure requested by the server associated with optimizing network parameters; and can perform the training procedure requested by the server based at least in part on receiving the request.

[0058] Figure 3 FIG. 3 is a diagram illustrating an example 300 associated with a training procedure of a network configuration, in accordance with various aspects of the present disclosure. Figure 3 The UEs 120 and the BSs 110 are configured to communicate at least one of downlink data communications or uplink data communications. The downlink data communications and the uplink data communications can include information associated with a training procedure of a network configuration.

[0059] The BSs 110 can communicate with an OAM server 310 deployed by a network provider of a LTE network or a 5G / NR network (e.g., a network). In some cases, the OAM server 310 can include, or be included within, an access and mobility management function (AMF) server. The OAM server 310 can manage the network to assist the network provider by managing the operation, administration, and maintenance of the network and by improving the performance of the network. In some aspects, the OAM server 310 can manage the network by, for example, managing coverage metrics provided in the network, managing handover procedures, updating network procedures, introducing new services, troubleshooting reported issues, and the like. To improve the performance of the network, the OAM server 310 can, for example, identify areas of improvement and / or issues, and implement solutions for improving the performance of the network. In some aspects, the OAM server 310 can assess the current performance of the network, and can improve the current performance.

[0060] As shown by reference number 320, the OAM server 310 can transmit, and the BS 110 can receive, a request for performing a training procedure requested by the server associated with optimizing network parameters to improve performance. The network parameters can include, for example, network performance associated with a service provided to UEs in the network (e.g., a positioning service), metrics of coverage to UEs in the network, data latency parameters, download speed parameters, handover services, and the like.

[0061] In this request, the OAM server 310 can provide information associated with performing the server-requested training procedure. For example, when the network parameters can be associated with providing positioning services for the UEs 120, the OAM server 310 can provide parameters to be used by the BSs 110 in performing the server-requested training procedure. Such parameters can include, for example, a requested geographic area for which the server-requested training procedure is to be performed, a requested accuracy measure of a result associated with performing the server-requested training procedure, a requested number of training samples to be collected and processed, a requested location information, a requested time frame associated with collecting and processing data, and the like.

[0062] In some aspects, as shown by reference number 330, the BS 110 can transmit, and the UE 120 can receive, configuration information associated with performing the BS-configured training procedure. In some aspects, one or more UEs, including the UE 120, can be in data communication with the BS 110, and the BS 110 can transmit, to the one or more UEs, respective configuration information associated with performing respective BS-configured training procedures. In other words, the BS 110 can implement distributed processing of data.

[0063] In some aspects, the respective configuration information can be based at least in part on respective capabilities of the one or more UEs. For example, the configuration information for a given UE can be based at least in part on whether the given UE is capable of performing the BS-configured training procedure with a machine learning (ML) model. Additionally or alternatively, the BS 110 can determine whether the given UE has sufficient processing power, sufficient hardware acceleration capacity, sufficient memory space, and the like to perform the BS-configured training procedure. In some aspects, the BS 110 can determine the capabilities of the one or more UEs based at least in part on checking data associated with the one or more UEs (e.g., capability bits including validation capability bits and / or interference capability bits).

[0064] In some aspects, prior to transmitting the configuration information, the BS 110 can determine whether the UE has previously provided consent to perform the BS-configured training procedure. In some aspects, the UE can provide such consent when initially establishing a connection with the BS 110. In some aspects, the UE can provide such consent when signing up to obtain services from a network provider. In this case, the OAM server 310 can indicate information about UEs that have provided such consent in the request. In some aspects, the BS 110 can refrain from transmitting configuration information associated with performing the BS-configured training procedure to UEs that have not previously provided such consent.

[0065] The configuration information can be received at the beginning of the data communication and / or during the data communication. In some aspects, the UE 120 can receive the configuration information via, for example, a control channel (e.g., a physical downlink control channel (PDCCH)) between the UE 120 and the BS 110. The configuration information can be received via radio resource control (RRC) signaling, medium access control (MAC) signaling, downlink control information (DCI) signaling, or a combination thereof (e.g., RRC configuration of a set of values for a parameter and DCI indication of a selected value for the parameter).

[0066] In some aspects, the configuration information can include an indication of one or more configuration parameters, for example, for the UE 120 to use to configure the UE 120 for the data communication and / or to perform a training procedure for the BS configuration. For example, the configuration information can include model information (e.g., training information, reporting information, and / or the like) to be utilized and / or evaluated by the UE 120 in performing the training procedure for the BS configuration. In some aspects, performing the training procedure for the BS configuration can include utilizing an ML model (e.g., an algorithm), and the model information can include, for example, initial weights associated with initial parameters provided to the ML model as input data for evaluation. For example, as discussed in further detail below, the model information can include a model definition including a list of nodes for one or more training layers and initial weights associated with the one or more training layers.

[0067] In examples related to improving settings for a positioning service, the initial parameters can include location data associated with movement of the UE 120, angle of arrival data associated with signals received by the UE 120, quality metrics associated with radio signaling data, and / or the like. In some aspects, the UE 120 can measure support data associated with the initial parameters in real-time in performing the training procedure for the BS configuration. In some aspects, the UE 120 can obtain support data associated with the initial parameters from internal memory (e.g., memory 282) storing, for example, a movement history of the UE 120.

[0068] The model information can also include parameters associated with one or more actions to be performed in performing the training procedure for the BS configuration. For example, the model information can include training information regarding updating the initial weights based at least in part on output data provided by the ML model. In some aspects, the information regarding updating the initial weights can include information regarding a frequency at which the initial weights are to be updated, a timeframe within which the initial weights are to be updated, and / or the like. The model information can also include information regarding a method to be used to validate an accuracy metric regarding an error level of the configuration. For example, the model information can indicate that the UE 120 is to use a mean squared error (MSE) method where N is a number of samples, Y is a known output, and X is a known input, to verify whether an accuracy measure associated with the updated weights fails to satisfy a configured error level (e.g., the accuracy measure is equal to or greater than the configured error level). In some aspects, the model information can indicate that the UE 120 is to update the initial weights when, for example, the accuracy measure associated with the updated weights fails to satisfy the configured error level.

[0069] In some aspects, the model information can include information associated with a configured region for which the UE 120 is to perform the training procedure of the BS configuration. In some aspects, the configured region can be based at least in part on a requested region indicated by the OAM server 310. For example, the requested region can include a region of a cell being served by the BS 110. Based at least in part on the region of the cell, the BS 110 can determine the configured region to be a portion of the region of the cell for which the UE 120 is to perform the training procedure of the BS configuration. Additionally, or alternatively, based at least in part on the region of the cell, the BS 110 can determine another configured region to be another portion of the region of the cell for which another UE is to perform another training procedure of another BS configuration. In some aspects, the configured region can be based at least in part on a cell list, a public land mobile network (PLMN) list, a RAN notification area (RNA) list, and / or a tracking area identification (TAI) list.

[0070] In some aspects, the model information can include information associated with starting and / or stopping (e.g., completing) performance of the training procedure of the BS configuration. For example, the model information can include a time at which the UE 120 is to start performing the training procedure of the BS configuration, a time at which the UE 120 is to stop (e.g., complete) performing the training procedure of the BS configuration, and / or a time frame within which the UE 120 is to complete performing the training procedure of the BS configuration. In some aspects, the model information can include a number of training rounds to be performed with the ML model. In some aspects, the model information can include information regarding a configured accuracy measure at which the UE 120 can stop (e.g., complete) performing the training procedure of the BS configuration.

[0071] In some aspects, the model information can include reporting information having trigger information regarding when the UE 120 is to provide a report associated with performing the BS-configured training procedure. For example, the model information can indicate (e.g., trigger) that the UE 120 is to provide the report periodically. Additionally, or alternatively, the model information can indicate that the UE 120 is to provide the report based at least in part on completing a configured number of training rounds while achieving a configured accuracy metric (e.g., trigger). In some aspects, the model information can include information regarding a method of providing the report. For example, the model information can indicate that the UE 120 is to provide the report by transmitting the report to the BS 110. Alternatively, the model information can indicate that the UE 120 is to transmit an indication to the BS 110 when the UE 120 has completed performing the BS-configured training procedure and / or when the report is available. Based at least in part on receiving the indication, the BS 110 can initiate a UE information request procedure (e.g., trigger) to obtain the report from the UE 120.

[0072] In some aspects, the BS 110 can transmit the configuration information to the UE 120 with a dedicated radio bearer (DRB) or a special signaling radio bearer (SRB) when an amount of data included in the configuration information satisfies a threshold data level (e.g., a quantity of data included in the configuration message is equal to or greater than the threshold data level). When utilizing a DRB, the BS 110 can indicate a location (e.g., a uniform resource identifier (URI)) to store the configuration information to enable the UE 120 to download the configuration information. Utilization of the DRB and / or the special SRB can allow the BS 110 to efficiently transmit the configuration information to the UE 120.

[0073] As shown by reference number 340, based at least in part on receiving the configuration information, the UE 120 can transmit and the BS 110 can receive a confirmation message to confirm receipt of the configuration information. In some aspects, the confirmation message can include an acceptance message to indicate consent from the UE 120 to perform the BS-configured training procedure. Alternatively, in some aspects, the confirmation message can include a rejection message to indicate that the UE 120 has rejected performing the BS-configured training procedure.

[0074] As shown by reference number 350, based at least in part on receiving the confirmation message from the UE 120, the BS 110 can transmit and the OAM server 310 can receive a response message to indicate that the UE 120 has consented or rejected performing the BS-configured training procedure. When the UE 120 consents, the response message can inform the OAM server 310 that the server-requested training procedure to be performed by the BS 110 can be based at least in part on the BS-configured training procedure performed by the UE 120.

[0075] As shown by reference number 360, based at least in part on the configuration information, the UE 120 can perform the BS-configured training procedure. In some aspects, performing the BS-configured training procedure can include utilizing the ML model to, for example, determine updated weights to update the initial weights. In some aspects, the UE 120 can utilize an internal processor (e.g., controller / processor 280) to utilize the ML model.

[0076] In some aspects, the UE 120 can provide data included in the model information (e.g., known input data (X), initial weights, known output data (Y), support data, etc.) to the ML model as training data. In some aspects, the UE 120 can measure support data associated with the initial parameters in real-time and can provide the measured support data to the ML model as training data. In some aspects, the UE 120 can retrieve support data stored in an internal memory (e.g., memory 282) and provide the retrieved support data to the ML model as training data.

[0077] In some aspects, the UE 120 can utilize the ML model to process and / or evaluate the training data using a ML algorithm. The ML algorithm can evaluate the training data to determine a function associated with processing the known input data (e.g., initial weights) to provide the known output data. In some aspects, determining the function can include iteratively determining updated weights (to update the initial weights) associated with the function. For example, in a first training round, the ML algorithm can determine first updated weights to update the initial weights, in a second training round, the ML algorithm can determine second updated weights to update the first updated weights, and so on. In some aspects, the ML algorithm can continue to iteratively determine updated weights until an accuracy measure associated with determining the function fails to satisfy a threshold error level (e.g., the accuracy measure is equal to or greater than the threshold error level). In some aspects, the threshold error level can be the same as the configured error level previously discussed and can be preconfigured by the BS 110 or the OAM server 310.

[0078] As shown by reference number 370, the UE 120 can provide and the BS 110 can receive a report associated with performing the BS-configured training procedure. In some aspects, the report can include results associated with performing the BS-configured training procedure. In some aspects, the report can include information related to the determined updated weights. In some aspects, as previously discussed, the UE 120 can provide the report based at least in part on information included in the model information.

[0079] As shown by reference number 380, based at least in part on receiving the reports, the BS 110 can perform the server-requested training procedure. In some aspects, receiving the reports can include receiving respective reports (including respective updated weights) from respective UEs that have performed the respective BS-configured training procedure with the respective model information. In some aspects, performing the server-configured training procedure can include utilizing the combined ML model to determine combined weights (e.g., multi-UE averaging) based at least in part on the respective updated weights, for example. In some aspects, the BS 110 can utilize an internal processor (e.g., controller / processor 240) to utilize the combined ML model.

[0080] In some aspects, the BS 110 can provide data included in the respective reports received from the one or more UEs (e.g., the respective updated weights) as training data to the combined ML model. In some aspects, in addition to known inputs, known outputs, and the like, the BS 110 can provide combined support data as training data. The combined support data can include information associated with network conditions applicable to the one or more UEs, such as handover conditions, traffic conditions, interference conditions, coverage conditions, and the like, as training data to the combined ML model. The support data can also include measured combined support data measured by the BS 110 in real-time. In some aspects, the support data can include retrieved combined support data retrieved by the BS 110 from internal memory (e.g., memory 242).

[0081] In some aspects, the BS 110 can utilize the combined ML model to process the training data using a combined machine learning algorithm (ML algorithm). The combined ML algorithm can evaluate the training data to determine a combined function associated with processing the known input data (e.g., X, the respective updated weights) to provide the known output data (e.g., Y). In some aspects, determining the combined function can include iteratively determining combined update weights (to update the respective updated weights). For example, in a first training round, the combined ML algorithm can determine first combined update weights to update the respective updated weights, in a second training round, the combined ML algorithm can determine second combined update weights to update the first combined update weights, and so on. In some aspects, the combined ML algorithm can continue to iteratively determine combined update weights until an accuracy measure associated with determining the combined function fails to satisfy a threshold combined error level (e.g., the accuracy measure is equal to or greater than the threshold combined error level). For example, the BS 110 can utilize a mean squared error method to verify whether the accuracy measure associated with determining the combined update weights fails to satisfy the threshold combined error level. In some aspects, the threshold combined error level can be preconfigured by the OAM server 310.

[0082] In some aspects, BS110 can utilize least squares methods and / or gradient methods to quickly determine combined update weights. In some aspects, BS110 can utilize combined ML models to determine combined update weights based at least in part on the requested geographic region, the requested accuracy metric, the requested number of training samples to be collected and processed, the requested location information, and / or the requested time frame associated with the collected and processed data.

[0083] As indicated by reference numeral 390, BS110 can provide OAM server 310 with results associated with the training process requested by the server. In some aspects, these results may include information associated with the determined combined update weights. Based at least in part on the results received from BS110, OAM server 310 can post-process the information included in the results to improve network performance. For example, regarding providing location services to one or more UEs, OAM server 310 can utilize the information associated with the combined update weights to, for example, improve the accuracy associated with determining location information and providing location information to one or more UEs.

[0084] As discussed in this paper, the network configuration training process enables network providers to improve network performance across networks involving multiple UEs communicating with multiple BSs. In some aspects, the distributed processing of data by multiple UEs and multiple BSs can produce results that the network provider can use to improve network performance without having to perform infeasible and costly data collection. In this way, the network configuration training process enables a convenient and cost-effective way to improve network performance.

[0085] As pointed out above, Figure 3 This is provided as an example. Other examples may differ from the one provided. Figure 3 Example of the description.

[0086] Figure 4 This is a diagram illustrating an example process 400 performed by a UE (e.g., UE 120) according to various aspects of this disclosure. Example process 400 is an example in which the UE performs operations associated with a training process for network configuration.

[0087] like Figure 4 As shown, in some aspects, process 400 may include receiving configuration information from the base station for performing a training process associated with optimizing network parameters (box 410). For example, the UE (e.g., using...) Figure 6 The receiving component 602 described herein can receive configuration information from the base station for performing a training process associated with optimizing network parameters, as described above.

[0088] As Figure 4 Further as Figure 6 illustrated, in some aspects, process 400 can include performing a BS configured training procedure based at least in part on the configuration information (block 420). For example, the UE (e.g., using performing component 608, depicted in FIG. 7) can perform a BS configured training procedure based at least in part on the configuration information, as described above.

[0089] Process 400 can 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.

[0090] In a first aspect, performing the BS configured training procedure includes performing the BS configured training procedure with a machine learning algorithm.

[0091] In a second aspect, alone or in combination with the first aspect, receiving the configuration information includes receiving model information associated with performing the BS configured training procedure.

[0092] In a third aspect, alone or in combination with one or more of the first and second aspects, the model information includes information related to one or more initial parameters to utilize when performing the BS configured training procedure.

[0093] In a fourth aspect, alone or in combination with one or more of the first through third aspects, the model information includes information related to performing an action when performing the BS configured training procedure.

[0094] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the model information includes information related to a geographic region associated with performing the BS configured training procedure.

[0095] In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, the model information includes information related to starting or stopping performance of the BS configured training procedure.

[0096] In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, the model information includes information related to providing a report associated with performing the BS configured training procedure.

[0097] In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, receiving the configuration information includes receiving the configuration information utilizing a dedicated radio bearer or a signaling radio bearer when an amount of data included in the configuration information satisfies a threshold data level.

[0098] In a ninth aspect, alone or in combination with one or more of the first through eighth aspects, process 400 includes providing, to the base station, a report associated with performing the BS-configured training procedure based at least in part on receiving a trigger or request for providing the report.

[0099] Although Figure 4 Example blocks of the process 400 are illustrated, but in some aspects, the process 400 can include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 4 Additionally or alternatively, two or more of the blocks of the process 400 can be performed in parallel.

[0100] Figure 5 FIG. 5 is a diagram illustrating an example process 500 performed, for example, by a base station (e.g., BS 110), in accordance with various aspects of the present disclosure. Example process 500 is an example where a base station performs operations associated with a network-configured training procedure.

[0101] As Figure 5 Further as shown, in some aspects, the process 500 can include receiving, from a server, a request for performing a server-requested training procedure associated with optimizing a network parameter (block 510). For example, the base station (e.g., using reception component 702 depicted in FIG. 7) can receive, from a server, a request for performing a server-requested training procedure associated with optimizing a network parameter, as described above. Figure 7

[0102] As Figure 5 Further as shown, in some aspects, the process 500 can include performing the server-requested training procedure based at least in part on receiving the request (block 520). For example, the base station (e.g., using execution component 708 depicted in FIG. 7) can perform the server-requested training procedure based at least in part on receiving the request, as described above. Figure 7

[0103] Process 500 can 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.

[0104] In a first aspect, the process 500 includes transmitting, to the server, a result associated with performing the server-requested training procedure.

[0105] In a second aspect, alone or in combination with the first aspect, performing the server-requested training procedure includes performing the server-requested training procedure with a machine learning algorithm.

[0106] ​​In a third aspect, alone or in combination with one or more of the first and second aspects, the process 500 includes transmitting configuration information associated with performing the BS-configured training procedure to the UE based at least in part on a capability of the UE to perform the BS-configured training procedure.

[0107] In a fourth aspect, alone or in combination with one or more of the first through third aspects, performing the server-requested training procedure includes updating a parameter associated with performing the server-requested training procedure based at least in part on the received report associated with performing the BS-configured training procedure by the user equipment.

[0108] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, performing the server-requested training procedure includes performing the server-requested training procedure based at least in part on an accuracy metric associated with performing the server-requested training procedure.

[0109] In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, performing the server-requested training procedure includes performing the server-requested training procedure based at least in part on a quantity of training samples associated with performing the server-requested training procedure.

[0110] In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, the process 500 includes transmitting respective configuration information associated with performing respective BS-configured training procedures to one or more user equipment (UEs) and receiving respective reports associated with performing the respective BS-configured training procedures from the one or more UEs.

[0111] In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, transmitting the respective configuration information includes transmitting respective model information associated with performing the respective BS-configured training procedures to the one or more UEs.

[0112] In a ninth aspect, alone or in combination with one or more of the first through eighth aspects, performing the server-requested training procedure includes averaging one or more results included in the respective reports.

[0113] Although Figure 5 Example blocks of the process 500 are illustrated, but in some aspects, the process 500 can include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figure 5 In addition or as an alternative, two or more of the blocks of the process 500 can be performed in parallel.

[0114] Figure 6is a block diagram of an example apparatus 600 for wireless communication (e.g., training a model used for wireless communication). The apparatus 600 can be a UE (e.g., a UE 120), or a UE can include the apparatus 600. In some aspects, the apparatus 600 includes a reception component 602 and a transmission component 604, which can be in communication with one another (for example, via one or more buses and / or one or more other components). As shown, the apparatus 600 can communicate with another apparatus 606 (such as a UE, a base station, or another wireless communication device) using the reception component 602 and the transmission component 604. As further shown, the apparatus 600 can include one or more of a performance component 608 and other examples.

[0115] In some aspects, the apparatus 600 can be configured to perform one or more operations described herein with reference to Figure 3 In some aspects, the apparatus 600 can be configured to perform one or more operations described herein with reference to Figure 4 In some aspects, the apparatus 600 and / or one or more components shown in Figure 6 may include one or more components of the UE described above in connection with Figure 2 In some aspects, one or more components illustrated in Figure 6 may be implemented within one or more components described above in connection with Figure 2 In some aspects, one or more components illustrated in may be implemented at least in part as software stored in a memory and executable by a controller or processor. For example, a component (or a portion of a component) can be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by a controller or processor to perform the functions or operations of the component.

[0116] Figure 2 The reception component 602 can receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 606. The reception component 602 can provide received communications to one or more other components of the apparatus 600. In some aspects, the reception component 602 can perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples), and can provide the processed signals to the one or more other components of the apparatus 606. In some aspects, the reception component 602 can include one or more antennas, a demodulator, a MIMO detector, a receive processor, a controller / processor, a memory, or a combination thereof, of the UE described above in connection with

[0117] Transmitting component 604 can transmit communications, such as reference signals, control information, data communications, or combinations thereof, to device 606. In some aspects, one or more other components of device 606 can generate communications and provide the generated communications to transmitting component 604 for transmission to device 606. In some aspects, transmitting component 604 can perform signal processing on the generated communications (e.g., filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, and other examples), and can transmit the processed signals to device 606. In some aspects, transmitting component 604 can include the combinations described above. Figure 2 The described UE includes one or more antennas, modulators, transmit MIMO processors, transmit processors, controllers / processors, memory, or combinations thereof. In some aspects, the transmit component 604 may be co-located with the receive component 602 in a transceiver.

[0118] The receiving component 602 can receive configuration information from the base station for performing a training process of BS configuration associated with optimizing network parameters. The executing component 608 can perform the BS configuration training process at least in part based on the configuration information.

[0119] The execution component 608 may provide the base station with a report related to the training process of executing the BS configuration, at least in part, based on receiving a trigger or request for providing a report.

[0120] exist Figure 6 The number and arrangement of components shown are provided as an example. In reality, they can exist in... Figure 6 The components shown are compared to additional components, fewer components, different components, or components arranged in a different manner. Furthermore, in Figure 6 The two or more components shown can be implemented within a single component, or in Figure 6 The single component shown can be implemented as multiple distributed components. Alternatively, in Figure 6 The set (one or more) components shown can perform actions described by [the following]: Figure 6 The other set of components shown performs one or more functions.

[0121] Figure 7This is a block diagram of an example device 700 for wireless communication. Device 700 may be a base station (e.g., BS110), or a base station may include device 700. In some aspects, device 700 includes a receiving component 702 and a transmitting component 704, which can communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, device 700 can use the receiving component 702 and the transmitting component 704 to communicate with another device 706 (such as a UE, a base station, or another wireless communication device). As further shown, device 700 may include an execution component 708 and one or more of the others in other examples.

[0122] In some respects, device 700 can be configured to perform the functions described herein. Figure 3 One or more operations described herein. Alternatively or concurrently, the apparatus 700 may be configured to perform one or more processes described herein, such as... Figure 5 The process 500 or a combination thereof. In some aspects, in Figure 7 The device 700 and / or one or more components shown may include the elements described above. Figure 2 One or more components of the described base station. Alternatively or in addition, in Figure 7 One or more components shown can be combined with the above. Figure 2 Implemented within one or more components described. Alternatively, one or more of the components in a set may be implemented at least partially as software stored in memory. For example, a component (or a portion of a component) may be implemented 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.

[0123] Receiver 702 may receive communications from device 706, such as reference signals, control information, data communications, or combinations thereof. Receiver 702 may provide the received communications to one or more other components of device 700. In some aspects, receiver 702 may perform signal processing on the received communications (e.g., filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, and other examples), and may provide the processed signal to one or more other components of device 706. In some aspects, receiver 702 may include the elements described above. Figure 2 One or more antennas, demodulators, MIMO detectors, receiver processors, controllers / processors, memories, or combinations thereof of the described base station (e.g., BS110).

[0124] Transmitting component 704 can transmit communications, such as reference signals, control information, data communications, or combinations thereof, to device 706. In some aspects, one or more other components of device 706 can generate communications and provide the generated communications to transmitting component 704 for transmission to device 706. In some aspects, transmitting component 704 can perform signal processing on the generated communications (e.g., filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, and other examples), and can transmit the processed signal to device 706. In some aspects, transmitting component 704 can include the combinations described above. Figure 2 The described base station (e.g., BS110) includes one or more antennas, modulators, transmit MIMO processors, transmit processors, controllers / processors, memory, or combinations thereof. In some aspects, the transmit component 704 may be co-located with the receive component 702 in a transceiver.

[0125] The receiving component 702 can receive a request from the server for performing a training process associated with optimizing network parameters. The execution component 708 can execute the server-requested training process at least in part based on the received request.

[0126] The sending component 704 can send the results associated with the training process that was requested by the server to the server.

[0127] The transmitting component 704 can transmit configuration information associated with the execution of the BS configuration training process to the UE, at least in part, based on the UE's ability to perform the BS configuration training process.

[0128] The transmitting component 704 can send relevant configuration information associated with the training process of performing the corresponding BS configuration to one or more UEs.

[0129] The receiving component 702 can receive corresponding reports from one or more UEs that are associated with the training process of performing the corresponding BS configuration.

[0130] exist Figure 7 The number and arrangement of components shown are provided as an example. In reality, they can exist in... Figure 7 The components shown are compared to additional components, fewer components, different components, or components arranged in a different manner. Furthermore, in Figure 7 The two or more components shown can be implemented within a single component, or in Figure 7 The single component shown can be implemented as multiple distributed components. Alternatively, in Figure 7 The set (one or more) components shown can perform actions described by [the following]: Figure 7 The other set of components shown performs one or more functions.

[0131] The following provides an overview of aspects of the disclosure:

[0132] Aspect 1 : A method of training a model performed by a UE, comprising: receiving, from a base station, configuration information for performing a training procedure of a BS configuration associated with optimizing network parameters; and performing the training procedure of the BS configuration based at least in part on the configuration information.

[0133] Aspect 2: The method of aspect 1, wherein performing the training procedure of the BS configuration comprises performing the training procedure of the BS configuration with a machine learning algorithm.

[0134] Aspect 3: The method of aspects 1-2, wherein receiving the configuration information comprises receiving model information associated with performing the training procedure of the BS configuration.

[0135] Aspect 4: The method of any of aspects 1-3, wherein the model information comprises information related to one or more initial parameters to utilize when performing the training procedure of the BS configuration.

[0136] Aspect 5: The method of any of aspects 1-4, wherein the model information comprises information related to performing an action when performing the training procedure of the BS configuration.

[0137] Aspect 6: The method of any of aspects 1-5, wherein the model information comprises information related to a geographic region associated with performing the training procedure of the BS configuration.

[0138] Aspect 7: The method of any of aspects 1-6, wherein the model information comprises information related to starting or stopping performance of the training procedure of the BS configuration.

[0139] Aspect 8: The method of any of aspects 1-7, wherein the model information comprises information related to providing a report associated with performing the training procedure of the BS configuration.

[0140] Aspect 9: The method of any of aspects 1-8, wherein receiving the configuration information comprises receiving the configuration information utilizing a dedicated radio bearer or a signaling radio bearer when an amount of data included in the configuration information satisfies a threshold data level.

[0141] Aspect 10: The method of any of aspects 1-9, further comprising providing, to the base station, the report associated with performing the training procedure of the BS configuration based at least in part on receiving a trigger or request for providing the report.

[0142] Aspect 11: A method of wireless communication performed by a base station, comprising: receiving, from a server, a request for performing a training procedure associated with a server request to optimize network parameters; and performing the training procedure of the server request based at least in part on receiving the request.

[0143] Aspect 12: The method of aspect 11, further comprising: transmitting, to the server, a result associated with performing the training procedure of the server request.

[0144] Aspect 13: The method of any of aspects 11 and 12, wherein performing the training procedure of the server request comprises: performing the training procedure of the server request with a machine learning algorithm.

[0145] Aspect 14: The method of any of aspects 11 to 13, further comprising: transmitting, to a UE, configuration information associated with performing a BS configured training procedure based at least in part on a capability of the UE to perform the BS configured training procedure.

[0146] Aspect 15: The method of any of aspects 11 to 14, wherein performing the training procedure of the server request comprises: updating a parameter associated with performing the training procedure of the server request based at least in part on a received report associated with performing a BS configured training procedure by a user equipment.

[0147] Aspect 16: The method of any of aspects 11 to 15, wherein performing the training procedure of the server request comprises: performing the training procedure of the server request based at least in part on an accuracy metric associated with performing the training procedure of the server request.

[0148] Aspect 17: The method of any of aspects 11 to 16, wherein performing the training procedure of the server request comprises: performing the training procedure of the server request based at least in part on a number of training samples associated with performing the training procedure of the server request.

[0149] Aspect 18: The method of any of aspects 11 to 17, further comprising: transmitting, to one or more user equipments (UEs), respective configuration information associated with performing respective BS configured training procedures; and receiving, from the one or more UEs, respective reports associated with performing the respective BS configured training procedures.

[0150] Aspect 19: The method of any of aspects 11 to 18, wherein transmitting the respective configuration information comprises: transmitting, to the one or more UEs, respective model information associated with performing the respective BS configured training procedures.

[0151] Aspect 20: The method of any of aspects 11 through 19, wherein performing the server-requested training procedure comprises averaging one or more results included in the respective reports.

[0152] Aspect 21 : An apparatus for wireless communication at a first device, comprising a processor; memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform the method of any of aspects 1 through 10.

[0153] Aspect 22: A user equipment for wireless communication, comprising a memory; and one or more processors coupled to the memory, the memory and the one or more processors configured to perform the method of any of aspects 1 through 10.

[0154] Aspect 23: An apparatus for wireless communication, comprising at least one means for performing a method of any of aspects 1 through 10.

[0155] Aspect 24: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform a method of any of aspects 1 through 10.

[0156] Aspect 25: A non-transitory computer-readable medium storing one or more instructions for wireless communication, the one or more instructions comprising one or more instructions that, when executed by one or more processors of a user equipment, cause the one or more processors to perform a method of any of aspects 1 through 10.

[0157] Aspect 26: An apparatus for wireless communication at a second device, comprising a processor; memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform the method of any of aspects 11 through 20.

[0158] Aspect 27: A base station for wireless communication, comprising a memory; and one or more processors coupled to the memory, the memory and the one or more processors configured to perform the method of any of aspects 11 through 20.

[0159] Aspect 28: An apparatus for wireless communication, comprising at least one means for performing a method of any of aspects 11 through 20.

[0160] Aspect 29: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method of any of aspects 11 through 20.

[0161] Aspect 30: A non-transitory computer-readable medium storing one or more instructions for wireless communication, the one or more instructions comprising one or more instructions that, when executed by one or more processors of a base station, cause the one or more processors to perform the method of any of aspects 11 through 20.

[0162] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise form disclosed. Modifications and variations can be possible in light of the above disclosure or from practicing the aspects.

[0163] As used herein, the term “component” is intended to be broadly interpreted to encompass hardware and / or a combination of hardware and software. “Software” shall be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, and / or functions, among other examples, 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 systems and / or methods described herein can be implemented in different forms of hardware and / or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the aspects. Thus, the operation and behavior of the systems and / or methods were described herein without reference to specific software code — it being understood that software and hardware can be designed to implement the systems and / or methods based, at least in part, on the description herein.

[0164] As used herein, depending on the context, satisfying 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, and / or the like.

[0165] Even if a particular feature is expressly identified as an aspect in a claim, among other places, such does not find limitation, and such a configuration should not be treated as being restricted to those aspects since the aspects can be combined with one another in any manner and / or quantity. Although each dependent claim listed below can only directly depend on one claim, the disclosure of each dependent claim includes each other claim in the set of claims. As used herein, the phrase “at least one of” a list of items means any combination of those items, including single members. As an example, “at least one of a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination of items from among a, b, and c (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 ordering of a, b, and c).

[0166] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and can be used interchangeably with “one or more.” Furthermore, as used herein, the article “the” is intended to include one or more items referenced, and can be used interchangeably with “the one or more.” Also, as used herein, the terms “set” and “group” are intended to include one or more items (e.g., related items, unrelated items, or a combination of related and unrelated items), and can 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, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series of items (e.g., “a, b, or c” or “a, b, and c”) unless explicitly stated otherwise (e.g., if used in the context “either a, b, or c, but not both”).

Claims

1. A method of training a model performed by a user equipment (UE), comprising: receiving, from a base station (BS), configuration information for performing a training procedure, the training procedure being configured by the BS and associated with optimizing a network parameter, wherein the configuration information comprises information associated with starting performance of the training procedure and stopping the performance of the training procedure, and wherein the configuration information is transmitted by the BS in response to a request from a server to the BS for performing a server-requested training procedure associated with optimizing the network parameter; performing the BS-configured training procedure based at least in part on the configuration information; and transmitting, to the BS, a report associated with performing the BS-configured training procedure.

2. The method of claim 1, wherein, Performing the BS-configured training procedure comprises performing the BS-configured training procedure with a machine learning algorithm.

3. The method of claim 1, wherein, Performing the BS-configured training procedure comprises fitting model parameters into a machine learning algorithm based at least in part on local model input data and local model output data.

4. The method of claim 1, wherein, The configuration information comprises model information, report information, and training information associated with performing the BS-configured training procedure.

5. The method of claim 1, wherein, The configuration information comprises information related to one or more initial parameters to utilize when performing the BS-configured training procedure, the one or more initial parameters comprising a model definition comprising a list of nodes for one or more training layers and initial weights associated with the one or more training layers.

6. The method of claim 1, wherein, The configuration information comprises information related to performing an action when performing the BS-configured training procedure.

7. The method of claim 1, wherein, The configuration information comprises information related to a zone associated with performing the BS-configured training procedure.

8. The method of claim 1, wherein, The configuration information comprises information related to providing the report associated with performing the BS-configured training procedure.

9. The method of claim 1, wherein, Receiving the configuration information comprises receiving the configuration information with a dedicated radio bearer or a signaling radio bearer when an amount of data included in the configuration information satisfies a threshold data level.

10. The method of claim 1, further comprising: providing, to the BS, the report associated with performing the BS-configured training procedure based at least in part on receiving a trigger or a request for providing a report.

11. A method of wireless communication performed by a base station (BS), comprising: receiving, from a server, a request for performing a server-requested training procedure associated with optimizing a network parameter; based on receiving the request, transmitting, to a user equipment (UE), configuration information associated with performing a BS-configured training procedure, the BS-configured training procedure being different from the server-requested training procedure, wherein the configuration information comprises information associated with starting performance of the BS-configured training procedure and stopping the performance of the BS-configured training procedure; based at least in part on transmitting the configuration information, receiving, from the UE, a report associated with performing the BS-configured training procedure; and performing the server-requested training procedure based at least in part on receiving the report.

12. The method of claim 11, further comprising: sending, to the server, a result associated with performing the server-requested training procedure.

13. The method of claim 11, wherein, performing the server-requested training procedure comprises performing the server-requested training procedure with a machine learning algorithm.

14. The method of claim 11, wherein: the configuration information is based at least in part on a capability of the UE to perform the BS-configured training procedure.

15. The method of claim 11, wherein, performing the server-requested training procedure comprises updating a parameter associated with performing the server-requested training procedure based at least in part on the received report associated with performing the BS-configured training procedure by the UE.

16. The method of claim 11, wherein, performing the server-requested training procedure comprises performing the server-requested training procedure based at least in part on an accuracy metric associated with performing the server-requested training procedure.

17. The method of claim 11, wherein, performing the server-requested training procedure comprises performing the server-requested training procedure based at least in part on a number of training samples associated with performing the server-requested training procedure.

18. The method of claim 11, further comprising: performing an information request procedure to obtain the report associated with the UE performing the BS-configured training procedure.

19. The method of claim 11, further comprising: sending, to another UE, additional configuration information associated with performing another BS-configured training procedure; and receiving, from the other UE, another report associated with performing the other BS-configured training procedure, wherein performing the server-requested training procedure is based at least in part on the report and the other report. sending the configuration information comprises sending, to the UE, model information associated with performing the BS-configured training procedure, and 20. The method of claim 19, wherein, wherein sending the additional configuration information comprises sending, to the other UE, additional model information associated with performing the other BS-configured training procedure. performing the server-requested training procedure comprises averaging one or more results included in the report and the other report.

21. The method of claim 19, wherein, 22. A user equipment (UE) for wireless communication, comprising: a memory; and one or more processors operatively coupled to the memory, the memory and the one or more processors configured to: receive, from a base station (BS), configuration information for performing a training procedure, the training procedure being configured by the BS and associated with optimizing a network parameter, wherein the configuration information comprises information associated with starting performance of the training procedure and stopping the performance of the training procedure, and wherein the configuration information is transmitted by the BS in response to a request from a server to the BS for performing a server-requested training procedure associated with optimizing the network parameter; perform the BS-configured training procedure based at least in part on the configuration information; and ​ ​ transmit, to the BS, a report associated with performing the BS configured training procedure.

23. The UE of claim 22, wherein, when performing the BS configured training procedure, the one or more processors are configured to perform the BS configured training procedure with a machine learning algorithm.

24. The UE of claim 22, wherein, when performing the BS configured training procedure, the one or more processors are configured to fit model parameters into a machine learning algorithm based at least in part on local model input data and local model output data.

25. The UE of claim 22, wherein, the configuration information includes model information, report information, and training information associated with performing the BS configured training procedure.

26. The UE of claim 22, wherein, the configuration information includes information related to one or more initial parameters to utilize when performing the BS configured training procedure, the one or more initial parameters including a model definition including a list of nodes for one or more training layers and initial weights associated with the one or more training layers.

27. The UE of claim 22, wherein, the configuration information includes information related to performing an action when performing the BS configured training procedure.

28. The UE of claim 22, wherein, the configuration information includes information related to a region associated with performing the BS configured training procedure.

29. The UE of claim 22, wherein, the configuration information includes information related to providing the report associated with performing the BS configured training procedure.

30. The UE of claim 22, wherein, when receiving the configuration information, the one or more processors are configured to receive the configuration information utilizing a dedicated radio bearer or a signaling radio bearer when an amount of data included in the configuration information satisfies a threshold data level.

31. The UE of claim 22, wherein, the one or more processors are further configured to: provide, to the BS, the report associated with performing the BS configured training procedure based at least in part on receiving a trigger or a request for providing a report.

32. A base station (BS) for wireless communication, comprising: memory; and one or more processors operatively coupled to the memory, the memory and the one or more processors configured to: receive, from a server, a request for performing a server requested training procedure associated with optimizing network parameters; based on receiving the request, transmit, to a user equipment (UE), configuration information associated with performing a BS configured training procedure, the BS configured training procedure being different from the server requested training procedure, wherein the configuration information includes information associated with starting performance of the BS configured training procedure and stopping the performance of the BS configured training procedure; based at least in part on transmitting the configuration information, receive, from the UE, a report associated with performing the BS configured training procedure; and perform the server requested training procedure based at least in part on receiving the report.

33. The BS of claim 32, wherein, the one or more processors are further configured to: transmit, to the server, a result associated with performing the server requested training procedure.

34. The BS of claim 32, wherein, when performing the server requested training procedure, the one or more processors are configured to perform the server requested training procedure with a machine learning algorithm.

35. The BS of claim 32, wherein, The configuration information is based at least in part on a capability of the UE to perform the BS configured training procedure.

36. The BS of claim 32, wherein, In performing the server requested training procedure, the one or more processors are configured to update a parameter associated with performing the server requested training procedure based at least in part on the received report associated with performing the BS configured training procedure by the UE.

37. The BS of claim 32, wherein, In performing the server requested training procedure, the one or more processors are configured to perform the server requested training procedure based at least in part on an accuracy metric associated with performing the server requested training procedure.

38. The BS of claim 32, wherein, In performing the server requested training procedure, the one or more processors are configured to perform the server requested training procedure based at least in part on a number of training samples associated with performing the server requested training procedure.

39. The BS of claim 32, wherein, The one or more processors are further configured to: perform an information request procedure to obtain the report associated with performing the BS configured training procedure by the UE.

40. The BS of claim 32, wherein, The one or more processors are further configured to: transmit, to another UE, additional configuration information associated with performing another BS configured training procedure; and receive, from the other UE, another report associated with performing the other BS configured training procedure, wherein performing the server requested training procedure is based at least in part on the report and the other report.

41. The BS of claim 40, wherein, In transmitting the configuration information, the one or more processors are configured to transmit, to the UE, model information associated with performing the BS configured training procedure, and wherein in transmitting the additional configuration information, the one or more processors are configured to transmit, to the other UE, additional model information associated with performing the other BS configured training procedure.

42. The BS of claim 40, wherein, In performing the server requested training procedure, the one or more processors are configured to average one or more results included in the report and the other report.

Images