Dynamic power loading

Abstract

Certain aspects of this disclosure provide techniques for dynamic power loading. A method for wireless communication by a device includes: obtaining a configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval; and using the transmit power, at least in part based on the configuration, to convey one or more signals.

Description

Cross-references to related applications

[0001] This application claims priority to Israeli Patent Application Serial No. 309093 entitled “Dynamic Power Loading”, filed on December 5, 2023, which is expressly incorporated herein by reference in its entirety. introduction Technical Field

[0003] Various aspects of this disclosure relate to wireless communication, and more specifically to techniques for transmission power allocation.

[0004] Related technical descriptions

[0005] Wireless communication systems are widely deployed to provide a variety of telecommunications services, such as telephone, video, data, messaging, broadcasting, or other similar services. These wireless communication systems may employ multiple access technologies that enable communication with several users by sharing available wireless communication system resources.

[0006] Despite significant technological advancements in wireless communication systems over the years, challenges remain. For example, complex and dynamic environments can still attenuate or block signals between wireless transmitters and receivers. Therefore, there is a continuous expectation for improving the technical performance of wireless communication systems, including, for example: improving communication speed and data carrying capacity; improving the efficiency of shared communication media; reducing the power used by transmitters and receivers during communication; improving the reliability of wireless communication; avoiding redundant transmission and / or reception and related processing; improving the coverage area of ​​wireless communication; increasing the number and types of devices that can access the wireless communication system; increasing the ability of different types of devices to communicate with each other; and increasing the number and types of available wireless communication media. Therefore, there is a need for further improvements to wireless communication systems to overcome the aforementioned technical challenges and other obstacles. Summary of the Invention

[0007] One aspect provides a method for wireless communication by a device. The method includes: obtaining a configuration indicating one or more first parameters for individually allocating transmission power to each of a plurality of frequency resource sets within a time interval; and conveying one or more signals using the transmission power at least partially based on the configuration.

[0008] Another aspect provides a method for wireless communication by a device. The method includes: transmitting a configuration indicating one or more first parameters for individually allocating transmission power to each of a plurality of frequency resource sets within a time interval; and conveying one or more signals using the transmission power, which is at least partially based on the configuration.

[0009] Other aspects provide: one or more means capable of operating to, configured to, or otherwise adapted to perform any portion of any method described herein (e.g., such that it can be implemented by only one means or in a distributed manner across multiple means); one or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors of the one or more means, cause the one or more means to perform any portion of any method described herein (e.g., such that the instructions can be included in only one computer-readable medium or in a distributed manner across multiple computer-readable media, such that the instructions can be executed by only one processor or by multiple processors in a distributed manner). Each of the one or more means may include one or more processors, and / or enable execution to be performed by only one means or in a distributed manner across multiple means; one or more computer program products embodied on one or more computer-readable storage media, the computer-readable storage media including code for performing any part of any method described herein (e.g., such that the code may be stored in only one computer-readable medium or in a distributed manner across computer-readable media); and / or one or more means, the one or more means including one or more components for performing any part of any method described herein (e.g., such that execution will be performed by only one means or by multiple means in a distributed manner). By way of example, an means may include a processing system, a device having a processing system, or a processing system cooperating via one or more networks. An means may include: one or more memories; and one or more processors configured to enable the means to perform any part of any method described herein. In some examples, one or more processors may be pre-configured to perform the various functions or operations described herein without being configured by software.

[0010] For illustrative purposes, the following description and figures illustrate certain features. Attached Figure Description

[0011] The accompanying drawings depict certain features of the various aspects described herein and should not be considered as limiting the scope of this disclosure.

[0012] Figure 1 An example wireless communication network is depicted.

[0013] Figure 2 An example decomposed base station architecture is described.

[0014] Figure 3 Various aspects of the example base station and example user equipment (UE) are described.

[0015] Figure 4A , Figure 4B , Figure 4C and Figure 4D Various example aspects of data structures used in wireless communication networks are described.

[0016] Figure 5 An example AI architecture is shown that can be used for artificial intelligence (AI) enhanced wireless communication.

[0017] Figure 6 An example AI architecture of a first wireless device communicating with a second wireless device is illustrated.

[0018] Figure 7 An example artificial neural network is shown.

[0019] Figure 8 Example transmit power allocations are shown on the various communication channels being transmitted.

[0020] Figure 9 An example operation is illustrated for allocating transmit power on each component channel of the transmission.

[0021] Figure 10 An example of AI-based dynamic power loading is shown.

[0022] Figure 11 An example system is shown for training an AI model to determine the transmit power of each component channel.

[0023] Figure 12 The process flow for communication between network entities and UEs in the system is described.

[0024] Figure 13 A method for wireless communication is described.

[0025] Figure 14 Another method for wireless communication is described.

[0026] Figure 15 Various aspects of the example communication device are described.

[0027] Figure 16 Various aspects of the example communication device are described. Detailed Implementation

[0028] This disclosure provides apparatus, methods, processing systems, and computer-readable media for dynamic power loading.

[0029] In some wireless communication systems (e.g., 5G New Radio (NR) systems), wireless communication devices (e.g., user equipment (UE) or base stations) transmit modulated signals using an equal power allocation across the entire allocated frequency resources (e.g., resource elements). Taking uplink transmit power control as an example, the UE can determine a minimum transmit power that enables adequate reception at a network entity (e.g., a base station) and minimizes interference encountered by other devices. The UE can calculate the uplink transmit power used for transmission using the network entity's request for received signal power at the network entity, an estimate of propagation loss on the uplink communication channel between the UE and the network entity, and other parameters such as the transmit bandwidth assigned by the resource. The UE can determine such transmit power for each scheduled transmission opportunity in the cell's carriers for resource allocation on the communication channel, and the UE can apply this transmit power according to the modulation distribution (e.g., any applicable amplitude modulation).

[0030] Technical challenges in transmit power allocation include, for example, the independent effects of signal propagation encountered in parallel communication channels. Since Orthogonal Frequency Division Multiplexing (OFDM) signals consist of multiple subcarriers, they may encounter varying degrees of frequency-dependent signal propagation effects on each subcarrier. These effects can include, for example, noise, interference, fading, scattering, and the Doppler effect. Therefore, applying the same transmit power to all subcarriers of an OFDM signal to compensate for propagation losses can lead to performance degradation when demodulating the signal at the receiver (such as a UE or base station). For example, due to independent signal propagation effects on a particular subcarrier, the constellation of the signal received on that subcarrier may not match the constellation mapping used for demodulation. This constellation misalignment affects wireless communication performance, such as received signal quality, received signal strength, block error rate (BLER), throughput, and latency.

[0031] The aspects described herein overcome the aforementioned technical problems by providing techniques for dynamic power loading. Wireless communication devices (e.g., UEs and / or network entities) can dynamically allocate transmit power across transmitted component channels. Dynamic transmit power allocation may involve setting a separate transmit power for each component channel being transmitted (e.g., modulated signal transmission). Each transmit power can be allocated to overcome certain independent signal propagation effects on the corresponding component channel (e.g., subcarrier or subcarrier group). Independent signal propagation effects may include the noise covariance of each component channel (e.g., subcarrier or subcarrier group). In some aspects, the transmit power allocation for each component channel (e.g., subcarrier or subcarrier group) can be determined as a function of the signal quality at the corresponding component channel (e.g., subcarrier or subcarrier group). For example, a mercury-filled technique can be used to determine the component channel transmit power allocation, as described herein with respect to... Figure 9 Further described. In some respects, artificial intelligence (AI) models can be used to determine the transmit power allocation for each component channel in a component channel (e.g., a subcarrier or a group of subcarriers), for example, as described herein with respect to... Figure 10 Further description. In some respects, network entities may configure schemes for dynamic power loading for the UE as described herein, or vice versa. In some respects, the UE may report channel state feedback to network entities that indicates subcarrier attributes for performing dynamic power loading on communication signals, or vice versa.

[0032] The techniques for dynamic power loading described herein can provide a variety of beneficial effects and / or advantages. Techniques for dynamic power loading can achieve improved wireless communication performance, such as improved signal quality, improved signal strength, increased throughput, and / or reduced latency. The improved wireless communication performance can be attributed to the dynamic power loading described herein, which adjusts the transmit power on each component channel based at least in part on the corresponding signal propagation effects on the transmitted component channels (e.g., OFDM subcarriers).

[0033] As used herein, the transmitted component channel may be or include OFDM subcarriers (e.g., resource elements) and / or groups of OFDM subcarriers (e.g., resource element groups) for transmitting signals (such as OFDM signals).

[0034] An introduction to wireless communication networks

[0035] The techniques and methods described herein can be used in a variety of wireless communication networks. Although aspects herein may be described using terms commonly associated with 3G, 4G, 5G, 6G and / or other generations of wireless technologies, aspects of this disclosure are equally applicable to other communication systems and standards not explicitly mentioned herein.

[0036] Figure 1 An example of a wireless communication network 100 in which the aspects described herein can be implemented is depicted.

[0037] Generally, wireless communication network 100 includes various network entities (alternatively, network elements or network nodes). Network entities are typically communication devices and / or communication functions performed by communication devices (e.g., user equipment (UE), base station (BS), components of the BS, servers, etc.). Since such communication devices are part of wireless communication network 100 and facilitate wireless communication, they may be referred to as wireless communication devices. For example, various functions of the network and various devices associated with and interacting with the network may be considered network entities. Furthermore, wireless communication network 100 includes terrestrial and non-terrestrial aspects (also referred to herein as non-terrestrial network entities). Terrestrial aspects include terrestrial network entities such as terrestrial network entities (e.g., BS 102), and non-terrestrial aspects include satellite 140 and / or airborne or spaceborne platforms, which may include onboard network entities (e.g., one or more BSs) capable of communicating with other network elements (e.g., terrestrial BSs) and UEs.

[0038] In the depicted example, wireless communication network 100 includes BS 102, UE 104 and one or more core networks (such as Evolved Packet Core (EPC) 160 and 5G Core (5GC) network 190) that interoperate to provide communication services over various communication links, including wired and wireless links.

[0039] Figure 1 Various example UEs 104 are described, which may more generally include: cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, global positioning systems, multimedia devices, video devices, digital audio players, cameras, game consoles, tablet computers, smart devices, wearable devices, vehicles, electricity meters, air pumps, large or small kitchen appliances, healthcare devices, implants, sensors / actuators, displays, Internet of Things (IoT) devices, always-on (AON) devices, edge processing devices, data centers, or other similar devices. UE 104 may also be more generally referred to as mobile devices, wireless devices, stations, mobile stations, subscriber stations, mobile subscriber stations, mobile units, subscriber units, wireless units, remote units, remote devices, access terminals, mobile terminals, wireless terminals, remote terminals, mobile phones, and others.

[0040] BS 102 communicates wirelessly with UE 104 via communication link 120 (e.g., transmitting or receiving signals to or from UE 104). Communication link 120 between BS 102 and UE 104 may include uplink (UL) (also known as reverse link) transmission from UE 104 to BS 102 and / or downlink (DL) (also known as forward link) transmission from BS 102 to UE 104. In various aspects, communication link 120 may utilize multiple-input multiple-output (MIMO) antenna technologies, including spatial multiplexing, beamforming, and / or transmit diversity.

[0041] BS 102 may typically include: NodeB, enhanced NodeB (eNB), next-generation enhanced NodeB (ng-eNB), next-generation NodeB (gNB or gNodeB), access point, transceiver base station, radio base station, radio transceiver, transceiver functionality, transmit / receive point, and / or others. Each of BS 102 provides communication coverage for a corresponding coverage area 110, which may sometimes be referred to as a cell, and in some cases may overlap (e.g., a small cell 102' may have a coverage area 110' that overlaps with the coverage area 110 of a macro cell). For example, BS may provide communication coverage for macro cells (covering a relatively large geographic area), pico cells (covering a relatively small geographic area, such as a stadium), femtocells (covering a relatively small geographic area (e.g., a home)), and / or other types of cells.

[0042] Generally, a cell can refer to a portion, partition, or segment of wireless communication coverage served by a network entity within a wireless communication network. A cell may have geographical characteristics (such as a geographical coverage area) and radio frequency characteristics (such as time and / or frequency resources dedicated to the cell). For example, a specific geographical coverage area may be covered by multiple cells employing different frequency resources (e.g., bandwidth portions) and / or different time resources. As another example, a specific geographical coverage area may be covered by a single cell. In some contexts (e.g., carrier aggregation scenarios and / or multi-connectivity scenarios), the terms "cell" or "serving cell" may refer to or correspond to a specific carrier frequency (e.g., component carrier) used for wireless communication, and "cell group" may refer to or correspond to multiple carriers used for wireless communication. For example, in a carrier aggregation scenario, a UE may communicate on multiple component carriers corresponding to multiple (serving) cells in the same cell group, and in a multi-connectivity (e.g., dual-connectivity) scenario, a UE may communicate on multiple component carriers corresponding to multiple cell groups.

[0043] Although BS 102 is described as a single communication device in various aspects, it can be implemented in various configurations. For example, to give a few examples, one or more components of the base station can be decomposed, including a central unit (CU), one or more distributed units (DU), one or more radio units (RU), a near real-time (near RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. In another example, various aspects of the base station can be virtualized. More generally, a base station (e.g., BS 102) can include components located at a single physical location or components located at various physical locations. In examples where the base station includes components located at various physical locations, the various components can each perform functions, such that the various components collectively achieve functionality similar to a base station located at a single physical location. In some aspects, a base station including components located at various physical locations can be referred to as a decomposed radio access network architecture (such as an open RAN (O-RAN) or virtualized RAN (VRAN) architecture). Figure 2 An example decomposed base station architecture is depicted and described.

[0044] Different BSs 102 within the wireless communication network 100 can also be configured to support different radio access technologies (such as 3G, 4G, and / or 5G). For example, a BS 102 configured for 4G LTE (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E-UTRAN)) can interface with EPC 160 via a first backhaul link 132 (e.g., S1 interface). A BS 102 configured for 5G (e.g., 5G NR or Next Generation RAN (NG-RAN)) can interface with 5GC 190 via a second backhaul link 184. BSs 102 can communicate directly or indirectly (e.g., via EPC 160 or 5GC 190) on a third backhaul link 134 (e.g., X2 interface), which can be wired or wireless.

[0045] Wireless communication network 100 can subdivide the electromagnetic spectrum into various categories, bands, channels, or other characteristics. In some aspects, subdivision is provided based on wavelength and frequency, where frequency may also be referred to as carrier, subcarrier, channel, tone, or subband. For example, 3GPP currently defines frequency range 1 (FR1) as including 410MHz-7125MHz, which is often (interchangeably) referred to as “sub-6GHz”. Similarly, 3GPP currently defines frequency range 2 (FR2) as including 24,250MHz to 71,000MHz, which is sometimes (interchangeably) referred to as “millimeter wave” (“mmW” or “mmWave”). In some cases, FR2 can be further defined according to subranges (such as a first subrange FR2-1 including 24,250MHz to 52,600MHz and a second subrange FR2-2 including 52,600MHz to 71,000MHz). Base stations configured to communicate using mmWave / near mmWave radio bands (e.g., mmWave base stations such as BS 180) can utilize beamforming (e.g., 182) with UEs (e.g., 104) to improve path loss and range.

[0046] The communication link 120 between BS 102 and, for example, UE 104 can be via one or more carriers, which may have different bandwidths (e.g., 5MHz, 10MHz, 15MHz, 20MHz, 100MHz, 400MHz and / or other MHz) and may be aggregated in various ways. The carriers may be adjacent to each other or may not be adjacent to each other. The allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated to DL compared to UL).

[0047] Compared to lower-frequency communication, communication using higher frequency bands may have higher path loss and shorter range. Therefore, some base stations (e.g., Figure 1The beamforming 182 of the BS 180 (180) with the UE 104 can be used to improve path loss and range. For example, the BS 180 and UE 104 may each include multiple antennas, such as antenna elements, antenna panels, and / or antenna arrays, to facilitate beamforming. In some cases, the BS 180 may transmit beamformed signals to the UE 104 in one or more transmit directions 182''. The UE 104 may receive beamformed signals from the BS 180 in one or more receive directions 182''. The UE 104 may also transmit beamformed signals to the BS 180 in one or more transmit directions 182''. The BS 180 may also receive beamformed signals from the UE 104 in one or more receive directions 182''. The BS 180 and UE 104 may then perform beamforming training to determine the optimal receive and transmit directions for each of the BS 180 and UE 104. It is worth noting that the transmit and receive directions of the BS 180 may be the same or different. Similarly, the sending and receiving directions of UE 104 may be the same or different.

[0048] The wireless communication network 100 further includes a Wi-Fi AP 150 that communicates with a Wi-Fi station (STA) 152 via a communication link 154 in, for example, unlicensed spectrum in 2.4 GHz and / or 5 GHz.

[0049] Some UEs 104 may use device-to-device (D2D) communication links 158 to communicate with each other. The D2D communication link 158 may use one or more sidelink channels, such as physical sidelink broadcast channel (PSBCH), physical sidelink discovery channel (PSDCH), physical sidelink shared channel (PSSCH), physical sidelink control channel (PSCCH), and / or physical sidelink feedback channel (PSFCH).

[0050] EPC 160 may include various functional components, including: Mobility Management Entity (MME) 162, other MMEs 164, Serving Gateway 166, Multimedia Broadcast Multicast Service (MBMS) Gateway 168, Broadcast Multicast Service Center (BM-SC) 170, and / or Packet Data Network (PDN) Gateway 172, as in the illustrated example. MME 162 may communicate with Home Subscriber Server (HSS) 174. MME 162 is the control node that handles signaling between UE 104 and EPC 160. Generally, MME 162 provides bearer and connectivity management.

[0051] Generally, user Internet Protocol (IP) packets are transmitted through Serving Gateway 166, which is itself connected to PDN Gateway 172. PDN Gateway 172 provides UE IP address allocation and other functions. PDN Gateway 172 and BM-SC 170 are connected to IP services 176, which may include, for example, the Internet, intranets, IP Multimedia Subsystem (IMS), packet-switched (PS) streaming services, and / or other IP services.

[0052] The BM-SC 170 provides functionality for MBMS user service dispatch and delivery. The BM-SC 170 can serve as an entry point for content provider MBMS transmissions, can be used to authorize and initiate MBMS bearer services within a Public Land Mobile Network (PLMN), and / or can be used to schedule MBMS transmissions. The MBMS gateway 168 can be used to distribute MBMS services to BS 102 in a Broadcast-Specific Service Single Frequency Network (MBSFN) area, and / or can be responsible for session management (start / stop) and collecting eMBMS-related billing information.

[0053] 5GC 190 may include various functional components, including: Access and Mobility Management Function (AMF) 192, other AMFs 193, Session Management Function (SMF) 194, and User Plane Function (UPF) 195. AMF 192 can communicate with Unified Data Management (UDM) 196.

[0054] AMF 192 is the control node that handles signaling between UE 104 and 5GC 190. AMF 192 provides services such as Quality of Service (QoS) flow and session management.

[0055] Internet Protocol (IP) packets are transmitted via UPF 195, which connects to IP service 197 and provides the UE with IP address allocation and other functions for 5GC 190. IP service 197 may include, for example, the Internet, intranet, IMS, PS streaming service, and / or other IP services.

[0056] In various aspects, to give a few examples, network entities or network nodes can be implemented as aggregated base stations, decomposed base stations, components of base stations, integrated access and backhaul (IAB) nodes, relay nodes, and sidelink nodes.

[0057] Figure 2An example decomposed base station 200 architecture is depicted. The decomposed base station 200 architecture may include one or more central units (CUs) 210, which may communicate directly with the core network 220 via a backhaul link, or indirectly with the core network 220 through one or more decomposed base station units, such as a near real-time (near-RT) RAN Intelligent Controller (RIC) 225 via an E2 link, or a non-real-time (non-RT) RIC 215 associated with a Service Management and Orchestration (SMO) framework 205, or both. CUs 210 may communicate with one or more distributed units (DUs) 230 via corresponding midhaul links (such as F1 interfaces). DUs 230 may communicate with one or more radio units (RUs) 240 via corresponding fronthaul links. RUs 240 may communicate with a corresponding UE 104 via one or more radio frequency (RF) access links. In some specific implementations, UE 104 may be served simultaneously by multiple RUs 240.

[0058] Each unit in a cell (e.g., CU 210, DU 230, RU 240, and near-RT RIC 225, non-RT RIC 215, and SMO frame 205) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the cells, or an associated processor or controller that provides instructions to the cell's communication interface, may be configured to communicate with one or more other cells via the transmission medium. For example, these cells may include a wired interface configured to receive signals or transmit signals to one or more other cells via a wired transmission medium. Additionally or alternatively, a cell may include a wireless interface that may include a receiver, transmitter, or transceiver (such as a radio frequency (RF) transceiver) configured to receive signals on a wireless transmission medium or transmit signals to one or more other cells, or both.

[0059] In some aspects, CU 210 can host one or more higher-level control functions. Such control functions may include Radio Resource Control (RRC), Packet Data Convergence Protocol (PDCP), Serving Data Adaptation Protocol (SDAP), etc. Each control function can be implemented using an interface configured to signal to other control functions hosted by CU 210. CU 210 can be configured to handle user plane functions (e.g., Central Unit-User Plane (CU-UP)), control plane functions (e.g., Central Unit-Control Plane (CU-CP)), or combinations thereof. In some implementations, CU 210 can be logically divided into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, CU-UP units can communicate bidirectionally with CU-CP units via an interface such as an E1 interface. CU 210 can be implemented to communicate with DU 230 for network control and signaling, as needed.

[0060] DU 230 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RU 240s. In some aspects, DU 230 may at least partially host one or more of the Radio Link Control (RLC) layer, Medium Access Control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) according to functional splits (such as those defined by the 3rd Generation Partnership Project (3GPP). In some aspects, DU 230 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by DU 230 or with control functions hosted by CU 210.

[0061] Lower-layer functionality can be implemented by one or more RU 240s. In some deployments, an RU240 controlled by a DU 230 may correspond to a logical node that hosts RF processing functions or low-PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, Physical Random Access Channel (PRACH) extraction and filtering, or both, based at least in part on functional decomposition (such as lower-layer functional decomposition). In such architectures, the RU 240 may be implemented to handle over-the-air (OTA) communications with one or more UE 104s. In some specific implementations, the real-time and non-real-time aspects of control plane and user plane communications with the RU 240 may be controlled by the corresponding DU 230. In some scenarios, this configuration enables the implementation of the DU 230 and CU 210 in cloud-based RAN architectures (such as vRAN architectures).

[0062] SMO framework 205 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, SMO framework 205 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operation and maintenance interfaces such as the O1 interface. For virtualized network elements, SMO framework 205 can be configured to interact with cloud computing platforms such as Open Cloud (O-Cloud) 290 to perform network element lifecycle management (such as instantiating virtualized network elements) via cloud computing platform interfaces such as the O2 interface. Such virtualized network elements may include, but are not limited to, CU 210, DU 230, RU 240, and near-RT RIC 225. In some specific implementations, SMO framework 205 may communicate with the hardware aspects of the 4G RAN (such as Open eNB (O-eNB) 211) via the O1 interface. Additionally, in some implementations, the SMO framework 205 may communicate directly with one or more DU 230s and / or one or more RU 240s via the O1 interface. The SMO framework 205 may also include a non-RT RIC 215 configured to support the functionality of the SMO framework 205.

[0063] The non-RT RIC 215 can be configured to include logical functions that enable non-real-time control and optimization of RAN elements and resources, including AI / ML workflows for model training and updates, or policy-based guidance for applications / features in the near-RT RIC 225. The non-RT RIC 215 can be coupled to or communicate with the near-RT RIC 225, such as via an A1 interface. The near-RT RIC 225 can be configured to include logical functions that enable near real-time control and optimization of RAN elements and resources via an interface, such as an E2 interface, connecting one or more CU 210s, one or more DU 230s, or both, and O-eNBs to the near-RT RIC 225.

[0064] In some implementations, to generate AI / ML models to be deployed in the near-RT RIC 225, the non-RT RIC 215 may receive parameters or external enrichment information from an external server. This information can be utilized by the near-RT RIC 225 and may be received from non-network data sources or network functions at the SMO framework 205 or the non-RT RIC 215. In some examples, the non-RT RIC 215 or the near-RT RIC 225 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 215 may monitor long-term trends and patterns in performance and use AI / ML models to perform corrective actions via the SMO framework 205 (such as reconfiguration via O1) or by creating RAN management policies (such as A1 policies).

[0065] Figure 3 Various aspects of examples BS 102 and UE 104 are described.

[0066] Generally, BS 102 includes various processors (e.g., 318, 320, 330, 338, and 340), antennas 334a-t (collectively referred to as 334), transceivers 332a-t (collectively referred to as 332) including modulators and demodulators, and other aspects that enable the wireless transmission of data (e.g., data source 312) and the wireless reception of data (e.g., data sink 314). For example, BS 102 can transmit and receive data between BS 102 and UE 104. BS 102 includes a controller / processor 340 that can be configured to implement the various functions described herein related to wireless communication.

[0067] Generally, UE 104 includes various processors (e.g., 358, 364, 366, 370, and 380), antennas 352a-r (collectively referred to as 352), transceivers 354a-r (collectively referred to as 354) including modulators and demodulators, and other aspects that enable the wireless transmission of data (e.g., retrieved from data source 362) and the wireless reception of data (e.g., provided to data sink 360). UE 104 includes a controller / processor 380 that can be configured to implement the various functions described herein related to wireless communication.

[0068] Regarding example downlink transmission, BS 102 includes a transmission processor 320 that can receive data from data source 312 and control information from controller / processor 340. This control information may be for a Physical Broadcast Channel (PBCH), Physical Control Format Indicator Channel (PCFICH), Physical Hybrid Automatic Repeat Request (HARQ) Indicator Channel (PHICH), Physical Downlink Control Channel (PDCCH), Group Common PDCCH (GC PDCCH), and / or others. In some examples, this data may be for a Physical Downlink Shared Channel (PDSCH).

[0069] The transmitter processor 320 can process (e.g., encode and symbol map) data and control information to obtain data symbols and control symbols, respectively. The transmitter processor 320 can also generate reference symbols (such as those for the primary synchronization signal (PSS), secondary synchronization signal (SSS), PBCH demodulation reference signal (DMRS), and channel state information reference signal (CSI-RS)).

[0070] The transmit (TX) multiple-input multiple-output (MIMO) processor 330 can perform spatial processing (e.g., pre-decoding) on ​​data symbols, control symbols, and / or reference symbols where applicable, and can provide the output symbol stream to the modulators (MODs) in transceivers 332a to 332t. Each modulator in transceivers 332a to 332t can process the corresponding output symbol stream to obtain an output sample stream. Each modulator can further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. The downlink signal from the modulators in transceivers 332a to 332t can be transmitted via antennas 334a to 334t, respectively.

[0071] To receive downlink transmissions, UE 104 includes antennas 352a to 352r that receive downlink signals from BS 102 and provide the received signals to demodulators (DEMODs) in transceivers 354a to 354r, respectively. Each demodulator in transceivers 354a-354r can adjust (e.g., filter, amplify, down-convert, and digitize) the corresponding received signal to obtain an input sample. Each demodulator can further process the input sample to obtain the received symbols.

[0072] The RX MIMO detector 356 acquires received symbols from all demodulators in transceivers 354a to 354r, performs MIMO detection on the received symbols where applicable, and provides the detected symbols. The receive processor 358 processes (e.g., demodulates, deinterleaves, and decodes) the detected symbols, provides the decoded data for UE 104 to data sink 360, and provides the decoded control information to controller / processor 380.

[0073] Regarding the example uplink transmission, UE 104 further includes a transmission processor 364 that receives and processes data from data source 362 (e.g., for PUSCH) and control information from controller / processor 380 (e.g., for Physical Uplink Control Channel (PUCCH)). Transmission processor 364 can also generate reference symbols for reference signals (e.g., for Sounding Reference Signal (SRS)). Symbols from transmission processor 364 may be pre-decoded by TX MIMO processor 366, where applicable, further processed by modulators in transceivers 354a-354r (e.g., for SC-FDM), and transmitted to BS 102.

[0074] At BS 102, uplink signals from UE 104 can be received by antennas 334a-t, processed by demodulators in transceivers 332a to 332t, detected where applicable by RX MIMO detector 336, and further processed by receiver processor 338 to obtain decoded data and control information transmitted by UE 104. Receiver processor 338 can provide the decoded data to data sink 314 and the decoded control information to controller / processor 340.

[0075] Memory 342 and memory 382 can store data and program code for BS 102 and UE 104, respectively.

[0076] Scheduler 344 can schedule UE to transmit data on the downlink and / or uplink.

[0077] In various respects, BS 102 can be described as transmitting and receiving various types of data associated with the methods described herein. In these contexts, "transmitting" can refer to various mechanisms that output data, such as from data source 312, scheduler 344, memory 342, transmit processor 320, controller / processor 340, TX MIMO processor 330, transceiver 332a-t, antenna 334a-t, and / or other aspects described herein. Similarly, "receiving" can refer to various mechanisms that acquire data, such as from antenna 334a-t, transceiver 332a-t, RX MIMO detector 336, controller / processor 340, receive processor 338, scheduler 344, memory 342, and / or other aspects described herein.

[0078] In various respects, UE 104 can also be described as transmitting and receiving various types of data associated with the methods described herein. In these contexts, “transmitting” can refer to various mechanisms that output data, such as outputting data from data source 362, memory 382, ​​transmit processor 364, controller / processor 380, TX MIMO processor 366, transceiver 354a-t, antenna 352a-t, and / or other aspects described herein. Similarly, “receiving” can refer to various mechanisms that acquire data, such as acquiring data from antenna 352a-t, transceiver 354a-t, RX MIMO detector 356, controller / processor 380, receive processor 358, memory 382, ​​and / or other aspects described herein.

[0079] In some respects, the processor can be configured to perform various operations (such as those associated with the methods described herein) and to send (output) data to or receive data from another interface configured to send or receive data, respectively.

[0080] In various aspects, artificial intelligence (AI) processors 318 and 370 may perform AI processing for BS 102 and / or UE 104, respectively. AI processor 318 may include AI accelerator hardware or circuitry, such as one or more neural processing units (NPUs), one or more neural network processors, one or more tensor processors, one or more deep learning processors, etc. AI processor 370 may also include AI accelerator hardware or circuitry. As an example, AI processor 370 may perform AI-based beam management, AI-based channel state feedback (CSF), AI-based antenna tuning, and / or AI-based positioning (e.g., Global Navigation Satellite System (GNSS) positioning). In some cases, AI processor 318 may use hardware-accelerated AI inference and / or AI training to process feedback (e.g., CSF) from UE 104. AI processor 318 may, for example, use hardware-accelerated AI inference associated with the CSF to decode compressed CSF from UE 104. In some cases, AI processor 318 may perform certain RAN-based functions, including, for example, network planning, network performance management, energy-efficient network operation, etc.

[0081] Figure 4A , Figure 4B , Figure 4C and Figure 4D Describes the use of wireless communication networks (such as Figure 1 All aspects of the data structure of the wireless communication network 100.

[0082] Specifically, Figure 4A Figure 400 is an example of the first subframe within a 5G (e.g., 5G NR) frame structure. Figure 4B Figure 430 illustrates an example of a DL channel within a 5G subframe. Figure 4C Figure 450 illustrates an example of the second subframe within a 5G frame structure, and Figure 4D Figure 480 illustrates an example of a UL channel within a 5G subframe.

[0083] Wireless communication systems can utilize Orthogonal Frequency Division Multiplexing (OFDM) with a cyclic prefix (CP) on both the uplink and downlink. Such systems can also support half-duplex operation using Time Division Duplex (TDD). OFDM and Single-Carrier Frequency Division Multiplexing (SC-FDM) will (e.g., as...) Figure 4B and Figure 4D The system bandwidth (as depicted in the text) is divided into multiple orthogonal subcarriers. Each subcarrier can be modulated with data. Modulation symbols can be transmitted in the frequency domain using OFDM and / or in the time domain using SC-FDM.

[0084] Wireless communication frame structures can be frequency division duplex (FDD), where for a specific set of subcarriers, subframes within that set are dedicated to either deep (DL) or ultra-low (UL). Wireless communication frame structures can also be time division duplex (TDD), where for a specific set of subcarriers, subframes within that set are dedicated to both DL and UL.

[0085] exist Figure 4A and Figure 4C In this example, the wireless communication frame structure is TDD, where D stands for DL, U for UL, and X is flexibly used between DL and UL. The UE can be configured using a slot format (dynamically via DL control information (DCI) or semi-statically / statically via Radio Resource Control (RRC) signaling) through the received Slot Format Indicator (SFI). In the depicted example, a 10ms frame is divided into 10 equal-sized 1ms subframes. Each subframe may include one or more slots. In some examples, each slot may include 12 or 14 symbols, depending on the Cyclic Prefix (CP) type (e.g., 12 symbols per slot for extended CP, or 14 symbols per slot for normal CP). Subframes may also include micro-slots, which typically have fewer symbols than the entire slot. Other wireless communication technologies may have different frame structures and / or different channels.

[0086] In some respects, the number of time slots within a subframe (e.g., the time slot duration within a subframe) is based on a parameter set that defines the frequency-domain subcarrier spacing and symbol duration, as further described herein. In some respects, given a parameter set μ, each subframe has 2 μ The number of time slots is 1. Therefore, parameter sets (µ) 0 through 6 allow for 1, 2, 4, 8, 16, 32, and 64 time slots per subframe, respectively. In some cases, extended CP (e.g., 12 symbols per time slot) can be used with specific parameter sets (e.g., parameter set 2, allowing 4 time slots per subframe). Subcarrier spacing and symbol length / duration are functions of the parameter sets. The subcarrier spacing can be equal to... kHz, where μ is the parameter set from 0 to 6. As an example, the parameter set... Corresponding to a subcarrier spacing of 15 kHz, and the parameter set This corresponds to a subcarrier spacing of 960 kHz. Symbol length / duration is negatively correlated with subcarrier spacing. Figure 4A , Figure 4B , Figure 4C and Figure 4D It provides a slot format with 14 symbols per slot (e.g., normal CP) and a parameter set with 4 slots per subframe. Example. In this case, the slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 μs.

[0087] like Figure 4A , Figure 4B , Figure 4C and Figure 4D As depicted, the resource grid can be used to represent the frame structure. Each time slot includes a resource block (RB) (also known as a physical RB (PRB)) extending for, for example, 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs). The number of bits carried by each RE depends on the modulation scheme, including, for example, quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM).

[0088] like Figure 4A As illustrated in the example, some REs in the RE carry information for the UE (e.g., Figure 1 and Figure 3 The reference (pilot) signal (RS) for the UE (104) may include a demodulation RS (DMRS) and / or a channel state information reference signal (CSI-RS) for channel estimation at the UE. The RS may also include a beam measurement RS (BRS), a beam refinement RS (BRRS), and / or a phase tracking RS (PT-RS).

[0089] Figure 4B Examples of various DL channels within a subframe of a frame are illustrated. The Physical Downlink Control Channel (PDCCH) carries the DCI within one or more Control Channel Elements (CCEs), each CCE comprising, for example, nine RE groups (REGs), each REG comprising, for example, four consecutive REs in an OFDM symbol.

[0090] The Primary Synchronization Signal (PSS) can be located within symbol 2 of a specific subframe of the frame. The PSS is generated by the UE (e.g., Figure 1 and Figure 3 104) is used to determine subframe / symbol timing and physical layer identifier.

[0091] The secondary synchronization signal (SSS) can be located within symbol 4 of a specific subframe of a frame. The SSS is used by the UE to determine the physical layer cell identification group number and radio frame timing.

[0092] Based on the Physical Layer Identifier and Physical Layer Cell Identifier Group Number, the UE can determine the Physical Cell Identifier (PCI). Based on the PCI, the UE can determine the location of the aforementioned DMRS. The Physical Broadcast Channel (PBCH), carrying the Master Information Block (MIB), can be logically grouped with the PSS and SSS to form a Synchronization Signal (SS) / PBCH block. The MIB provides the number of RBs and the System Frame Number (SFN) in the system bandwidth. The Physical Downlink Shared Channel (PDSCH) carries user data, broadcast system information (such as System Information Block (SIB)) not transmitted via the PBCH, and / or paging messages.

[0093] like Figure 4C As illustrated, some REs in the REs carry DMRS for channel estimation at the base station (indicated as R for a particular configuration, but other DMRS configurations are possible). The UE can transmit DMRS for PUCCH and DMRS for PUSCH. PUSCH DMRS can be transmitted, for example, in the first or second symbol before the PUSCH. PUCCH DMRS can be transmitted in different configurations depending on whether a short or long PUCCH is being transmitted and depending on the specific PUCCH format used. UE104 can transmit a Sounding Reference Signal (SRS). SRS can be transmitted, for example, in the last symbol of a subframe. SRS can have a comb structure, and the UE can transmit SRS on one of the comb teeth. SRS can be used by the base station for channel quality estimation to enable frequency-dependent scheduling of the UL.

[0094] Figure 4D Examples of various UL channels within a subframe of a frame are illustrated. The PUCCH can be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, channel quality indicators (CQI), pre-decoding matrix indicators (PMI), rank indicators (RI), and HARQ ACK / NACK feedback. The PUSCH carries data and may additionally be used to carry buffer status reports (BSR), power clearance reports (PHR), and / or UCI.

[0095] Example of artificial intelligence for wireless communication

[0096] Some aspects described in this paper can be implemented, at least in part, using some form of artificial intelligence (AI), such as the process of using a machine learning (ML) model to infer or predict output data based on input data. Example ML models may include mathematical representations of one or more relationships between various objects to provide outputs representing one or more predictions or inferences. Once an ML model has been trained, it can be deployed to process data that is wholly or partially similar to or related to the training data, and to provide outputs representing one or more predictions or inferences based on the input data.

[0097] ML is typically characterized by a learning type that generates a specific type of learning model that performs a particular type of task. For example, different types of machine learning include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

[0098] Supervised learning algorithms typically model the relationships and dependencies between input features (e.g., feature vectors) and one or more target outputs. Supervised learning uses labeled training data, which consists of data including one or more inputs and the desired output. Supervised learning can be used to train models to perform tasks such as classification (where the goal is to predict discrete values) or regression (where the goal is to predict continuous values). Some example supervised learning algorithms include nearest neighbor, Naive Bayes, decision trees, linear regression, support vector machines (SVM), and artificial neural networks (ANN).

[0099] Unsupervised learning algorithms process unlabeled input data and train models that take the input and transform it into output to solve real-world problems. Examples of unsupervised learning tasks are clustering (where the model's output might be cluster labels), dimensionality reduction (where the model's output is an output feature vector with fewer features than the input feature vector), and outlier detection (where the model's output is a value indicating how the input differs from typical examples in the dataset). An example unsupervised learning algorithm is k-means.

[0100] Semi-supervised learning algorithms process datasets containing both labeled and unlabeled examples, where the number of unlabeled examples is typically much greater than the number of labeled examples. However, the goal of semi-supervised learning is to achieve the goals of supervised learning. Typically, a semi-supervised model involves a model trained to generate pseudo-labels for unlabeled data, which are then combined with labeled data to train a second classifier that leverages a larger volume of overall training data to improve task performance.

[0101] Reinforcement learning algorithms use observations gathered by an agent from its interactions with the environment to take actions that maximize reward or minimize risk. Reinforcement learning is a continuous and iterative process in which the agent learns from its experience with the environment until it has explored, for example, the entire range of possible states. An example type of reinforcement learning algorithm is adversarial networks. Reinforcement learning can be particularly beneficial when used to improve or attempt to optimize the behavior of models deployed in dynamically changing environments, such as wireless communication networks.

[0102] ML models can be deployed in one or more devices (e.g., network entities such as base stations and / or user equipment) to support various wired and / or wireless communication aspects of a communication system. For example, ML models can be trained to identify patterns and relationships in data corresponding to networks, devices, air interfaces, etc. ML models can improve operations associated with one or more aspects, such as transceiver circuitry control, frequency synchronization, timing synchronization, channel state estimation, channel equalization, channel state feedback, modulation, demodulation, device location, transceiver tuning, beamforming, signal decoding / decoding, network routing, load balancing, and energy saving (to name just a few). AI-enhanced transceiver circuitry control may include, for example, filter tuning, transmit power control, gain control (including automatic gain control), phase control, power management, etc.

[0103] The aspects described herein can be used to describe technical solutions for performing certain tasks and various technical problems by applying specific types of ML models, such as ANNs. However, it should be understood that other types of AI models can be used as a complement or alternative to ANNs or machine learning. An ML model can be an example of an AI model, and other AI models can be used as a complement or alternative to any ML model described herein. Therefore, unless explicitly stated otherwise, the topic of ML models is not necessarily intended to be limited to ANN solutions or machine learning. Furthermore, it should be understood that, unless otherwise specified, terms such as “AI model,” “ML model,” “AI / ML model,” or “trained ML model” are intended to be used interchangeably.

[0104] Figure 5 This is a diagram illustrating an example AI architecture 500 that can be used for AI-enhanced wireless communication. As illustrated, architecture 500 includes multiple logical entities, such as a model training host 502, a model inference host 504, a data source 506, and an agent 508. The AI ​​architecture can be used in any of the various use cases for wireless communication, such as those listed above.

[0105] The model inference host 504 in architecture 500 is configured to run an ML model based on inference data 512 provided by data source 506. The model inference host 504 may produce an output 514 (e.g., a prediction or inference, such as discrete or continuous values) based on the inference data 512, and then provide it as input to agent 508.

[0106] Agent 508 can be a component or entity of a wireless communication system, including, for example, a radio access network (RAN), a wireless local area network, a device-to-device (D2D) communication system, etc. As an example, agent 508 can be user equipment (e.g., Figure 1 UE 104 in the base station (e.g., Figure 1 The BS 102 or any of its decomposed network entities, including centralized units (CUs), distributed units (DUs) and / or radio units (RUs), access points, radio stations, RAN intelligent controllers (RICs) in cloud-based RANs, etc. Additionally, the type of agent 508 may also depend on the type of task performed by the model inference host 504, the type of inference data 512 provided to the model inference host 504, and / or the type of output 514 generated by the model inference host 504.

[0107] For example, if the output 514 from the model inference host 504 is associated with beam management, then the agent 508 may be or include a UE, DU, or RU. As another example, if the output 514 from the model inference host 504 is associated with transmit and / or receive scheduling, then the agent 508 may be a CU or DU.

[0108] After agent 508 receives output 514 from model inference host 504, agent 508 may determine whether to take action based on the output. For example, if agent 508 is a DU or RU, and the output from model inference host 504 is associated with beam management, agent 508 may determine whether to change or modify the transmit and / or receive beams based on output 514. If agent 508 determines to take action based on output 514, agent 508 may instruct the action to at least one action subject 510. For example, if agent 508 determines to change or modify the transmit and / or receive beams used for communication between agent 508 and action subject 510 (e.g., UE), agent 508 may transmit a beam switching instruction to action subject 510 (e.g., UE). As another example, agent 508 may be a UE, and output 514 from model inference host 504 may be one or more predicted channel characteristics of one or more beams. For example, model inference host 504 may predict the channel characteristics of a beam set based on measurements of another beam set. Based on predicted channel characteristics, an intelligent agent 508, such as a UE, can transmit a request to an action subject 510, such as a BS, to switch to a different beam for communication. In some cases, the intelligent agent 508 and the action subject 510 are the same entity.

[0109] Data source 506 can be configured to collect data that can be used as training data 516 for training an ML model or as inference data 512 for feeding ML model inference operations. Specifically, data source 506 can collect data from any of various entities that may include action subject 510 (e.g., UE and / or BS) and provide the collected data to model training host 502 for ML model training. For example, after action subject 510 (e.g., UE) receives beam configuration from agent 508, action subject 510 can provide data source 506 with performance feedback associated with the beam configuration, where the performance feedback can be used by model training host 502 to monitor and / or evaluate ML model performance, such as whether the output 514 provided to agent 508 is accurate. In some examples, if the output 514 provided to agent 508 is inaccurate (or the accuracy is below an accuracy threshold), model training host 502 can determine, for example, to modify or retrain the ML model used by model inference host 504 via ML model deployment / update.

[0110] In some respects, the model training host 502 may be deployed at the same or a different entity as the entity where the model inference host 504 is deployed, or deployed with the same or a different entity. For example, to offload model training processing that may affect the performance of the model inference host 504, the model training host 502 may be deployed at a model server, as further described herein. Furthermore, in some cases, training and / or inference may be distributed among devices in a decentralized or federated manner.

[0111] In some respects, ML models are deployed at or on network entities for dynamic power loading. More specifically, the model infers the host (such as...) Figure 5 The model inference host (504) can be deployed at or on a network entity to allocate transmit power for each component channel of transmission (e.g., transmission of modulated signals), as further described herein.

[0112] In some other respects, ML models are deployed at or on the UE for dynamic power loading. More specifically, the model infers host (such as...) Figure 5 The model inference host (504) can be deployed at or on the UE to allocate transmit power for each component channel of the modulated signal, as further described herein.

[0113] Figure 6 An example AI architecture for a first wireless device 602 communicating with a second wireless device 604 is illustrated. The first wireless device 602 can be, as described herein, relative to... Figure 1 and Figure 3The example of UE 104 described herein. Similarly, the second wireless device 604 can be as described herein with respect to... Figures 1 to 3 Examples of the described BS 102 or any of its decomposed entities. Note that the AI ​​architecture of the first wireless device 602 can be applied to the second wireless device 604.

[0114] The first wireless device 602 may be or may include a chip, a system-on-a-chip (SoC), a system-in-package (SiP), a chipset, a package, or a device, which includes one or more processors, processing blocks, or processing elements (collectively, “processor 610”) and one or more memory blocks or elements (collectively, “memory 620”).

[0115] As an example, in transmission mode, processor 610 can transform information (e.g., packets or data blocks) into modulated symbols. As digital baseband signals (e.g., digital in-phase (I) and / or quadrature (Q) baseband signals representing corresponding symbols), processor 610 can output the modulated symbols to transceiver 640. Processor 610 can be coupled to transceiver 640 for transmitting and / or receiving signals via one or more antennas 646. In this example, transceiver 640 includes radio frequency (RF) circuitry 642, which can be coupled to antenna 646 via interface 644. As an example, interface 644 can include a switch, duplexer, double-ended converter, multiplexer, etc. RF circuitry 642 can, for example, use a digital-to-analog converter to convert digital signals into analog baseband signals. RF circuitry 642 can include any circuitry of various types, including, for example, baseband filters, mixers, frequency synthesizers, power amplifiers, and / or low-noise amplifiers. In some cases, RF circuitry 642 can up-convert baseband signals to one or more carrier frequencies for transmission. Antenna 646 can transmit RF signals, which can be received at the second wireless device 604.

[0116] In receive mode, RF signals received via antenna 646 (e.g., from a second wireless device 604) can be amplified and converted to a baseband frequency (e.g., down-conversion). The received baseband signal can be filtered and converted to digital I or Q signals for digital signal processing. Modem 610 can receive the digital I or Q signals and further process them, for example, by demodulating them.

[0117] One or more ML models 630 (hereinafter referred to as "ML models 630") may be stored in memory 620 and accessible by processor 610. In some cases, different ML models 630 with different characteristics may be stored in memory 620, and a particular ML model 630 may be selected based on its characteristics and / or application and the characteristics and / or conditions of the first wireless device 602 (e.g., power state, mobility state, battery reserve, temperature, etc.). For example, ML models 630 may have different inference data and output pairings (e.g., different types of inference data produce different types of outputs), and predictions (e.g., Figure 5 The output of 514 is associated with different accuracy levels (e.g., 80%, 90%, or 95% accuracy), different latency associated with generating predictions (e.g., processing time less than 10ms, 100ms, or 1 second), different ML model sizes (e.g., file size), different coefficients or weights, etc.

[0118] Processor 610 can use ML model 630 based on input data (e.g., Figure 5 The inferred data 512) is used to generate output data (e.g., Figure 5 (of 514), for example, as this article is relative to Figure 5 The inference host 504 is described. The ML model 630 can be used to perform any AI augmentation task in a variety of AI augmentation tasks, such as those listed above.

[0119] As an example, the ML model 630 may obtain input data, including, for example, one or more channel attributes (e.g., signal quality) for each transmitted component channel; and the ML model 630 may provide output data, including, for example, the transmit power allocation for each component channel, as further described herein. It should be noted that other input and / or output data may be used as a supplement to or alternative to the examples described herein.

[0120] In some respects, model server 650 may perform any of the various ML model lifecycle management (LCM) tasks for first wireless device 602 and / or second wireless device 604. Model server 650 may operate as model training host 502 and use training data to update ML model 630. In some cases, model server 650 may operate as data source 506 to collect and host training data, inference data, and / or performance feedback associated with ML model 630. In some respects, model server 650 may host various types and / or versions of ML model 630 for download by first wireless device 602 and / or second wireless device 604.

[0121] In some cases, model server 650 can monitor and evaluate the performance of ML model 630 to trigger one or more LCM tasks. For example, model server 650 can determine whether to activate or deactivate the use of a specific ML model at first wireless device 602 and / or second wireless device 604, and model server 650 can provide such instructions to the respective first wireless device 602 and / or second wireless device 604. In some cases, model server 650 can determine whether to switch to a different ML model 630 used at first wireless device 602 and / or second wireless device 604, and model server 650 can provide such instructions to the respective first wireless device 602 and / or second wireless device 604. In yet other examples, model server 650 can also act as a central server for decentralized machine learning tasks such as federated learning.

[0122] Example Artificial Intelligence Model

[0123] Figure 7 This is an exemplary block diagram of an example artificial neural network (ANN) 700.

[0124] ANN 700 may receive input data 706, which may include one or more bits of data 702, preprocessed data (optionally) output from preprocessor 704, or some combination thereof. Here, data 702 may include training data, validation data, application-related data, etc., for example, depending on the development and / or deployment phase of ANN 700. In some other embodiments, preprocessor 704 may be included within ANN 700. Preprocessor 704 may, for example, process all or part of data 702, which may result in some data in data 702 being changed, replaced, deleted, etc. In some embodiments, preprocessor 704 may add additional data to data 702.

[0125] ANN 700 includes at least one first layer 708 of artificial neurons 710 (e.g., perceptrons) to process input data 706 and provide the resulting first layer output data to at least a portion of at least one second layer 714 via edges 712 (e.g., synapses). The second layer 714 processes data received via edges 712 and provides second layer output data to at least a portion of at least one third layer 718 via edges 716. The third layer 718 processes data received via edges 716 and provides third layer output data to at least a portion of a final layer 722 comprising one or more neurons via edges 720 to provide output data 724. All or part of the output data 724 may be further processed in some way by (optionally) a post-processor 726. Thus, in some examples, ANN 700 may provide output data 728 based on output data 724, post-processed data output from post-processor 726, or some combination thereof. In some other embodiments, post-processor 726 may be included within ANN 700. Post-processor 726 may, for example, process all or part of the output data 724, which may result in output data 728 being at least partially different from output data 724, for example, due to data being altered, replaced, deleted, etc. In some specific embodiments, post-processor 726 may be configured to add additional data to output data 724. In this example, the second layer 714 and the third layer 718 represent intermediate or hidden layers that may be arranged in a hierarchical or other similar structure. Although not explicitly shown, one or more additional intermediate layers may exist between the second layer 714 and the third layer 718.

[0126] The structure and training of the artificial neurons 710 in each layer can be customized according to the specific requirements of the application. Within a given layer of an ANN, some or all of the neurons can be configured to process the information provided to that layer and output corresponding transformed information from that layer. For example, the transformed information from a layer can represent a weighted sum of input information associated with a nonlinear activation function or another activation function used to “activate” the artificial neurons in the next layer, or otherwise based on that nonlinear activation function or the other activation function used to “activate” the artificial neurons in the next layer. Artificial neurons in such layers can be activated by or in response to weights and biases that can be adjusted during the training process. The weights of various artificial neurons can act as parameters controlling the connection strength between layers or between artificial neurons, while the biases can act as parameters controlling the connection direction between layers or between artificial neurons. Activation functions can select or determine whether an artificial neuron sends its output to the next layer in response to the data received by the artificial neuron. Different activation functions can be used to model different types of nonlinear relationships. By introducing nonlinearity into the ML model, activation functions allow the ML model to “learn” the input data (e.g., Figure 5Complex patterns and relationships in (506). Some non-exhaustive example activation functions include linear functions, binary step functions, sigmoid, hyperbolic tangent (tanh), rectified linear unit (ReLU) and its variants, exponential linear unit (ELU), Swish, Softmax, etc.

[0127] Design tools (such as computer applications, programs, etc.) can be used to select the appropriate structure and number of layers for the ANN 700, as well as the number of artificial neurons in each layer, and to select activation functions, loss functions, training procedures, etc. Once the initial model is designed, it can be trained using training data. Training data may include one or more datasets within which the ANN 700 can detect, determine, identify, or discover patterns. Training data can represent various types of information, including written, visual, audio, environmental context, operational attributes, etc. During training, the parameters of the artificial neurons 710 can be changed, such as minimizing or otherwise reducing the loss function or cost function. The training process can be repeated multiple times to fine-tune the ANN 700 with each iteration.

[0128] Various ANN model architectures are available for consideration. For example, in a feedforward ANN architecture, each artificial neuron 710 in a layer receives information from the previous layer and similarly generates information for the next layer. In a convolutional ANN architecture, some layers can be organized as filters that extract features from data (e.g., training data and / or input data). In a recursive ANN architecture, some layers may have connections that allow data to be processed across time, such as for processing information with temporal structure (e.g., time series data prediction).

[0129] In the autoencoder ANN architecture, compact representations of data can be processed, and models can be trained to make predictions or potentially reconstruct the original data from a reduced set of features. The autoencoder ANN architecture can be used for tasks related to dimensionality reduction and data compression.

[0130] Generative adversarial network (GAN) architectures can include generator ANNs and discriminator ANNs trained to compete against each other. GANs are ANN architectures that can be used for tasks related to generating synthetic data or improving the performance of other models.

[0131] Transformer ANN structures utilize an attention mechanism that enables the model to process input sequences in a parallel and efficient manner. The attention mechanism allows the model to focus on different parts of the input sequence at different times. This attention mechanism can be implemented using a series of layers called attention layers to compute, compute, determine, or select a weighted sum of input features based on the similarity between different elements of the input sequence. A transformer ANN structure may include a series of feedforward ANN layers that "learn" the non-linear relationship between the input and output sequences. The output of the transformer ANN structure can be obtained by applying a linear transformation to the output of the final attention layer. Transformer ANN structures are particularly useful for tasks involving sequence modeling or other similar processing.

[0132] Another example type of ANN structure is a model with one or more invertible layers. This type of model can be inverted or "unfolded" to reveal the input data used to generate the output of the layers.

[0133] Other examples of ANN model architectures include fully connected neural networks (FCNN) and long short-term memory (LSTM) networks.

[0134] ANN 700 or other ML models can be implemented in various types of processing circuits, as well as their memory and applicable instructions, for example, as described in this paper relative to... Figure 5 and Figure 6 As described. For example, the model can be implemented using general-purpose hardware circuitry such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). One or more ML accelerators, such as tensor processing units (TPUs), embedded neural processing units (eNPUs) or other dedicated processors, and / or field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., can also be used to develop ANN models. Various programming tools can be used to develop ANN models.

[0135] Various aspects of artificial intelligence model training

[0136] There are deployable ML models (such as...) Figure 7 The various model training techniques and processes used at some point before or after the ANN 700.

[0137] As part of the model development process, information may be collected or otherwise created in a suitable training data format for training the ML model accordingly. For example, training data may be collected or otherwise created relating to data on received / transmitted signal strength, interference, and resource usage, as well as any other relevant data that can be used to train the model to solve one or more problems or challenges in a communication system. In some cases, all or part of the training data may originate from one or more user equipment (UEs), one or more network entities, or one or more other devices in a wireless communication system. In some cases, all or part of the training data may be aggregated from multiple sources, such as one or more UEs, one or more network entities, the Internet, etc. For example, wireless network architectures (such as self-organizing networks (SONs) or mobile-driven test (MDT) networks) may be adapted to support data collection for ML model applications. In another example, training data may be generated or collected online, offline, or both online and offline by UEs, network entities, or other devices, and all or part of such training data may be transmitted or shared (real-time or near real-time), such as through store-and-forward functions. Offline training may refer to, for example, creating and using a static training dataset in a batch manner, while online training may refer to collecting and using training data in real-time or near real-time. For example, online or offline training can be used to train and / or fine-tune the ML model at a network device (e.g., a UE). For offline training, data collection and training can occur offline at the network side (e.g., at a base station or other network entity) or at the UE side. For online training, training of the ML model at the UE side can be performed locally at the UE or by a server device (e.g., a server hosted by the UE vendor) based on data provided from the UE to the server device, in a real-time or near real-time manner.

[0138] In some cases, all or part of the training data may be shared within the wireless communication system, or even shared (or obtained from outside) the wireless communication system.

[0139] Once the ML model has been trained on the training data, its performance can be evaluated. In some scenarios, evaluation / validation tests can be conducted using a validation dataset, which may include data not present in the training data, to compare the model's performance against a baseline or other benchmark information. If the model's performance is deemed unsatisfactory, fine-tuning the model may be beneficial, for example, by changing its architecture, retraining it on the data, or using different optimization techniques. Once the model's performance is deemed satisfactory, it can be deployed accordingly. In some cases, the model can be updated in some way, for example, all or part of the model can be changed or replaced, or it can undergo further training, to name just a few examples.

[0140] As for use in ANNs (such as Figure 7 As part of the training process of an ANN (Advanced Neural Network 700), parameters affecting the operation of artificial neurons and layers can be tuned. For example, backpropagation can be used to train an ANN by iteratively adjusting the weights and / or biases of certain artificial neurons associated with the error between the model's predicted output and the expected output, which may be known or otherwise considered acceptable. Backpropagation may include forward propagation, loss function, backward propagation, and parameter updates that can be performed during training iterations. This process can be repeated a certain number of times for each training dataset until the weights of the artificial neurons / layers are properly tuned.

[0141] Backpropagation, associated with the loss function, measures how well a model can predict the desired output for a given input. Optimization algorithms can be used during training to adjust weights and / or biases to reduce or minimize the loss function, which improves model performance. Various optimization algorithms exist that can be used with backpropagation or other training techniques. Some initial examples include gradient descent-based and stochastic gradient descent-based optimization algorithms. Stochastic gradient descent (or ascent) can be used to adjust weights / biases to minimize or otherwise reduce the loss function. Mini-batch gradient descent, a variant of gradient descent, involves updating weights / biases using mini-batch training data instead of the entire dataset. Momentum techniques can accelerate the optimization process by adding momentum terms to update or otherwise influence certain weights / biases.

[0142] Adaptive learning rate techniques adjust the learning rate of an optimization algorithm that is associated with one or more characteristics of the training data. Batch normalization techniques can be used to normalize the input to a model in order to stabilize the training process and potentially improve the model's performance.

[0143] The "drop-out" technique can be used to randomly discard some of the artificial neurons from the model during the training process, for example, to reduce overfitting and potentially improve the model's generalization.

[0144] The “early stopping” technique can be used to stop an ongoing training process early, such as when the performance of a model using a validation dataset begins to degrade.

[0145] Another example technique includes data augmentation, which generates additional training data by applying transformations to all or part of the training information.

[0146] Transfer learning techniques can be used, which involve using a pre-trained model as a starting point for training a new model. This can be useful when training data is limited or when there are multiple tasks that are related to each other.

[0147] Multi-task learning techniques can be used, which involve training a model to perform multiple tasks simultaneously to potentially improve the model's performance on one or more of these tasks. In some cases, hyperparameters can be input and applied during the training process.

[0148] Another example technique that can be useful for ML models is some form of "pruning." Pruning techniques, which can be performed during the training process or after the model has been trained, involve removing unnecessary (e.g., because they have no effect on the output), less necessary (e.g., because their effect on the output is negligible), or potentially redundant features from the model. In some cases, pruning techniques can reduce the complexity of the model or improve its efficiency without compromising its expected performance.

[0149] Pruning techniques can be particularly useful in the context of wireless communication, where available resources, such as power and bandwidth, may be limited. Some example pruning techniques include weight pruning, neuron pruning, layer pruning, structural pruning, and dynamic pruning. Pruning techniques can, for example, reduce the amount of data corresponding to a model that may need to be transmitted or stored.

[0150] Weight pruning techniques may involve removing some weights from a model. Neuron pruning techniques may involve removing some neurons from a model. Layer pruning techniques may involve removing some layers from a model. Structural pruning techniques may involve removing some connections between neurons in a model. Dynamic pruning techniques may involve adapting a pruning strategy for a model to one or more characteristics associated with the data or environment. For example, in some wireless communication devices, dynamic pruning techniques may be more aggressive in pruning models used in low-power or low-bandwidth environments and less aggressive in pruning models used in high-power or high-bandwidth environments. In some aspects, pruning techniques may also be applied to training data, such as to remove outliers. In some specific implementations, preprocessing techniques on all or part of the training dataset can improve model performance or facilitate faster model convergence. For example, training data may be preprocessed to alter or remove unnecessary, irrelevant, incorrect, or otherwise identifiable data. Such preprocessing of training data can, for example, lead to a reduction in potential overfitting or otherwise improve the performance of the trained model.

[0151] One or more of the example training techniques presented above can be used as part of the training process. As mentioned above, some example training processes that can be used to train ML models include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning techniques.

[0152] Decentralized, distributed, or shared learning, such as federated learning, enables training on data distributed across multiple devices or organizations without the need for centralized data or training. Federated learning can be particularly useful in data-sensitive or privacy-constrained situations, or when centralized data is impractical, inefficient, or expensive. For example, in the context of wireless communications, federated learning can be used to improve performance by allowing ML models to be trained on data collected from a wide range of devices and environments. For instance, ML models can be trained on data collected from a large number of wireless devices in a network, such as distributed wireless communication nodes, smartphones, or Internet of Things (IoT) devices, to improve network performance and efficiency. With federated learning, a user equipment (UE) or other device can receive a full or partial copy of the model and perform local training on such a copy of the model using locally available training data. Such devices can provide updated information about the locally trained model (e.g., trainable parameter gradients) to one or more other devices, such as network entities or servers, where updates from other similar devices (such as other UEs) can be aggregated and used to provide updates to shared models, etc. The federated learning process can be iteratively repeated until all or part of the model achieves a satisfactory level of performance. Federated learning enables devices to protect the privacy and security of local data while supporting collaboration on training and updating shared models, in whole or in part.

[0153] In some implementations, one or more devices or services may support processes related to the use, maintenance, activation, or reporting of ML models. In some cases, all or part of a dataset or model may be shared across multiple devices, for example, to provide or otherwise enhance or improve processing. In some examples, signaling mechanisms may be used at various nodes in a wireless network to signal capabilities for performing specific functions related to the ML model, support for a particular ML model, capabilities for collecting, creating, and transmitting training data, or other ML-related capabilities. ML models in wireless communication systems may, for example, support decisions related to wireless resource allocation or selection, wireless channel condition estimation, interference mitigation, beam management, positioning accuracy, energy saving, or modulation or decoding schemes. In some implementations, model deployment may be conducted jointly or individually at various network levels, such as central units (CUs), distributed units (DUs), radio units (RUs), etc.

[0154] Various aspects related to dynamic power loading

[0155] Various aspects of this disclosure provide techniques for dynamic power loading (e.g., transmit power allocation) of channels for transmitting modulated signals.

[0156] Figure 8An example transmit power allocation 800 is illustrated on n communication component channels 802a-n (hereinafter referred to as "channels 802"). In this example, the transmit power allocation 800 is represented according to a communication system having n channels 802. The transfer function of the communication system can represent the input-output relationship on the n channels 802, which may correspond to certain time-frequency resources for transmission. The input-output relationship on a given channel means that a transmitter, as the input side, transmits a signal on the channel, and a receiver, as the output side, receives a signal on the channel. In some aspects, a channel (e.g., channel 802a) may correspond to one or more subcarriers across a specific time interval (e.g., one or more symbols). As an example, for an OFDM scheme, the n channels 802 may correspond to OFDM resource elements 804a-n (collectively referred to as "resource elements 804") in an OFDM resource grid 806, for example, as described herein with respect to... Figures 4A to 4D As described. In some cases, each of the n channels 802 may correspond to a specific resource element (e.g., resource element 804a) in resource grid 806. In other cases, each of the n channels 802 may correspond to multiple resource elements 804 in resource grid 806. Each resource element 804 may represent a time-frequency cell in resource grid 806. For example, a resource element (e.g., resource element 804a) may represent one or more subcarriers 808 spanning time interval 810 (e.g., one or more symbols).

[0157] The transfer function of the i-th channel can be expressed as follows:

[0158] (1)

[0160] in These are output signals, such as received signals 812a and 812n obtained at the receiving entity (e.g., UE 104); This represents communication channels 802a and 802n; These are input signals, such as transmit signals 814a and 814n output at the transmitting entity (e.g., BS 102); and These are noise signals 816a and 816n in the communication channel. (Transmitted signal) This can be represented based on the modulation and distribution of the transmission power as follows:

[0161] (2)

[0163] in It refers to the transmit power 818a, 818n allocated to a given channel; and These are the subcarrier components of the modulated signals 820a and 820n for a given channel, such as resource element components, which may correspond to OFDM subcarriers and symbols as discussed above. As an example, It can represent the orthogonal subcarriers of the transmitted OFDM signal and correspond to a specific constellation in the modulation scheme. It may have a specific amplitude and / or phase determined by the modulation scheme (e.g., QPSK or QAM). This can depend on the total transmission power allocated for transmission, for example, ,in It is the normalized power of a given channel, and This is the total transmit power allocated to transmission. The total transmit power can be allocated to mitigate interference, reduce power consumption at the transmitter, avoid saturation at the receiver, and / or keep radio frequency (RF) radiation at a level safe for humans.

[0164] For MIMO communication, the transfer function of the i-th channel can be expressed as follows:

[0165] (3)

[0167] in It is the received signal vector of the i-th channel (e.g., resource element i), and its size is: [(R x number of antennas) × 1]; It is the receive channel matrix of the i-th channel (e.g., resource element i), and its size is: [(R x number of antennas) × (number of streams)]; It is the transmission signal vector of the i-th channel (e.g., resource element i), with a size of [(number of streams) × 1]; and Let be the received noise vector of the i-th channel (e.g., resource element i), with a size of [(R x number of antennas) × 1]. The transmitted signal vector can be represented as follows:

[0168] ,

[0169] in

[0170] ,

[0171] (4)

[0173] in It is the quantity of the flow; It refers to the quantity of resource elements; It is a portion of the power allocated to resource element i; and It is the vector of the transmitted symbols at resource element i, and its size is: [(number of streams) x 1].

[0174] In some respects, the transmit power of a specific component channel (For example, transmit power 812a, 812n) can be allocated based on certain signal propagation effects encountered in the component channel, including, for example, noise, interference, scattering, fading, Doppler effects, etc., as further described herein. Transmit power for a specific channel (For example, transmit power 812a) can be allocated to the modulated signal within a time interval (such as time interval 810). Component channels (e.g., corresponding to channel 802a). In some cases, the transmit power of a specific channel... Assignments can be made using a function of one or more channel properties (e.g., signal quality, which may include signal-to-noise ratio (SNR)), as described in this paper relative to... Figure 9 Further as described. In some cases, the transmit power of a particular channel... AI models can be used for allocation, for example, as described in this paper relative to... Figure 10 As described.

[0175] It should be noted that transmit power allocation 800 is an example of allocating transmit power across resource element 804 (e.g., Each resource element 804). Other transmit power allocation schemes can be applied as a supplement to or alternative to the example transmit power allocation 800. In some cases, the transmit power of the transmitted component channels can be allocated on each group of subcarriers or resource elements (e.g., allocated to each group of resource elements 804 within a time interval). ).

[0176] In some respects, the UE and network entities may employ one or more error correction techniques associated with dynamic transmit power allocation. Since each of the UE and network entities can independently determine the dynamic power allocation for transmission (e.g., the transmitting entity and the receiving entity determine separate power allocations), mismatches in the dynamic power allocations determined at the UE and network entities are possible. For example, in downlink communication, the UE may determine a different transmit power allocation for demodulation than the one used for transmission at the network entity, or vice versa (for uplink communication).

[0177] To detect mismatches between power allocations and perform error correction, a transmitting entity (e.g., a network entity) may send an indication to a receiving entity (e.g., a UE) of the power allocations used at the transmitting entity. The receiving entity may compare the indication of the power allocations with the power allocations determined at the receiving entity. If a mismatch exists between these power allocations, the receiving entity may adjust the transmit power used for demodulation.

[0178] As an example, each transmit power in the transmitted component channel can be quantized as a binary number, for example, using 4 bits to represent 16 values ​​in the range of 0 to 1. The binary numbers of transmit powers can be combined (e.g., concatenated) into a bit stream. The transmitting entity can encode the bit stream using an error correction encoder, including, for example, convolutional codes, Reed-Solomon codes, or any other suitable error correction code. The transmitting entity can determine the Cyclic Redundancy Check (CRC) of the encoded bit stream. The transmitting entity can transmit the CRC of the encoded bit stream to the receiving entity. The receiving entity can perform the same encoding and CRC generation on the power allocation determined at the receiving entity. The receiving entity can compare the calculated CRC with the CRC obtained from the transmitting entity. If these CRCs are the same, it likely means there is no mismatch between the power allocations of the transmitting and receiving entities. If there is a mismatch between these CRCs, the receiving entity can correct any errors based on the error correction capabilities of the encoder / decoder.

[0179] Example power loading based on signal quality function

[0180] In some respects, wireless communication devices may use a function of one or more channel properties (such as the signal quality of each channel) to determine the transmission power of each component of the communication signal (e.g., the modulated signal) of the channel. This function can be used to configure the transmitter and receiver. The transmitter can use this function to determine the individual transmit power, and the receiver can use this function to demodulate the received signal.

[0181] As an example, a wireless communication device (e.g., a UE or a network entity) may determine the signal quality of each transmitted OFDM subcarrier or group of subcarriers. Signal quality may be or include the signal-to-noise ratio (SNR). In some aspects, the network entity and the UE may calculate the signal quality for each resource element. To reduce signaling overhead, the UE may send an indication to the network entity of the broadband noise covariance observed (measured) at the UE. In some aspects, the UE may transmit an indication to the network entity of the signal quality for each component channel (e.g., subcarrier, resource element, subcarrier group, and / or resource element group). For example, the UE may report the broadband noise covariance periodically via uplink control information (UCI), which may be pre-configured or configured by the network entity. In some cases, the network entity may send an indication to the UE to report the broadband noise covariance non-periodically, for example, via downlink control information (DCI) and / or in response to a triggering event.

[0182] In some cases, the signal quality of a given component channel (e.g., a resource element or a group of resource elements) can be determined based on... The arithmetic mean of the diagonal matrix is ​​expressed as follows:

[0183] (5)

[0185] in It represents the signal quality of the corresponding component channel; It refers to the number of flows (layers); ; It is an element (i, i) of M; It is the reciprocal of the broadband noise covariance reported by the UE; It is the channel matrix; and It is the transpose and conjugate of the channel matrix. The constellation can be known to both the network entity and the UE. In some cases, the network entity and the UE may recalculate it for each OFDM symbol.

[0186] Figure 9 An example operation 900 for allocating transmit power across component channels (e.g., resource elements or groups of resource elements) for modulation transmission is illustrated. In this example, transmit power may be allocated based on a function of the signal quality of each component channel (such as a mercury / waterfilling technique or a water-filling technique). Operation 900 may be performed by a wireless communication device (such as UE 104 and / or BS 102 or their decomposition entities). The example mercury / waterfilling technique is described in Lozano, A. et al., Mercury / Waterfilling: Optimum Power Allocation with Arbitrary Input Constellations, International Symposium on Information Theory, Proceedings, September 4, 2005, 1773-1777, IEEE.

[0187] At 902, the wireless device can determine the signal quality of each component channel in the component channels. ), and can be based on the reciprocal of the corresponding signal quality ( (represented as solid 908) and modulation (represented as mercury 914) to effectively adjust each channel in the channel at the effective water level 910 (e.g., The following is the available power margin (e.g., the injected water), where This is a factor used to determine the water level 910 (which corresponds to the total available power allocated to the channel), and can be determined as further described below. The available power margin below the effective water level 910 can represent the allocated transmit power that can be set for individual component channels. For example, for each component channel in the component channels, containers 912a, 912n can be filled with unit-based solid 908 until ( The height of the solid 908 can effectively replace a portion of the effective water level 910 in the corresponding containers 912a, 912n of the component channel.

[0188] At 904, for each channel in the channel, the available power margin up to the effective level 910 can be effectively adjusted based on the representation of the input distribution (e.g., modulation). For example, mercury 914 can be poured into each of the channel containers 912a-912n until the height of mercury 914 and solid 908 is reached. )achieve 916. Given any input distribution of channel (i), it can be represented as follows:

[0189] (6)

[0191] in It is the effective transmit power of the i-th channel; and It is the inverse minimum mean square error of the i-th channel. The minimum mean square error of a given channel (i) can be expressed as follows:

[0192] (7)

[0194] Where m is the constellation size of the modulation scheme; y is the l-th symbol of the constellation; and y is the received power of the received signal on the i-th channel. The following expression can be used as a constraint to determine:

[0195] (8)

[0197] in This refers to the number of channel or resource elements used in modulation transmission. In some respects, this can be pre-calculated for each constellation. The value is then filled into the lookup table.

[0198] At 906, for each channel in the channel, the transmission power can be the remaining power up to the water level 910 (e.g., 918). For example, water 918 can be filled in each of containers 912a to 912n until water 918 reaches the water level 910 (e.g., The water height 920 above the mercury 914 in the i-th containers 912a and 912n is equal to the transmission power p allocated to channel (i). i It should be noted that Operation 900 is an example technique for allocating separate transmit power to each component channel of the modulated signal. As a relative... Figure 9Other technologies, such as water injection techniques, may be used as a supplement or alternative to the described mercury injection technique.

[0199] Example of AI-based dynamic power loading

[0200] In some respects, AI models can be used to determine the transmit power for each component channel of a modulated signal. AI models can be used to configure transmitters and receivers. Transmitters can use AI models to determine the individual transmit power allocated to each component channel of the modulated signal, and receivers can use AI models to demodulate the received signal.

[0201] As an example, a network entity may transmit to the UE an indication of the AI ​​model to be used for dynamic power loading. The AI ​​model may be indicated by neural network coefficients (e.g., weights and / or coefficients associated with synapses in the neural network) and / or an index or identifier identifying a particular AI model. The UE may obtain the indication of the AI ​​model via control signaling, including, for example, DCI, sidelink control information (SCI), radio resource control signaling, media access control signaling, and / or system information. In some cases, the UE may obtain the indication of the AI ​​model as part of establishing an RRC connection or before establishing an RRC connection. The UE and the network entity may use the same AI model to determine the transmit power for each component channel used for transmission and / or demodulation, as described herein with respect to... Figure 12 As further described herein. In some respects, the UE may report noise covariance to network entities, as described herein.

[0202] Figure 10 An example of AI-based dynamic power loading 1000 is illustrated. In this example, AI model 1002 obtains input data 1004 associated with dynamic power loading. AI model 1002 can be... Figure 6An example of ML model 630. Input data 1004 may include a modulation and decoding scheme (MCS) 1008 for transmission. In some aspects, MCS 1008 may define or indicate the total number of transmit component channels for which individual transmit power is to be allocated. In some aspects, input data 1004 may include channel attributes for each component channel of the modulated signal. For example, input data 1004 may include the signal quality (e.g., SNR) of each component channel 1010, which may be calculated as described above. The set of component channels 1010 associated with the channel attributes may include one or more resource elements and / or one or more OFDM subcarriers. In some aspects, input data 1004 may include indications of the quality of service (QoS) level or priority 1012 associated with transmission, such as a QoS identifier. For example, AI model 1002 can be trained to allocate more transmission power to transmissions with high QoS levels or priorities (e.g., Ultra-Reliable Low-Latency Communication (URLLC), Extended Reality (XR) communication, etc.); and AI model 1002 can be trained to allocate less power to transmissions with low QoS levels or priorities (e.g., voice chat, text messaging services, etc.). In some cases, as a supplement to or alternative to those described above, input data 1004 may include any other suitable features or parameters, such as the expected received signal power at the receiving entity, channel capacity, maximum allowed transmission power, one or more gain factors for a specific component channel (e.g., subcarriers or groups of subcarriers), etc.

[0203] Trainable AI model 1002 can predict or infer the dynamic power settings of modulated signals (e.g., P). i 818a–n), for example, as this article relates to Figure 8 As described above, AI model 1002 provides output data 1006, which includes the transmit power of each component channel (e.g., resource element and / or group of resource elements) of the modulated signal. As discussed above, the transmit power may depend on the total transmit power defined for transmission.

[0204] Aspects of training a machine learning model for dynamic power loading

[0205] Figure 11 An example of a system 1100 for training an AI model 1102 to determine the transmit power of each component channel is illustrated. System 1100 may be or include a model training host (e.g., Figure 5 (Model training host 502). In some respects, AI model 1102 can be relative to the model training host 502 in this paper. Figures 5 to 7 and Figure 10 An example of the described AI model. The model training host can train AI model 1102, as described in this paper relative to... Figure 7 The discussion.

[0206] The model training host obtains training data 1104, which includes training input data 1106 and, in some cases, corresponding labels 1108 for the training input data 1106. The training input data 1106 may include one or more channel attributes, MCS, and / or any other parameters associated with dynamic power loading for each component channel of the modulated signal, such as those described herein with respect to… Figure 10 As described. In some cases, the training input data 1106 may be simulated (e.g., computer-generated) and / or collected from measurements performed on one or more wireless communication devices (e.g., one or more UEs and / or network entities). The training input data 1106 may be obtained from various communication scenarios, including, for example, various combinations of MCS, channel conditions (e.g., interference, scattering, fading, etc.), UE mobility scenarios, frequency ranges (e.g., FR1 and / or FR2), line-of-sight communication paths, non-line-of-sight communication paths, beamform, beam orientation, etc.

[0207] The model training host can use label 1108 to evaluate the performance of AI model 1102 and adjust the configuration of AI model 1102 (e.g., Figure 7 The weights, coefficients, activation functions, and / or synapses of the ANN 700 are as described herein. Label 1108 may be or include the expected output corresponding to the training input data 1106. In some cases, label 1108 may be or include the transmit power obtained from function-based techniques such as water-filling and / or mercury-filling techniques. For example, for training input data of a specific MCS and channel condition for each component channel, label 1108 may include the expected value of the transmit power for each component channel. Each label in label 1108 may be associated with at least one set of training input data 1106. In some cases, each label in label 1108 may include the expected value of the transmit power for each component channel, which achieves target performance at the receiving entity. For example, target performance may be or include specified signal quality and / or signal strength of the received signal.

[0208] The model training host provides training input data 1106 to the AI ​​model 1102. For example, the model training host may provide the AI ​​model 1102 with a set of SNRs for component channels and transmit MCS, where the set of SNRs corresponds to a specific communication scenario as discussed above. The AI ​​model 1102 provides output data 1110, which may include the transmit power of each transmitted component channel.

[0209] At 1112, the model training host can evaluate the performance of AI model 1102. For example, the model training host can evaluate the quality and / or accuracy of output data 1110. In some aspects, the model training host can evaluate the performance of the AI ​​model at least in part based on the output data 1110 and / or the label 1108 using a cost or loss function 1114 (hereinafter referred to as "cost function 1114"). In some aspects, cost function 1114 can calculate the cost of output data 1110 based on one or more performance metrics, including, for example, the difference between label 1108 and output data 1110, received signal quality, received signal strength, block error rate (BLER) of received signal, etc. In some aspects, cost function 1114 can calculate the cost based on the difference between a performance metric of dynamic power loading and a target performance. The model training host can adjust AI model 1102 (e.g., any weights in the weights of layers of a neural network) to reduce the cost associated with AI model 1102. As an example, adjusting AI model 1102 may involve adjusting any weights in the weights of the neural network, any activation functions applied at the neurons, and / or any synapses coupled to the neurons, etc.

[0210] The model training host can continue to provide training input data 1106 to the AI ​​model 1102 and adjust the AI ​​model 1102 until its cost meets a threshold and / or reaches a minimum cost. In some aspects, the model training host can apply the Adam optimizer to minimize the cost associated with the transmission power. In some cases, the model training host can determine whether the output data 1110 matches the corresponding label of the training input data 1106. For example, the model training host can determine whether the predicted transmission power is correct based on the label (e.g., expected transmission power) associated with the SNR set provided as input to the AI ​​model 1102. In some aspects, the model training host can train the AI ​​model 1102 to meet certain criteria. In some cases, the model training host can train the AI ​​model 1102 to meet the target performance of the communication channel.

[0211] In some respects, a model training host can train multiple AI models. These AI models can be trained to have different performance characteristics, different input-output schemes, and / or different applications (e.g., communication scenarios). For example, AI models can be trained to predict the transmission power of each component channel at different levels of accuracy (e.g., 85%, 90%, or 99% accuracy) that meet performance objectives, different latency (e.g., processing time for predicting transmission power), and / or different throughput (e.g., the ability to predict transmission power from one or more sets of input data).

[0212] In some cases, the model training host can train AI models for specific applications or communication scenarios (e.g., based on channel conditions, UE mobility, line-of-sight communication, etc.). For example, an AI model can be trained to predict the transmit power of the component channel of a first MCS (e.g., QPSK), and another AI model can be trained to predict the transmit power of the component channel of a second MCS (e.g., QAM).

[0213] In some cases, AI models can be trained to calculate the transmit power of a component channel using a specific input-output scheme. For example, a first AI model can be configured to calculate the transmit power using the SNR of each transmitted resource element, while a second AI model can be configured to calculate the transmit power using the SNR of each group of transmitted resource elements. Therefore, wireless communication devices can select AI models capable of calculating transmit power based on certain performance characteristics, applications, and / or input-output schemes as described above.

[0214] Example operation of dynamic power loading in a communication system

[0215] Figure 12 A process flow 1200 for communication between network entity 1202 and user equipment (UE) 1204 in the system is described. In some aspects, network entity 1202 may be relative to... Figure 1 and Figure 3 The BS 102 depicted and described, or relative to Figure 2 Examples of decomposed base stations depicted and described. Similarly, UE 1204 could be about... Figure 1 and Figure 3 An example of UE 104 is depicted and described. However, in other respects, UE 1204 may be another type of wireless communication device, and network entity 1202 may be another type of network entity or network node, such as those described herein. Note that any operation illustrated with dashed lines indicates that the operation may be optional or alternative.

[0216] At 1206, UE 1204 transmits capability information to network entity 1202, indicating that 1204 is capable of performing the dynamic power loading described herein, for example, using function-based power loading techniques, AI-based power loading techniques, and / or any suitable power loading techniques. In some cases, the capability information may indicate that UE 1204 is capable of performing function-based power loading, for example, as described herein with respect to... Figure 9 As described. For example, capability information may indicate the types of functions supported by UE 1204 (e.g., mercury injection and / or water injection). In some cases, capability information may indicate that UE 1204 is capable of performing AI-based power loading, for example, as described herein relative to... Figure 10As described. In some respects, capability information can indicate the type of AI model (e.g., neural network) supported by UE 1204.

[0217] At 1208, UE 1204 obtains a configuration from network entity 1202 that indicates one or more parameters for allocating transmit power individually to each component channel (e.g., subcarrier or subcarrier group) during each transmission time interval. In some respects, this configuration may indicate the time interval for each dynamic power allocation, such as one or more OFDM symbols (e.g., 1 OFDM symbol or 2 OFDM symbols) or one or more time slots (e.g., 1 time slot or 2 time slots).

[0218] This configuration may indicate the type of dynamic power loading used for communication, such as function-based or AI-based dynamic power loading. In some aspects, this configuration may indicate one or more AI models used to determine transmit power allocation. The indication of the AI ​​model may be or include information reproducible to the AI ​​model (e.g., weights and / or structural information). The indication of the AI ​​model may be or include an index or identifier identifying the specific AI model used to determine transmit power allocation. In some cases, this configuration may indicate the input-output scheme of the AI ​​model, such as the set of input features from the available set of input features (e.g., SNR and MCS for each component channel). This configuration may indicate when channel state feedback is transmitted to network entity 1202 and / or what is included in the channel state feedback (e.g., periodically, semi-persistently, and / or aperiodically). The channel state feedback may include noise covariance reporting, such as wideband noise covariance that can be used to determine the signal quality (e.g., SNR) of the component channel. This configuration can instruct that broadband noise covariance matrix reports will be transmitted periodically (e.g., in time slots (e.g., 10, 20, 40, 80 time slots)).

[0219] In some respects, this configuration may indicate one or more parameters for detecting power allocation mismatch between UE 1204 and network entity 1202. This configuration may indicate the total number of bits used to quantize the power of each transmitted component channel (e.g., resource element or group of resource elements). This configuration may indicate details of the code used to encode the power allocation bits and / or details of the CRC used to calculate the CRC on the encoded power allocation bits.

[0220] At 1210, UE 1204 establishes a communication link (e.g., an RRC connection) with network entity 1210. In some cases, UE 1204 and / or network entity 1202 may perform dynamic power loading as described herein for communication on the channel associated with the RRC connection.

[0221] At 1212, UE 1204 transmits channel state feedback to network entity 1202, including, for example, a channel attribute report. The channel attribute report may include the wideband noise covariance of a frequency band (e.g., one or more frequency resources), the wideband signal quality of the frequency band, and the subcarrier signal quality for each component channel (e.g., OFDM subcarriers). UE 1204 may transmit the channel attribute report periodically or in response to aperiodic triggers (e.g., DCI triggers and / or specific events). In some cases, UE 1204 may report channel attributes in response to receiving a DCI message from network entity 1202. The channel state feedback may be transmitted via uplink control information (UCI). In some cases, the wideband noise covariance report may have… The size of the bits, where This is the total number of Rx antennas at the UE.

[0222] At 1214, network entity 1202 determines one or more channel attributes (e.g., signal quality or SNR) for each transmitted component channel (e.g., resource element or group of resource elements) based at least in part on the channel attribute report obtained at 1212. As an example, one or more channel attributes for each component channel can be determined according to expression (5).

[0223] At 1216, network entity 1202 determines the transmit power of each component channel based at least in part on one or more channel attributes of each component channel determined at 1214. The transmit power may be determined using function-based techniques and / or AI-based techniques, depending on the configuration communicated at 1208.

[0224] At 1218, network entity 1202 transmits an indication to UE 1204 of the transmit power allocation determined at 1216. In some respects, this indication may be transmitted via control signaling, including, for example, DCI. In some cases, the indication may include a coded quantized CRC of the transmit power allocation as discussed above.

[0225] At 1220, network entity 1202 transmits one or more signals to UE 1204 using the transmit power determined according to dynamic transmit power allocation, for example, as described herein with respect to... Figure 8 As described. For example, network entity 1202 may transmit signals across multiple symbols in multiple subcarriers (e.g., component channels) during a transmission timing. Network entity 1202 may use independent transmit power (e.g., P) for each OFDM subcarrier in each symbol of the transmission timing. i The transmit power can compensate for the independent signal propagation effects encountered on OFDM subcarriers.

[0226] At 1222, UE 1204 receives a signal from network entity 1202, and UE 1204 demodulates the signal based on dynamic transmit power allocation. As an example, at 1224, UE 1204 determines the channel attributes of each component channel; and at 1226, UE 1204 determines the transmit power of each component channel based on the configuration received at 1208. Then, using the transmit power, UE 1204 decodes the constellation of the modulated signal.

[0227] At 1228, UE 1204 communicates with network entity 1202 via an uplink communication channel using dynamic power loading as described herein. For example, UE 1204 may use function-based and / or AI-based techniques as described herein to determine the transmit power for each component channel. UE 1204 transmits one or more signals to network entity 1202 using the transmit power determined according to the dynamic transmit power allocation, for example, as described herein relative to... Figure 8 As described, network entity 1202 demodulates the received signal based on dynamic transmit power allocation. It should be noted that the dynamic power loading described herein can also be applied to sidelink communication between multiple UEs.

[0228] The dynamic power loading described herein enables improved wireless communication performance between UE 1204 and network entity 1202, such as increased throughput, reduced latency, enhanced signal quality, and / or enhanced signal strength.

[0229] Example operation of dynamic power loading

[0230] Figure 13 It shows a device (such as) Figure 1 and Figure 3 Method 1300 for wireless communication of UE 104.

[0231] Method 1300 begins at block 1305 and obtains a configuration indicating how to allocate transmit power (e.g., P) individually to each of a plurality of frequency resource sets within a time interval. i One or more first parameters of ). In some respects, a frequency resource set may include one or more resource elements, for example, as described herein with respect to Figures 4A to 4D and Figure 8As described. In some aspects, the time interval may be or include one or more symbols or one or more time slots. In some aspects, multiple frequency resource sets are located in at least one of a carrier or a bandwidth portion of a carrier. In some aspects, one or more first parameters include one or more of the following: the total number of frequency resources in each of the multiple frequency resource sets; the duration of the time interval; the power allocation type used to allocate transmit power; one or more coefficients of an AI model used to allocate transmit power; or the total transmit power allocated across the multiple frequency resource sets within the time interval.

[0232] Then, method 1300 proceeds to block 1310, using a transmit power at least partially based on the configuration to transmit one or more signals. In some aspects, block 1310 includes: transmitting one or more signals on a plurality of frequency resource sets using a corresponding transmit power for each of the plurality of frequency resource sets, each corresponding transmit power based on the configuration. In some aspects, block 1310 includes: acquiring one or more signals; and demodulating one or more signals based on a corresponding transmit power for each of the plurality of frequency resource sets, each corresponding transmit power based on the configuration. In some aspects, method 1300 further includes determining a corresponding transmit power for each of the plurality of frequency resource sets based on the noise covariance of the plurality of frequency resource sets.

[0233] In some aspects, method 1300 also includes monitoring multiple sets of frequency resources. In some aspects, method 1300 also includes transmitting an indication of the noise covariance of the multiple sets of frequency resources.

[0234] In some respects, method 1300 also includes obtaining an indication of the periodicity of the noise covariance used to report multiple sets of frequency resources.

[0235] In some aspects, method 1300 further includes conveying (e.g., transmitting or obtaining) an indication of transmit power allocated for each of a plurality of frequency resource sets. In some aspects, method 1300 further includes comparing a first CRC associated with the transmit power with a second CRC, the indication of the allocated transmit power including the second CRC.

[0236] In some aspects, method 1300 further includes adjusting at least one of the transmission powers in response to any difference between the first CRC and the second CRC. In some aspects, adjusting at least one of the transmission powers includes: decoding an indication of allocated transmission power; and adjusting at least one of the transmission powers based at least in part on the decoded indication of allocated transmission power.

[0237] In some respects, the indication of transmit power allocated for each of the multiple frequency resource sets is configured according to one or more second parameters, which include one or more of the following: the number of quantized power levels for each allocated transmit power; the number of bits for the indication of each allocated transmit power; the encoding type for the indication of the allocated transmit power; or the CRC for the indication of the allocated transmit power.

[0238] In some respects, method 1300 also includes transmission capability information that indicates the device's ability to allocate transmission power individually to different sets of frequency resources.

[0239] In some respects, method 1300 further includes: determining the transmit power of each of the plurality of frequency resource sets based on the signal quality of each of the plurality of frequency resource sets, for example, as described herein relative to... Figure 9 As described.

[0240] In some respects, method 1300 also includes determining the transmit power of each of the multiple frequency resource sets by an AI model, for example, as described herein relative to... Figure 10 As described.

[0241] In some respects, method 1300 or any aspect thereof may be made possible by means of a device (such as...) Figure 15 The communication device 1500 performs the method, which includes various components operable to, configured to, or adapted to perform the method 1300. The communication device 1500 is described in further detail below.

[0242] It should be noted that Figure 13 This is merely one example of a method, and other methods that include fewer, additional, or alternative operations may also be consistent with this disclosure.

[0243] Figure 14 It shows a device (such as) Figure 1 and Figure 3 BS 102 or relative to Figure 2 The method for wireless communication using the decomposed base station discussed in the discussion 1400.

[0244] Method 1400 begins at block 1405 and transmits a configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval. In some aspects, a frequency resource set may include one or more resource elements, for example, as described herein with respect to… Figures 4A to 4D and Figure 8As described. In some aspects, the time interval may be or include one or more symbols or one or more time slots. In some aspects, multiple frequency resource sets are located in at least one of a carrier or a bandwidth portion of a carrier. In some aspects, one or more first parameters include one or more of the following: the total number of frequency resources in each of the multiple frequency resource sets; the duration of the time interval; the power allocation type used to allocate transmit power; one or more coefficients of an AI model used to allocate transmit power; or the total transmit power allocated across the multiple frequency resource sets within the time interval.

[0245] Then, method 1400 proceeds to block 1410, using a transmit power at least partially based on the configuration to transmit one or more signals. In some aspects, block 1410 includes transmitting one or more signals on multiple frequency resource sets using a corresponding transmit power for each of the multiple frequency resource sets, each corresponding transmit power based on the configuration. In some aspects, block 1410 includes: acquiring one or more signals; and demodulating one or more signals based on a corresponding transmit power for each of the multiple frequency resource sets, each corresponding transmit power based on the configuration. In some aspects, method 1400 further includes determining a corresponding transmit power for each of the multiple frequency resource sets based on the noise covariance of the multiple frequency resource sets.

[0246] In some respects, method 1400 also includes obtaining an indication of the noise covariance (e.g., broadband noise covariance) for multiple sets of frequency resources.

[0247] In some respects, method 1400 also includes transmitting an indication of the periodicity of the noise covariance used to report multiple sets of frequency resources.

[0248] In some aspects, method 1400 further includes conveying an indication of transmit power allocated for each of a plurality of frequency resource sets. In some aspects, method 1400 further includes comparing a first CRC associated with the transmit power with a second CRC, the indication of the allocated transmit power including the second CRC. In some aspects, method 1400 further includes adjusting at least one of the transmit powers in response to any difference between the first CRC and the second CRC. In some aspects, adjusting at least one of the transmit powers includes: decoding the indication of the allocated transmit power; and adjusting at least one of the transmit powers based at least in part on the decoded indication of the allocated transmit power.

[0249] In some respects, the indication of transmit power allocated for each of the multiple frequency resource sets is configured according to one or more second parameters, which include one or more of the following: the number of quantized power levels for each allocated transmit power; the number of bits for the indication of each allocated transmit power; the encoding type for the indication of the allocated transmit power; or the CRC for the indication of the allocated transmit power.

[0250] In some respects, method 1400 also includes obtaining capability information that indicates the device is capable of allocating transmission power individually to different sets of frequency resources.

[0251] In some aspects, method 1400 also includes determining the transmit power of each of the plurality of frequency resource sets based on the signal quality of each of the plurality of frequency resource sets, for example, as described herein with respect to Figure 9 As described.

[0252] In some respects, method 1400 also includes determining the transmit power of each of the multiple frequency resource sets by an artificial intelligence model, for example, as described herein relative to... Figure 10 As described.

[0253] In some respects, method 1400 or any aspect thereof may be made possible by means of a device (such as...) Figure 16 The communication device 1600 performs the method, which includes various components operable to, configured to, or adapted to perform the method 1400. The communication device 1600 is described in further detail below.

[0254] It should be noted that Figure 14 This is merely one example of a method, and other methods that include fewer, additional, or alternative operations may also be consistent with this disclosure.

[0255] Example communication device

[0256] Figure 15 Various aspects of the example communication device 1500 are described. In some aspects, the communication device 1500 is user equipment, such as those described above relative to... Figure 1 and Figure 3 The UE 104 described.

[0257] Communication device 1500 includes a processing system 1502 coupled to a transceiver 1538 (e.g., a transmitter and / or receiver). Transceiver 1538 is configured to transmit and receive signals for communication device 1500 via antenna 1540, such as various signals as described herein. Processing system 1502 may be configured to perform processing functions of communication device 1500, including processing signals received by communication device 1500 and / or to be transmitted by the communication device.

[0258] Processing system 1502 includes one or more processors 1504. In various aspects, the one or more processors 1504 may represent one or more of a receive processor 358, a transmit processor 364, a TX MIMO processor 366, and / or a controller / processor 380, as relative to... Figure 3 As described. One or more processors 1504 are coupled to a computer-readable medium / memory 1520 via a bus 1536. In some aspects, the computer-readable medium / memory 1520 is configured to store instructions (e.g., computer-executable code) that, when executed by one or more processors 1504, enable one or more processors 1504 to execute and cause the one or more processors to perform relative to... Figure 13 The described method 1300 or any aspect thereof, including regarding Figure 13 Any additional operations described. Note that references to processors performing the functions of communication device 1500 may include one or more processors, such as performing the functions of communication device 1500 in a distributed manner.

[0259] In the depicted example, computer-readable medium / memory 1520 stores code 1522 for acquisition, code 1524 for communication, code 1526 for determination, code 1528 for monitoring, code 1530 for transmission, code 1532 for comparison, and code 1534 for adjustment. Processing of codes 1522 to 1534 enables communication device 1500 to perform and allows the communication device to perform relative to Figure 13 The described method 1300 or any aspect related to that method.

[0260] One or more processors 1504 include circuitry configured to implement (e.g., execute) code stored in computer-readable medium / memory 1520. This circuitry includes circuitry 1506 for acquisition, circuitry 1508 for communication, circuitry 1510 for determination, circuitry 1512 for monitoring, circuitry 1514 for transmission, circuitry 1516 for comparison, and circuitry 1518 for adjustment. Processing using circuitry 1506 to 1518 enables communication device 1500 to perform and allow the communication device to perform relative to… Figure 13The described method 1300 or any aspect related to that method.

[0261] More generally, components used for conveying, sending, transmitting, or outputting for transmission may include Figure 3 The UE104 illustrated includes a transceiver 354, an antenna 352, a transmit processor 364, a TX MIMO processor 366, and / or a controller / processor 380. Figure 15 The transceiver 1538 and / or antenna 1540 of the communication device 1500 and / or Figure 15 The communication device 1500 includes one or more processors 1504. Components used for communication, receiving, monitoring, or acquiring may include... Figure 3 The UE 104 illustrated includes a transceiver 354, an antenna 352, a receiver processor 358, and / or a controller / processor 380. Figure 15 The transceiver 1538 and / or antenna 1540 of the communication device 1500 and / or Figure 15 The communication device 1500 includes one or more processors 1504. Components for determining, monitoring, comparing, or adjusting may include... Figure 3 The controller / processor 380 and / or of the illustrated UE 104 Figure 15 One or more processors 1504 of the communication device 1500 in the middle.

[0262] Figure 16 Various aspects of the example communication device 1600 are described. In some aspects, the communication device 1600 is a network entity, such as... Figure 1 and Figure 3 BS 102 or about Figure 2 The decomposed base station under discussion.

[0263] Communication device 1600 includes a processing system 1605 coupled to a transceiver 1685 (e.g., a transmitter and / or receiver) and / or a network interface 1695. Transceiver 1685 is configured to transmit and receive signals for communication device 1600 via antenna 1690, such as various signals as described herein. Network interface 1695 is configured to transmit and receive signals for communication device 1600 via a communication link (such as those described herein). Figure 2 The processing system 1605 is configured to perform processing functions of the communication device 1600, including processing signals received by the communication device 1600 and / or to be transmitted by the communication device 1600.

[0264] Processing system 1605 includes one or more processors 1610. In various aspects, the one or more processors 1610 may represent one or more of the following: receive processor 338, transmit processor 320, TX MIMO processor 330, and / or controller / processor 340, as relative to... Figure 3 As described. One or more processors 1610 are coupled to a computer-readable medium / memory 1645 via a bus 1680. In some aspects, the computer-readable medium / memory 1645 is configured to store instructions (e.g., computer-executable code) that, when executed by one or more processors 1610, enable one or more processors 1610 to execute and cause the one or more processors to perform relative to... Figure 14 The described method 1400 or any aspect thereof, including regarding Figure 14 Any additional operations described. Note that references to the processor of the communication device 1600 performing the function may include one or more processors of the communication device 1600, such as those performing the function in a distributed manner.

[0265] In the depicted example, computer-readable medium / memory 1645 stores code 1650 for transmission, code 1655 for communication, code 1660 for determination, code 1665 for acquisition, code 1670 for comparison, and code 1675 for adjustment. Processing of codes 1650 to 1675 enables communication device 1600 to execute and allows the communication device to perform relative to... Figure 14 The described method 1400 or any aspect related to that method.

[0266] One or more processors 1610 include circuitry configured to implement (e.g., execute) code stored in a computer-readable medium / memory 1645. This circuitry includes circuitry 1615 for transmission, circuitry 1620 for communication, circuitry 1625 for determination, circuitry 1630 for acquisition, circuitry 1635 for comparison, and circuitry 1640 for adjustment. Processing using circuitry 1615 to 1640 enables communication device 1600 to perform and allow the communication device to perform relative to… Figure 14 The described method 1400 or any aspect related to that method.

[0267] More generally, components used for conveying, sending, transmitting, or outputting for transmission may include Figure 3 The BS102 illustrated includes a transceiver 332, an antenna 334, a transmit processor 320, a TX MIMO processor 330, and / or a controller / processor 340. Figure 16 The transceiver 1685 and / or antenna 1690 of the communication device 1600 and / or Figure 16One or more processors 1610 of the communication device 1600. Components for transmitting, receiving, or acquiring may include... Figure 3 The BS 102 illustrated includes transceiver 332, antenna 334, receiver processor 338, and / or controller / processor 340. Figure 16 The transceiver 1685 and / or antenna 1690 of the communication device 1600 and / or Figure 16 One or more processors 1610 of the communication device 1600. Components for determining, comparing, or adjusting may include... Figure 3 The controller / processor 340 of the illustrated BS 102 and / or Figure 16 One or more processors 1610 of the communication device 1600 in the middle.

[0268] Example Terms

[0269] Specific implementation examples are described in the following numbered clauses:

[0270] Clause 1: A method for wireless communication by a device, the method comprising: obtaining a configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval; and using the transmit power at least in part based on the configuration to transmit one or more signals.

[0271] Clause 2: The method according to Clause 1 further includes determining the corresponding transmit power of each of the plurality of frequency resource sets based on the noise covariance of the plurality of frequency resource sets.

[0272] Clause 3: The method according to Clause 2 further includes: monitoring the plurality of frequency resource sets; and transmitting an indication of the noise covariance of the plurality of frequency resource sets.

[0273] Clause 4: The method according to any one of Clauses 1 to 3, the method further comprising: obtaining an indication of the periodicity of the noise covariance used to report the plurality of frequency resource sets.

[0274] Clause 5: The method according to any one of Clauses 1 to 4, wherein the plurality of frequency resource sets are located in at least one of a carrier or a bandwidth portion of the carrier.

[0275] Clause 6: The method according to any one of Clauses 1 to 5, wherein the one or more first parameters include one or more of the following: the total number of frequency resources in each of the plurality of frequency resource sets; the duration of the time interval; a power allocation type for allocating transmit power; one or more coefficients of an AI model for allocating transmit power; or the total transmit power allocated on the plurality of frequency resource sets during the time interval.

[0276] Clause 7: The method according to any one of Clauses 1 to 6, the method further comprising: conveying an indication of the transmit power allocated for each of the plurality of frequency resource sets.

[0277] Clause 8: The method according to Clause 7 further comprises: comparing a first CRC associated with the transmit power with a second CRC, wherein the indication of the allocated transmit power includes the second CRC; and adjusting at least one of the transmit powers in response to any difference between the first CRC and the second CRC.

[0278] Clause 9: The method according to Clause 7 or 8, wherein adjusting the at least one of the transmission powers comprises: decoding the indication of the allocated transmission power; and adjusting the at least one of the transmission powers based at least in part on the indication of the decoded allocated transmission power.

[0279] Clause 10: The method according to any one of Clauses 7 to 9, wherein the indication of the transmit power allocated for each of the plurality of frequency resource sets is configured according to one or more second parameters, the one or more second parameters including one or more of the following: the number of quantized power levels for each allocated transmit power; the number of bits for the indication of each allocated transmit power; the encoding type for the indication of the allocated transmit power; or the CRC for the indication of the allocated transmit power.

[0280] Clause 11: The method according to any one of Clauses 1 to 10, the method further comprising: transmitting capability information, the capability information indicating that the device is capable of allocating transmission power individually to different sets of frequency resources.

[0281] Clause 12: The method according to any one of Clauses 1 to 11, the method further comprising: determining the transmission power of each of the plurality of frequency resource sets by an AI model.

[0282] Clause 13: The method according to any one of Clauses 1 to 12, the method further comprising: determining the transmission power of each of the plurality of frequency resource sets based on the signal quality of each of the plurality of frequency resource sets.

[0283] Clause 14: The method according to any one of Clauses 1 to 13, wherein transmitting the one or more signals comprises: transmitting the one or more signals on the plurality of frequency resource sets using a corresponding transmit power of each of the plurality of frequency resource sets, each corresponding transmit power being based on the configuration.

[0284] Clause 15: The method according to any one of Clauses 1 to 14, wherein transmitting the one or more signals comprises: obtaining the one or more signals; and demodulating the one or more signals based on a corresponding transmit power of each of the plurality of frequency resource sets, each corresponding transmit power being based on the configuration.

[0285] Clause 16: A method for wireless communication by a device, the method comprising: transmitting a configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval; and using the transmit power at least in part based on the configuration to transmit one or more signals.

[0286] Clause 17: The method according to Clause 16 further includes determining the corresponding transmit power of each of the plurality of frequency resource sets based on the noise covariance of the plurality of frequency resource sets.

[0287] Clause 18: The method according to Clause 16 or 17 further includes: obtaining an indication of the noise covariance of the plurality of frequency resource sets.

[0288] Clause 19: The method according to any one of Clauses 16 to 18, the method further comprising: transmitting an indication of the periodicity of the noise covariance for reporting the plurality of frequency resource sets.

[0289] Clause 20: The method according to any one of Clauses 16 to 19, wherein the plurality of frequency resource sets are located in at least one of a carrier or a bandwidth portion of the carrier.

[0290] Clause 21: The method according to any one of Clauses 16 to 20, wherein the one or more first parameters include one or more of the following: the total number of frequency resources in each of the plurality of frequency resource sets; the duration of the time interval; a power allocation type for allocating transmit power; one or more coefficients of an AI model for allocating transmit power; or the total transmit power allocated on the plurality of frequency resource sets during the time interval.

[0291] Clause 22: The method according to any one of Clauses 16 to 21, the method further comprising: conveying an indication of a transmit power allocated for each of the plurality of frequency resource sets.

[0292] Clause 23: The method according to Clause 22 further comprises: comparing a first CRC associated with the transmit power with a second CRC, wherein the indication of the allocated transmit power includes the second CRC; and adjusting at least one of the transmit powers in response to any difference between the first CRC and the second CRC.

[0293] Clause 24: The method according to Clause 22 or 23, wherein adjusting the at least one of the transmission powers comprises: decoding the indication of the allocated transmission power; and adjusting the at least one of the transmission powers based at least in part on the indication of the decoded allocated transmission power.

[0294] Clause 25: The method according to any one of Clauses 22 to 24, wherein the indication of the transmit power allocated for each of the plurality of frequency resource sets is configured according to one or more second parameters, the one or more second parameters including one or more of the following: the number of quantized power levels for each allocated transmit power; the number of bits for the indication of each allocated transmit power; the encoding type for the indication of the allocated transmit power; or the CRC for the indication of the allocated transmit power.

[0295] Clause 26: The method according to any one of Clauses 16 to 25, the method further comprising: obtaining capability information, the capability information indicating that the device is capable of allocating transmission power individually to different sets of frequency resources.

[0296] Clause 27: The method according to any one of Clauses 16 to 26, the method further comprising: determining the transmission power of each of the plurality of frequency resource sets by an artificial intelligence model.

[0297] Clause 28: The method according to any one of Clauses 16 to 27, the method further comprising: determining the transmission power of each of the plurality of frequency resource sets based on the signal quality of each of the plurality of frequency resource sets.

[0298] Clause 29: The method according to any one of Clauses 16 to 28, wherein transmitting the one or more signals comprises transmitting the one or more signals on the plurality of frequency resource sets using a corresponding transmit power of each of the plurality of frequency resource sets, each corresponding transmit power being based on the configuration.

[0299] Clause 30: The method according to any one of Clauses 16 to 29, wherein conveying the one or more signals comprises: obtaining the one or more signals; and demodulating the one or more signals based on a corresponding transmit power of each of the plurality of frequency resource sets, each corresponding transmit power being based on the configuration.

[0300] Clause 31: One or more means comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to cause the one or more means to perform the method according to any one of Clauses 1 to 30.

[0301] Clause 32: One or more apparatuses, said apparatus including components for performing the method according to any one of Clauses 1 to 30.

[0302] Clause 33: One or more non-transitory computer-readable media, the non-transitory computer-readable media comprising executable instructions that, when executed by one or more processors of one or more devices, cause the one or more devices to perform the method according to any one of Clauses 1 to 30.

[0303] Clause 34: One or more computer program products embodied on one or more computer-readable storage media, said computer-readable storage media including code for performing the method according to any one of Clauses 1 to 30.

[0304] Additional Notes

[0305] The foregoing description is provided to enable any person skilled in the art to practice the various aspects described herein. The examples discussed herein do not limit the scope, applicability, or aspects set forth in the claims. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. For example, the function and arrangement of the elements discussed may be changed without departing from the scope of this disclosure. Various processes or components may be omitted, substituted, or added as appropriate in the various examples. For example, the described methods may be performed in a different order than described, and various actions may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined in some other examples. For example, any number of aspects set forth herein may be used to implement an apparatus or practice. Additionally, the scope of this disclosure is intended to cover such apparatuses or methods practiced using other structures, functionalities, or structures and functionalities that complement or replace the various aspects of this disclosure set forth herein. It should be understood that any aspect of the disclosure herein may be embodied by one or more elements of the claims.

[0306] The various exemplary logic blocks, modules, and circuits described in this disclosure can be implemented or executed using a general-purpose processor, AI processor, digital signal processor (DSP), ASIC, field-programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic element, discrete hardware component, or any combination thereof designed to perform the functions described herein. While the general-purpose processor may be a microprocessor, in alternative embodiments, the processor may be any commercially available processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working in conjunction with a DSP core, a system-on-a-chip (SoC), or any other such configuration.

[0307] As used in this article, the phrase “at least one of” in a list of items refers to any combination of these entries, including a single member. As an example, “at least one of a, b, or c” is intended to cover: a, b, c, ab, ac, bc, and abc, as well as any combination with multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbb, bbc, cc, and ccc, or any other ordering of a, b, and c).

[0308] As used herein, the term "determine" encompasses a wide variety of actions. For example, "determine" can include calculation, operation, processing, deduction, investigation, lookup (e.g., searching in a table, database, or other data structure), assertion, etc. Additionally, "determine" can include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), etc. Furthermore, "determine" can include parsing, selecting, picking, building, etc.

[0309] As used herein, unless otherwise stated, “coupled to” and “coupled with” generally encompass both direct and indirect coupling (e.g., including intermediate aspects of coupling). For example, stating that a processor is coupled to memory allows for direct coupling or coupling via an intermediate aspect such as a bus.

[0310] The methods disclosed herein include one or more actions for implementing the methods. These method actions may be interchanged without departing from the scope of the claims. In other words, unless a specified order of actions is given, the order and / or use of a particular action may be modified without departing from the scope of the claims. Furthermore, the various operations of the methods described above can be performed by any suitable component capable of performing the corresponding function. This component may include various hardware and / or software components and / or modules, including but not limited to circuits, application-specific integrated circuits (ASICs), or processors.

[0311] The following claims are not intended to be limited to the aspects shown herein, but should be given the full scope consistent with the language of the claims. References to singular elements are not intended to mean “only one” (unless specifically stated as “only one”), but rather “one or more”. Unless otherwise expressly stated, definite articles (e.g., “the” or “described”) subsequently used with an element (e.g., “processor”) are not intended to introduce a singular meaning (e.g., “only one”) for that element. For example, unless specifically stated otherwise, references to elements (e.g., “processor”, “controller”, “memory”, “transceiver”, “antenna”, “the processor”, “the controller”, “the memory”, “the transceiver”, “the antenna”, etc.) should be understood to refer to one or more elements (e.g., “one or more processors”, “one or more controllers”, “one or more memories”, “one or more transceivers”, etc.). The terms “set” and “group” are intended to include one or more elements and are used interchangeably with “one or more”. In the case of references to one or more elements performing a function (e.g., steps of a method), one element may perform all the functions, or more than one element may collectively perform those functions. When more than one element performs these functions together, each function does not need to be performed by every single element (e.g., different functions can be performed by different elements), and / or each function does not need to be performed by only one element overall (e.g., different elements can perform different sub-functions of a function). Similarly, when referring to one or more elements configured to cause another element (e.g., a device) to perform a function, one element may be configured to cause another element to perform all functions, or more than one element may be jointly configured to cause another element to perform these functions. Unless otherwise specifically stated, the term "some" refers to one or more. All structural and functional equivalents of the elements of the various aspects described throughout this disclosure that are currently or hereafter known to those skilled in the art are intended to be covered by the claims. Furthermore, nothing disclosed herein is intended to be offered to the public, whether or not such disclosure is explicitly stated in the claims.

Claims

1. An apparatus configured for wireless communication, the apparatus comprising: One or more memory units; and One or more processors, said one or more processors coupled to said one or more memories, said one or more processors being configured to cause the device to: Obtain a configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval; and One or more signals are transmitted using a transmission power that is at least partially based on the configuration.

2. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to determine the corresponding transmit power of each of the plurality of frequency resource sets based on the noise covariance of the plurality of frequency resource sets.

3. The apparatus of claim 2, wherein the one or more processors are configured to cause the apparatus to: Monitoring the multiple frequency resource sets; and Transmit an indication of the noise covariance of the plurality of frequency resource sets.

4. The apparatus of claim 3, wherein the one or more processors are configured to cause the apparatus to: obtain an indication of the periodicity of the noise covariance for reporting the plurality of frequency resource sets.

5. The apparatus of claim 2, wherein the plurality of frequency resource sets are located in at least one of a carrier or a bandwidth portion of the carrier.

6. The apparatus of claim 1, wherein the one or more first parameters include one or more of the following: The total number of frequency resources in each of the plurality of frequency resource sets; The duration of the time interval, Power allocation type used to allocate transmit power; One or more coefficients of an artificial intelligence (AI) model used to allocate transmission power; or Total transmit power allocated across the plurality of frequency resource sets within the time interval.

7. The apparatus of claim 2, wherein the one or more processors are configured to cause the apparatus to: convey an indication of transmit power allocated for each of the plurality of frequency resource sets.

8. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: transmit capability information indicating that the apparatus is capable of allocating transmission power individually to different sets of frequency resources.

9. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: determine the transmission power of each of the plurality of frequency resource sets by an artificial intelligence (AI) model.

10. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: determine the transmission power of each of the plurality of frequency resource sets based on the signal quality of each of the plurality of frequency resource sets.

11. An apparatus configured for wireless communication, the apparatus comprising: One or more memory units; and One or more processors, said one or more processors coupled to said one or more memories, said one or more processors being configured to cause the device to: Transmission configuration, the configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval; and One or more signals are transmitted using a transmission power that is at least partially based on the configuration.

12. The apparatus of claim 11, wherein the one or more processors are configured to cause the apparatus to determine the corresponding transmit power of each of the plurality of frequency resource sets based on the noise covariance of the plurality of frequency resource sets.

13. The apparatus of claim 12, wherein the one or more processors are configured to cause the apparatus to: obtain an indication of the noise covariance of the plurality of frequency resource sets.

14. The apparatus of claim 13, wherein the one or more processors are configured to cause the apparatus to: transmit an indication of the periodicity of the noise covariance for reporting the plurality of frequency resource sets.

15. The apparatus of claim 11, wherein the one or more first parameters include one or more of the following: The total number of frequency resources in each of the plurality of frequency resource sets; The duration of the time interval, Power allocation type used to allocate transmit power; One or more coefficients of an artificial intelligence (AI) model used to allocate transmission power; or Total transmit power allocated across the plurality of frequency resource sets within the time interval.

16. The apparatus of claim 12, wherein the one or more processors are configured to cause the apparatus to: convey an indication of transmit power allocated for each of the plurality of frequency resource sets.

17. The apparatus of claim 11, wherein the one or more processors are configured to cause the apparatus to: obtain capability information indicating that the apparatus is capable of allocating transmission power individually to different sets of frequency resources.

18. The apparatus of claim 11, wherein the one or more processors are configured to cause the apparatus to: determine the transmission power of each of the plurality of frequency resource sets by an artificial intelligence model.

19. The apparatus of claim 11, wherein the one or more processors are configured to cause the apparatus to: determine the transmission power of each of the plurality of frequency resource sets based on the signal quality of each of the plurality of frequency resource sets.

20. A method for wireless communication by a device, the method comprising: Obtain a configuration indicating one or more first parameters for individually allocating transmit power to each of a plurality of frequency resource sets within a time interval; as well as One or more signals are transmitted using a transmission power that is at least partially based on the configuration.

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