Dynamic power loading

Abstract

Certain aspects of the present disclosure provide techniques for dynamic power loading. A method for wireless communications by an apparatus includes obtaining a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicating one or more signals using transmit powers based at least part on the configuration.

Description

DYNAMIC POWER LOADINGCROSS REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to Israel Patent Application Serial No. 309093, filed on December 05, 2023, entitled "Dynamic Power Loading," which is hereby expressly incorporated by reference herein in its entirety.IntroductionField of the Disclosure

[0002] Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for transmit power allocation.Description of Related Art

[0003] Wireless communications systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, broadcasts, or other similar types of services. These wireless communications systems may employ multiple-access technologies capable of supporting communications with multiple users by sharing available wireless communications system resources with those users.

[0004] Although wireless communications systems have made great technological advancements over many years, challenges still exist. For example, complex and dynamic environments can still attenuate or block signals between wireless transmitters and wireless receivers. Accordingly, there is a continuous desire to improve the technical performance of wireless communications systems, including, for example: improving speed and data carrying capacity of communications, improving efficiency of the use of shared communications mediums, reducing power used by transmitters and receivers while performing communications, improving reliability of wireless communications, avoiding redundant transmissions and / or receptions and related processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access wireless communications systems, increasing the ability for different types of devices to intercommunicate, increasing the number and type of wireless communications mediums available for use, and the like. Consequently, there exists a need for further improvements in wireless communications systems to overcome the aforementioned technical challenges and others.SUMMARY

[0005] One aspect provides a method for wireless communications by an apparatus. The method includes obtaining a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicating one or more signals using transmit powers based at least in part on the configuration.

[0006] Another aspect provides a method for wireless communications by an apparatus. The method includes sending a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicating one or more signals using transmit powers based at least in part on the configuration.

[0007] Other aspects provide: one or more apparatuses operable, configured, or otherwise adapted to perform any portion of any method described herein (e.g., such that performance may be by only one apparatus or in a distributed fashion across multiple apparatuses); one or more non-transitory, computer-readable media comprising instructions that, when executed by one or more processors of one or more apparatuses, cause the one or more apparatuses to perform any portion of any method described herein (e.g., such that instructions may be included in only one computer-readable medium or in a distributed fashion across multiple computer-readable media, such that instructions may be executed by only one processor or by multiple processors in a distributed fashion, such that each apparatus of the one or more apparatuses may include one processor or multiple processors, and / or such that performance may be by only one apparatus or in a distributed fashion across multiple apparatuses); one or more computer program products embodied on one or more computer-readable storage media comprising code for performing any portion of any method described herein (e.g., such that code may be stored in only one computer-readable medium or across computer-readable media in a distributed fashion); and / or one or more apparatuses comprising one or more means for performing any portion of any method described herein (e.g., such that performance would be by only one apparatus or by multiple apparatuses in a distributed fashion). By way of example, an apparatus may comprise a processing system, a device with a processing system, or processing systems cooperating over one or more networks. An apparatus may comprise one or more memories; and one or more processors configured to cause the apparatus to perform any portion of any method described herein. In some examples, one or more ofthe processors may be preconfigured to perform various functions or operations described herein without requiring configuration by software.

[0008] The following description and the appended figures set forth certain features for purposes of illustration.BRIEF DESCRIPTION OF DRAWINGS

[0009] The appended figures depict certain features of the various aspects described herein and are not to be considered limiting of the scope of this disclosure.

[0010] FIG. 1 depicts an example wireless communications network.

[0011] FIG. 2 depicts an example disaggregated base station architecture.

[0012] FIG. 3 depicts aspects of an example base station and an example user equipment (UE).

[0013] FIGS. 4A, 4B, 4C, and 4D depict various example aspects of data structures for a wireless communications network.

[0014] FIG. 5 illustrates an example artificial intelligence (Al) architecture that may be used for Al-enhanced wireless communications.

[0015] FIG. 6 illustrates an example Al architecture of a first wireless device that is in communication with a second wireless device.

[0016] FIG. 7 illustrates an example artificial neural network.

[0017] FIG. 8 illustrates an example transmit power allocation across communication channels of a transmission.

[0018] FIG. 9 illustrates example operations for allocating transmit powers across component channels of a transmission.

[0019] FIG. 10 illustrates an example of Al-based dynamic power loading.

[0020] FIG. 11 illustrates an example system for training an Al model to determine a transmit power per component channel.

[0021] FIG. 12 depicts a process flow for communications in a system between a network entity and a UE.

[0022] FIG. 13 depicts a method for wireless communications.

[0023] FIG. 14 depicts another method for wireless communications.

[0024] FIG. 15 depicts aspects of an example communications device.

[0025] FIG. 16 depicts aspects of an example communications device.DETAILED DESCRIPTION

[0026] Aspects of the present disclosure provide apparatuses, methods, processing systems, and computer-readable mediums for dynamic power loading.

[0027] In certain wireless communications systems (e.g., 5G New Radio (NR) systems), a wireless communications device (e.g., a user equipment (UE) or base station) transmits a modulated signal using a power allocation that is equal over the entire allocated frequency resources (e.g., resource elements). As an example with respect to uplink transmit power control, a UE may determine a lowest transmit power that enables adequate reception at a network entity (e.g., a base station) and minimizes interference encountered by other devices. The UE may calculate the uplink transmit power for a transmission using the network entity’s request for the received signal power at the network entity, an estimate of the propagation loss on the uplink communication channel between the UE and the network entity, and other parameters such as the transmission bandwidth of the resource assignment. The UE may determine such a transmit power for each scheduled transmission occasion in a carrier of a cell with respect to the resource assignment on the communication channel, and the UE may apply this transmit power in accordance with a modulation distribution (e.g., any applicable amplitude modulation).

[0028] Technical problems for transmit power allocation include, for example, parallel communication channels encountering independent effects of signal propagation. As an orthogonal frequency-division multiplexing (OFDM) signal is made up of multiple subcarriers, such a signal may encounter frequency dependent signal propagation effects at varying degrees per subcarrier. Such signal propagation effects may include, for example, noise, interference, fading, scattering, Doppler effects, etc. Thus, applying the same transmit power to compensate for propagation losses across all of the subcarriers of an OFDM signal can lead to reduced performance in demodulating a signal at a receiver, such as a UE or base station. For example, the constellation of a received signal at a particular subcarrier may not align with the constellation mapping used for demodulating due to independent signal propagation effects on that subcarrier. This constellationmisalignment can affect the wireless communication performance, such as received signal quality, received signal strength, block error rate (BLER), throughput, latency, etc.

[0029] Aspects described herein overcome the aforementioned technical problem(s) by providing techniques for dynamic power loading. A wireless communications device (e.g., a UE and / or a network entity) may dynamically allocate transmit powers across component channels of a transmission. The dynamic transmit power allocation may involve setting an individual transmit power per component channel of a transmission (e.g., a modulated signal transmission). Each of the transmit powers may be allocated to overcome certain independent signal propagation effects on the corresponding component channel (e.g., subcarrier or group of subcarriers). The independent signal propagation effects may include a noise covariance per component channel (e.g., subcarrier or group of subcarriers). In certain aspects, the transmit power allocation for each of the component channels (e.g., subcarriers or groups of subcarriers) may be determined according to a function of a signal quality at the respective component channel (e.g., subcarrier or group subcarriers). For example, a mercury-waterfdling technique may be used to determine the component channel transmit power allocations as further described herein with respect to FIG. 9. In certain aspects, the transmit power allocation for each of the component channels (e.g., subcarriers or groups of subcarriers) may be determined using an artificial intelligence (Al) model, for example, as further described herein with respect to FIG. 10. In certain aspects, a network entity may configure a UE with a scheme for dynamic power loading, as described herein, or vice versa. In certain aspects, a UE may report, to the network entity, channel state feedback that indicates subcarrier properties to perform the dynamic power loading for communication signals, or vice versa.

[0030] The techniques for dynamic power loading described herein may provide various beneficial effects and / or advantages. The techniques for dynamic power loading may enable improved wireless communication performance, such as improved signal quality, improved signal strength, increased throughput, and / or reduced latency. The improved wireless communication performance may be attributable to the dynamic power loading described herein that adjusts the transmit powers across component channels (e.g., OFDM subcarriers) of a transmission based at least in part on the respective signal propagation effects on the component channels.

[0031] As used herein, component channels of a transmission may be or include OFDM subcarriers (e.g., resource elements) and / or groups of OFDM subcarriers (e.g., groups of resource elements) used for the transmission of a signal, such as an OFDM signal.Introduction to Wireless Communications Networks

[0032] The techniques and methods described herein may be used for various wireless communications networks. While aspects may be described herein using terminology commonly associated with 3G, 4G, 5G, 6G, and / or other generations of wireless technologies, aspects of the present disclosure may likewise be applicable to other communications systems and standards not explicitly mentioned herein.

[0033] FIG. 1 depicts an example of a wireless communications network 100, in which aspects described herein may be implemented.

[0034] Generally, wireless communications network 100 includes various network entities (alternatively, network elements or network nodes). A network entity is generally a communications device and / or a communications function performed by a communications device (e.g., a user equipment (UE), a base station (BS), a component of a BS, a server, etc.). As such communications devices are part of wireless communications network 100, and facilitate wireless communications, such communications devices may be referred to as wireless communications devices. For example, various functions of a network as well as various devices associated with and interacting with a network may be considered network entities. Further, wireless communications network 100 includes terrestrial aspects, such as ground-based network entities (e.g., BSs 102), and non-terrestrial aspects (also referred to herein as nonterrestrial network entities), such as satellite 140 and / or aerial or spaceborne platform(s), which may include network entities on-board (e.g., one or more BSs) capable of communicating with other network elements (e.g., terrestrial BSs) and UEs.

[0035] In the depicted example, wireless communications network 100 includes BSs 102, UEs 104, and one or more core networks, such as an Evolved Packet Core (EPC) 160 and 5G Core (5GC) network 190, which interoperate to provide communications services over various communications links, including wired and wireless links.

[0036] FIG. 1 depicts various example UEs 104, which may more generally include: a cellular phone, smart phone, session initiation protocol (SIP) phone, laptop, personaldigital assistant (PDA), satellite radio, global positioning system, multimedia device, video device, digital audio player, camera, game console, tablet, smart device, wearable device, vehicle, electric meter, gas pump, large or small kitchen appliance, healthcare device, implant, sensor / actuator, display, internet of things (loT) devices, always on (AON) devices, edge processing devices, data centers, or other similar devices. UEs 104 may also be referred to more generally as a mobile device, a wireless device, a station, a mobile station, a subscriber station, a mobile subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a remote device, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, and others.

[0037] BSs 102 wirelessly communicate with (e.g., transmit signals to or receive signals from) UEs 104 via communications links 120. The communications links 120 between BSs 102 and UEs 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to a BS 102 and / or downlink (DL) (also referred to as forward link) transmissions from a BS 102 to a UE 104. The communications links 120 may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity in various aspects.

[0038] BSs 102 may generally include: a NodeB, enhanced NodeB (eNB), next generation enhanced NodeB (ng-eNB), next generation NodeB (gNB or gNodeB), access point, base transceiver station, radio base station, radio transceiver, transceiver function, transmission reception point, and / or others. Each of BSs 102 may provide communications coverage for a respective coverage area 110, which may sometimes be referred to as a cell, and which may overlap in some cases (e.g., small cell 102’ may have a coverage area 110’ that overlaps the coverage area 110 of a macro cell). A BS may, for example, provide communications coverage for a macro cell (covering relatively large geographic area), a pico cell (covering relatively smaller geographic area, such as a sports stadium), a femto cell (relatively smaller geographic area (e.g., a home)), and / or other types of cells.

[0039] Generally, a cell may 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 geographic characteristics, such as a geographic coverage area, as well as radio frequency characteristics, such as time and / or frequency resources dedicated to the cell. For example, a specific geographic coverage area may be covered by multiple cells employing different frequency resources (e.g., bandwidth parts) and / ordifferent time resources. As another example, a specific geographic coverage area may be covered by a single cell. In some contexts (e.g., a carrier aggregation scenario and / or multi-connectivity scenario), the terms “cell” or “serving cell” may refer to or correspond to a specific carrier frequency (e.g., a component carrier) used for wireless communications, and a “cell group” may refer to or correspond to multiple carriers used for wireless communications. As examples, 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.

[0040] While BSs 102 are depicted in various aspects as unitary communications devices, BSs 102 may be implemented in various configurations. For example, one or more components of a base station may be disaggregated, including a central unit (CU), one or more distributed units (DUs), one or more radio units (RUs), a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC), or a Non-Real Time (Non-RT) RIC, to name a few examples. In another example, various aspects of a base station may be virtualized. More generally, a base station (e.g., BS 102) may include components that are located at a single physical location or components located at various physical locations. In examples in which a base station includes components that are located at various physical locations, the various components may each perform functions such that, collectively, the various components achieve functionality that is similar to a base station that is located at a single physical location. In some aspects, a base station including components that are located at various physical locations may be referred to as a disaggregated radio access network architecture, such as an Open RAN (O-RAN) or Virtualized RAN (VRAN) architecture. FIG. 2 depicts and describes an example disaggregated base station architecture.

[0041] Different BSs 102 within wireless communications network 100 may also be configured to support different radio access technologies, such as 3G, 4G, and / or 5G. For example, BSs 102 configured for 4G ETE (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E- UTRAN)) may interface with the EPC 160 through first backhaul links 132 (e.g., an SI interface). BSs 102 configured for 5G (e.g., 5G NR or Next Generation RAN (NG-RAN)) may interface with 5GC 190 through second backhaul links 184. BSs 102 maycommunicate directly or indirectly (e.g., through the EPC 160 or 5GC 190) with each other over third backhaul links 134 (e.g., X2 interface), which may be wired or wireless.

[0042] Wireless communications network 100 may subdivide the electromagnetic spectrum into various classes, bands, channels, or other features. In some aspects, the subdivision is provided based on wavelength and frequency, where frequency may also be referred to as a carrier, a subcarrier, a frequency channel, a tone, or a subband. For example, 3GPP currently defines Frequency Range 1 (FR1) as including 410 MHz - 7125 MHz, which is often referred to (interchangeably) as “Sub-6 GHz”. Similarly, 3GPP currently defines Frequency Range 2 (FR2) as including 24,250 MHz - 71,000 MHz, which is sometimes referred to (interchangeably) as a “millimeter wave” (“mmW” or “mmWave”). In some cases, FR2 may be further defined in terms of sub-ranges, such as a first sub-range FR2-1 including 24,250 MHz - 52,600 MHz and a second sub-range FR2-2 including 52,600 MHz - 71,000 MHz. A base station configured to communicate using mmWave / near mmWave radio frequency bands (e.g., a mmWave base station such as BS 180) may utilize beamforming (e.g., 182) with a UE (e.g., 104) to improve path loss and range.

[0043] The communications links 120 between BSs 102 and, for example, UEs 104, may be through one or more carriers, which may have different bandwidths (e.g., 5, 10, 15, 20, 100, 400, and / or other MHz), and which may be aggregated in various aspects. Carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated for DL than for UL).

[0044] Communications using higher frequency bands may have higher path loss and a shorter range compared to lower frequency communications. Accordingly, certain base stations (e.g., 180 in FIG. 1) may utilize beamforming 182 with a UE 104 to improve path loss and range. For example, BS 180 and the UE 104 may each include a plurality of antennas, such as antenna elements, antenna panels, and / or antenna arrays to facilitate the beamforming. In some cases, BS 180 may transmit a beamformed signal to UE 104 in one or more transmit directions 182’. UE 104 may receive the beamformed signal from the BS 180 in one or more receive directions 182”. UE 104 may also transmit a beamformed signal to the BS 180 in one or more transmit directions 182”. BS 180 may also receive the beamformed signal from UE 104 in one or more receive directions 182’. BS 180 and UE 104 may then perform beam training to determine the best receive andtransmit directions for each of BS 180 and UE 104. Notably, the transmit and receive directions for BS 180 may or may not be the same. Similarly, the transmit and receive directions for UE 104 may or may not be the same.

[0045] Wireless communications network 100 further includes a Wi-Fi AP 150 in communication with Wi-Fi stations (STAs) 152 via communications links 154 in, for example, a 2.4 GHz and / or 5 GHz unlicensed frequency spectrum.

[0046] Certain UEs 104 may communicate with each other using device-to-device (D2D) communications link 158. D2D communications link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), a physical sidelink control channel (PSCCH), and / or a physical sidelink feedback channel (PSFCH).

[0047] EPC 160 may include various functional components, including: a Mobility Management Entity (MME) 162, other MMEs 164, a Serving Gateway 166, a Multimedia Broadcast Multicast Service (MBMS) Gateway 168, a Broadcast Multicast Service Center (BM-SC) 170, and / or a Packet Data Network (PDN) Gateway 172, such as in the depicted example. MME 162 may be in communication with a Home Subscriber Server (HSS) 174. MME 162 is the control node that processes the signaling between the UEs 104 and the EPC 160. Generally, MME 162 provides bearer and connection management.

[0048] Generally, user Internet protocol (IP) packets are transferred through Serving Gateway 166, which itself is connected to PDN Gateway 172. PDN Gateway 172 provides UE IP address allocation as well as other functions. PDN Gateway 172 and the BM-SC 170 are connected to IP Services 176, which may include, for example, the Internet, an intranet, an IP Multimedia Subsystem (IMS), a Packet Switched (PS) streaming service, and / or other IP services.

[0049] BM-SC 170 may provide functions for MBMS user service provisioning and delivery. BM-SC 170 may serve as an entry point for content provider MBMS transmission, may be used to authorize and initiate MBMS Bearer Services within a public land mobile network (PLMN), and / or may be used to schedule MBMS transmissions. MBMS Gateway 168 may be used to distribute MBMS traffic to the BSs 102 belonging to a Multicast Broadcast Single Frequency Network (MBSFN) areabroadcasting a particular service, and / or may be responsible for session management (start / stop) and for collecting eMBMS related charging information.

[0050] 5GC 190 may include various functional components, including: an Access and Mobility Management Function (AMF) 192, other AMFs 193, a Session Management Function (SMF) 194, and a User Plane Function (UPF) 195. AMF 192 may be in communication with Unified Data Management (UDM) 196.

[0051] AMF 192 is a control node that processes signaling between UEs 104 and 5GC 190. AMF 192 provides, for example, quality of service (QoS) flow and session management.

[0052] Internet protocol (IP) packets are transferred through UPF 195, which is connected to the IP Services 197, and which provides UE IP address allocation as well as other functions for 5GC 190. IP Services 197 may include, for example, the Internet, an intranet, an IMS, a PS streaming service, and / or other IP services.

[0053] In various aspects, a network entity or network node can be implemented as an aggregated base station, as a disaggregated base station, a component of a base station, an integrated access and backhaul (IAB) node, a relay node, a sidelink node, to name a few examples.

[0054] FIG. 2 depicts an example disaggregated base station 200 architecture. The disaggregated base station 200 architecture may include one or more central units (CUs) 210 that can communicate directly with a core network 220 via a backhaul link, or indirectly with the core network 220 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 225 via an E2 link, or aNon-Real Time (Non-RT) RIC 215 associated with a Service Management and Orchestration (SMO) Framework 205, or both). A CU 210 may communicate with one or more distributed units (DUs) 230 via respective midhaul links, such as an Fl interface. The DUs 230 may communicate with one or more radio units (RUs) 240 via respective fronthaul links. The RUs 240 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, the UE 104 may be simultaneously served by multiple RUs 240.

[0055] Each of the units, e.g., the CUs 210, the DUs 230, the RUs 240, as well as the Near-RT RICs 225, the Non-RT RICs 215 and the SMO Framework 205, may include one or more interfaces or be coupled to one or more interfaces configured to receive ortransmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communications interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units. Additionally or alternatively, the units can include a wireless interface, which may include a receiver, a transmitter or transceiver (such as a radio frequency (RF) transceiver), configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other units.

[0056] In some aspects, the CU 210 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 210. The CU 210 may be configured to handle user plane functionality (e.g., Central Unit - User Plane (CU-UP)), control plane functionality (e.g., Central Unit - Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 210 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as the El interface when implemented in an O-RAN configuration. The CU 210 can be implemented to communicate with the DU 230, as necessary, for network control and signaling.

[0057] The DU 230 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 240. In some aspects, the DU 230 may host one or more of a radio link control (REC) layer, a 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, or the like) depending, at least in part, on a functional split, such as those defined by the 3rdGeneration Partnership Project (3 GPP). In some aspects, the DU 230 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 230, or with the control functions hosted by the CU 210.

[0058] Lower-layer functionality can be implemented by one or more RUs 240. In some deployments, an RU 240, 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 fdtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU(s) 240 can be implemented to handle over the air (OTA) communications with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RU(s) 240 can be controlled by the corresponding DU 230. In some scenarios, this configuration can enable the DU(s) 230 and the CU 210 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0059] The SMO Framework 205 may be configured to support RAN deployment and provisioning of non- virtualized and virtualized network elements. For non- virtualized network elements, the SMO Framework 205 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an 01 interface). For virtualized network elements, the SMO Framework 205 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 290) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an 02 interface). Such virtualized network elements can include, but are not limited to, CUs 210, DUs 230, RUs 240 and Near-RT RICs 225. In some implementations, the SMO Framework 205 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 211, via an 01 interface. Additionally, in some implementations, the SMO Framework 205 can communicate directly with one or more DUs 230 and / or one or more RUs 240 via an 01 interface. The SMO Framework 205 also may include aNon-RT RIC 215 configured to support functionality of the SMO Framework 205.

[0060] The Non-RT RIC 215 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, Artificial Intelligence / Machine Learning (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the Near-RT RIC 225. The Non-RT RIC 215 may be coupled to or communicate with (such as via an Al interface)the Near-RT RIC 225. The Near-RT RIC 225 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 210, one or more DUs 230, or both, as well as an O-eNB, with the Near-RT RIC 225.

[0061] In some implementations, to generate AI / MT models to be deployed in the Near-RT RIC 225, the Non-RT RIC 215 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 225 and may be received at the SMO Framework 205 or the Non-RT RIC 215 from nonnetwork data sources or from network functions. 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 for performance and employ AI / ML models to perform corrective actions through the SMO Framework 205 (such as reconfiguration via 01) or via creation of RAN management policies (such as Al policies).

[0062] FIG. 3 depicts aspects of an example BS 102 and a UE 104.

[0063] Generally, BS 102 includes various processors (e.g., 318, 320, 330, 338, and 340), antennas 334a-t (collectively 334), transceivers 332a-t (collectively 332), which include modulators and demodulators, and other aspects, which enable wireless transmission of data (e.g., data source 312) and wireless reception of data (e.g., data sink 314). For example, BS 102 may send and receive data between BS 102 and UE 104. BS 102 includes controller / processor 340, which may be configured to implement various functions described herein related to wireless communications.

[0064] Generally, UE 104 includes various processors (e.g., 358, 364, 366, 370, and 380), antennas 352a-r (collectively 352), transceivers 354a-r (collectively 354), which include modulators and demodulators, and other aspects, which enable wireless transmission of data (e.g., retrieved from data source 362) and wireless reception of data (e.g., provided to data sink 360). UE 104 includes controller / processor 380, which may be configured to implement various functions described herein related to wireless communications.

[0065] In regards to an example downlink transmission, BS 102 includes a transmit processor 320 that may receive data from a data source 312 and control information froma controller / processor 340. The control information may be for the 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. The data may be for the physical downlink shared channel (PDSCH), in some examples.

[0066] Transmit processor 320 may process (e.g., encode and symbol map) the data and control information to obtain data symbols and control symbols, respectively. Transmit processor 320 may also generate reference symbols, such as for the primary synchronization signal (PSS), secondary synchronization signal (SSS), PBCH demodulation reference signal (DMRS), and channel state information reference signal (CSI-RS).

[0067] Transmit (TX) multiple-input multiple-output (MIMO) processor 330 may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, and / or the reference symbols, if applicable, and may provide output symbol streams to the modulators (MODs) in transceivers 332a-332t. Each modulator in transceivers 332a- 332t may process a respective output symbol stream to obtain an output sample stream. Each modulator may further process (e.g., convert to analog, amplify, fdter, and upconvert) the output sample stream to obtain a downlink signal. Downlink signals from the modulators in transceivers 332a-332t may be transmitted via the antennas 334a-334t, respectively.

[0068] In order to receive the downlink transmission, UE 104 includes antennas 352a- 352r that may receive the downlink signals from the BS 102 and may provide received signals to the demodulators (DEMODs) in transceivers 354a-354r, respectively. Each demodulator in transceivers 354a-354r may condition (e.g., fdter, amplify, downconvert, and digitize) a respective received signal to obtain input samples. Each demodulator may further process the input samples to obtain received symbols.

[0069] RX MIMO detector 356 may obtain received symbols from all the demodulators in transceivers 354a-354r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. Receive processor 358 may process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for the UE 104 to a data sink 360, and provide decoded control information to a controller / processor 380.

[0070] In regards to an example uplink transmission, UE 104 further includes a transmit processor 364 that may receive and process data (e.g., for the PUSCH) from a data source 362 and control information (e.g., for the physical uplink control channel (PUCCH)) from the controller / processor 380. Transmit processor 364 may also generate reference symbols for a reference signal (e.g., for the sounding reference signal (SRS)). The symbols from the transmit processor 364 may be precoded by a TX MIMO processor 366 if applicable, further processed by the modulators in transceivers 354a-354r (e.g., for SC-FDM), and transmitted to BS 102.

[0071] At BS 102, the uplink signals from UE 104 may be received by antennas 334a- t, processed by the demodulators in transceivers 332a-332t, detected by a RX MIMO detector 336 if applicable, and further processed by a receive processor 338 to obtain decoded data and control information sent by UE 104. Receive processor 338 may provide the decoded data to a data sink 314 and the decoded control information to the controller / processor 340.

[0072] Memories 342 and 382 may store data and program codes for BS 102 and UE 104, respectively.

[0073] Scheduler 344 may schedule UEs for data transmission on the downlink and / or uplink.

[0074] In various aspects, BS 102 may be described as transmitting and receiving various types of data associated with the methods described herein. In these contexts, “transmitting” may refer to various mechanisms of outputting data, such as outputting data from data source 312, scheduler 344, memory 342, transmit processor 320, controller / processor 340, TX MIMO processor 330, transceivers 332a-t, antenna 334a-t, and / or other aspects described herein. Similarly, “receiving” may refer to various mechanisms of obtaining data, such as obtaining data from antennas 334a-t, transceivers 332a-t, RX MIMO detector 336, controller / processor 340, receive processor 338, scheduler 344, memory 342, and / or other aspects described herein.

[0075] In various aspects, UE 104 may likewise be described as transmitting and receiving various types of data associated with the methods described herein. In these contexts, “transmitting” may refer to various mechanisms of outputting data, such as outputting data from data source 362, memory 382, transmit processor 364, controller / processor 380, TX MIMO processor 366, transceivers 354a-t, antenna 352a-t,and / or other aspects described herein. Similarly, “receiving” may refer to various mechanisms of obtaining data, such as obtaining data from antennas 352a-t, transceivers 354a-t, RX MIMO detector 356, controller / processor 380, receive processor 358, memory 382, and / or other aspects described herein.

[0076] In some aspects, a processor may be configured to perform various operations, such as those associated with the methods described herein, and transmit (output) to or receive (obtain) data from another interface that is configured to transmit or receive, respectively, the data.

[0077] In various aspects, artificial intelligence (Al) processors 318 and 370 may perform Al processing for BS 102 and / or UE 104, respectively. The Al processor 318 may include Al 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. The Al processor 370 may likewise include Al accelerator hardware or circuitry. As an example, the Al processor 370 may perform AI- based beam management, Al-based channel state feedback (CSF), Al-based antenna tuning, and / or Al-based positioning (e.g., global navigation satellite system (GNSS) positioning). In some cases, the Al processor 318 may process feedback from the UE 104 (e.g., CSF) using hardware accelerated Al inferences and / or Al training. The Al processor 318 may decode compressed CSF from the UE 104, for example, using a hardware accelerated Al inference associated with the CSF. In certain cases, the Al processor 318 may perform certain RAN-based functions including, for example, network planning, network performance management, energy-efficient network operations, etc.

[0078] FIGS. 4A, 4B, 4C, and 4D depict aspects of data structures for a wireless communications network, such as wireless communications network 100 of FIG. 1.

[0079] In particular, FIG. 4A is a diagram 400 illustrating an example of a first subframe within a 5G (e.g., 5GNR) frame structure, FIG. 4B is a diagram 430 illustrating an example of DL channels within a 5G subframe, FIG. 4C is a diagram 450 illustrating an example of a second subframe within a 5G frame structure, and FIG. 4D is a diagram 480 illustrating an example of UL channels within a 5G subframe.

[0080] Wireless communications systems may utilize orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) on the uplink and downlink. Such systems may also support half-duplex operation using time division duplexing (TDD). OFDM andsingle-carrier frequency division multiplexing (SC-FDM) partition the system bandwidth (e.g., as depicted in FIGS. 4B and 4D) into multiple orthogonal subcarriers. Each subcarrier may be modulated with data. Modulation symbols may be sent in the frequency domain with OFDM and / or in the time domain with SC-FDM.

[0081] A wireless communications frame structure may be frequency division duplex (FDD), in which, for a particular set of subcarriers, subframes within the set of subcarriers are dedicated for either DL or UL. Wireless communications frame structures may also be time division duplex (TDD), in which, for a particular set of subcarriers, subframes within the set of subcarriers are dedicated for both DL and UL.

[0082] In FIG. 4A and 4C, the wireless communications frame structure is TDD where D is DL, U is UL, and X is flexible for use between DL / UL. UEs may be configured with a slot format through a received slot format indicator (SFI) (dynamically through DL control information (DCI), or semi-statically / statically through radio resource control (RRC) signaling). In the depicted examples, a 10 ms frame is divided into 10 equally sized 1 ms subframes. Each subframe may include one or more time 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 an extended CP or 14 symbols per slot for a normal CP). Subframes may also include mini-slots, which generally have fewer symbols than an entire slot. Other wireless communications technologies may have a different frame structure and / or different channels.

[0083] In certain aspects, the number of slots within a subframe (e.g., a slot duration in a subframe) is based on a numerology, which may define a frequency domain subcarrier spacing and symbol duration as further described herein. In certain aspects, given a numerology p, there are 2gslots per subframe. Thus, numerologies (p) 0 to 6 may allow for 1, 2, 4, 8, 16, 32, and 64 slots, respectively, per subframe. In some cases, the extended CP (e.g., 12 symbols per slot) may be used with a specific numerology, e.g., numerology 2 allowing for 4 slots per subframe. The subcarrier spacing and symbol length / duration are a function of the numerology. The subcarrier spacing may be equal to 211x 15 kHz, where p is the numerology 0 to 6. As an example, the numerology p = 0 corresponds to a subcarrier spacing of 15 kHz, and the numerology p = 6 corresponds to a subcarrier spacing of 960 kHz. The symbol length / duration is inversely related to the subcarrier spacing. FIGS. 4A, 4B, 4C, and 4D provide an example of a slot format having 14 symbols per slot (e.g., a normal CP) and a numerology p = 2 with 4 slots persubframe. In such a case, the slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 ps.

[0084] As depicted in FIGS. 4A, 4B, 4C, and 4D, a resource grid may be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as physical RBs (PRBs)) that extends, 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).

[0085] As illustrated in FIG. 4A, some of the REs carry reference (pilot) signals (RS) for a UE (e.g., UE 104 of FIGS. 1 and 3). The RS may include demodulation RS (DMRS) and / or channel state information reference signals (CSI-RS) for channel estimation at the UE. The RS may also include beam measurement RS (BRS), beam refinement RS (BRRS), and / or phase tracking RS (PT-RS).

[0086] FIG. 4B illustrates an example of various DE channels within a subframe of a frame. The physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs), each CCE including, for example, nine RE groups (REGs), each REG including, for example, four consecutive REs in an OFDM symbol.

[0087] A primary synchronization signal (PSS) may be within symbol 2 of particular subframes of a frame. The PSS is used by a UE (e.g., 104 of FIGS. 1 and 3) to determine subframe / symbol timing and a physical layer identity.

[0088] A secondary synchronization signal (SSS) may be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine a physical layer cell identity group number and radio frame timing.

[0089] Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI). Based on the PCI, the UE can determine the locations of the aforementioned DMRS. The physical broadcast channel (PBCH), which carries a master information block (MIB), may be logically grouped with the PSS and SSS to form a synchronization signal (SS) / PBCH block. The MIB provides a number of RBs in the system bandwidth and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs), and / or paging messages.

[0090] As illustrated in FIG. 4C, some of the REs carry DMRS (indicated as R for one particular configuration, but other DMRS configurations are possible) for channel estimation at the base station. The UE may transmit DMRS for the PUCCH and DMRS for the PUS CH. The PUS CH DMRS may be transmitted, for example, in the first one or two symbols of the PUSCH. The PUCCH DMRS may be transmitted in different configurations depending on whether short or long PUCCHs are transmitted and depending on the particular PUCCH format used. UE 104 may transmit sounding reference signals (SRS). The SRS may be transmitted, for example, in the last symbol of a subframe. The SRS may have a comb structure, and a UE may transmit SRS on one of the combs. The SRS may be used by a base station for channel quality estimation to enable frequency-dependent scheduling on the UE.

[0091] FIG. 4D illustrates an example of various UE channels within a subframe of a frame. The PUCCH may be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK / NACK feedback. The PUSCH carries data, and may additionally be used to carry a buffer status report (BSR), a power headroom report (PHR), and / or UCI.Example Artificial Intelligence for Wireless Communications

[0092] Certain aspects described herein may be implemented, at least in part, using some form of artificial intelligence (Al), e.g., the process of using a machine learning (ME) model to infer or predict output data based on input data. An example ME model may include a mathematical representation of one or more relationships among various objects to provide an output representing one or more predictions or inferences. Once an ME model has been trained, the ME model may be deployed to process data that may be similar to, or associated with, all or part of the training data and provide an output representing one or more predictions or inferences based on the input data.

[0093] ME is often characterized in terms of types of learning that generate specific types of learned models that perform specific types of tasks. For example, different types of machine learning include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

[0094] Supervised learning algorithms generally model relationships and dependencies between input features (e.g., a feature vector) and one or more targetoutputs. Supervised learning uses labeled training data, which are data including one or more inputs and a desired output. Supervised learning may be used to train models to perform tasks like 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 (SVMs), and artificial neural networks (ANNs).

[0095] Unsupervised learning algorithms work on unlabeled input data and train models that take an input and transform it into an output to solve a practical problem. Examples of unsupervised learning tasks are clustering, where the output of the model may be a cluster identification, dimensionality reduction, where the output of the model is an output feature vector that has fewer features than the input feature vector, and outlier detection, where the output of the model is a value indicating how the input is different from a typical example in the dataset. An example unsupervised learning algorithm is k- Means.

[0096] Semi-supervised learning algorithms work on datasets containing both labeled and unlabeled examples, where often the quantity of unlabeled examples is much higher than the number of labeled examples. However, the goal of a semi-supervised learning is that of supervised learning. Often, a semi-supervised model includes a model trained to produce pseudo-labels for unlabeled data that is then combined with the labeled data to train a second classifier that leverages the higher quantity of overall training data to improve task performance.

[0097] Reinforcement Learning algorithms use observations gathered by an agent from an interaction with an environment to take actions that may maximize a reward or minimize a risk. Reinforcement learning is a continuous and iterative process in which the agent learns from its experiences with the environment until it explores, for example, a full range of possible states. An example type of reinforcement learning algorithm is an adversarial network. Reinforcement learning may be particularly beneficial when used to improve or attempt to optimize a behavior of a model deployed in a dynamically changing environment, such as a wireless communication network.

[0098] ML models may be deployed in one or more devices (e.g., network entities such as base station(s) and / or user equipment(s)) to support various wired and / or wireless communication aspects of a communication system. For example, an ML model may betrained to identify patterns and relationships in data corresponding to a network, a device, an air interface, or the like. An ML model may improve operations relating to one or more aspects, such as transceiver circuitry controls, frequency synchronization, timing synchronization, channel state estimation, channel equalization, channel state feedback, modulation, demodulation, device positioning, transceiver tuning, beamforming, signal coding / decoding, network routing, load balancing, and energy conservation (to name just a few) associated with communications devices, services, and / or networks. Al-enhanced transceiver circuitry controls may include, for example, fdter tuning, transmit power controls, gain controls (including automatic gain controls), phase controls, power management, and the like.

[0099] Aspects described herein may describe the performance of certain tasks and the technical solution of various technical problems by application of a specific type of ML model, such as an ANN. It should be understood, however, that other type(s) of Al models may be used in addition to or instead of an ANN or machine learning. An ML model may be an example of an Al model, and other Al models may be used in addition to or instead of any of the ML models described herein. Hence, unless expressly recited, subject matter regarding an ML model is not necessarily intended to be limited to just an ANN solution or machine learning. Further, it should be understood that, unless otherwise specifically stated, terms such “Al model,” “ML model,” “AI / ML model,” “trained ML model,” and the like are intended to be interchangeable.

[0100] FIG. 5 is a diagram illustrating an example Al architecture 500 that may be used for Al-enhanced wireless communications. As illustrated, the architecture 500 includes multiple logical entities, such as a model training host 502, a model inference host 504, data source(s) 506, and an agent 508. The Al architecture may be used in any of various use cases for wireless communications, such as those listed above.

[0101] The model inference host 504, in the architecture 500, is configured to run an ML model based on inference data 512 provided by data source(s) 506. The model inference host 504 may produce an output 514 (e.g., a prediction or inference, such as a discrete or continuous value) based on the inference data 512, that is then provided as input to the agent 508.

[0102] The agent 508 may be an element or an entity of a wireless communication system including, for example, a radio access network (RAN), a wireless local areanetwork, a device-to-device (D2D) communications system, etc. As an example, the agent 508 may be a user equipment (e.g., UE 104 in FIG. 1), a base station (e.g., the BS 102 in FIG. 1 or any disaggregated network entity thereof including a centralized unit (CU), a distributed unit (DU), and / or a radio unit (RU)), an access point, a wireless station, a RAN intelligent controller (RIC) in a cloud-based RAN, among some examples. Additionally, the type of agent 508 may also depend on the type of tasks performed by the model inference host 504, the type of inference data 512 provided to model inference host 504, and / or the type of output 514 produced by model inference host 504.

[0103] For example, if output 514 from the model inference host 504 is associated with beam management, the agent 508 may be or include a UE, a DU, or an RU. As another example, if output 514 from model inference host 504 is associated with transmission and / or reception scheduling, the agent 508 may be a CU or a DU.

[0104] After the agent 508 receives output 514 from the model inference host 504, agent 508 may determine whether to act based on the output. For example, if agent 508 is a DU or an RU and the output from model inference host 504 is associated with beam management, the agent 508 may determine whether to change or modify a transmit and / or receive beam based on the output 514. If the agent 508 determines to act based on the output 514, agent 508 may indicate the action to at least one subject of the action 510. For example, if the agent 508 determines to change or modify a transmit and / or receive beam for a communication between the agent 508 and the subject of action 510 (e.g., a UE), the agent 508 may send a beam switching indication to the subject of action 510 (e.g., a UE). As another example, the agent 508 may be a UE, the output 514 from model inference host 504 may be one or more predicted channel characteristics for one or more beams. For example, the model inference host 504 may predict channel characteristics for a set of beams based on the measurements of another set of beams. Based on the predicted channel characteristics, the agent 508, such as the UE, may send, to the subject of action 510, such as a BS, a request to switch to a different beam for communications. In some cases, the agent 508 and the subject of action 510 are the same entity.

[0105] The data sources 506 may be configured for collecting data that is used as training data 516 for training an ML model, or as inference data 512 for feeding an ML model inference operation. In particular, the data sources 506 may collect data from any of various entities (e.g., the UE and / or the BS), which may include the subject of action 510, and provide the collected data to a model training host 502 for ML model training.For example, after a subject of action 510 (e.g., a UE) receives a beam configuration from agent 508, the subject of action 510 may provide performance feedback associated with the beam configuration to the data sources 506, where the performance feedback may be used by the model training host 502 for monitoring and / or evaluating the 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), the model training host 502 may determine to modify or retrain the ML model used by model inference host 504, such as via an ML model deployment / update.

[0106] In certain aspects, the model training host 502 may deployed at or with the same or a different entity than that in which the model inference host 504 is deployed. For example, in order to offload model training processing, which can impact the performance of the model inference host 504, the model training host 502 may be deployed at a model server as further described herein. Further, in some cases, training and / or inference may be distributed amongst devices in a decentralized or federated fashion.

[0107] In some aspects, an ML model is deployed at or on a network entity for dynamic power loading. More specifically, a model interference host, such as model inference host 504 in FIG. 5, may be deployed at or on the network entity for allocation of transmit power per component channel of a transmission (e.g., transmission of a modulated signal) as further described herein.

[0108] In some other aspects, an ML model is deployed at or on a UE for dynamic power loading. More specifically, a model inference host, such as model inference host 504 in FIG. 5, may be deployed at or on the UE for allocation of transmit power per component channel of a modulated signal as further described herein.

[0109] FIG. 6 illustrates an example Al architecture of a first wireless device 602 that is in communication with a second wireless device 604. The first wireless device 602 may be an example of the UE 104 as described herein with respect to FIGS. 1 and 3. Similarly, the second wireless device 604 may be an example of the BS 102 or any disaggregated entity thereof as described herein with respect to FIGS. 1-3. Note that the Al architecture of the first wireless device 602 may be applied to the second wireless device 604.

[0110] The first wireless device 602 may be, or may include, a chip, system on chip (SoC), a system in package (SiP), chipset, package or device that includes one or more processors, processing blocks or processing elements (collectively “the processor 610”) and one or more memory blocks or elements (collectively “the memory 620”).

[0111] As an example, in a transmit mode, the processor 610 may 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 representative of the respective symbols), the processor 610 may output the modulated symbols to a transceiver 640. The processor 610 may be coupled to the transceiver 640 for transmitting and / or receiving signals via one or more antennas 646. In this example, the transceiver 640 includes radio frequency (RF) circuitry 642, which may be coupled to the antennas 646 via an interface 644. As an example, the interface 644 may include a switch, a duplexer, a diplexer, a multiplexer, and / or the like. The RF circuitry 642 may convert the digital signals to analog baseband signals, for example, using a digital-to-analog converter. The RF circuitry 642 may include any of various circuitry, including, for example, baseband filter(s), mixer(s), frequency synthesizer(s), power amplifier(s), and / or low noise amplifier(s). In some cases, the RF circuitry 642 may upconvert the baseband signals to one or more carrier frequencies for transmission. The antennas 646 may emit RF signals, which may be received at the second wireless device 604.

[0112] In receive mode, RF signals received via the antenna 646 (e.g., from the second wireless device 604) may be amplified and converted to a baseband frequency (e.g., downconverted). The received baseband signals may be filtered and converted to digital I or Q signals for digital signal processing. The processor 610 may receive the digital I or Q signals and further process the digital signals, for example, demodulating the digital signals.

[0113] One or more MF models 630 (hereinafter “the ML model 630”) may be stored in the memory 620 and accessible to the processor(s) 610. In certain cases, different ML models 630 with different characteristics may be stored in the memory 620, and a particular ML model 630 may be selected based on its characteristics and / or application as well as characteristics and / or conditions of first wireless device 602 (e.g., a power state, a mobility state, a battery reserve, a temperature, etc.). For example, the ML models 630 may have different inference data and output pairings (e.g., different types of inference data produce different types of output), different levels of accuracies (e.g., 80%, 90%, or95% accurate) associated with the predictions (e.g., the output 514 of FIG. 5), different latencies (e.g., processing times of less than 10 ms, 100 ms, or 1 second) associated with producing the predictions, different ML model sizes (e.g., fde sizes), different coefficients or weights, etc.

[0114] The processor 610 may use the ML model 630 to produce output data (e.g., the 514 of FIG. 5) based on input data (e.g., the inference data 512 of FIG. 5), for example, as described herein with respect to the inference host 504 of FIG. 5. The ML model 630 may be used to perform any of various Al-enhanced tasks, such as those listed above.

[0115] As an example, the ML model 630 may obtain input data including, for example, one or more channel properties (e.g., a signal quality) per component channel of a transmission; and the ML model 630 may provide output data including, for example, a transmit power allocation per component channel, for example, as further described herein. Note that other input data and / or output data may be used in addition to or instead of the examples described herein.

[0116] In certain aspects, the model server 650 may perform any of various ML model lifecycle management (LCM) tasks for the first wireless device 602 and / or the second wireless device 604. The model server 650 may operate as the model training host 502 and update the ML model 630 using training data. In some cases, the model server 650 may operate as the data source 506 to collect and host training data, inference data, and / or performance feedback associated with an ML model 630. In certain aspects, the model server 650 may host various types and / or versions of the ML models 630 for the first wireless device 602 and / or the second wireless device 604 to download.

[0117] In some cases, the model server 650 may monitor and evaluate the performance of the ML model 630 to trigger one or more LCM tasks. For example, the model server 650 may determine whether to activate or deactivate the use of a particular ML model at the first wireless device 602 and / or the second wireless device 604, and the model server 650 may provide such an instruction to the respective first wireless device 602 and / or the second wireless device 604. In some cases, the model server 650 may determine whether to switch to a different ML model 630 being used at the first wireless device 602 and / or the second wireless device 604, and the model server 650 may provide such an instruction to the respective first wireless device 602 and / or the second wirelessdevice 604. In yet further examples, the model server 650 may also act as a central server for decentralized machine learning tasks, such as federated learning.Example Artificial Intelligence Model

[0118] FIG. 7 is an illustrative block diagram of an example artificial neural network (ANN) 700.

[0119] ANN 700 may receive input data 706 which may include one or more bits of data 702, pre-processed data output from pre-processor 704 (optional), or some combination thereof. Here, data 702 may include training data, verification data, application-related data, or the like, e.g., depending on the stage of development and / or deployment of ANN 700. Pre-processor 704 may be included within ANN 700 in some other implementations. Pre-processor 704 may, for example, process all or a portion of data 702 which may result in some of data 702 being changed, replaced, deleted, etc. In some implementations, pre-processor 704 may add additional data to data 702.

[0120] ANN 700 includes at least one first layer 708 of artificial neurons 710 (e.g., perceptrons) to process input data 706 and provide resulting first layer output data via edges 712 (e.g., synapses) to at least a portion of at least one second layer 714. Second layer 714 processes data received via edges 712 and provides second layer output data via edges 716 to at least a portion of at least one third layer 718. Third layer 718 processes data received via edges 716 and provides third layer output data via edges 720 to at least a portion of a final layer 722 including one or more neurons to provide output data 724. All or part of output data 724 may be further processed in some manner by (optional) post-processor 726. Thus, in certain examples, ANN 700 may provide output data 728 that is based on output data 724, post-processed data output from post-processor 726, or some combination thereof. Post-processor 726 may be included within ANN 700 in some other implementations. Post-processor 726 may, for example, process all or a portion of output data 724 which may result in output data 728 being different, at least in part, to output data 724, e.g., as result of data being changed, replaced, deleted, etc. In some implementations, post-processor 726 may be configured to add additional data to output data 724. In this example, second layer 714 and third layer 718 represent intermediate or hidden layers that may be arranged in a hierarchical or other like structure. Although not explicitly shown, there may be one or more further intermediate layers between the second layer 714 and the third layer 718.

[0121] The structure and training of artificial neurons 710 in the various layers may be tailored to specific requirements of an application. Within a given layer of an ANN, some or all of the neurons may be configured to process information provided to the layer and output corresponding transformed information from the layer. For example, transformed information from a layer may represent a weighted sum of the input information associated with or otherwise based on a non-linear activation function or other activation function used to “activate” artificial neurons of a next layer. Artificial neurons in such a layer may be activated by or be responsive to weights and biases that may be adjusted during a training process. Weights of the various artificial neurons may act as parameters to control a strength of connections between layers or artificial neurons, while biases may act as parameters to control a direction of connections between the layers or artificial neurons. An activation function may select or determine whether an artificial neuron transmits its output to the next layer or not in response to its received data. Different activation functions may be used to model different types of non-linear relationships. By introducing non-linearity into an ML model, an activation function allows the ML model to “learn” complex patterns and relationships in the input data (e.g., 506 in FIG. 5). Some non-exhaustive example activation functions include a linear function, binary step function, sigmoid, hyperbolic tangent (tanh), a rectified linear unit (ReLU) and variants, exponential linear unit (ELU), Swish, Softmax, and others.

[0122] Design tools (such as computer applications, programs, etc.) may be used to select appropriate structures for ANN 700 and a number of layers and a number of artificial neurons in each layer, as well as selecting activation functions, a loss function, training processes, etc. Once an initial model has been designed, training of the model may be conducted using training data. Training data may include one or more datasets within which ANN 700 may detect, determine, identify or ascertain patterns. Training data may represent various types of information, including written, visual, audio, environmental context, operational properties, etc. During training, parameters of artificial neurons 710 may be changed, such as to minimize or otherwise reduce a loss function or a cost function. A training process may be repeated multiple times to finetune ANN 700 with each iteration.

[0123] Various ANN model structures are available for consideration. For example, in a feedforward ANN structure each artificial neuron 710 in a layer receives information from the previous layer and likewise produces information for the next layer. In aconvolutional ANN structure, some layers may be organized into filters that extract features from data (e.g., training data and / or input data). In a recurrent ANN structure, some layers may have connections that allow for processing of data across time, such as for processing information having a temporal structure, such as time series data forecasting.

[0124] In an autoencoder ANN structure, compact representations of data may be processed and the model trained to predict or potentially reconstruct original data from a reduced set of features. An autoencoder ANN structure may be useful for tasks related to dimensionality reduction and data compression.

[0125] A generative adversarial ANN structure may include a generator ANN and a discriminator ANN that are trained to compete with each other. Generative-adversarial networks (GANs) are ANN structures that may be useful for tasks relating to generating synthetic data or improving the performance of other models.

[0126] A transformer ANN structure makes use of attention mechanisms that may enable the model to process input sequences in a parallel and efficient manner. An attention mechanism allows the model to focus on different parts of the input sequence at different times. Attention mechanisms may be implemented using a series of layers known as attention layers to compute, calculate, determine or select weighted sums of input features based on a similarity between different elements of the input sequence. A transformer ANN structure may include a series of feedforward ANN layers that may learn non-linear relationships between the input and output sequences. The output of a transformer ANN structure may be obtained by applying a linear transformation to the output of a final attention layer. A transformer ANN structure may be of particular use for tasks that involve sequence modeling, or other like processing.

[0127] Another example type of ANN structure, is a model with one or more invertible layers. Models of this type may be inverted or “unwrapped” to reveal the input data that was used to generate the output of a layer.

[0128] Other example types of ANN model structures include fully connected neural networks (FCNNs) and long short-term memory (LSTM) networks.

[0129] ANN 700 or other ML models may be implemented in various types of processing circuits along with memory and applicable instructions therein, for example, as described herein with respect to FIGS. 5 and 6. For example, general-purposehardware circuits, such as, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs) may be employed to implement a model. One or more ML accelerators, such as tensor processing units (TPUs), embedded neural processing units (eNPUs), or other special-purpose processors, and / or field- programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), or the like also may be employed. Various programming tools are available for developing ANN models.Aspects of Artificial Intelligence Model Training

[0130] There are a variety of model training techniques and processes that may be used prior to, or at some point following, deployment of an ML model, such as ANN 700 of FIG. 7.

[0131] As part of a model development process, information in the form of applicable training data may be gathered or otherwise created for use in training an ML model accordingly. For example, training data may be gathered or otherwise created regarding information associated with received / transmitted signal strengths, interference, and resource usage data, as well as any other relevant data that might be useful for training a model to address one or more problems or issues in a communication system. In certain instances, all or part of the training data may originate in one or more user equipments (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 (e.g., 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 drive test (MDT) networks, may be adapted to support collection of data for ML model applications. In another example, training data may be generated or collected online, offline, or both online and offline by a UE, network entity, or other device(s), and all or part of such training data may be transferred or shared (in real or near-real time), such as through store and forward functions or the like. Offline training may refer to creating and using a static training dataset, e.g., in a batched manner, whereas online training may refer to a real-time or near-real-time collection and use of training data. For example, an ML model at a network device (e.g., a UE) may be trained and / or fine-tuned using online or offline training. For offline training, data collection and training can occur in an offline manner at the network side (e.g., at a base station or other network entity) or at the UE side. For online training, the training of a UE-side ML modelmay be performed locally at the UE or by a server device (e.g., a server hosted by a UE vendor) in a real-time or near-real-time manner based on data provided to the server device from the UE.

[0132] In certain instances, all or part of the training data may be shared within a wireless communication system, or even shared (or obtained from) outside of the wireless communication system.

[0133] Once an ML model has been trained with training data, its performance may be evaluated. In some scenarios, evaluation / verification tests may use a validation dataset, which may include data not in the training data, to compare the model’s performance to baseline or other benchmark information. If model performance is deemed unsatisfactory, it may be beneficial to fine-tune the model, e.g., by changing its architecture, re-training it on the data, or using different optimization techniques, etc. Once a model’ s performance is deemed satisfactory, the model may be deployed accordingly. In certain instances, a model may be updated in some manner, e.g., all or part of the model may be changed or replaced, or undergo further training, just to name a few examples.

[0134] As part of a training process for an ANN, such as ANN 700 of FIG. 7, parameters affecting the functioning of the artificial neurons and layers may be adjusted. For example, backpropagation techniques may be used to train the ANN by iteratively adjusting weights and / or biases of certain artificial neurons associated with errors between a predicted output of the model and a desired output that may be known or otherwise deemed acceptable. Backpropagation may include a forward pass, a loss function, a backward pass, and a parameter update that may be performed in training iteration. The process may be repeated for a certain number of iterations for each set of training data until the weights of the artificial neurons / layers are adequately tuned.

[0135] Backpropagation techniques associated with a loss function may measure how well a model is able to predict a desired output for a given input. An optimization algorithm may be used during a training process to adjust weights and / or biases to reduce or minimize the loss function which should improve the performance of the model. There are a variety of optimization algorithms that may be used along with backpropagation techniques or other training techniques. Some initial examples include a gradient descent based optimization algorithm and a stochastic gradient descent based optimization algorithm. A stochastic gradient descent (or ascent) technique may be used to adjustweights / biases in order to minimize or otherwise reduce a loss function. A mini-batch gradient descent technique, which is a variant of gradient descent, may involve updating weights / biases using a small batch of training data rather than the entire dataset. A momentum technique may accelerate an optimization process by adding a momentum term to update or otherwise affect certain weights / biases.

[0136] An adaptive learning rate technique may adjust a learning rate of an optimization algorithm associated with one or more characteristics of the training data. A batch normalization technique may be used to normalize inputs to a model in order to stabilize a training process and potentially improve the performance of the model.

[0137] A “dropout” technique may be used to randomly drop out some of the artificial neurons from a model during a training process, e.g., in order to reduce overfitting and potentially improve the generalization of the model.

[0138] An “early stopping” technique may be used to stop an on-going training process early, such as when a performance of the model using a validation dataset starts to degrade.

[0139] Another example technique includes data augmentation to generate additional training data by applying transformations to all or part of the training information.

[0140] A transfer learning technique may be used which involves using a pre-trained model as a starting point for training a new model, which may be useful when training data is limited or when there are multiple tasks that are related to each other.

[0141] A multi-task learning technique may be used which involves training a model to perform multiple tasks simultaneously to potentially improve the performance of the model on one or more of the tasks. Hyperparameters or the like may be input and applied during a training process in certain instances.

[0142] Another example technique that may be useful with regard to an ML model is some form of a “pruning” technique. A pruning technique, which may be performed during a training process or after a model has been trained, involves the removal of unnecessary (e.g., because they have no impact on the output) or less necessary (e.g., because they have negligible impact on the output), or possibly redundant features from a model. In certain instances, a pruning technique may reduce the complexity of a model or improve efficiency of a model without undermining the intended performance of the model.

[0143] Pruning techniques may be particularly useful in the context of wireless communication, where the available resources (such as power and bandwidth) may be limited. Some example pruning techniques include a weight pruning technique, a neuron pruning technique, a layer pruning technique, a structural pruning technique, and a dynamic pruning technique. Pruning techniques may, for example, reduce the amount of data corresponding to a model that may need to be transmitted or stored.

[0144] Weight pruning techniques may involve removing some of the 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 of a model associated with one or more characteristics of the data or the environment. For example, in certain wireless communication devices, a dynamic pruning technique may more aggressively prune a model for use in a low-power or low-bandwidth environment, and less aggressively prune the model for use in a high-power or high-bandwidth environment. In certain aspects, pruning techniques also may be applied to training data, e.g., to remove outliers, etc. In some implementations, pre-processing techniques directed to all or part of a training dataset may improve model performance or promote faster convergence of a model. For example, training data may be pre-processed to change or remove unnecessary data, extraneous data, incorrect data, or otherwise identifiable data. Such pre-processed training data may, for example, lead to a reduction in potential overfitting, or otherwise improve the performance of the trained model.

[0145] One or more of the example training techniques presented above may be employed as part of a training process. As above, some example training processes that may be used to train an ML model include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning technique.

[0146] Decentralized, distributed, or shared learning, such as federated learning, may enable training on data distributed across multiple devices or organizations, without the need to centralize data or the training. Federated learning may be particularly useful in scenarios where data is sensitive or subject to privacy constraints, or where it is impractical, inefficient, or expensive to centralize data. In the context of wireless communication, for example, federated learning may be used to improve performance by allowing an ML model to be trained on data collected from a wide range of devices andenvironments. For example, an ML model may 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 (loT) devices, to improve the network's performance and efficiency. With federated learning, a user equipment (UE) or other device may receive a copy of all or part of a model and perform local training on such copy of all or part of the model using locally available training data. Such a device may provide update information (e.g., trainable parameter gradients) regarding the locally trained model to one or more other devices (such as a network entity or a server) where the updates from other-like devices (such as other UEs) may be aggregated and used to provide an update to a shared model or the like. A federated learning process may be repeated iteratively until all or part of a model obtains a satisfactory level of performance. Federated learning may enable devices to protect the privacy and security of local data, while supporting collaboration regarding training and updating of all or part of a shared model.

[0147] In some implementations, one or more devices or services may support processes relating to a ML model’s usage, maintenance, activation, reporting, or the like. In certain instances, all or part of a dataset or model may be shared across multiple devices, e.g., to provide or otherwise augment or improve processing. In some examples, signaling mechanisms may be utilized at various nodes of wireless network to signal the capabilities for performing specific functions related to ML model, support for specific ML models, capabilities for gathering, creating, transmitting training data, or other ML related capabilities. ML models in wireless communication systems may, for example, be employed to support decisions relating to wireless resource allocation or selection, wireless channel condition estimation, interference mitigation, beam management, positioning accuracy, energy savings, or modulation or coding schemes, etc. In some implementations, model deployment may occur jointly or separately at various network levels, such as, a central unit (CU), a distributed unit (DU), a radio unit (RU), or the like.Aspects Related to Dynamic Power Loading

[0148] Aspects of the present disclosure provide techniques for dynamic power loading (e.g., transmit power allocation) across channels of a modulated signal transmission.

[0149] FIG. 8 illustrates an example transmit power allocation 800 across n communication component channels 802a-n (hereinafter “the channels 802”) of a transmission. In this example, the transmit power allocation 800 is represented in terms of a communication system having n channels 802. A transfer function of the communication system may represent the input-output relationship on n channels 802, which may correspond to certain time-frequency resources of the transmission. The inputoutput relationship on a given channel refers to transmission of a signal on the channel by a transmitter as the input side and reception of the signal on the channel by a receiver as the output side. In certain aspects, a channel (e.g., the channel 802a) may correspond to one or more subcarriers across a specific time interval (e.g., one or more symbols). As an example, with respect to an OFDM scheme, the n channels 802 may correspond to OFDM resource elements 804a-n (collectively “the resource elements 804”) in an OFDM resource grid 806, for example, as described herein with respect to FIGS. 4A-4D. In some cases, each of the n channels 802 may correspond to a specific resource element (e.g., the resource element 804a) in the resource grid 806. In certain cases, each of the n channels 802 may correspond to multiple resource elements 804 in the resource grid 806. Each of the resource elements 804 may represent a time-frequency unit in the resource grid 806. For example, a resource element (e.g., the resource element 804a) may represent one or more subcarriers 808 across a time interval 810 (e.g., one or more symbols).

[0150] The transfer function of the ith channel can be expressed as follows:where Kj is the output signal, for example, the received signal 812a, 812n obtained at a receiving entity (e.g., the UE 104); hLis a representation of the communication channel 802a, 802n; XLis the input signal, for example, the transmitted signal 814a, 814n output at a transmitting entity (e.g., the BS 102); andis the noise 816a, 816n across the communication channel. The transmitted signal XLcan be expressed in terms of the modulation and allocated transmit power as follows:Xt= PiSt(2)where Ptis the allocated transmit power 818a, 818n for a given channel; and SLis a subcarrier component of the modulated signal 820a, 820n for the given channel, for example, a resource element component, which may correspond to an OFDM subcarrier and symbol as discussed above. As an example, SLmay represent an orthogonal subcarrier of the transmitted OFDM signal and correspond to a particular constellation in the modulation scheme. SLmay have a specific amplitude and / or phase dictated by the modulation scheme, for example, QPSK or QAM. Ptmay be dependent on an aggregate transmit power allocated for the transmission, for example, Pt= / PiP, where pLis the normalized power of a given channel, and P is an aggregate transmit power allocated to the transmission. The aggregate transmit power may be allocated to mitigate against interference, reduce power consumption at the transmitter, avoid saturation at the receiver, and / or maintain radio frequency (RF) radiation at a safe level for humans.

[0151] With respect to MIMO communications, the transfer function of the ith channel can be expressed as follows:where ytis the received signal vector for the ith channel (e.g., resource element i) having a size: [(number of Rx antennas)xl]; HLis the received channel matrix for the ith channel (e.g., resource element i) having a size: [(number of Rx antennas)x(number of streams)]; is the transmitted signal vector for the ith channel (e.g., resource element i) having a size: [(number of streams)xl]; andis the received noise vector for the ith channel (e.g., resource element i) having a size: [(number of Rx antennas)xl]. The transmitted signal vector can be expressed as follows:wherePi = piP ,where NLis the number of streams; NREis the number of resource elements; pps the part of the power that is allocated to resource element z;is the vector of transmitted symbols at resource element i having a size: [(number of streams)xl].

[0152] In certain aspects, the transmit power Ptfor a specific component channel (e.g., the transmit power 812a, 812n) may be allocated based on certain signal propagation effects encountered on the component channel including, for example, noise, interference, scattering, fading, Doppler effects, etc., as further described herein. The transmit power Pi for a specific channel (e.g., the transmit power 812a) may be assigned to the component channel (e.g., corresponding to the channel 802a) of the modulated signal SLfor a time interval, such as the time interval 810. In some cases, the transmit power Ptfor a specific channel may be allocated using a function of one or more channel properties (e.g., a signal quality, which may include signal-to-noise ratio (SNR)) as further described herein with respect to FIG. 9. In certain cases, the transmit power Ptfor a specific channel may be allocated using an Al model, for example, as described herein with respect to FIG. 10.

[0153] Note that the transmit power allocation 800 is an example of allocating transmit powers across resource elements 804 (e.g., Pt per resource element 804). Other transmit power allocation schemes may be applied in addition to or instead of the example transmit power allocation 800. In some cases, the transmit power for a component channel of a transmission may be allocated across groups of subcarriers or resource elements (e.g., Pt per a group of resource elements 804 across a time interval).

[0154] In certain aspects, the UE and network entity may employ one or more techniques for error correction associated with dynamic transmit power allocation. As each of the UE and network entity may separately determine a dynamic power allocation for a transmission (e.g., the transmitting entity and the receiving entity determine separate power allocations), there may be occasions when the dynamic power allocation determined at the UE and network entity are mismatched. As an example with respect to downlink communications, the UE may determine a different transmit power allocation for demodulation than the transmit power allocation used at the network entity for transmission, or vice versa (for uplink communications).

[0155] In order to detect a mismatch between the power allocations and perform error correction, the transmitting entity (e.g., a network entity) may send, to the receiving entity (e.g., a UE), an indication of the power allocation used at the transmitting entity. Thereceiving entity may compare the indication of the power allocation with the power allocation determined at the receiving entity. If there is a mismatch between the power allocations, the receiving entity may adjust the transmit powers used for demodulation.

[0156] As an example, each of the transmit powers for component channels of a transmission may be quantized as a binary number, for example, having 16 values in 4 bits representing the range of 0 to 1. The binary numbers of the transmit powers may be combined (e.g., concatenated) into a bit stream. The transmitting entity may encode the bit stream using an error correcting encoder including, for example, a convolutional code, a Reed-Salomon code, or any other suitable error correction code. The transmitting entity may determine a cyclic redundancy check (CRC) of the encoded bit stream. The transmitting entity may send, to the receiving entity, the CRC of the encoded bit stream. The receiving entity may perform the same encoding and CRC generation on the power allocation determined at the receiving entity. The receiving entity may compare the calculated CRC and the CRC obtained from the transmitting entity. If the CRCs are the same, it may mean with high probability that there is no mismatch between the transmitting entity and receiving entity power allocations. If there is a mismatch between the CRCs, the receiving entity may correct any errors based on the error correction capabilities of the encoder / decoder.Example Power Loading Based on Function of Signal Quality

[0157] In certain aspects, a wireless communications device may determine the transmit power per component channel (St) of a communication signal (e.g., a modulated signal) using a function of one or more channel properties, such as the signal quality per channel. A transmitter and receiver may be configured with the function. The transmitter may use the function to determine the individual transmit powers, and the receiver may use the function to demodulate the received signal.

[0158] As an example, a wireless communications device (e.g., the UE or network entity) may determine the signal quality per OFDM subcarrier or groups of subcarriers of a transmission. The signal quality may be or include a signal-to-noise ratio (SNR). In certain aspects, a network entity and UE may calculate the signal quality per resource element. For reduced signaling overhead, the UE may send, to the network entity, an indication of the wideband noise covariance observed (measured) at the UE. In certain aspects, the UE may send, to the network entity an indication of a signal quality percomponent channel (e.g., subcarrier, resource element, group of subcarriers, and / or group of resource elements). For example, the UE may report the wideband noise covariance via uplink control information (UCI) at some periodicity, which may be preconfigured or configured by the network entity. In some cases, the network entity may send, to the UE, an indication to report the wideband noise covariance aperiodically, for example, triggered via downlink control information (DCI) and / or in response to a triggering event.

[0159] In some cases, the signal quality for a given component channel (e.g., resource element or group of resource elements) may be expressed in terms of an arithmetic mean of the diagonal matrix of M as follows:where y is a representation of the signal quality for the respective component channel; NLis number of streams (layers); M[NL NL] = HHR^H ; [M]u is the (i, i) element of M; R^n is the inverse of the wideband noise covariance reported by the UE; H is the channel matrix; and HHis the transpose conjugate of the channel matrix. The constellation may be known to the network entity and the UE. In some cases, the network entity and the UE may repeat the calculation for each OFDM symbol.

[0160] FIG. 9 illustrates example operations 900 for allocating transmit powers across component channels (e.g., resource elements or groups of resource elements) of a modulated transmission. In this example, the transmit powers may be allocated based on a function of the signal quality per component channel, such as a mercury-waterfdling technique or a waterfdling technique. The operations 900 may be performed by a wireless communications device, such as the UE 104 and / or BS 102 or a disaggregated entity thereof. An example mercury-waterfdling technique is described in Lozano, A. et al., Mercury / Waterfilling: Optimum Power Allocation with Arbitrary Input Constellations, International Symposium on Information Theory, Proceedings, Sep. 4, 2005, 1773-1777, IEEE.

[0161] At 902, the wireless device may determine the signal quality (y,) for each of the component channels, and an available power margin (e.g., fdled water) up to an effective water level 910 (e.g., 1 / z?) for each of the channels may be effectively adjustedbased on the reciprocal of the corresponding signal quality (1 / Pi represented as a solid 908) and the modulation (represented as the mercury 914), where Y is a factor for determining the water level 910 (which corresponds to the total available power for allocation to a channel) and can be determined as further described below. The available power margin up to the effective water level 910 may represent the allocated transmit power that can be set to an individual component channel. For example, for each of the component channels, a vessel 912a, 912n may be fdled with a unit-base solid 908 up to a height of (1 / Fi). The solid 908 may effectively displace a portion of the effective water level 910 in the respective vessel 912a, 912n of a component channel.

[0162] At 904, for each of the channels, the available power margin up to the effective water level 910 may be effectively adjusted based on a representation of the input distribution (e.g., the modulation). For example, mercury 914 may be poured into each of the channel vessels 912a-912n until the height of the mercury 914 and the solid 908 (1 / Pi) reaches Gt (— j / Yt 916. An arbitrary input distribution of a given channel (z) may be expressed as follows:where p is an effective transmit power for the ith channel; and MMSE^1is an inverse minimum means square error for the ith channel. The minimum means square error of a given channel (z) may be expressed as follows:where m is the constellation size of the modulation scheme; stis the 1’th symbol of the constellation; and y is the received power of the received signal for the ith channel. Y can be determined using the following expression as a contraint:(8) where NREis the number of channels or resource elements used in the modulated transmission. In certain aspects, values of the MMSE^1can be pre-calculated for each constellation and populated in a look-up table.

[0163] At 906, for each of the channels, the transmit power may be the remaining power (e.g., 918) up to the water level 910. For example, water 918 may be fdled in each of the vessels 912a-912n until the water 918 reaches the water level 910 (e.g., I / 77). The water height 920 over the mercury 914 on the ith vessel 912a, 912n equals the allocated transmit power p, for the channel (z). Note that the operations 900 are an example technique of allocating separate transmit powers per component channel of a modulated signal. Other techniques may be used in addition to or instead of the mercury-waterfdling technique described with respect to FIG. 9, such as a waterfdling technique.Example Al-based Dynamic Power Loading

[0164] In certain aspects, an Al model may be used to determine the transmit power per component channel of a modulated signal. A transmitter and receiver may be configured with the Al model. The transmitter may use the Al model to determine the individual transmit powers allocated to each of the component channels of a modulated signal, and the receiver may use the Al model to demodulate the received signal.

[0165] As an example, a network entity may send, to a UE, an indication of the Al model to use for the dynamic power loading. The Al model may be indicated by neural network coefficients (e.g., weights and / or coefficients associated with synapses of the neural network) and / or an index or identifier that identifies a particular Al model. The UE may obtain the indication of the Al model via control signaling including, for example, DCI, sidelink control information (SCI), radio resource control signaling, medium access control signaling, and / or system information. In some cases, the UE may obtain the indication of the Al model as part of establishing an RRC connection or prior to establishing an RRC connection. The UE and the network entity may use the same Al model to determine the transmit power per component channel for transmission and / or demodulation as further described herein with respect to FIG. 12. In certain aspects, the UE may report the noise covariance to the network entity as described herein.

[0166] FIG. 10 illustrates an example of Al-based dynamic power loading 1000. In this example, an Al model 1002 obtains input data 1004 associated with dynamic powerloading. The Al model 1002 may be an example of the ML model(s) 630 of FIG. 6. The input data 1004 may include a modulation and coding scheme (MCS) 1008 used for the transmission. In certain aspects, the MCS 1008 may define or indicate the total number of component channels of a transmission for which to allocate individual transmit powers. In certain aspects, the input data 1004 may include a channel property per component channel of the modulated signal. For example, the input data 1004 may include a signal quality (e.g., SNR) per set of component channels 1010, which may be calculated as described above. The set of component channels 1010 associated with a channel property may include one or more resource elements and / or one or more OFDM subcarriers. In certain aspects, the input data 1004 may include an indication of a quality of service (QoS) level or priority 1012 associated with the transmission, such as a QoS identifier. For example, the Al model 1002 may be trained to allocate more transmit power to a transmission with a high QoS level or priority (e.g., ultra-reliable low latency communications (URLLC), extended reality (XR) communications, etc.); and the Al model 1002 may be trained to allocate less power to a transmission with a low QoS level or priority (e.g., conversational voice, text messaging traffic, etc.). In some cases, the input data 1004 may include any other suitable feature or parameter in addition to or instead of those described above, such as an expected received signal power at the receiving entity, channel capacity, a maximum allowed transmit power, one or more gain factors for specific component channels (e.g., subcarriers or groups of subcarriers), etc.

[0167] The Al model 1002 may be trained to predict or infer the dynamic power settings (e.g., Pi 818a-n) of a modulated signal, for example, as described herein with respect to FIG. 8. The Al model 1002 provides output data 1006 that includes a transmit power per component channel (e.g., resource element and / or group of resource elements) of the modulated signal. As discussed above, the transmit powers may be depend on an aggregate transmit power defined for the transmission.Aspects of Training a Machine Learning Model for Dynamic Power Loading

[0168] FIG. 11 illustrates example a system 1100 for training an Al model 1102 to determine a transmit power per component channel. The system 1100 may be or include a model training host (e.g., the model training host 502 of FIG. 5). In certain aspects, the Al model 1102 may be an example of the Al model(s) described herein with respect to FIG. 5-7 and 10. The model training host may train the Al model 1102 as discussed herein with respect to FIG. 7.

[0169] The model training host obtains training data 1104 including 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 properties per component channel of a modulated signal, the MCS, and / or any other parameters associated with the dynamic power loading, for example, as described herein with respect to FIG. 10. 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 communications devices (e.g., one or more UEs and / or network entities). The training input data 1106 may be obtained from various communications scenarios including, for example, various combinations of MCSs, 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, beam shapes, beam orientations, etc.

[0170] The model training host may use the labels 1108 to evaluate the performance of the Al model 1102 and adjust a configuration of the Al model 1102 (e.g., weights, coefficient(s), activation function(s), and / or synapses of the ANN 700 of FIG. 7) as described herein. The labels 1108 may be or include the expected output of corresponding training input data 1106. In some cases, the labels 1108 may be or include the transmit powers obtained from a function based technique, such as a water filling technique and / or mercury-waterfilling technique. For example, for training input data of a specific MCS and channel conditions across the component channels, the labels 1108 may include expected values for the transmit powers per component channels. Each of the labels 1108 may be associated with at least one set of training input data 1106. In certain cases, each of the labels 1108 may include expected values for the transmit power per component channel that enable a target performance at a receiving entity. For example, the target performance may be or include a specified signal quality and / or a signal strength of the received signal.

[0171] The model training host provides the training input data 1106 to the Al model 1102. For example, the model training host may provide, to the Al model 1102, a set of SNRs for component channels and the MCS of a transmission, where the set of SNRs correspond to a specific communications scenario as discussed above. The Al model 1102 provides output data 1110, which may include the transmit power per component channel of the transmission.

[0172] At 1112, the model training host may evaluate the performance of the Al model 1102. For example, the model training host may evaluate the quality and / or accuracy of the output data 1110. In certain aspects, the model training host may evaluate the performance of the Al model using a cost or loss function 1114 (hereinafter “the cost function 1114”) based at least in part on the output data 1110 and / or the labels 1108. In certain aspects, the cost function 1114 may calculate a cost of the output data 1110 in terms of one or more performance indicator(s) including, for example, a difference between the labels 1108 and the output data 1110, a received signal quality, a received signal strength, a block error rate (BLER) of the received signal, etc. In certain aspects, the cost function 1114 may calculate the cost based on a difference between a performance indicator and a target performance of the dynamic power loading. The model training host may adjust the Al model 1102 (e.g., any of the weights in a layer of a neural network) to reduce the cost associated with the Al model 1102. As an example, adjusting the Al model 1102 may involve adjusting any of the weights of a neural network, any of the activation functions applied at a neuron, and / or any of the synapses coupled to a neuron, etc.

[0173] The model training host may continue to provide the training input data 1106 to the Al model 1102 and adjust the Al model 1102 until the cost of the Al model 1102 satisfies a threshold and / or reaches a minimum cost. In certain aspects, the model training host may apply an Adam optimizer to minimize the cost associated with the transmit powers. In some cases, the model training host may determine whether the output data 1110 matches the corresponding label of the training input data 1106. For example, the model training host may determine whether the predicted transmit powers are correct based on the label (e.g., the expected transmit powers) associated with the set of SNRs supplied as input to the Al model 1102. In certain aspects, the model training host may train the Al model 1102 to satisfy certain criteria. In some cases, the model training host may train the Al model 1102 to satisfy a target performance of a communication channel.

[0174] In certain aspects, the model training host may train multiple Al models. The Al models may be trained to have different performance characteristics, different inputoutput schemes, and / or different applications (e.g., communications scenarios). For example, the Al models may be trained to predict the transmit power per component channel with different levels of accuracy (e.g., accuracies of 85%, 90%, or 99%) of meeting a performance target, different latencies (e.g., the processing time to predict thetransmit powers), and / or different throughputs (e.g., the capacity to predict the transmit powers from one or more sets of input data).

[0175] In some cases, the model training host may train an Al model for a specific application or communications scenario, for example, based on channel conditions, UE mobility, line of sight communications, etc. For example, an Al model may be trained to predict transmit powers for component channels of a first MCS (e.g., QPSK), and another Al model may be trained to predict transmit powers for component channels of a second MCS (e.g., QAM).

[0176] In certain cases, the Al model may be trained to calculate the transmit powers for component channels with a specific input-output scheme. For example, a first Al model may be configured to calculate the transmit powers using an SNR per resource element of a transmission, whereas a second Al model may be configured to calculate the transmit powers using an SNR per group of resource elements of a transmission. Thus, a wireless communications device may select the Al model that is capable of calculating the transmit powers in accordance with certain performance characteristic(s), applications, and / or input-output schemes as described above.Example Operations of Dynamic Power Loading in a Communications System

[0177] FIG. 12 depicts a process flow 1200 for communications in a system between a network entity 1202 and a user equipment (UE) 1204. In some aspects, the network entity 1202 may be an example of the BS 102 depicted and described with respect to FIG. 1 and 3 or a disaggregated base station depicted and described with respect to FIG. 2. Similarly, the UE 1204 may be an example of UE 104 depicted and described with respect to FIG. 1 and 3. However, in other aspects, UE 1204 may be another type of wireless communications device and network entity 1202 may be another type of network entity or network node, such as those described herein. Note that any operations illustrated with dashed lines indicates that that operation may be optional or an alternative.

[0178] At 1206, the UE 1204 sends, to the network entity 1202, capability information indicating that the 1204 is capable of performing dynamic power loading described herein, for example, using a function-based power loading technique an AI- based power loading technique, and / or any suitable power loading technique technique. In some cases, the capability information may indicate that the UE 1204 is capable of performing function-based power loading, for example, as described herein with respectto FIG. 9. For example, the capability information may indicate the type of function (e.g., mercury-waterfilling and / or waterfilling) supported by the UE 1204. In certain cases, the capability information may indicate that the UE 1204 is capable of performing Al-based power loading, for example, as described herein with respect to FIG. 10. In certain aspects, the capability information may indicate the type of Al model (e.g., a neural network) supported by the UE 1204.

[0179] At 1208, the UE 1204 obtains, from the network entity 1202, a configuration indicating one or more parameters for individually allocating transmit power to each component channel (e.g., subcarrier or group of subcarriers) per time interval of a transmission. In certain aspects, the configuration may indicate the time interval for each dynamic power allocation, for example, one or more OFDM symbols (e.g., 1 OFDM symbol or 2 OFDM symbols) or one or more slots (e.g., 1 slot or 2 slots).

[0180] The configuration may indicate the type of dynamic power loading to use for communications, such as the function-based or Al-based dynamic power loading. In certain aspects, the configuration may indicate one or more Al models to use for determination of a transmit power allocation. The indication of the Al model(s) may be or include information that can reproduce an Al model (e.g., weights and / or structure information). The indication of the Al model(s) may be or include an index or identifier that identifies a specific Al model to use for determination of the transmit power allocation. In some cases, the configuration may indicate the input-output scheme of the Al model, such as a set of input features (e.g., SNR per component channel and MCS) from available input feature sets. The configuration may indicate when to send and / or what to include in channel state feedback (e.g., periodically, semi-persistently, and / or aperiodically) to the network entity 1202. The channel state feedback may include a noise covariance report, such as a wideband noise covariance that can be used to determine the signal quality (e.g., SNR) of component channels. The configuration may indicate that a wideband noise covariance matrix report is to be sent with periodicity, for example, in terms of slots (e.g., 10, 20, 40, 80 slots).

[0181] In certain aspects, the configuration may indicate one or more parameters for detection of power allocation mismatches between the UE 1204 and the network entity 1202. The configuration may indicate the total number of bits to quantize the power for each component channel (e.g., resource element or group of resource elements) of a transmission. The configuration may indicate details of the code for encoding the powerallocation bits and / or details of the CRC for calculating a CRC over encoded power allocation bits.

[0182] At 1210, the UE 1204 establishes a communication link (e.g., an RRC connection) with the network entity 1210. In some cases, the UE 1204 and / or network entity 1202 may perform the dynamic power loading as described herein for communications on a channel associated with an RRC connection.

[0183] At 1212, the UE 1204 sends, to the network entity 1202, channel state feedback including, for example, a channel property report. The channel property report may include a wideband noise covariance of a frequency band (e.g., one or more frequency resources), a wideband signal quality of a frequency band, subcarrier signal quality per component channel (e.g., OFDM subcarrier). The UE 1204 may send the channel property report on a periodic basis or in response to an aperiodic trigger (e.g., a DCI trigger and / or a specific event). In some cases, the UE 1204 may report the channel property in response to receiving a DCI message from the network entity 1202. The channel state feedback may be sent via uplink control information (UCI). In some cases, the wideband noise covariance report may have a bit size of (NRX~+ NRx, whereNRXis the total number of Rx antennas at the UE.

[0184] At 1214, the network entity 1202 determines one or more channel properties (e.g., signal quality or SNR) per component channel (e.g., resource element or group of resource elements) of a transmission based at least in part on the channel property report obtained at 1212. As an example, the one or more channel properties per component channel may be determined according to Expression (5).

[0185] At 1216, the network entity 1202 determines the transmit powers per component channel of a transmission based at least in part on the one or more channel properties per component channel determined at 1214. The transmit powers may be determined using the function-based technique and / or the Al-based technique in accordance with the configuration communicated at 1208.

[0186] At 1218, the network entity 1202 sends, to the UE 1204, an indication of the transmit power allocation determined at 1216. In certain aspects, the indication may be sent via control signaling including, for example, DCI. In some cases, the indication may include a CRC of an encoded quantization of the transmit power allocation as discussed above.

[0187] At 1220, the network entity 1202 sends, to the UE 1204, one or more signals using transmit powers determined in accordance with the dynamic transmit power allocation, for example, as described herein with respect to FIG. 8. For example, the network entity 1202 may send signal in a transmission occasion across multiple symbols in multiple subcarriers (e.g., component channels). The network entity 1202 may use an independent transmit power (e.g., Pi) per OFDM subcarrier in each of the symbols of the transmission occasions. The transmit powers may compensate for the independent signal propagation effects encountered on the OFDM subcarriers.

[0188] At 1222, the UE 1204 receives the signals from the network entity 1202, and the UE 1204 demodulates the signals based on the dynamic transmit power allocation. As an example, at 1224, the UE 1204 may determine the channel properties per component channel; and at 1226, the UE 1204 may determine the transmit power per component channel in accordance with the configuration received at 1208. Then, using the transmit powers, the UE 1204 may decode the constellations of the modulated signal.

[0189] At 1228, the UE 1204 communicates with the network entity 1202 via uplink communication channel(s) using dynamic power loading as described herein. For example, the UE 1204 may determine the transmit powers per component channel using the function-based technique and / or Al-based technique as described herein. The UE 1204 sends, to the network entity 1202, one or more signals using transmit powers determined in accordance with the dynamic transmit power allocation, for example, as described herein with respect to FIG. 8. The network entity 1202 demodulates the received signals based on the dynamic transmit power allocation. Note that the dynamic power loading described herein may also be applied to sidelink communications between multiple UEs.

[0190] The dynamic power loading described herein may enable improved wireless communication performance between the UE 1204 and the network entity 1202, such as increased throughput, reduced latency, enhanced signal quality, and / or enhanced signal strength.Example Operations of Dynamic Power Loading

[0191] FIG. 13 shows a method 1300 for wireless communications by an apparatus, such as UE 104 of FIGS. 1 and 3.

[0192] Method 1300 begins at block 1305 with obtaining a configuration indicating one or more first parameters for individually allocating transmit power (e.g., Pi) to each set of frequency resources of a plurality of sets of frequency resources over a time interval. In certain aspects, a set of frequency resources may include one or more resource elements, for example, as described herein with respect to FIGS. 4A-4D and 8. In certain aspects, the time interval may be or include one or more symbols or one or more slots. In certain aspects, the plurality of sets of frequency resources are in at least one of a carrier or a bandwidth part of the carrier. In certain aspects, the one or more first parameters comprise one or more of: a total number of frequency resources in each of the plurality of sets of frequency resources; a duration of the time interval; a type of power allocation for allocating transmit power; one or more coefficients for an Al model for allocating transmit power; or a total transmit power for allocation across the plurality of sets of frequency resources over the time interval.

[0193] Method 1300 then proceeds to block 1310 with communicating one or more signals using transmit powers based at least in part on the configuration. In certain aspects, block 1310 includes: sending the one or more signals on the plurality of sets of frequency resources using a corresponding transmit power for each set of frequency resources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration. In certain aspects, block 1310 includes: obtaining the one or more signals; and demodulating the one or more signals based on a corresponding transmit power for each set of frequency resources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration. In certain aspects, method 1300 further includes determining a corresponding transmit power for each set of the frequency resources of the plurality of sets of frequency resources based on a noise covariance for the plurality of sets of frequency resources.

[0194] In certain aspects, method 1300 further includes monitoring the plurality of sets of frequency resources. In certain aspects, method 1300 further includes sending an indication of the noise covariance for the plurality of sets of frequency resources.

[0195] In certain aspects, method 1300 further includes obtaining an indication of a periodicity for reporting noise covariance for the plurality of sets of frequency resources.

[0196] In certain aspects, method 1300 further includes communicating (e.g., sending or obtaining) an indication of allocated transmit power for each set of frequency resourcesof the plurality of sets of frequency resources. In certain aspects, method 1300 further includes comparing a first CRC associated with the transmit powers to a second CRC, the indication of the allocated transmit power comprising the second CRC.

[0197] In certain aspects, method 1300 further includes adjusting at least one of the transmit powers in response to any difference between the first CRC and the second CRC. In certain aspects, adjusting the at least one of the transmit powers comprises: decoding the indication of the allocated transmit power; and adjusting the at least one of the transmit powers based at least in part on the decoded indication of the allocated transmit power.

[0198] In certain aspects, the indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources is configured according to one or more second parameters comprising one or more of: a number of quantized power levels for each allocated transmit power; a number of bits for indication of each allocated transmit power; a type of encoding for the indication of allocated transmit power; or a CRC for the indication of allocated transmit power.

[0199] In certain aspects, method 1300 further includes sending capability information indicating that the apparatus is capable of individually allocating transmit power to different sets of frequency resources.

[0200] In certain aspects, method 1300 further includes determining, as a function of signal quality of each set of frequency resources of the plurality of sets of frequency resources, a transmit power for each set of frequency resources of the plurality of sets of frequency resources, for example, as described herein with respect to FIG. 9.

[0201] In certain aspects, method 1300 further includes determining, by an Al model, a transmit power for each set of frequency resources of the plurality of sets of frequency resources, for example, as described herein with respect to FIG. 10.

[0202] In certain aspects, method 1300, or any aspect related to it, may be performed by an apparatus, such as communications device 1500 of FIG. 15, which includes various components operable, configured, or adapted to perform the method 1300. Communications device 1500 is described below in further detail.

[0203] Note that FIG. 13 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.

[0204] FIG. 14 shows a method 1400 for wireless communications by an apparatus, such as BS 102 of FIGS. 1 and 3, or a disaggregated base station as discussed with respect to FIG. 2.

[0205] Method 1400 begins at block 1405 with sending a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval. In certain aspects, a set of frequency resources may include one or more resource elements, for example, as described herein with respect to FIGS. 4A-4D and 8. In certain aspects, the time interval may be or include one or more symbols or one or more slots. In certain aspects, the plurality of sets of frequency resources are in at least one of a carrier or a bandwidth part of the carrier. In certain aspects, the one or more first parameters comprise one or more of: a total number of frequency resources in each of the plurality of sets of frequency resources; a duration of the time interval; a type of power allocation for allocating transmit power; one or more coefficients for an Al model for allocating transmit power; or a total transmit power for allocation across the plurality of sets of frequency resources over the time interval.

[0206] Method 1400 then proceeds to block 1410 with communicating one or more signals using transmit powers based at least in part on the configuration. In certain aspects, block 1410 includes sending the one or more signals on the plurality of sets of frequency resources using a corresponding transmit power for each set of frequency resources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration. In certain aspects, block 1410 includes: obtaining the one or more signals; and demodulating the one or more signals based on a corresponding transmit power for each set of frequency resources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration. In certain aspects, method 1400 further includes determining a corresponding transmit power for each set of the frequency resources of the plurality of sets of frequency resources based on a noise covariance for the plurality of sets of frequency resources.

[0207] In certain aspects, method 1400 further includes obtaining an indication of the noise covariance (e.g., a wideband noise covariance) for the plurality of sets of frequency resources.

[0208] In certain aspects, method 1400 further includes sending an indication of a periodicity for reporting noise covariance for the plurality of sets of frequency resources.

[0209] In certain aspects, method 1400 further includes communicating an indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources. In certain aspects, method 1400 further includes comparing a first CRC associated with the transmit powers to a second CRC, the indication of the allocated transmit power comprising the second CRC. In certain 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 certain aspects, adjusting the at least one of the transmit powers comprises decoding the indication of the allocated transmit power; and adjusting the at least one of the transmit powers based at least in part on the decoded indication of the allocated transmit power.

[0210] In certain aspects, the indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources is configured according to one or more second parameters comprising one or more of: a number of quantized power levels for each allocated transmit power; a number of bits for indication of each allocated transmit power; a type of encoding for the indication of allocated transmit power; or a CRC for the indication of allocated transmit power.

[0211] In certain aspects, method 1400 further includes obtaining capability information indicating that the apparatus is capable of individually allocating transmit power to different sets of frequency resources.

[0212] In certain aspects, method 1400 further includes determining, as a function of signal quality of each set of frequency resources of the plurality of sets of frequency resources, a transmit power for each set of frequency resources of the plurality of sets of frequency resources, for example, as described herein with respect to FIG. 9.

[0213] In certain aspects, method 1400 further includes determining, by an artificial intelligence model, a transmit power for each set of frequency resources of the plurality of sets of frequency resources, for example, as described herein with respect to FIG. 10.

[0214] In certain aspects, method 1400, or any aspect related to it, may be performed by an apparatus, such as communications device 1600 of FIG. 16, which includes various components operable, configured, or adapted to perform the method 1400. Communications device 1600 is described below in further detail.

[0215] Note that FIG. 14 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.Example Communications Devices

[0216] FIG. 15 depicts aspects of an example communications device 1500. In some aspects, communications device 1500 is a user equipment, such as UE 104 described above with respect to FIGS. 1 and 3.

[0217] The communications device 1500 includes a processing system 1502 coupled to a transceiver 1538 (e.g., a transmitter and / or a receiver). The transceiver 1538 is configured to transmit and receive signals for the communications device 1500 via an antenna 1540, such as the various signals as described herein. The processing system 1502 may be configured to perform processing functions for the communications device 1500, including processing signals received and / or to be transmitted by the communications device 1500.

[0218] The processing system 1502 includes one or more processors 1504. In various aspects, the one or more processors 1504 may be representative of one or more of receive processor 358, transmit processor 364, TX MIMO processor 366, and / or controller / processor 380, as described with respect to FIG. 3. The one or more processors 1504 are coupled to a computer-readable medium / memory 1520 via a bus 1536. In certain aspects, the computer-readable medium / memory 1520 is configured to store instructions (e.g., computer-executable code) that when executed by the one or more processors 1504, enable and cause the one or more processors 1504 to perform the method 1300 described with respect to FIG. 13, or any aspect related to it, including any additional operations described in relation to FIG. 13. Note that reference to a processor performing a function of communications device 1500 may include one or more processors performing that function of communications device 1500, such as in a distributed fashion.

[0219] In the depicted example, computer-readable medium / memory 1520 stores code for obtaining 1522, code for communicating 1524, code for determining 1526, code for monitoring 1528, code for sending 1530, code for comparing 1532, and code for adjusting 1534. Processing of the code 1522-1534 may enable and cause the communications device 1500 to perform the method 1300 described with respect to FIG. 13, or any aspect related to it.

[0220] The one or more processors 1504 include circuitry configured to implement (e.g., execute) the code stored in the computer-readable medium / memory 1520, including circuitry for obtaining 1506, circuitry for communicating 1508, circuitry for determining 1510, circuitry for monitoring 1512, circuitry for sending 1514, circuitry for comparing 1516, and circuitry for adjusting 1518. Processing with circuitry 1506-1518 may enable and cause the communications device 1500 to perform the method 1300 described with respect to FIG. 13, or any aspect related to it.

[0221] More generally, means for communicating, transmitting, sending or outputting for transmission may include the transceivers 354, antenna(s) 352, transmit processor 364, TX MIMO processor 366, and / or controller / processor 380 of the UE 104 illustrated in FIG. 3, transceiver 1538 and / or antenna 1540 of the communications device 1500 in FIG. 15, and / or one or more processors 1504 of the communications device 1500 in FIG. 15. Means for communicating, receiving, monitoring, or obtaining may include the transceivers 354, antenna(s) 352, receive processor 358, and / or controller / processor 380 of the UE 104 illustrated in FIG. 3, transceiver 1538 and / or antenna 1540 of the communications device 1500 in FIG. 15, and / or one or more processors 1504 of the communications device 1500 in FIG. 15. Means for determining, monitoring, comparing, or adjusting may include the controller / processor 380 of the UE 104 illustrated in FIG. 3, and / or one or more processors 1504 of the communications device 1500 in FIG. 15.

[0222] FIG. 16 depicts aspects of an example communications device 1600. In some aspects, communications device 1600 is a network entity, such as BS 102 of FIGS. 1 and 3, or a disaggregated base station as discussed with respect to FIG. 2.

[0223] The communications device 1600 includes a processing system 1605 coupled to a transceiver 1685 (e.g., a transmitter and / or a receiver) and / or a network interface 1695. The transceiver 1685 is configured to transmit and receive signals for the communications device 1600 via an antenna 1690, such as the various signals as described herein. The network interface 1695 is configured to obtain and send signals for the communications device 1600 via communications link(s), such as a backhaul link, midhaul link, and / or fronthaul link as described herein, such as with respect to FIG. 2. The processing system 1605 may be configured to perform processing functions for the communications device 1600, including processing signals received and / or to be transmitted by the communications device 1600.

[0224] The processing system 1605 includes one or more processors 1610. In various aspects, one or more processors 1610 may be representative of one or more of receive processor 338, transmit processor 320, TX MIMO processor 330, and / or controller / processor 340, as described with respect to FIG. 3. The one or more processors 1610 are coupled to a computer-readable medium / memory 1645 via a bus 1680. In certain aspects, the computer-readable medium / memory 1645 is configured to store instructions (e.g., computer-executable code) that when executed by the one or more processors 1610, enable and cause the one or more processors 1610 to perform the method 1400 described with respect to FIG. 14, or any aspect related to it, including any additional operations described in relation to FIG. 14. Note that reference to a processor of communications device 1600 performing a function may include one or more processors of communications device 1600 performing that function, such as in a distributed fashion.

[0225] In the depicted example, the computer-readable medium / memory 1645 stores code for sending 1650, code for communicating 1655, code for determining 1660, code for obtaining 1665, code for comparing 1670, and code for adjusting 1675. Processing of the code 1650-1675 may enable and cause the communications device 1600 to perform the method 1400 described with respect to FIG. 14, or any aspect related to it.

[0226] The one or more processors 1610 include circuitry configured to implement (e.g., execute) the code stored in the computer-readable medium / memory 1645, including circuitry for sending 1615, circuitry for communicating 1620, circuitry for determining 1625, circuitry for obtaining 1630, circuitry for comparing 1635, and circuitry for adjusting 1640. Processing with circuitry 1615-1640 may enable and cause the communications device 1600 to perform the method 1400 described with respect to FIG. 14, or any aspect related to it.

[0227] More generally, means for communicating, transmitting, sending or outputting for transmission may include the transceivers 332, antenna(s) 334, transmit processor 320, TX MIMO processor 330, and / or controller / processor 340 of the BS 102 illustrated in FIG. 3, transceiver 1685 and / or antenna 1690 of the communications device 1600 in FIG. 16, and / or one or more processors 1610 of the communications device 1600 in FIG. 16. Means for communicating, receiving, or obtaining may include the transceivers 332, antenna(s) 334, receive processor 338, and / or controller / processor 340 of the BS 102 illustrated in FIG. 3, transceiver 1685 and / or antenna 1690 of the communications device 1600 in FIG. 16, and / or one or more processors 1610 of thecommunications device 1600 in FIG. 16. Means for determining, comparing, or adjusting may include the controller / processor 340 of the BS 102 illustrated in FIG. 3, and / or one or more processors 1610 of the communications device 1600 in FIG. 16.Example Clauses

[0228] Implementation examples are described in the following numbered clauses:

[0229] Clause 1 : A method for wireless communications by an apparatus comprising: obtaining a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicating one or more signals using transmit powers based at least in part on the configuration.

[0230] Clause 2: The method of Clause 1, further comprising determining a corresponding transmit power for each set of the frequency resources of the plurality of sets of frequency resources based on a noise covariance for the plurality of sets of frequency resources.

[0231] Clause 3: The method of Clause 2, further comprising: monitoring the plurality of sets of frequency resources; and sending an indication of the noise covariance for the plurality of sets of frequency resources.

[0232] Clause 4: The method of any of Clauses 1-3, further comprising: obtaining an indication of a periodicity for reporting noise covariance for the plurality of sets of frequency resources.

[0233] Clause 5: The method of any of Clauses 1-4, wherein the plurality of sets of frequency resources are in at least one of a carrier or a bandwidth part of the carrier.

[0234] Clause 6: The method of any of Clauses 1-5, wherein the one or more first parameters comprise one or more of: a total number of frequency resources in each of the plurality of sets of frequency resources; a duration of the time interval; a type of power allocation for allocating transmit power; one or more coefficients for an Al model for allocating transmit power; or a total transmit power for allocation across the plurality of sets of frequency resources over the time interval.

[0235] Clause 7: The method of any of Clause 1-6, further comprising: communicating an indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources.

[0236] Clause 8: The method of Clause 7, further comprising: comparing a first CRC associated with the transmit powers to a second CRC, the indication of the allocated transmit power comprising 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.

[0237] Clause 9: The method of Clause 7 or 8, wherein adjusting the at least one of the transmit powers comprises: decoding the indication of the allocated transmit power; and adjusting the at least one of the transmit powers based at least in part on the decoded indication of the allocated transmit power.

[0238] Clause 10: The method of any of Clauses 7-9, wherein the indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources is configured according to one or more second parameters comprising one or more of: a number of quantized power levels for each allocated transmit power; a number of bits for indication of each allocated transmit power; a type of encoding for the indication of allocated transmit power; or a CRC for the indication of allocated transmit power.

[0239] Clause 11 : The method of any of Clauses 1-10, further comprising: sending capability information indicating that the apparatus is capable of individually allocating transmit power to different sets of frequency resources.

[0240] Clause 12: The method of any of Clauses 1-11, further comprising: determining, by an Al model, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

[0241] Clause 13: The method of any of Clauses 1-12, further comprising: determining, as a function of signal quality of each set of frequency resources of the plurality of sets of frequency resources, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

[0242] Clause 14: The method of any of Clauses 1-13, wherein communicating the one or more signals comprises: sending the one or more signals on the plurality of sets of frequency resources using a corresponding transmit power for each set of frequencyresources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration.

[0243] Clause 15: The method of any of Clauses 1-14, wherein communicating 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 for each set of frequency resources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration.

[0244] Clause 16: A method for wireless communications by an apparatus comprising: sending a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicating one or more signals using transmit powers based at least in part on the configuration.

[0245] Clause 17: The method of Clause 16, further comprising determining a corresponding transmit power for each set of the frequency resources of the plurality of sets of frequency resources based on a noise covariance for the plurality of sets of frequency resources.

[0246] Clause 18: The method of Clause 16 or 17, further comprising: obtaining an indication of the noise covariance for the plurality of sets of frequency resources.

[0247] Clause 19: The method of any of Clauses 16-18, further comprising: sending an indication of a periodicity for reporting noise covariance for the plurality of sets of frequency resources.

[0248] Clause 20: The method of any of Clauses 16-19, wherein the plurality of sets of frequency resources are in at least one of a carrier or a bandwidth part of the carrier.

[0249] Clause 21 : The method of any of Clauses 16-20, wherein the one or more first parameters comprise one or more of: a total number of frequency resources in each of the plurality of sets of frequency resources; a duration of the time interval; a type of power allocation for allocating transmit power; one or more coefficients for an Al model for allocating transmit power; or a total transmit power for allocation across the plurality of sets of frequency resources over the time interval.

[0250] Clause 22: The method of any of Clauses 16-21, further comprising: communicating an indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources.

[0251] Clause 23: The method of Clause 22, further comprising: comparing a first CRC associated with the transmit powers to a second CRC, the indication of the allocated transmit power comprising 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.

[0252] Clause 24: The method of Clause 22 or 23, wherein adjusting the at least one of the transmit powers comprises decoding the indication of the allocated transmit power; and adjusting the at least one of the transmit powers based at least in part on the decoded indication of the allocated transmit power.

[0253] Clause 25: The method of any of Clauses 22-24, wherein the indication of allocated transmit power for each set of frequency resources of the plurality of sets of frequency resources is configured according to one or more second parameters comprising one or more of: a number of quantized power levels for each allocated transmit power; a number of bits for indication of each allocated transmit power; a type of encoding for the indication of allocated transmit power; or a CRC for the indication of allocated transmit power.

[0254] Clause 26: The method of any of Clauses 16-25, further comprising: obtaining capability information indicating that the apparatus is capable of individually allocating transmit power to different sets of frequency resources.

[0255] Clause 27: The method of any of Clauses 16-26, further comprising: determining, by an artificial intelligence model, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

[0256] Clause 28: The method of any of Clauses 16-27, further comprising: determining, as a function of signal quality of each set of frequency resources of the plurality of sets of frequency resources, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

[0257] Clause 29: The method of any of Clauses 16-28, wherein communicating the one or more signals comprises sending the one or more signals on the plurality of sets of frequency resources using a corresponding transmit power for each set of frequencyresources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration.

[0258] Clause 30: The method of any of Clauses 16-29, wherein communicating 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 for each set of frequency resources of the plurality of sets of frequency resources, each corresponding transmit power based on the configuration.

[0259] Clause 31 : One or more apparatuses, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to cause the one or more apparatuses to perform a method in accordance with any one of clauses 1- 30.

[0260] Clause 32: One or more apparatuses, comprising means for performing a method in accordance with any one of clauses 1-30.

[0261] Clause 33: One or more non-transitory computer-readable media comprising executable instructions that, when executed by one or more processors of one or more apparatuses, cause the one or more apparatuses to perform a method in accordance with any one of clauses 1-30.

[0262] Clause 34: One or more computer program products embodied on one or more computer-readable storage media comprising code for performing a method in accordance with any one of clauses 1-30.Additional Considerations

[0263] The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. The examples discussed herein are not limiting of the scope, applicability, or aspects set forth in the claims. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various actions may be added, omitted, or combined. Also, features described with respect to some examples may becombined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0264] The various illustrative logical blocks, modules and circuits described in connection with the present disclosure may be implemented or performed with a general purpose processor, an Al processor, a digital signal processor (DSP), an ASIC, a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, a system on a chip (SoC), or any other such configuration.

[0265] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0266] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

[0267] As used herein, “coupled to” and “coupled with” generally encompass direct coupling and indirect coupling (e.g., including intermediary coupled aspects) unlessstated otherwise. For example, stating that a processor is coupled to a memory allows for a direct coupling or a coupling via an intermediary aspect, such as a bus.

[0268] The methods disclosed herein comprise one or more actions for achieving the methods. The method actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of actions is specified, the order and / or use of specific actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and / or software component(s) and / or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor.

[0269] The following claims are not intended to be limited to the aspects shown herein, but are to be accorded the full scope consistent with the language of the claims. Reference to an element in the singular is not intended to mean only one unless specifically so stated, but rather “one or more.” The subsequent use of a definite article (e.g., “the” or “said”) with an element (e.g., “the processor”) is not intended to invoke a singular meaning (e.g., “only one”) on the element unless otherwise specifically stated. For example, reference to an element (e.g., “a processor,” “a controller,” “a memory,” “a transceiver,” “an antenna,” “the processor,” “the controller,” “the memory,” “the transceiver,” “the antenna,” etc.), unless otherwise specifically stated, 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 more transceivers,” etc.). The terms “set” and “group” are intended to include one or more elements, and may be used interchangeably with “one or more.” Where reference is made to one or more elements performing functions (e.g., steps of a method), one element may perform all functions, or more than one element may collectively perform the functions. When more than one element collectively performs the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements) and / or each function need not be performed in whole by only one element (e.g., different elements may perform different sub- functions of a function). Similarly, where reference is made to one or more elements configured to cause another element (e.g., an apparatus) to perform functions, one element may be configured to cause the other element to perform all functions, or more than one element may collectively be configured to causethe other element to perform the functions. Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

CLAIMS1. An apparatus configured for wireless communications, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors being configured to cause the apparatus to: obtain a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicate one or more signals using transmit powers based at least in part on the configuration.

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

3. The apparatus of claim 2, wherein the one or more processors are configured to cause the apparatus to: monitor the plurality of sets of frequency resources; and send an indication of the noise covariance for the plurality of sets of frequency resources.

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

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

6. The apparatus of claim 1, wherein the one or more first parameters comprise one or more of: a total number of frequency resources in each of the plurality of sets of frequency resources;a duration of the time interval; a type of power allocation for allocating transmit power; one or more coefficients for an artificial intelligence (Al) model for allocating transmit power; or a total transmit power for allocation across the plurality of sets of frequency resources over the time interval.

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

8. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: send capability information indicating that the apparatus is capable of individually allocating transmit power 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, by an artificial intelligence (Al) model, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

10. The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to: determine, as a function of signal quality of each set of frequency resources of the plurality of sets of frequency resources, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

11. An apparatus configured for wireless communications, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors being configured to cause the apparatus to: send a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicate one or more signals using transmit powers based at least in part on the configuration.

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

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 for the plurality of sets of frequency resources.

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

15. The apparatus of claim 11, wherein the one or more first parameters comprise one or more of: a total number of frequency resources in each of the plurality of sets of frequency resources; a duration of the time interval; a type of power allocation for allocating transmit power; one or more coefficients for an artificial intelligence (Al) model for allocating transmit power; or a total transmit power for allocation across the plurality of sets of frequency resources over the time interval.

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

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 individually allocating transmit power 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, by an artificial intelligence model, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

19. The apparatus of claim 11, wherein the one or more processors are configured to cause the apparatus to: determine, as a function of signal quality of each set of frequency resources of the plurality of sets of frequency resources, a transmit power for each set of frequency resources of the plurality of sets of frequency resources.

20. A method of wireless communications by an apparatus, comprising: obtaining a configuration indicating one or more first parameters for individually allocating transmit power to each set of frequency resources of a plurality of sets of frequency resources over a time interval; and communicating one or more signals using transmit powers based at least in part on the configuration.

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