Ai / ml model id signaling for rrm enhancement

Abstract

An apparatus of a base station (BS), the apparatus comprising one or more processors coupled to memory and configured to decode, at the BS, a UE capability message received from a UE, the UE capability message comprising: one or more radio resource management (RRM) artificial intelligence (AI) model IDs of RRM AI models supported by the UE. The RRM AI models are configured to map to transmit beam patterns for a cell for RRM. The processors and the memory can encode, at the BS, assistance information for the UE from a serving cell of the BS, wherein the assistance information comprises: the RRM AI model IDs of the RRM AI models for the BS; and SSB index beam mappings for the RRM AI models by the BS. The UE can be performed RRM using the RRM AI models for the BS with the SSB index beam mappings.

Description

Technical Field

[0001] Embodiments of the present invention relate to wireless communication, including apparatus, systems, and methods for mapping radio resource management (RRM) artificial intelligence (AI) models to transmit beam patterns for cells used in RRM.

[0002] Related technical descriptions The use of wireless communication systems is growing rapidly. In recent years, wireless devices, such as smartphones and tablets, have become increasingly sophisticated. In addition to supporting telephone calls, many mobile devices now offer access to the Internet, email, text messaging, and navigation using the Global Positioning System (GPS), and are capable of operating complex applications that utilize these functionalities.

[0003] Long Term Evolution (LTE) has become the technology of choice for most wireless network operators worldwide, providing mobile broadband data and high-speed internet access to their subscriber base. LTE was first proposed in 2004 and standardized for the first time in 2008. Since then, with the exponential growth in the use of wireless communication systems, the demand from wireless network operators to support higher capacity for a higher density of mobile broadband users has also increased. In 2015, research into new radio access technologies began, and in 2017, the first version of 5G New Radio (5G NR) was standardized.

[0004] 5G-NR (also known as NR for short) offers higher capacity for higher density mobile broadband users compared to LTE, while also supporting ultra-reliable and massive machine-type communication between devices, as well as lower latency and / or lower battery consumption. Additionally, NR allows for more flexible UE scheduling compared to current LTE. Therefore, ongoing development of 5G-NR is underway to take advantage of the potentially higher throughput at higher frequencies.

[0005] Wireless communication systems provide mobility by enabling user equipment (UEs) to move between cells via a process known as handover. Handover occurs when a mobile UE switches from one cell to another neighboring cell. Mechanisms have been established to help ensure a smooth transition between cells. NR supports different types of handover not supported in previous 4G LTE specifications. The basic handover in NR is based on LTE handover mechanisms, in which the network controls UE mobility based on UE measurement reports. These measurement reports typically involve Layer 3 (L3) measurements of neighboring cells and reports from the UE to the eNB. Summary of the Invention

[0006] The implementation scheme relates to wireless communication, and more specifically to apparatus, systems, and methods for a base station (BS), the apparatus including one or more processors coupled to memory and configured to decode UE capability messages received from a UE at the BS. The UE capability messages include one or more RRM AI model IDs of a Radio Resource Management (RRM) Artificial Intelligence (AI) model supported by the UE. The RRM AI model is configured to be mapped to a transmit beam pattern of a cell used for RRM. The processor and memory can encode auxiliary information for the UE from the serving cell of the BS at the BS, wherein the auxiliary information includes: the RRM AI model ID of the RRM AI model for the BS; and an SSB index beam mapping of the BS for the RRM AI model. RRM can be performed on the UE using the RRM AI model for a BS with an SSB index beam mapping.

[0007] Other embodiments relate to an apparatus for a user equipment (UE) including: one or more processors coupled to a memory, configured to: encode a UE capability message for transmission to a serving cell having a base station (BS), the UE capability message including one or more RRM AI model identifiers (IDs) of one or more RRM artificial intelligence (AI) models available at the UE for radio resource management (RRM) measurements, wherein each RRM AI model is configured to be mapped to a transmission beam pattern of a cell for RRM; decode auxiliary information from the serving cell at the UE, wherein the auxiliary information includes: an RRM AI model ID for one or more neighboring cells; and a transmission beam pattern for one or more neighboring cells such that the transmission beam pattern can be mapped to an RRM AI model; and perform RRM for one or more neighboring cells using the RRM AI model of the transmission beam pattern mapped to the RRM AI model ID associated with the one or more neighboring cells.

[0008] The technologies described herein can be implemented in and / or used with a variety of different types of devices, including but not limited to unmanned aerial vehicles (UAVs), unmanned controllers (UACs), base stations, access points, cellular phones, tablet computers, wearable computing devices, portable media players, and any of a variety of other computing devices.

[0009] The present invention is intended to provide a brief overview of some of the subjects described in this document. Therefore, it should be understood that the above features are merely illustrative and should not be construed as narrowing the scope or substance of the subjects described herein in any way. Other features, aspects, and advantages of the subjects described herein will become apparent from the following detailed description, drawings, and claims. Attached Figure Description

[0010] A better understanding of the subject matter can be obtained by considering the following detailed description of various embodiments in conjunction with the accompanying drawings, in which: Figure 1A Example wireless communication systems according to some implementation schemes are illustrated.

[0011] Figure 1B Examples of base stations and access points communicating with user equipment (UE) devices according to some implementation schemes are illustrated.

[0012] Figure 2 Example block diagrams of base stations according to some implementation schemes are shown.

[0013] Figure 3 Example block diagrams of servers according to some implementation schemes are shown.

[0014] Figure 4 Example block diagrams of a UE according to some implementation schemes are shown.

[0015] Figure 5 Example block diagrams of cellular communication circuits according to some implementation schemes are shown.

[0016] Figure 6 Examples of baseband processor architectures for UEs according to some implementation schemes are illustrated.

[0017] Figure 7 Example block diagrams illustrating the interface of a baseband circuit according to some implementation schemes are shown.

[0018] Figure 8 Example diagrams illustrating a conventional approach-based radio resource monitoring (RRM) process according to some implementation schemes are shown.

[0019] Figure 9 Example illustrations of a synchronization signal block (SSB) according to some implementation schemes are shown.

[0020] Figure 10 Examples of AI / ML RRM measurement enhancements for reducing overhead are illustrated according to some implementation schemes.

[0021] Figure 11 Examples of AI / ML RRM measurement enhancements for reducing overhead and latency are illustrated according to some implementation schemes.

[0022] Figure 12 Examples of AI / ML RRM measurement enhancements for reducing latency and further reducing overhead are illustrated according to some implementation schemes.

[0023] Figure 13 Examples of AI / ML RRM measurement enhancements for further reducing latency and overhead are illustrated according to some implementation schemes.

[0024] Figure 14 Examples of using AI models to interpolate between reduced measurements are illustrated according to some implementation schemes.

[0025] Figure 15 Examples are shown of serving cells using a selected mode to perform RRM measurements according to some implementation schemes.

[0026] Figure 16 Examples are given of serving cells and neighboring cells having different Tx beam characteristics according to some implementation schemes.

[0027] Figure 17 Examples of communication and message exchange between a UE, a serving cell, one or more of L neighboring cells, and a server are illustrated according to some implementation schemes.

[0028] Figure 18 An example flowchart illustrates a method for mapping a radio resource management (RRM) artificial intelligence (AI) model to a transmission beam pattern of a cell used for RRM, according to some implementation schemes.

[0029] Figure 19 Another example flowchart illustrates a method for mapping a radio resource management (RRM) artificial intelligence (AI) model to a transmission beam pattern of a cell used for RRM, according to some implementation schemes.

[0030] Although the features described herein may be subject to various modifications and alternatives, specific embodiments thereof are shown by way of example in the accompanying drawings and described in detail herein. However, it should be understood that the drawings and their detailed description are not intended to limit one to the specific forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the substance and scope of the subject matter as defined by the appended claims. Detailed Implementation

[0031] the term The following is a glossary of terms used in this disclosure: Memory media—any of various types of nontransitory memory devices or storage devices. The term "memory media" is intended to include mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory; magnetic media, such as hard disk drives or optical storage devices; registers or other similar types of memory elements, etc. Memory media may also include other types of nontransitory memory or combinations thereof. Furthermore, memory media may reside in a first computer system executing a program, or may reside in a different second computer system connected to the first computer system via a network such as the Internet. In the latter example, the second computer system may provide program instructions to the first computer for execution. The term "memory media" may include two or more memory media residing in different locations in different computer systems connected via, for example, a network. Memory media may store program instructions (e.g., embodied in a computer program) that can be executed by one or more processors.

[0032] Carrier media—such as memory media as described above, and physical transmission media such as buses, networks, and / or other physical transmission media that transmit signals such as electrical signals, electromagnetic signals, or digital signals.

[0033] Programmable hardware elements encompass a variety of hardware devices, which consist of multiple programmable functional blocks connected via programmable interconnects. Examples include FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), FPOAs (Field-Programmable Object Arrays), and CPLDs (Complex PLDs). Programmable functional blocks can range from fine-grained (combinational logic or lookup tables) to coarse-grained (arithmetic logic units or processor cores). Programmable hardware elements may also be referred to as "reconfigurable logic units."

[0034] Computer system (or computer) — any of the various types of computing or processing systems, including personal computer systems (PCs), mainframe computer systems, workstations, network appliances, internet-connected appliances, personal digital assistants (PDAs), television systems, grid computing systems, or other devices or combinations thereof. In general, the term "computer system" can be broadly defined to encompass any device (or combination of devices) having at least one processor that executes instructions from a memory medium.

[0035] User equipment (UE) (or “UE device”) — any of various types of computer system devices that are mobile or portable and perform wireless communication. Examples of UE devices include mobile phones or smartphones (e.g., iPhone). ™Based on Android ™ Telephones), portable gaming devices (e.g., Nintendo DS) ™ PlayStation Portable ™ Gameboy Advance ™ iPhone ™ ), laptops, wearable devices (e.g., smartwatches, smart glasses), PDAs, portable internet devices, music players, data storage devices, other handheld devices, unmanned aerial vehicles (UAVs) (e.g., drones), UAV controllers (UACs), etc. Generally speaking, the term "UE" or "UE device" can be broadly defined to encompass any electronic device, computing device, and / or telecommunications device (or combination of devices) that is easily transportable by the user and capable of wireless communication.

[0036] Base station—The term “base station” has the full range of its common meaning and includes at least a wireless communication station that is installed in a fixed location and is used for communication as part of a wireless telephone system or radio system.

[0037] A processing element (or processor) is a component or combination of components capable of performing the functions of a device such as a user equipment or cellular network device. A processing element may include, for example: a processor and associated memory, portions or circuitry of individual processor cores, an entire processor core, a processor array, circuitry such as an ASIC (Application-Specific Integrated Circuit), programmable hardware components such as a Field-Programmable Gate Array (FPGA), and any combination thereof.

[0038] A channel is a medium used to transmit information from a transmitter to a receiver. It should be noted that because the characteristics of the term "channel" can vary depending on the wireless protocol, the term "channel" as used herein can be considered to be used in a standard manner consistent with the type of device to which the term is referenced. In some standards, channel bandwidth can be variable (e.g., depending on device capabilities, frequency band conditions, etc.). For example, LTE can support scalable channel bandwidths from 1.4 MHz to 20 MHz. 5G NR can support scalable channel bandwidths from 5 MHz to 100 MHz in Frequency Range 1 (FR1) and up to 400 MHz in FR2. In other radio access technologies, WLAN channels can be 22 MHz wide, while Bluetooth channels can be 1 MHz wide. Other protocols and standards may include different definitions of channels. Furthermore, some standards may define and use multiple types of channels, for example, different channels for uplink or downlink and / or different channels for different purposes such as data, control information, etc.

[0039] Frequency band—The term “frequency band” has the full range of its general meaning and includes at least a segment of spectrum (e.g., radio frequency spectrum) in which a channel is used or reserved for the same purpose.

[0040] Automatic—means that an action or operation is performed by a computer system (e.g., software executed by the computer system) or device (e.g., circuits, programmable hardware elements, ASICs, etc.) without requiring direct specification or execution of the action or operation through user input. Therefore, the term "automatically" is the opposite of an operation performed or specified manually by a user, where the user provides input to directly perform the operation. An automatic process can be initiated by user-provided input, but the subsequent actions performed "automatically" are not specified by the user; that is, they are not performed "manually," where the user specifies each action to be performed. For example, a user filling out a form by selecting each field and providing input specifying information (e.g., by typing information, selecting a checkbox, radio selection, etc.) is considered manually filling out the form, even though the computer system will update the form in response to the user's actions. The form can be automatically filled out by a computer system, where the computer system (e.g., software executed on the computer system) analyzes the fields of the form and fills out the form without any user input specifying answers for the fields. As indicated above, the user can invoke the automatic filling of the form but does not participate in the actual filling of the form (e.g., the user does not manually specify answers for the fields, but they are completed automatically). This manual provides various examples of operations that are automatically performed in response to actions taken by the user.

[0041] Approximately—means a value close to the correct or precise value. For example, approximately could mean a value within 1% to 10% of the precise (or expected) value. However, it should be noted that the actual threshold (or tolerance) can be application-dependent. For example, in some implementations, “approximately” could mean within 0.1% of some specified or expected value, while in various other implementations, the threshold could be, for example, 2%, 3%, 5%, etc., depending on the expectations or settings of the specific application.

[0042] Concurrency refers to the parallel execution or implementation of tasks, processes, or programs in a manner that at least partially overlaps. For example, concurrency can be achieved using “strong” or strict parallelism, where tasks are executed in parallel (at least partially) on corresponding computing elements; or using “weak parallelism,” where tasks are executed in an interleaved manner (e.g., by time multiplexing of execution threads).

[0043] Complete Tx codebook: Represents the entire set of Tx beams at each BS.

[0044] Detector / pilot codebook: A sparse subset of the complete Tx codebook or another set of beams with a wider beamwidth, which is used to form a spatial beam and calculate RSRP values ​​and serve as input to the AI / ML model.

[0045] Various components can be described as being "configured" to perform one or more tasks. In this context, "configured" is a broad expression generally meaning "having a structure" that performs one or more tasks during operation. Therefore, a component can be configured to perform a task even when it is not currently performing one (e.g., a set of electrical conductors can be configured to electrically connect one module to another, even when the two modules are not connected). In some contexts, "configured" can be a broad expression generally meaning a structure that "has a circuit" that performs one or more tasks during operation. Therefore, a component can be configured to perform a task even when it is not currently switched on. Generally, the circuit forming the structure corresponding to "configured" can include hardware circuitry.

[0046] For ease of description, various components may be described as performing one or more tasks. Such descriptions should be interpreted as including the phrase "configured to". Statements describing a component as configured to perform one or more tasks are explicitly intended not to invoke the interpretation of 35 USC § 112(f) for that component.

[0047] The example embodiment can be further understood by referring to the following description and related figures, in which the same elements have the same reference numerals. The example embodiment involves the sharing of measurement opportunities between layer 1 and layer 3.

[0048] Example implementations are described regarding communication between a base station (BS) and a user equipment (UE). However, references to the BS or UE are provided for illustrative purposes only. The example implementations can be used with any electronic components capable of establishing a connection to a network and configured using hardware, software, and / or firmware to support gapless RRM measurements. Therefore, the BS or UE described herein is used to represent any suitable type of electronic component.

[0049] Example implementations are also described regarding fifth-generation (5G) New Radio (NR) networks that can configure UEs to control measurement opportunity sharing between L3 and L1 measurements based on a network-configurable sharing factor. However, references to 5G NR networks are provided for illustrative purposes only. The example implementations can be utilized with any suitable type of network.

[0050] Throughout this specification, various information elements (IEs) are referred to by specific names. It should be understood that these names are merely examples, and IEs carrying information mentioned throughout this specification may be referred to by various entities under other names.

[0051] Figure 1A and Figure 1B Communication system Figure 1A A simplified example wireless communication system according to some implementation schemes is illustrated. It should be noted that... Figure 1A The system described herein is merely one example of a possible system, and the features of this disclosure can be implemented in any of the various systems as needed.

[0052] As shown in the figure, the example wireless communication system includes a base station 102A, which communicates with one or more user equipments 106A, 106B to 106N, etc., via a transmission medium. Each user equipment may be referred to herein as a "user equipment" (UE). Therefore, user equipment 106 is referred to as a UE or UE device.

[0053] Base station (BS) 102A may be a transceiver base station (BTS) or a cell site (“cellular base station”), and may include hardware that enables wireless communication with UE 106A to UE 106N.

[0054] The communication area (or coverage area) of a base station may be referred to as a "cell". Base station 102A and UE 106 can be configured to communicate via a transmission medium using any of a variety of Radio Access Technologies (RATs), also known as wireless communication technologies or telecommunications standards, such as GSM, UMTS (associated with air interfaces such as WCDMA or TD-SCDMA), LTE, LTE-Advanced (LTE-A), 5G New Radio (5G NR), HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD), etc. Note that if base station 102A is implemented in the context of LTE, also known as Evolved Universal Terrestrial Radio Access Network (E-UTRAN), its alternative location may be referred to as an "eNodeB" or "eNB". Note that if base station 102A is implemented in a 5G NR environment, its alternative location may be referred to as a "gNodeB" or "gNB".

[0055] As shown in the figure, base station 102A can also be configured to communicate with network 100 (e.g., in various possibilities, the core network of a cellular service provider, telecommunications networks such as the Public Switched Telephone Network (PSTN), and / or the Internet). Therefore, base station 102A facilitates communication between user equipments and / or between user equipments and network 100. Specifically, cellular base station 102A can provide UE 106 with various telecommunications capabilities, such as voice, SMS, and / or data services.

[0056] Base station 102A and other similar base stations (such as base stations 102B...102N) operating according to the same or different cellular communication standards can therefore be provided as a network of cells that can provide continuous or nearly continuous overlapping services to UE 106A to UE 106N and similar devices over a geographical area via one or more cellular communication standards.

[0057] Therefore, although base station 102A can act as such Figure 1A The example illustrates the "serving cells" of UEs 106A to UE 106N, but each UE 106 may also be able to receive signals (and possibly within its communication range) from one or more other cells (which may be provided by base stations 102B to 102N and / or any other base stations), which may be referred to as "neighboring cells." Such cells may also facilitate communication between user equipments and / or between user equipments and network 100. These cells may include "macro" cells, "micro" cells, "pecimen" cells, and / or any other cells of various other granularities providing service area size. For example, in Figure 1A Base stations 102A to 102B illustrated can be macro cells, while base station 102N can be a micro cell. Other configurations are also possible.

[0058] In some implementations, base station 102A may be a next-generation base station, such as a 5G New Radio (5G NR) base station or a “gNB”. In some implementations, the BS may be connected to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network. Furthermore, the BS cell may include one or more transition and receive points (TRPs). Additionally, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more BSs.

[0059] It should be noted that UE 106 may be able to communicate using multiple wireless communication standards. For example, UE 106 may be configured to communicate using wireless networking (e.g., Wi-Fi) and / or peer-to-peer wireless communication protocols (e.g., Bluetooth, Wi-Fi peer-to-peer, etc.) other than at least one cellular communication protocol (e.g., GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces), LTE, LTE-A, 5G NR, HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD, etc.)). If desired, UE 106 may also be configured, or alternatively, to communicate using one or more Global Navigation Satellite Systems (GNSS, such as GPS or GLONASS), one or more mobile television broadcasting standards (e.g., ATSC-M / H or DVB-H) and / or any other wireless communication protocol. Other combinations of wireless communication standards (including more than two wireless communication standards) are also possible.

[0060] Figure 1B User equipment 106 (e.g., one of devices 106A to 106N) communicating with base station 102 and access point 112 according to some embodiments is illustrated. UE 106 can be a device with cellular and non-cellular communication capabilities (e.g., Bluetooth, Wi-Fi, etc.), such as a mobile phone, handheld device, computer or tablet computer, or virtually any type of wireless device.

[0061] UE 106 may include a processor configured to execute program instructions stored in memory. UE 106 may execute any method implementation of the method implementations described herein by executing such stored instructions. Alternatively or additionally, UE 106 may include programmable hardware elements, such as a field-programmable gate array (FPGA) configured to perform any of the method implementations described herein or any portion thereof.

[0062] UE 106 may include one or more antennas for communicating using one or more wireless communication protocols or technologies. In some embodiments, UE 106 may be configured to communicate using, for example, CDMA2000 (1xRTT / 1xEV-DO / HRPD / eHRPD), LTE / Advanced LTE, or 5G NR using a single shared radio component and / or GSM, LTE, Advanced LTE, or 5G NR using a single shared radio component. The shared radio component may be coupled to a single antenna or to multiple antennas (e.g., for MIMO) for performing wireless communication. Generally, the radio component may include any combination of baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc.) or digital processing circuitry (e.g., for digital modulation and other digital processing). Similarly, the radio component may use the aforementioned hardware to implement one or more receive chains and transmit chains. For example, UE 106 may share one or more portions of the receive chain and / or transmit chain among multiple wireless communication technologies (such as those discussed above).

[0063] In some implementations, UE 106 may include independent transmit and / or receive chains (e.g., including independent antennas and other radio components) for each wireless communication protocol configured to communicate therewith. As another possibility, UE 106 may include one or more radio components shared among multiple wireless communication protocols, as well as one or more radio components uniquely used by a single wireless communication protocol. For example, UE 106 may include shared radio components for communication using either LTE or 5G NR (or LTE or 1xRTT, or LTE or GSM), and separate radio components for communication using each of Wi-Fi and Bluetooth. Other configurations are also possible.

[0064] Figure 2 Block diagram of a base station Figure 2 Example block diagrams of base station 102 according to some implementation schemes are shown. It should be noted that... Figure 2 The base station shown is merely one example of a possible base station. As illustrated, base station 102 may include processor 204, which executes program instructions for base station 102. Processor 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from processor 204 and translate these addresses into locations in memory (e.g., memory 260 and read-only memory (ROM) 250) or into other circuitry or devices.

[0065] Base station 102 may include at least one network port 270. Network port 270 may be configured to couple to a telephone network and provide access to multiple devices, such as UE device 106, as described above in Figure 1 and... Figure 2 Access to the telephone network described in the text.

[0066] Network port 270 (or an additional network port) may also be configured, or alternatively configured, to couple to a cellular network, such as the core network of a cellular service provider. The core network may provide mobility-related services and / or other services to multiple devices, such as UE device 106. In some cases, network port 270 may be coupled to a telephone network via the core network, and / or the core network may provide a telephone network (e.g., in other UE devices served by a cellular service provider).

[0067] In some implementations, base station 102 may be a next-generation base station, such as a 5G New Radio (5G NR) base station, or “gNB”. In such implementations, base station 102 may connect to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network. Furthermore, base station 102 may be considered a 5G NR cell and may include one or more transition and receive points (TRPs). Additionally, a UE capable of operating according to 5G NR may connect to one or more TRPs within one or more BSs.

[0068] Base station 102 may include at least one antenna 234, and may include multiple antennas. At least one antenna 234 may be configured to operate as a wireless transceiver and may also be configured to communicate with UE device 106 via radio component 230. Antenna 234 communicates with radio component 230 via communication link 232. Communication link 232 may be a receive link, a transmit link, or both. Radio component 230 may be configured to communicate via various wireless communication standards, including but not limited to 5G NR, LTE, LTE-A, GSM, UMTS, CDMA2000, Wi-Fi, etc.

[0069] Base station 102 can be configured to perform wireless communication using multiple wireless communication standards. In some instances, base station 102 may include multiple radio components that enable base station 102 to communicate according to multiple wireless communication technologies. For example, as one possibility, base station 102 may include an LTE radio component for performing communication according to LTE and a 5G NR radio component for performing communication according to 5G NR. In this case, base station 102 may be able to operate as both an LTE base station and a 5G NR base station. As another possibility, base station 102 may include a multimode radio component capable of performing communication according to any of multiple wireless communication technologies (e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc.).

[0070] As further described herein, BS 102 may include hardware and software components for implementing or supporting specific implementations of the features described herein. The processor 204 of base station 102 may be configured, for example, to implement or support some or all of the methods described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, processor 204 may be configured as a programmable hardware element such as an FPGA (Field-Programmable Gate Array), or as an ASIC (Application-Specific Integrated Circuit), or a combination thereof. Alternatively (or further), in conjunction with one or more of other components 230, 232, 234, 240, 250, 260, 270, the processor 204 of BS 102 may be configured to implement or support some or all of the features described herein.

[0071] Furthermore, as described herein, processor 204 may comprise one or more processing elements. In other words, one or more processing elements may be included in processor 204. Therefore, processor 204 may include one or more integrated circuits (ICs) configured to perform the functions of processor 204. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 204.

[0072] Furthermore, as described herein, radio component 230 may comprise one or more processing elements. In other words, one or more processing elements may be included in radio component 230. Therefore, radio component 230 may include one or more integrated circuits (ICs) configured to perform the functions of radio component 230. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of radio component 230.

[0073] In some implementations, the base station or BS 102 and / or its processor 204 may be able to and configured to perform RRM on the UE 106, which is configured to use an RRM AI model with an RRM AI model ID for the BS 102 having an SSB index beammap.

[0074] Figure 3 Server block diagram Figure 3 Example block diagrams of server 104 according to some implementation schemes are shown. Note that... Figure 3 The server shown is merely one example of a possible server. As illustrated, server 104 may include processor 344 capable of executing program instructions for server 104. Processor 344 may also be coupled to memory management unit (MMU) 374, which may be configured to receive addresses from processor 344 and translate these addresses into locations in memory (e.g., memory 364 and read-only memory (ROM) 354) or into other circuitry or devices.

[0075] Server 104 can be configured to provide network access functionality to multiple devices, such as base station 102 and UE device 106, for example, as further described herein.

[0076] In some implementations, server 104 may be part of a radio access network, such as a 5G New Radio (5G NR) access network. In some implementations, server 104 may be connected to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network.

[0077] As described herein, server 104 may include hardware and software components for implementing or supporting the implementation of the features described herein. Processor 344 of server 104 may be configured, for example, to implement or support some or all of the methods described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium). Alternatively, processor 344 may be configured as a programmable hardware element such as a FPGA (Field-Programmable Gate Array), or as an ASIC (Application-Specific Integrated Circuit), or a combination thereof. Alternatively (or further), in conjunction with one or more of other components 354, 364, and / or 374, processor 344 of server 104 may be configured to implement or support some or all of the features described herein.

[0078] Furthermore, as described herein, processor 344 may comprise one or more processing elements. In other words, one or more processing elements may be included in processor 344. Therefore, processor 344 may include one or more integrated circuits (ICs) configured to perform the functions of processor 344. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 344.

[0079] Figure 4 User Equipment Block Diagram Figure 4 A simplified block diagram of a communication device 106 according to some implementation schemes is shown. Note that... Figure 4 The block diagram of the communication device is merely one example of possible communication devices. According to embodiments, communication device 106 may be a user equipment (UE) device, mobile device or mobile station, wireless device or wireless station, desktop computer or computing device, mobile computing device (e.g., laptop computer, notebook computer, or portable computing device), tablet computer, unmanned aerial vehicle (UAV), UAV controller (UAC), and / or a combination of devices, and other devices. As shown, communication device 106 may include a set of components 400 configured to perform core functions. For example, this set of components may be implemented as a system-on-a-chip (SOC), which may include portions for various purposes. Alternatively, this set of components 400 may be implemented as individual components or groups of components for various purposes. This set of components 400 may be (e.g., communicatively; directly or indirectly) coupled to various other circuitry of communication device 106.

[0080] For example, communication device 106 may include various types of memory (e.g., including NAND flash memory 410), input / output interfaces such as connector I / F 420 (e.g., for connection to a computer system; docking station; charging station; input devices such as microphone, camera, keyboard; output devices such as speaker; etc.), a display 460 that may be integrated with or external to the communication device 106, and cellular communication circuitry 430 such as for 5G NR, LTE, GSM, etc., and short- to medium-range wireless communication circuitry 429 (e.g., Bluetooth). ™ (and WLAN circuitry). In some embodiments, communication device 106 may include wired communication circuitry (not shown), such as, for example, a network interface card for Ethernet.

[0081] Cellular communication circuitry 430 may be coupled (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 shown. Short-to-medium-range wireless communication circuitry 429 may also be coupled (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 437 and 438 shown. Alternatively, short-to-medium-range wireless communication circuitry 429 may be coupled (e.g., communicatively; directly or indirectly) to antennas 435 and 436 in addition to or instead of being coupled to antennas 437 and 438. Short-to-medium-range wireless communication circuitry 429 and / or cellular communication circuitry 430 may include multiple receive chains and / or multiple transmit chains for receiving and / or transmitting multiple spatial streams, such as in a multiple-input multiple-output (MIMO) configuration.

[0082] In some embodiments, as further described below, the cellular communication circuit 430 may include dedicated receive chains for multiple RATs (including and / or coupled to (e.g., communicatively; directly or indirectly) dedicated processors and / or radio components) (e.g., a first receive chain for LTE and a second receive chain for 5G NR). Furthermore, in some embodiments, the cellular communication circuit 430 may include a single transmit chain that can be switched between radio components dedicated to a particular RAT. For example, a first radio component may be dedicated to a first RAT, such as LTE, and may communicate with a dedicated receive chain and a transmit chain shared with additional radio components, such as a second radio component that may be dedicated to a second RAT (e.g., 5G NR) and may communicate with a dedicated receive chain and a shared transmit chain.

[0083] The communication device 106 may also include one or more user interface elements and / or be configured to be used with one or more user interface elements. The user interface elements may include any of a variety of elements, such as a display 460 (which may be a touch screen display), a keyboard (which may be a separate keyboard or may be implemented as part of a touch screen display), a mouse, a microphone and / or a speaker, one or more cameras, one or more buttons, and / or any of a variety of other elements capable of providing information to the user and / or receiving or interpreting user input.

[0084] The communication device 106 may also include one or more smart cards 445 with SIM (Subscriber Identity Module) functionality, such as one or more UICC (Universal Integrated Circuit Card) 445. It should be noted that the term "SIM" or "SIM entity" is intended to include any of various types of SIM implementations or SIM functionality, such as one or more UICC cards 445, one or more eUICCs, one or more eSIMs, removable or embedded, etc. In some embodiments, the UE 106 may include at least two SIMs. Each SIM may execute one or more SIM applications and / or otherwise implement SIM functionality. Thus, each SIM may be a single smart card that can be embedded, for example, soldered to a circuit board in the UE 106, or each SIM 410 may be implemented as a removable smart card. Therefore, a SIM may be one or more removable smart cards (such as UICC cards, sometimes referred to as "SIM cards"), and / or SIM 410 may be one or more embedded cards (such as embedded UICCs (eUICCs), sometimes referred to as "eSIMs" or "eSIM cards"). In some implementations (such as when the SIM includes an eUICC), one or more SIMs within the SIM can implement embedded SIM (eSIM) functionality; in such implementations, a single SIM within the SIM can execute multiple SIM applications. Each SIM may include components such as a processor and / or memory; instructions for performing SIM / eSIM functionality may be stored in memory and executed by the processor. In some implementations, UE 106 may include, as needed, a combination of removable smart cards and fixed / non-removable smart cards (such as one or more eUICC cards implementing eSIM functionality). For example, UE 106 may include two embedded SIMs, two removable SIMs, or a combination of one embedded SIM and one removable SIM. Various other SIM configurations are also envisioned.

[0085] As described above, in some implementations, UE 106 may include two or more SIMs. Including two or more SIMs in UE 106 allows UE 106 to support two different phone numbers and allows UE 106 to communicate on two or more corresponding networks. For example, the first SIM may support a first RAT such as LTE, and the second SIM 106 may support a second RAT such as 5G NR. Other specific implementations and RATs are also possible. In some implementations, when UE 106 includes two SIMs, UE 106 may support Dual SIM Dual Standby (DSDA) functionality. DSDA functionality allows UE 106 to connect to two networks simultaneously (and use two different RATs), or allows two connections supported by two different SIMs using the same or different RATs to be maintained simultaneously on the same or different networks. DSDA functionality also allows UE 106 to receive voice calls or data traffic simultaneously on either phone number. In some implementations, voice calls may be packet-switched communications. In other words, voice calls can be received using LTE-based Voice (VoLTE) technology and / or NR-based Voice (VoNR) technology. In some implementations, UE 106 may support Dual SIM Dual Standby (DSDS) functionality. DSDS functionality allows either of the two SIMs in UE 106 to standby awaiting a voice call and / or data connection. In DSDS, when a call / data connection is established on one SIM, the other SIM is no longer active. In some implementations, DSDx functionality (DSDA or DSDS functionality) can be implemented using a single SIM (e.g., eUICC) that performs multiple SIM applications for different carriers and / or RATs.

[0086] As shown, the SOC 400 may include a processor 402 and display circuitry 404. The processor executes program instructions from the communication device 106, and the display circuitry performs graphics processing and provides display signals to the display 460. The processor 402 may also be coupled to a memory management unit (MMU) 440 (which may be configured to receive addresses from the processor 402 and translate those addresses into locations in memory (e.g., memory 406, read-only memory (ROM) 450, NAND flash memory 410)), and / or coupled to other circuitry or devices (such as display circuitry 404, short-to-medium range wireless communication circuitry 429, cellular communication circuitry 430, connector I / F 420, and / or display 460). The MMU 440 may be configured to perform memory protection and page table translation or setup. In some embodiments, the MMU 440 may be included as part of the processor 402.

[0087] As described herein, communication device 106 may include hardware and software components for implementing the features described above to communicate a scheduling profile for power saving to the network. For example, by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium), processor 402 of communication device 106 may be configured to implement some or all of the features described herein. Alternatively (or further), processor 402 may be configured as a programmable hardware element such as a FPGA (Field-Programmable Gate Array) or as an ASIC (Application-Specific Integrated Circuit). Alternatively (or further), in conjunction with one or more of other components 400, 404, 406, 410, 420, 429, 430, 440, 445, 450, 460, processor 402 of communication device 106 may be configured to implement some or all of the features described herein.

[0088] Furthermore, as described herein, processor 402 may include one or more processing elements. Therefore, processor 402 may include one or more integrated circuits (ICs) configured to perform the functions of processor 402. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 402.

[0089] Additionally, as described herein, the cellular communication circuit 430 and the short-to-medium-range wireless communication circuit 429 may each include one or more processing elements. In other words, one or more processing elements may be included in the cellular communication circuit 430, and similarly, one or more processing elements may be included in the short-to-medium-range wireless communication circuit 429. Therefore, the cellular communication circuit 430 may include one or more integrated circuits (ICs) configured to perform the functions of the cellular communication circuit 430. Furthermore, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of the cellular communication circuit 430. Similarly, the short-to-medium-range wireless communication circuit 429 may include one or more ICs configured to perform the functions of the short-to-medium-range wireless communication circuit 429. Furthermore, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of the short-to-medium-range wireless communication circuit 429.

[0090] In some implementations, UE 106 and / or its processor 402 may be configured and / or able to perform RRM on the UE, wherein the UE is configured to use an RRM AI model with an RRM AI model ID for a BS having an SSB index beammap, as described herein.

[0091] Figure 5 Block diagram of cellular communication circuit Figure 5Simplified block diagrams of cellular communication circuits according to some implementation schemes are shown. Note that... Figure 5 The block diagram of the cellular communication circuit is merely one example of a possible cellular communication circuit. According to the implementation, the cellular communication circuit 530 (which may be the cellular communication circuit 430) may be included in a communication device such as the communication device 106 described above. As noted above, among other devices, the communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop computer, notebook computer, or portable computing device), a tablet computer, and / or a combination of these devices.

[0092] Cellular communication circuit 530 may (e.g., communicatively; directly or indirectly) be coupled to one or more antennas, such as ( Figure 4 Antennas 435a-b and 436 are shown in the diagram. In some embodiments, cellular communication circuitry 530 may include dedicated receive chains for various RATs (including and / or coupled to (e.g., communicatively ground; directly or indirectly) dedicated processors and / or radio components) (e.g., a first receive chain for LTE and a second receive chain for 5G NR). For example, as... Figure 5 As shown, the cellular communication circuit 530 may include a modem 510 and a modem 520. The modem 510 may be configured for communication according to a first RAT (e.g., such as LTE or LTE-A), and the modem 520 may be configured for communication according to a second RAT (e.g., such as 5G NR).

[0093] As shown, modem 510 may include one or more processors 512 and memory 516 communicating with processors 512. Modem 510 may communicate with radio frequency (RF) front end 535. RF front end 535 may include circuitry for transmitting and receiving radio signals. For example, RF front end 535 may include receiver circuitry (RX) 532 and transmitter circuitry (TX) 534. In some embodiments, receiver circuitry 532 may communicate with downlink (DL) front end 550, which may include circuitry for receiving radio signals via antenna 335a.

[0094] Similarly, modem 520 may include one or more processors 522 and memory 526 communicating with processor 522. Modem 520 may communicate with RF front end 540. RF front end 540 may include circuitry for transmitting and receiving radio signals. For example, RF front end 540 may include receiving circuitry 542 and transmitting circuitry 544. In some embodiments, receiving circuitry 542 may communicate with DL front end 560, which may include circuitry for receiving radio signals via antenna 335b.

[0095] In some implementations, switch 570 may couple transmitting circuitry 534 to uplink (UL) front-end 572. Additionally, switch 570 may couple transmitting circuitry 544 to UL front-end 572. UL front-end 572 may include circuitry for transmitting radio signals via antenna 336. Therefore, when cellular communication circuitry 530 receives an instruction to transmit according to a first RAT (e.g., supported by modem 510), switch 570 may be switched to a first state allowing modem 510 to transmit signals according to the first RAT (e.g., via a transmission chain including transmitting circuitry 534 and UL front-end 572). Similarly, when cellular communication circuitry 530 receives an instruction to transmit according to a second RAT (e.g., supported by modem 520), switch 570 may be switched to a second state allowing modem 520 to transmit signals according to the second RAT (e.g., via a transmission chain including transmitting circuitry 544 and UL front-end 572).

[0096] As described herein, modem 510 may include hardware and software components for implementing the features described above or for UL data used in time-division multiplexing NSANR operation, as well as various other techniques described herein. For example, processor 512 may be configured to implement some or all of the features described herein by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable storage medium). Alternatively (or additionally), processor 512 may be configured as a programmable hardware element such as a FPGA (Field-Programmable Gate Array) or as an ASIC (Application-Specific Integrated Circuit). Alternatively (or additionally), processor 512 may be configured to implement some or all of the features described herein in conjunction with one or more of other components 530, 532, 534, 535, 550, 570, 572, 335a, 335b, and 336.

[0097] Furthermore, as described herein, processor 512 may include one or more processing elements. Therefore, processor 512 may include one or more integrated circuits (ICs) configured to perform the functions of processor 512. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 512.

[0098] For example, by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium), processor 522 may be configured to implement some or all of the features described herein. Alternatively (or further), processor 522 may be configured as a programmable hardware element such as a FPGA (Field-Programmable Gate Array) or as an ASIC (Application-Specific Integrated Circuit). Alternatively (or further), in conjunction with one or more of other components 540, 542, 544, 550, 570, 572, 335a, 335b, and 336, processor 522 may be configured to implement some or all of the features described herein.

[0099] Furthermore, as described herein, processor 522 may include one or more processing elements. Therefore, processor 522 may include one or more integrated circuits (ICs) configured to perform the functions of processor 522. Additionally, each integrated circuit may include circuitry (e.g., a first circuit, a second circuit, etc.) configured to perform the functions of processor 522.

[0100] In some implementations, processors 512, 522 may be configured to perform RRM on UE 106, which is configured to use an RRM AI model with an RRM AI model ID for BS 102 with SSB index beam mapping, as further described herein.

[0101] Figure 6 Block diagram of the baseband processor architecture for UE Figure 6 Example components of device 600 according to some implementation schemes are illustrated. It should be noted that... Figure 6 The device described is merely one example of a possible system, and the features of this disclosure can be implemented in any UE of various types as needed.

[0102] In some embodiments, device 600 may include application circuitry 602, baseband circuitry 604, radio frequency (RF) circuitry 606, front-end module (FEM) circuitry 608, one or more antennas 610, and power management circuitry (PMC) 612 (at least coupled together as shown). Components exemplified by device 600 may be included in UE 106 or RAN node 102A. In some embodiments, device 600 may include fewer components (e.g., the RAN node may not utilize application circuitry 602, but instead include a processor / controller to process IP data received from the EPC). In some embodiments, device 600 may include additional components such as, for example, memory / storage devices, displays, cameras, sensors, or input / output (I / O) interfaces. In other embodiments, the following components may be included in more than one device (e.g., the circuitry may be individually included in more than one device for a cloud-RAN (C-RAN) specific implementation).

[0103] Application circuitry 602 may include one or more application processors. For example, application circuitry 602 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The one or more processors may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, etc.). These processors may be coupled to or may include memory / storage devices and may be configured to execute instructions stored in the memory / storage device to enable various applications or operating systems to run on device 600. In some embodiments, the processor of application circuitry 602 may process IP data packets received from the EPC.

[0104] Baseband circuitry 604 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. Baseband circuitry 604 may include one or more baseband processors or control logic components to process baseband signals received from the receive signal path of RF circuitry 606 and generate baseband signals for the transmit signal path of RF circuitry 606. Baseband processing circuitry 604 may interact with application circuitry 602 to generate and process baseband signals and control the operation of RF circuitry 606. For example, in some embodiments, baseband circuitry 604 may include a third-generation (3G) baseband processor 604A, a fourth-generation (4G) baseband processor 604B, a fifth-generation (5G) baseband processor 604C, or other existing, under development, or future generations of baseband processors 604D (e.g., second-generation (2G), sixth-generation (6G), etc.). Baseband circuitry 604 (e.g., one or more of baseband processors 604A to 604D) may handle various radio control functions to implement communication with one or more radio networks via RF circuitry 606. In other embodiments, some or all of the functionality of the baseband processors 604A to 604D may be included in modules stored in memory 604G and executed via a central processing unit (CPU) 604E. Radio control functions may include, but are not limited to, signal modulation / demodulation, encoding / decoding, RF shifting, etc. In some embodiments, the modulation / demodulation circuitry of baseband circuitry 604 may include Fast Fourier Transform (FFT), precoding, or constellation mapping / demapping functionality. In some embodiments, the encoding / decoding circuitry of baseband circuitry 604 may include convolution, tail-biting convolution, turbo, Viterbi, or low-density parity-check (LDPC) encoder / decoder functionality. Implementations of the modulation / demodulation and encoder / decoder functions are not limited to these examples, and other suitable functionality may be included in other embodiments.

[0105] In some embodiments, the baseband circuitry 604 may include one or more audio digital signal processors (DSPs) 604F. The audio DSP 604F may include elements for compression / decompression and echo cancellation, and in other embodiments may include other suitable processing elements. In some embodiments, components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on the same circuit board. In some embodiments, some or all of the components of the baseband circuitry 604 and the application circuitry 602 may be implemented together, for example, on a system-on-a-chip (SoC).

[0106] In some implementations, baseband circuit 604 can provide communication compatible with one or more radio technologies. For example, in some implementations, baseband circuit 604 can support communication with the Evolved Universal Terrestrial Radio Access Network (EUTRAN) or other Wireless Metropolitan Area Networks (WMAN), Wireless Local Area Networks (WLAN), or Wireless Personal Area Networks (WPAN). Implementations in which baseband circuit 604 is configured to support radio communication with more than one radio protocol may be referred to as multimode baseband circuits.

[0107] RF circuit 606 enables communication with a wireless network via a non-solid medium using modulated electromagnetic radiation. In various embodiments, RF circuit 606 may include switches, filters, amplifiers, etc., to facilitate communication with the wireless network. RF circuit 606 may include a receive signal path that includes circuitry for down-converting the RF signal received from FEM circuit 608 and providing a baseband signal to baseband circuit 604. RF circuit 606 may also include a transmit signal path that includes circuitry for up-converting the baseband signal provided by baseband circuit 604 and providing an RF output signal for transmission to FEM circuit 608.

[0108] In some embodiments, the receive signal path of RF circuit 606 may include mixer circuit 606a, amplifier circuit 606b, and filter circuit 606c. In some embodiments, the transmit signal path of RF circuit 606 may include filter circuit 606c and mixer circuit 606a. RF circuit 606 may also include synthesizer circuit 606d for synthesizing frequencies used by mixer circuit 606a in both the receive and transmit signal paths. In some embodiments, mixer circuit 606a in the receive signal path may be configured to down-convert the RF signal received from FEM circuit 608 based on the synthesized frequency provided by synthesizer circuit 606d. Amplifier circuit 606b may be configured to amplify the down-converted signal, and filter circuit 606c may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signal to generate an output baseband signal. The output baseband signal may be provided to baseband circuit 604 for further processing. In some implementations, these output baseband signals may be zero-frequency baseband signals, but this is not necessary. In some implementations, the mixer circuit 606a in the receiving signal path may include a passive mixer, but the scope of the implementations is not limited in this respect.

[0109] In some implementations, the mixer circuit 606a of the transmit signal path may be configured to up-convert the input baseband signal based on the synthesis frequency provided by the synthesizer circuit 606d to generate an RF output signal for the FEM circuit 608. The baseband signal may be provided by the baseband circuit 604 and may be filtered by the filter circuit 606c.

[0110] In some embodiments, the mixer circuit 606a for the receive signal path and the mixer circuit 606a for the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and quadrature upconversion, respectively. In some embodiments, the mixer circuit 606a for the receive signal path and the mixer circuit 606a for the transmit signal path may include two or more mixers and may be arranged for image rejection (e.g., Hartley image rejection). In some embodiments, the mixer circuit 606a for the receive signal path and the mixer circuit 606a for the transmit signal path may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuit 606a for the receive signal path and the mixer circuit 606a for the transmit signal path may be configured for superheterodyne operation.

[0111] In some embodiments, the output baseband signal and the input baseband signal may be analog baseband signals, but the scope of the embodiments is not limited in this respect. In some alternative embodiments, the output baseband signal and the input baseband signal may be digital baseband signals. In these alternative embodiments, RF circuit 606 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry, and baseband circuit 604 may include a digital baseband interface for communicating with RF circuit 606.

[0112] In some dual-mode implementations, separate radio IC circuits may be provided to process signals for each spectrum, but the scope of the implementation is not limited in this respect.

[0113] In some implementations, synthesizer circuit 606d may be a fractional-N synthesizer or a fractional-N / N+1 synthesizer, but the scope of implementations is not limited in this respect, as other types of frequency synthesizers may also be suitable. For example, synthesizer circuit 606d may be a Δ-∑ synthesizer, a frequency multiplier, or a synthesizer including a phase-locked loop with a frequency divider.

[0114] Synthesizer circuit 606d can be configured to synthesize an output frequency based on a frequency input and a divider control input for use by mixer circuit 606a of RF circuit 606. In some embodiments, synthesizer circuit 606d may be a fractional N / N+1 synthesizer.

[0115] In some implementations, the frequency input may be provided by a voltage-controlled oscillator (VCO), but this is not necessary. The divider control input may be provided by the baseband circuit 604 or the application processor 602 according to the desired output frequency. In some implementations, the divider control input (e.g., N) may be determined from a lookup table based on the channel indicated by the application processor 602.

[0116] The synthesizer circuit 606d of the RF circuit 606 may include a frequency divider, a delay-locked loop (DLL), a multiplexer, and a phase accumulator. In some embodiments, the frequency divider may be a dual-mode divider (DMD), and the phase accumulator may be a digital phase accumulator (DPA). In some embodiments, the DMD may be configured to divide the input signal by N or N+1 (e.g., based on carry) to provide a fractional division ratio. In some example embodiments, the DLL may include a set of cascaded, tunable delay elements, a phase detector, a charge pump, and a D-type flip-flop. In these embodiments, the delay elements may be configured to divide the VCO cycle into Nd equal phase groups, where Nd is the number of delay elements in the delay line. Thus, the DLL provides negative feedback to help ensure that the total delay through the delay line is one VCO cycle.

[0117] In some embodiments, synthesizer circuitry 606d may be configured to generate a carrier frequency as the output frequency, while in other embodiments, the output frequency may be a multiple of the carrier frequency (e.g., twice the carrier frequency, four times the carrier frequency) and used in conjunction with quadrature generator and frequency divider circuitry to generate multiple signals having multiple different phases relative to each other at the carrier frequency. In some embodiments, the output frequency may be the LO frequency (fLO). In some embodiments, RF circuitry 606 may include an IQ / polarity converter.

[0118] FEM circuit 608 may include a receive signal path, which may include circuitry configured to operate on RF signals received from one or more antennas 610, amplify the received signals, and provide an amplified version of the received signals to RF circuit 606 for further processing. FEM circuit 608 may also include a transmit signal path, which may include circuitry configured to amplify transmit signals provided by RF circuit 606 for transmission by one or more of the one or more antennas 610. In various embodiments, amplification via the transmit or receive signal path may be performed only in RF circuit 606, only in FEM 608, or in both RF circuit 606 and FEM 608.

[0119] In some embodiments, FEM circuit 608 may include a TX / RX switch for switching between transmit and receive mode operation. The FEM circuit may include a receive signal path and a transmit signal path. The receive signal path of the FEM circuit may include an LNA for amplifying the received RF signal and providing the amplified received RF signal as an output (e.g., provided to RF circuit 606). The transmit signal path of FEM circuit 608 may include a power amplifier (PA) for amplifying (e.g., provided by RF circuit 606) the input RF signal; and one or more filters for generating an RF signal for subsequent transmission (e.g., through one or more antennas in one or more antennas 610).

[0120] In some implementations, the PMC 612 can manage the power supplied to the baseband circuitry 604. Specifically, the PMC 612 can control power selection, voltage scaling, battery charging, or DC-DC conversion. The PMC 612 is typically included when the device 600 can be powered by a battery, for example, when the device is included in a UE. The PMC 612 can improve power conversion efficiency while providing the desired implementation size and thermal characteristics.

[0121] Although Figure 6 A PMC 612 is shown coupled only to the baseband circuit 604; however, in other embodiments, the PMC 612 may be additionally or alternatively coupled to other components such as, but not limited to, the application circuit 602, the RF circuit 606, or the FEM 608, and perform similar power management operations for these other components.

[0122] In some implementations, PMC 612 can control or otherwise become part of various power-saving mechanisms of device 600. For example, if device 600 is in the radio resource control_Connected (RRC_Connected) state, where the device is still connected to the RAN node because it expects to receive traffic immediately, the device can enter a state called discontinuous reception mode (DRX) after a period of inactivity. During this state, device 600 can be powered down for short intervals, thereby saving power.

[0123] If there is no data traffic activity during the extended period, device 600 can transition to the RRC_Idle state, in which the device disconnects from the network and does not perform operations such as channel quality feedback or handover. Device 600 enters a very low power state and performs paging, in which the device periodically wakes up again to listen to the network, and then powers off again. Device 600 may be unable to receive data in this state, and to receive data, it will transition back to the RRC_Connected state.

[0124] An additional power-saving mode renders the device unusable for a period exceeding the paging interval (from seconds to hours). During this time, the device is completely unconnected to the network and may be completely powered off. Any data transmitted during this period will incur significant latency, which is assumed to be acceptable.

[0125] The processor of application circuit 602 and the processor of baseband circuit 604 can be used to execute elements of one or more instances of the protocol stack. For example, the processor of baseband circuit 604 can be used individually or in combination to perform layer 3, layer 2, or layer 1 functions, while the processor of application circuit 604 can utilize data received from these layers (e.g., packet data) and further perform layer 4 functions (e.g., transmit communication protocol (TCP) and user datagram protocol (UDP) layers). As mentioned herein, layer 3 (L3) may include the Radio Resource Control (RRC) layer, which will be described in further detail below. As mentioned herein, layer 2 (L2) may include the Media Access Control (MAC) layer, the Radio Link Control (RLC) layer, and the Packet Data Convergence Protocol (PDCP) layer, which will be described in further detail below. As mentioned herein, layer 1 (L1) may include the physical (PHY) layer of the UE / RAN node, which will be described in further detail below. Therefore, baseband circuit 604 can be used to encode messages for transmission between the UE and the BS, or to decode messages received between the UE and the BS.

[0126] For example, baseband circuitry 604 can be used at the BS to decode a UE capability message received from the UE, the UE capability message including one or more RRM AI model IDs of Radio Resource Management (RRM) Artificial Intelligence (AI) models supported by the UE, wherein each RRM AI model is configured to be mapped to a transmit beam pattern for the cell used for RRM; and at the BS to encode auxiliary information for the UE from the serving cell of the BS, wherein the auxiliary information includes: the RRM AI model ID of the RRM AI model for the BS; and the SSB index beam mapping of the BS for the RRM AI model. These examples are not intended to be limiting. The baseband circuitry can be used as previously described.

[0127] Figure 7 Block diagram of the baseband circuit interface Figure 7 Example interfaces of baseband circuits according to some implementation schemes are illustrated. Note that... Figure 7 The baseband circuit is merely one example of a possible circuit, and the features of this disclosure can be implemented in any system of various types as needed.

[0128] As discussed above, Figure 6The baseband circuit 604 may include processors 604A to 604E and a memory 604G utilized by the processors. Each of the processors 604A to 604E may respectively include a memory interface 704A to 704E for transferring / receiving data to / from the memory 604G.

[0129] Baseband circuit 604 may further include: one or more interfaces for communicatively coupling to other circuits / devices, such as memory interface 712 (e.g., an interface for transferring / receiving data to / from a memory external to baseband circuit 604); application circuit interface 714 (e.g., for transferring / receiving data to / from a memory external to baseband circuit 604); and application circuit interface 714 (e.g., for transferring / receiving data to / from a memory external to baseband circuit 604). Figure 6 Application circuit 602 is an interface for transmitting / receiving data; RF circuit interface 716 (e.g., for sending / receiving data to / from...). Figure 6 RF circuit 606 is an interface for transmitting / receiving data; wireless hardware connection interface 718 (e.g., for sending / receiving data to / from near field communication (NFC) components, Bluetooth). ® Components (e.g., Bluetooth) ® Low power consumption, Wi-Fi ® Interfaces for transmitting / receiving data to / from components and other communication components); and power management interface 720 (e.g., an interface for transmitting / receiving power or control signals to / from PMC 612).

[0130] Figures 8 to 17 AI / ML model ID signaling for RRM enhancement The transition from 3GPP LTE to NR offers the prospect of significantly increased bandwidth, providing greater download and upload speeds and reduced latency. One technique used to achieve this is the use of higher frequency bands. The NR specification is divided into two frequency bands: Frequency Range 1 (FR1), which covers the frequency range from 410MHz to 7.125GHz; and Frequency Range 2 (FR2), which covers the frequency range greater than 7.125GHz, including bands with center frequencies from 28GHz to 60GHz, and single-channel bandwidths from 50MHz to 400MHz, and even up to 2000MHz for band n263.

[0131] The millimeter-wave frequencies in FR2, compared to the smaller 3GPP bands in FR1, offer significantly greater bandwidth and transmission speeds to user equipment. However, the higher frequency range in FR2 also results in much greater signal loss due to absorption of the millimeter-wave carrier signal in the atmosphere.

[0132] To overcome the significant signal loss in FR2 while still meeting the specific absorption rate (SAR) transmit power limits at the UE in each country, the NR specification has adopted the use of beamforming. By transmitting power in a relatively narrow beam, the signal can travel a greater distance to the receiver compared to transmission using omnidirectional or wide-angle antennas.

[0133] In traditional beam management, the UE typically performs measurements across a set of transmit and receive (Tx, Rx) beam pairs. In one example, the number of Tx beams is M, and the number of UE beams is N. Measuring the Layer 3 (L3) Received Signal Received Power (RSRP) of all M×N beam pairs and selecting the optimal beam can result in significant measurement overhead.

[0134] In the current 3GPP NR specification, the overhead incurred by using reference signals (RS) such as Channel State Information Reference Signal (CSI-RS) or Synchronization Signal Block (SSB) for Radio Resource Monitoring (RRM) is quite high. For example, the associated SMTC overhead in FR2 is 25% when the SSB Measurement Timing Configuration (SMTC) period is 20 milliseconds (ms), with a 5 ms SSB burst. Therefore, reducing the number of beams at both the Tx and / or Rx sides is highly beneficial in L3 correlated measurements.

[0135] Artificial intelligence and machine learning (AI / ML) can be used to enhance the measurement and reporting of L3-related measurements of Tx / Rx beam pairs. AI / ML can be used to reduce latency in L3 measurements by minimizing the Tx / Rx beam scan set (spatial beam prediction). By reducing the beam scan factor, the SMTC window duration can be shortened. This reduces measurement latency.

[0136] Furthermore, AI / ML can be used to assist in L3 measurement reduction by periodically skipping Tx / Rx beam scans. This increases the periodicity of the SMTC window, thereby reducing overhead. As a result of minimizing L3 measurements, scheduling constraints can also be reduced, thus increasing throughput. Therefore, the use of AI / ML models can increase periodicity, increase throughput, and reduce measurement latency.

[0137] However, modern NR networks can include a wide variety of cell types and radio access networks (RANs). Each cell can be configured with a different number (4, 8, or 64) of consecutive synchronization signal blocks (SSBs) used for beam management, depending on the frequency range (FR1 or FR2) the cell is operating in. Furthermore, the order in which beams are transmitted can differ between cells. When a UE moves through a 5G network and handover occurs from cell to cell, SSB beam index mapping can be used to enable the design, training, and use of different AI / ML models across different cells in the cellular network, based on the configuration of each cell.

[0138] Figure 8An example diagram of a conventional Radio Resource Monitoring (RRM) procedure is provided. In a conventional RRM procedure, the BS is configured to transmit a sequence of M Tx beams 802 with different directions. The BS can transmit each of the M Tx beams during an SMTC window 806. Each beam is associated with a specific SSB. The UE (such as UE 106) can be configured to receive only once on a single receive chain. To enable the measurement of each Rx beam using each Tx beam, the UE can be configured to guide individual Rx beams 808 in different directions during each SMTC window 806. In this example, there are N=4 Rx beams (with N different directions) and M=8 Tx beams (with 8 different directions). A delay 810 exists within the period used to perform RRM measurements on each of the 4×8 Rx, Tx beam combinations.

[0139] The overhead for performing measurements can be as high as 25%. Furthermore, cells in FR2 can be relatively small (a few hundred feet in diameter). When the UE moves through different cells, significant overhead will be used to perform RRM measurements based on traditional schemes.

[0140] Figure 9 An example illustration of a Synchronization Signal Block (SSB) 900 used in NR to assist UE 106 in establishing a connection with BS 102 in the cell is provided. The SSB 900 comprises 4 OFDM symbols in the time domain and 20 Resource Blocks (RBs) in the frequency domain. Each RB includes 12 subcarriers. The SSB includes a Primary Synchronization Signal (PSS), a Secondary Synchronization Signal (SSS), and a Physical Broadcast Channel (PBCH). The PBCH may include a Demodulation Reference Signal (DRMS) and data. In NR, there are many different cases of time-domain patterns for SSB transmission for different frequency ranges and subcarrier spacings (SCS). The maximum number of SSBs within an SSB set (i.e., within a 5 ms period) is specified as 4 for a frequency range up to 3 GHz, 8 for 3 GHz to 7.125 GHz, or 64 for 7.125 GHz to 71.0 GHz, to achieve a trade-off between coverage and resource overhead.

[0141] When performing beam scanning, multiple SSBs are transmitted at specific intervals, known as SSB burst sets. An SSB burst set is a group of SSBs transmitted within a 5 ms window of SSB transmission. Each SSB is identified by a unique number called an SSB beam index. Each SSB is transmitted via a specific beam radiating in a specific direction. Therefore, each beam direction is associated with a specific SSB beam index.

[0142] Figure 10An example illustration of AI / ML RRM measurement enhancements for reducing overhead is provided. In this example, for Rx beams 2 and 3, Rx / Tx RRM beam measurements are not performed in SMTC window 806. For Rx beams 1 and 4, RRM measurements are still performed during SMTC window 806. During SMTC window 806, each of the M=8 Tx beams 802 is measured for both Rx beams 1 and 4. This results in an overhead reduction of approximately 50% because measurements are performed only for half of the N=4 Rx beams 808. However, since the first (Rx beam 1) and last (Rx beam 4) SMTC windows are used to perform RRM measurements, the total delay period 810 is reduced compared to... Figure 8 The total delay period is the same in the traditional process illustrated in the example.

[0143] Figure 11 Another example illustration of AI / ML RRM measurement enhancements is provided to reduce both overhead and latency. In this example, Rx / Tx RRM beam measurements are not performed during the SMTC window for Rx beams 3 and 4. During the SMTC window for Rx beams 1 and 2, M=8 Tx beams are measured for both Rx beams 1 and 2. Figure 10 As shown, this is relative to Figure 8 This reduces overhead by approximately 50%. Furthermore, because the first two SMTC windows were measured, while the last two were not, the latency is significantly smaller, resulting in a latency of 1110 that is significantly smaller (by up to 50%). Figure 8 and Figure 10 The delay is 802.

[0144] Figure 12 Another example illustration of AI / ML RRM measurement enhancements for reducing latency and further reducing overhead is provided. In this example, four Tx beams out of M=8 Tx beams are measured for both Rx beam 1 and Rx beam 2 during the SMTC window for Rx beam 1 and Rx beam 2. In this example, the beams associated with SSB 2, SSB 4, SSB 6, and SSB 8 are measured for both Rx beam 1 and Rx beam 2. This is relative to... Figure 10 and Figure 11 Further, expenses were reduced by approximately 50% (relative to) Figure 8 (25%). In addition, such as Figure 11 As shown, the delay is reduced.

[0145] Figure 13 Another example illustration of AI / ML RRM measurement enhancements is provided to further reduce latency and overhead. In this example, every other complete measurement cycle is skipped. This is relative to... Figure 12Examples show that this reduces overhead and latency by at least 50%. Simply skipping all measurements can significantly reduce accuracy over time and decrease the resolution of the data used to perform computations. To limit the impact of data reduction, the remaining data can be used to train an AI model to infer the missing data (skipped measurements). A trained AI model can be very accurate in inferring outputs when used relative to the current data for training purposes. By using a trained AI model to infer the values ​​of skipped measurements, the reduction in accuracy and resolution can be minimized.

[0146] Figure 14 An example illustration is provided that uses an AI model to interpolate between reduced measurements to provide a full-resolution map of RSRP measurements, where the Tx codebook and Rx codebook have the same resolution as the full RSRP map. This example illustrates the use of [a specific method / method]. A complete RSRP map 1402 for 10 data points, with up to 64 measurements for the Tx codebook and up to 16 measurements for the Rx codebook. However, as previously discussed, such a large number of measurements can lead to potentially excessive overhead levels when communicating in a cellular network. Therefore, the measurements can be downsampled to obtain a downsampled RSRP image 1404, where the gaps between downsampled measurements can include both the Rx and Tx codebooks. This may result in a RSRP map with reduced resolution. Reduced resolution reduces accuracy.

[0147] In one example, the output of the reduced RSRP map 1406 And the output of the complete RSRP mapping diagram 1402 It can be input into artificial intelligence / machine learning (AI / ML) models In model 1408 (referred to as the RRM AI model in this paper), deep learning can be used to train the model, where data from the full RSRP map 1402 is used to generate data as the true labels (sprite labels), and data from the reduced RSRP map 1406 is used as input. The optimal N {Tx, Rx} beam pair can be obtained from the interpolated image map. Found in 1410. The optimal {Tx, Rx} beam can be expressed as... The data originates from sprite tags (also known as real tags). The interpolated image map 1410 may include an Rx codebook and a Tx codebook with the same resolution as the RSRP map, where some RSRP measurements are derived from an AI / ML model. 1408 inferred. However, in AI / ML models... With the assistance of 1408, the overhead and latency of performing physical measurements can still be significantly reduced, as previously discussed.

[0148] One challenge in using the RRM AI model 1408 is that the AI ​​model's output can be highly dependent on the type of input and the training performed. When a UE moves between cells, different cells can perform RRM measurements differently from other cells. For RRM measurements, the size of the full Tx codebook, the size of the probe codebook, and the beamwidth can differ between the serving cell and neighboring cells. However, it may be difficult to have a dedicated, trained RRM AI model for each UE and cell combination. Therefore, it may be challenging to use the RRM AI model for mobile UEs.

[0149] Figure 15 An example illustration is provided showing how the serving cell performs RRM measurements using the selected pattern. Each angle of the serving cell can be associated with an SSB beam index. In this example, the serving cell transmits SSB beam index 1502 in numerical order from 1 to 64. Some beams illustrated in a star configuration are called pilot beams. Pilot beams are selected beams with physical measurements. For example, in... Figure 12 In this process, SSB1, SSB3, SSB5, and SSB7 perform physical measurements, while SSB2, SSB4, SSB6, and SSB8 are skipped. Physical measurements can be selected as pilot beams. The RSRP measurements of the pilot beams can be used as input to an AI model with a selected ID (such as ID1 in this example).

[0150] Return to Figure 15 Neighboring cells can send SSB beam indices in a different order than the serving cell. For example, neighboring cell signals with SSB indices = 1, 2, 3, 4, 5 spatially correspond to the serving cell's SSB indices 63, 60, 62, 64, and 3, respectively, when training the AI / ML model.

[0151] According to one implementation, the SSB beam index transmission 1504 of a neighboring cell can be mapped to the SSB beam index transmission of the serving cell 1502. In this example, a subset of the complete SSB beam index can be selected and mapped. However, this is not intended to be restrictive. A larger subset or the complete subset of the SSB index can be mapped. Alternatively, the RRM AI model can have predetermined inputs relative to the SSB beam index or Tx codebook pattern. Each serving cell or neighboring cell can be configured to identify the Tx pattern used by that cell. This Tx pattern can be mapped to the RRM AI model input such that the input to the RRM AI model is the same for each cell. For example, the TX pattern can be mapped to the RRM AI model input using the SSB index or a selected input relative to the Tx codebook. Thus, each cell using the same Tx codebook can map the cell's RRM measurements to the input of the RRM AI model, enabling the RRM AI model to be used to infer RRM measurement outputs for which no actual physical RRM measurements have been performed, as previously discussed.

[0152] exist Figure 15 In the example, SSB beam indices can be mapped for the pilot beams of serving cell 1502 and neighboring cell 1504. In the SSB beam index transmission of serving cell 1502, the pilot beams are selected as SSB beam indices 3, 6, and 61. In the SSB beam index transmission of neighboring cell 1504, the SSB beam indices are associated with different beams. For example, SSB3 is the 5th transmitted beam, SSB 6 is the 7th transmitted beam, and so on. The SSB index mapping of neighboring cells can be communicated to the serving cell. The serving cell can then pass this information to the UE so that the UE can achieve similar outputs using the same RRM AI model with the same inputs. Any serving cell and neighboring cell sharing the same transmission (Tx) codebook pattern can be mapped using SSB beam indices to enable the use of the same RRM AI model. Therefore, the same RRM AI model with the same RRM AI model ID can be used with the SSB beam index mapping between the serving cell and neighboring cells.

[0153] exist Figure 16 In the example, the SSB beam index transmission of serving cell 1602 includes 64 beams. However, the SSB beam index transmission of neighboring cell 1604 includes 16 or 32 beams. The fewer beams transmitted by the neighboring cell are also wider than the 64 beams transmitted by the serving cell. If an AI / ML model has already been trained with the serving cell's Tx beam pattern, reusing the same model for neighboring cell measurements will result in prediction errors.

[0154] Different RRM AI models can be used when beam physics differs or different numbers of beams are used. Furthermore, different Tx codebooks are typically used. Different RRM AI models can be trained using the outputs of neighboring cells. Figure 16 In the example, the RRM AI model used for SSB beam index transmission in neighboring cell 1604 can be assigned a different ID than the RRM AI model used for the serving cell, such as ID2. In this example, the serving cell RRM AI model is ID1. Therefore, different models can be created for cells with different codebooks or different types or numbers of transmissions for SSB beam index transmission. Each different model can be trained using transmissions from the cell associated with the model.

[0155] Figure 17 Example illustrations of communication and message exchange between UE 106, serving cell 1702, L neighboring cells 1704…1704L, and server 1706 are provided. In this example, UE 106 can transmit a UE capability message 1708 to the serving cell, which indicates the RRM AI model ID available at the UE for performing RRM measurements with serving cell 1702 and the L neighboring cells 1704L. Furthermore, training information can be transmitted from serving cell 1702 to UE 106 to train the RRM AI model for use with the serving cell. However, training can also be performed offline. This will be discussed further in the preceding paragraphs.

[0156] UE 106 can receive configuration information 1710 from the serving cell. The configuration information may include auxiliary information and RRM AI model IDs for the UE to use when performing RRM on the serving cell 1702. The configuration information may also include RRM AI model IDs to be used for L neighboring cells 1704...1704L.

[0157] As previously discussed, when cells have a similar number of beams transmitted (e.g., 64, 32, 16, 8, 4, 2, or 1) and use the same Tx codebook, each RRM AI model is available to the UE for performing RRM on selected cells (e.g., serving cell, neighboring cell 1… neighboring cell L). Cells can be transmitted using a specific beam pattern referred to as the Tx beam pattern. Each beam pattern can be mapped to the RRM AI model input so that the input to the RRM AI model is the same for each cell. For example, the TX beam pattern can be mapped to the RRM AI model input using the SSB index or the selected input relative to the Tx codebook.

[0158] When a mobile UE moves through neighboring cells, the serving cell can receive Tx beam pattern information 1724 from neighboring cell 1704L. The serving cell 1702 can identify the RRM AI model ID 1724 associated with the Tx beam pattern information 1722. The Tx beam pattern can be associated with a known RRM AI model (one of K RRM AI models). In one example, each RRM AI model can be mapped to an SSB index of the Tx beam pattern information 1724 used for RRM. Figure 15 The beam pattern can also be associated with multiple pilot beams, each with an SSB beam index. Auxiliary information 1710 can be used to enable the UE to perform RRM on neighboring cells using the correct RRM AI model.

[0159] If the Tx beam pattern information 1722 for the neighboring cell 1704L is new, and there is currently no RRM AI model 1724K configured for that Tx beam pattern, a new RRM AI model can be created. This can be done in cells with different numbers of Tx beams (1604, Figure 16 This occurs when different Tx codebooks are used. In one example, serving cell 1702 may transmit training information 1712 for a new RRM AI model and provide a new RRM AI model ID 1726 to the UE. Serving cell 1702 may transmit a request to neighboring cells to provide initial training 1720.

[0160] Neighboring cell 1704L with new Tx mode information 1704 can communicate training information to server 1706 via a non-3GPP air interface (such as a wired link to neighboring cell 1704L or the network of neighboring cell 1704L). Server 1706 can use the new RRM AI model ID 1726 to train the new RRM AI model. The server can then transmit the new RRM AI model 1718 to UE 106. The UE can signal 1714 the new capabilities, including the new RRM AI model ID that the UE is configured to use for RRM execution.

[0161] The UE may also include an RRM AI model timer 1723. Timer 1723 can be used to refresh the RRM model as it ages. The RRM model is based on inputs that can vary over time and distance as the channel used by the UE changes. Therefore, the RRM AI model used by the UE may change over time and may become less accurate as the RRM AI model ages. In one example, server 1706 can be used to receive current training data and Tx beam pattern information 1722 for different RRM AI models used by the UE. The RRM AI model can be periodically updated and transmitted to the UE 1718. Furthermore, for more urgent updates, model training can also be performed via the air interface between UE 106 and serving cell 1702. The UE can then signal 1714 new capabilities, including a new RRM AI model ID configured for UE 106 to perform RRM. UE 106 can maintain RRM AI models of recently visited neighboring cells to allow them to be reused in future neighboring cells 1704L.

[0162] In some embodiments, an apparatus for a base station (BS) is disclosed, comprising one or more processors coupled to a memory, configured to decode UE capability messages received from a UE at the BS. The UE capability messages include one or more RRM AI model IDs 1724K of a Radio Resource Management (RRM) Artificial Intelligence (AI) model supported by UE 106. Each RRM AI model is configured to be mapped to transmission mode information 1722 of a cell 1704L for RRM. The one or more processors are also configured to encode auxiliary information from the serving cell 1702 of the BS 102 for transmission to UE 106 at the BS 102. The auxiliary information may include: an RRM AI model ID 1724 for the RRM AI model of the BS 102; and an SSB index beam mapping for the RRM AI model of the BS. The one or more processors can perform RRM for UE 106, which is configured to use an RRM AI model with RRM AI model ID 1724 for the BS 102 with SSB index beam mapping.

[0163] In some implementations, one or more processors at the BS are further configured to: decode beam transmission patterns from one or more neighboring cells at the BS; associate the beam transmission patterns with existing RRM AI model IDs at the BS; and encode the RRM AI model IDs based on the SSB index mapped to the beam transmission patterns for transmission to the UE, enabling the UE to perform RRM on one or more neighboring cells using an RRM AI model with the RRM AI model ID. This allows the RRM AI model to be used with multiple neighboring cells having a similar number of transmission beams. In one example, multiple neighboring cells may share the same Tx codebook, as previously discussed.

[0164] In some implementations, one or more processors of the BS are further configured to: decode beam transmission patterns from one or more neighboring cells at the BS; determine at the BS that the beam transmission patterns are not associated with an existing RRM AI model; transmit the beam transmission patterns to a server so that a new RRM AI model can be trained; associate the new RRM AI model with a new RRM AI model ID; and encode the new RRM AI model ID based on an SSB index mapped to the beam transmission patterns for transmission to the user equipment (UE) so that the UE can use the new RRM AI model with the new RRM AI model ID to perform RRM on one or more neighboring cells.

[0165] In some implementations, one or more processors of the BS are also configured to encode an initiation training message at the BS for transmission to one or more neighboring cells, thereby initiating the training of a new RRM AI model for the one or more neighboring cells.

[0166] In some implementations, one or more processors of the BS are also configured to: train a new RRM AI model via an air interface with one or more neighboring cells; or train a new RRM AI model via a wired link with one or more neighboring cells; or train a new RRM AI model via a wired link with a server configured to train an RRM AI model.

[0167] In some implementations, one or more processors of the BS are also configured to encode training data at the BS for transmission to the UE, thereby training an RRM AI model for the BS.

[0168] In some implementations, each RRM AI model is associated with a Synchronization Signal Block (SSB) index that maps to the beam transmission pattern of the cell used for RRM, and the beam transmission pattern is associated with multiple pilot beams, each of which has an SSB beam index.

[0169] In some implementations, one or more processors of the BS are further configured to map the beam transmission patterns of the multiple neighboring cells to a single RRM AI model by mapping the pilot beams of the multiple neighboring cells to the input of a single RRM AI model having a single RRM AI model ID, so that the multiple neighboring cells can use a single RRM AI model, wherein each of the multiple neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

[0170] In some embodiments, an apparatus for a user equipment (UE) is disclosed, comprising: one or more processors coupled to a memory, configured to: encode a UE capability message for transmission to a serving cell having a base station (BS), the UE capability message including one or more RRM AI model identifiers (IDs) of one or more RRM AI models available at the UE for radio resource management (RRM) measurements, wherein each RRM AI model is configured to be mapped to a transmit beam pattern of a cell for RRM. The one or more processors are also configured to decode auxiliary information from the serving cell at the UE, wherein the auxiliary information includes: an RRM AI model ID for one or more neighboring cells; and a transmit beam pattern for one or more neighboring cells, such that the transmit beam pattern can be mapped to an RRM AI model. The one or more processors are further configured to perform RRM for one or more neighboring cells using the RRM AI models of transmit beam patterns mapped to the RRM AI model IDs associated with the one or more neighboring cells.

[0171] In some implementations, one or more processors of the UE are further configured to: receive a new RRM AI model trained for the cell from a server, enabling the UE to: perform RRM on the cell using the new RRM AI model with a transmit beam pattern mapped to a new RRM AI model ID associated with the cell; and encode a UE capability message for transmission to the BS, the UE capability message including the RRM AI model ID of the new RRM AI model, to notify the BS of the UE's updated capabilities.

[0172] In some implementations, one or more processors of the UE are also configured to operate an RRM model timer and, when the RRM model timer expires, to perform a refresh of one or more RRM AI models at one or more of the UE or servers.

[0173] In some implementations, one or more processors of the UE are further configured to: train one or more refreshed RRM AI models via an air interface with the BS or one or more neighboring cells; or receive one or more refreshed RRM AI models from a server configured to train one or more refreshed RRM AI models via a wired link with one or more neighboring cells or serving cells of the BS.

[0174] In some implementations, one or more processors of the UE are also configured to decode training data received from the BS at the UE, thereby training an RRM AI model for the BS. Each RRM AI model may be associated with a Synchronization Signal Block (SSB) index mapped to a beam transmission pattern for the cell used for RRM. The beam transmission pattern may be associated with multiple pilot beams, each of which has an SSB beam index.

[0175] In some implementations, one or more processors of the UE are further configured to map the beam transmission patterns of the multiple neighboring cells to a single RRM AI model by mapping the pilot beams of the multiple neighboring cells to the input of a single RRM AI model having a single RRM AI model ID, so that the multiple neighboring cells can use a single RRM AI model, wherein each of the multiple neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

[0176] Figure 18 Flowchart of a method for mapping an RRM AI model to a transmit beam pattern for a cell used in RRM Figure 18 A flowchart illustrating an example of mapping a Radio Resource Management (RRM) artificial intelligence (AI) model to a transmit beam pattern for a cell used in RRM, according to some implementation schemes, is shown. Among other devices, Figure 18 The methods shown can also be used in conjunction with any of the systems, methods, or devices illustrated in the figures. In various embodiments, some of the method elements shown may be executed concurrently in a different order than shown, or may be omitted. Additional method elements may also be executed as needed.

[0177] According to one embodiment, a method 1800 is disclosed for mapping Radio Resource Management (RRM) Artificial Intelligence (AI) models to transmit beam patterns of cells used for RRM. The method includes: decoding at a BS the UE capability message received from a UE, the UE capability message including one or more RRM AI model IDs of Radio Resource Management (RRM) Artificial Intelligence (AI) models supported by the UE, wherein each RRM AI model is configured to be mapped to a transmit beam pattern of a cell used for RRM, as shown in box 1810. Method 1800 further includes: encoding at the BS auxiliary information for the UE from the serving cell of the BS, wherein the auxiliary information includes: the RRM AI model ID of the RRM AI model for the BS; and the SSB index beam mapping of the BS for the RRM AI model, as shown in box 1820. Method 1800 further includes: performing RRM on the UE, the UE being configured to use an RRM AI model with an RRM AI model ID for a BS having an SSB index beam mapping, as shown in box 1830.

[0178] In some implementations, method 1800 may further include: decoding beam transmission patterns from one or more neighboring cells at the BS; associating the beam transmission patterns with an existing RRM AI model ID at the BS; and encoding the RRM AI model ID based on an SSB index mapped to the beam transmission pattern for transmission to the UE, so that the UE can use the RRM AI model with the RRM AI model ID to perform RRM on one or more neighboring cells.

[0179] In some implementations, method 1800 may further include: decoding beam transmission patterns from one or more neighboring cells at the BS; determining at the BS that the beam transmission patterns are not associated with an existing RRM AI model; transmitting the beam transmission patterns to a server so that a new RRM AI model can be trained; associating the new RRM AI model with a new RRM AI model ID; and encoding the new RRM AI model ID based on an SSB index mapped to the beam transmission patterns for transmission to a user equipment (UE) so that the UE can use the new RRM AI model with the new RRM AI model ID to perform RRM on one or more neighboring cells.

[0180] In some implementations, method 1800 may further include: encoding an initiation training message at the BS for transmission to one or more neighboring cells, thereby initiating training of a new RRM AI model for the one or more neighboring cells.

[0181] In some implementations, method 1800 may further include: training a new RRM AI model via an air interface with one or more neighboring cells; or training a new RRM AI model via a wired link with one or more neighboring cells; or training a new RRM AI model via a wired link with a server configured to train an RRM AI model.

[0182] In some implementations, method 1800 may further include: encoding training data at the BS for transmission to the UE, thereby training an RRM AI model for the BS. Each RRM AI model may be associated with a Synchronization Signal Block (SSB) index mapped to a beam transmission pattern of a cell for RRM, and the beam transmission pattern is associated with a plurality of pilot beams, each of which has an SSB beam index. The method may further include: mapping the beam transmission patterns of the plurality of neighboring cells to a single RRM AI model by mapping the pilot beams of the plurality of neighboring cells to the input of a single RRM AI model having a single RRM AI model ID, such that the plurality of neighboring cells can use a single RRM AI model, wherein each of the plurality of neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

[0183] In some embodiments, an apparatus is disclosed that is configured to cause the user equipment (UE) to perform any of the operations of method 1800.

[0184] In some embodiments, a computer program product is disclosed that includes computer instructions that, when executed by one or more processors, perform any of the operations described in method 1900.

[0185] Figure 19 Flowchart of a method for using a dynamic measurement opportunity sharing scheme at the UE Figure 19 A flowchart illustrating an example of a method for mapping a Radio Resource Management (RRM) artificial intelligence (AI) model to a transmission beam pattern for a cell used in RRM, according to some implementation schemes. Among other devices, Figure 19 The methods shown can also be used in conjunction with any of the systems, methods, or devices illustrated in the figures. In various embodiments, some of the method elements shown may be executed concurrently in a different order than shown, or may be omitted. Additional method elements may also be executed as needed.

[0186] According to the implementation, method 1900 includes: encoding a UE capability message at a user equipment (UE) for transmission to a serving cell having a base station (BS), the UE capability message including one or more RRM AI model identifiers (IDs) of one or more RRM AI models available at the UE for radio resource management (RRM) measurements, wherein each RRM AI model is configured to map to a transmission beam pattern of the cell used for RRM, as shown in box 1910. Method 1900 further includes: decoding auxiliary information from the serving cell at the UE, wherein the auxiliary information includes: an RRM AI model ID for one or more neighboring cells; and a transmission beam pattern for one or more neighboring cells, such that the transmission beam pattern can be mapped to an RRM AI model, as shown in box 1920. Method 1900 further includes: performing RRM for one or more neighboring cells using an RRM AI model with a transmission beam pattern mapped to an RRM AI model ID associated with one or more neighboring cells, as shown in box 1930.

[0187] Method 1900 may further include: decoding training information from the BS at the UE, wherein the training information includes a new RRM AI model ID for the new RRM AI model; and receiving a new RRM AI model trained for the cell from the server, so that the UE can perform RRM on the cell using the new RRM AI model having a new RRM AI model ID associated with the cell.

[0188] Method 1900 may further include: receiving from a server a new RRM AI model trained for the cell, enabling the UE to: perform RRM on the cell using the new RRM AI model that maps to a transmission beam pattern associated with the cell; and encoding a UE capability message for transmission to a BS, the UE capability message including the new RRM AI model ID of the new RRM AI model, to notify the BS of the UE's updated capabilities.

[0189] Method 1900 may further include: operating an RRM model timer; and when the RRM AI model timer expires, performing a refresh of one or more RRM AI models at one or more of the UE or a server. Method 1900 also includes: training one or more refreshed RRM AI models via an air interface with a BS or one or more neighboring cells; or receiving one or more refreshed RRM AI models from a server configured to train one or more refreshed RRM AI models via a wired link with one or more neighboring cells or a serving cell.

[0190] Method 1900 may further include: decoding training data from the BS at the UE to train an RRMAI model for the BS.

[0191] Each RRM AI model can be associated with a Synchronization Signal Block (SSB) index that maps to the beam transmission pattern of the cell used for RRM, and the beam transmission pattern is associated with multiple pilot beams, each of which has an SSB beam index.

[0192] Method 1900 may further include: mapping the beam transmission patterns of the plurality of neighboring cells to a single RRM AI model by mapping the pilot beams of the plurality of neighboring cells to the input of a single RRM AI model having a single RRM AI model ID, such that the plurality of neighboring cells can use a single RRM AI model, wherein each of the plurality of neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

[0193] In some embodiments, an apparatus is disclosed that is configured to cause the user equipment (UE) to perform any of the operations of method 1900.

[0194] In some embodiments, a computer program product is disclosed that includes computer instructions that, when executed by one or more processors, perform any of the operations described in method 1900.

[0195] Embodiments of this disclosure may be implemented in any of a variety of forms. For example, some embodiments may be implemented as computer-implemented methods, computer-readable storage media, or computer systems. Other embodiments may be implemented using one or more custom-designed hardware devices such as ASICs. Other embodiments may be implemented using one or more programmable hardware elements such as FPGAs.

[0196] In some embodiments, a non-transitory computer-readable storage medium may be configured to store program instructions and / or data, wherein, if executed by a computer system, the program instructions cause the computer system to perform a method, such as any method embodiment of the method embodiments described herein, or any combination of method embodiments described herein, or any subset or combination of any such subset of any method embodiments described herein.

[0197] In some implementations, the device (e.g., UE 106) may be configured to include a processor (or a set of processors) and a memory medium, wherein the memory medium stores program instructions, and the processor is configured to read from and execute the program instructions from the memory medium, wherein the program instructions are executable to implement any of the various method implementations described herein (or any combination of the method implementations described herein, or any subset of any of the method implementations described herein, or any combination of such subsets). The device may be implemented in any of the various forms.

[0198] By interpreting each message / signal X received by the user equipment (UE) in the downlink as a message / signal X sent by the base station, and interpreting each message / signal Y sent by the UE in the uplink as a message / signal Y received by the base station, any method described herein for operating the UE can serve as the basis for a corresponding method for operating the base station.

[0199] Although the above embodiments have been described in considerable detail, many variations and modifications will become apparent to those skilled in the art once the above disclosure is fully understood. It is intended that the following claims be construed as encompassing all such variations and modifications.

Claims

1. A method for mapping a radio resource management (RRM) artificial intelligence (AI) model to a transmit beam pattern for a cell used in RRM, the method comprising: The UE capability message is encoded at the user equipment (UE) for transmission to a serving cell with a base station (BS). The UE capability message includes one or more RRM AI model identifiers (IDs) of one or more RRM artificial intelligence (AI) models available at the UE for radio resource management (RRM) measurements, wherein each RRM AI model is configured to be mapped to a transmission beam pattern of the cell for RRM. The auxiliary information from the serving cell is decoded at the UE, wherein the auxiliary information includes: RRM AI model ID for one or more neighboring cells; and For the transmission beam patterns of the one or more neighboring cells, such that the transmission beam patterns can be mapped to the RRM AI model; and The RRM AI model, which maps to the transmit beam pattern associated with the RRM AI model ID of the one or more neighboring cells, is used to perform RRM for the one or more neighboring cells.

2. The method according to claim 1, further comprising: The training information from the BS is decoded at the UE, wherein the training information includes a new RRM AI model ID for the new RRM AI model; and Receive the new RRM AI model trained for the cell from the server, so that the UE can: RRM is performed on the cell using the new RRM AI model that has the new RRM AI model ID associated with the cell.

3. The method according to claim 1, further comprising: Receive a new RRM AI model trained for the cell from the server, enabling the UE to: RRM is performed on the cell using the new RRMAI model of the transmit beam pattern mapped to the new RRM AI model ID associated with the cell; and The UE capability message is encoded for transmission to the BS, the UE capability message including the new RRM AI model ID of the new RRM AI model, to notify the BS of the UE's updated capabilities.

4. The method according to claim 1, further comprising: Operate the RRM model timer; as well as When the RRM AI model timer expires, a refresh of one or more RRM AI models is performed at one or more of the UE or servers.

5. The method according to claim 4, further comprising: One or more refreshed RRMAI models are trained via the air interface with the BS or one or more neighboring cells. or The server receives one or more refreshed RRM AI models, the server being configured to train one or more refreshed RRM AI models via wired links with the one or more neighboring cells or the serving cell.

6. The method according to claim 1, further comprising: The training data from the BS is decoded at the UE to train the RRM AI model for the BS.

7. The method of claim 1, wherein each RRM AI model is associated with a Synchronization Signal Block (SSB) index mapped to a beam transmission pattern for the cell used for RRM, and the beam transmission pattern is associated with a plurality of pilot beams, wherein each pilot beam has an SSB beam index.

8. The method according to claim 7, further comprising: The beam transmission patterns of the multiple neighboring cells are mapped to the single RRM AI model by mapping the pilot beams of multiple neighboring cells to the input of a single RRM AI model with a single RRM AI model ID, so that the multiple neighboring cells can use the single RRM AI model, wherein each of the multiple neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

9. An apparatus configured to cause a user equipment (UE) to perform any of the methods described according to claims 1 to 8.

10. A user equipment (UE) configured to perform any of the operations described herein.

11. A base station (BS) configured to perform any of the operations described herein.

12. A computer program product comprising computer instructions that, when executed by one or more processors, perform any of the operations described herein.

13. A baseband processor configured to perform one or more of the methods of claims 1 to 8.

14. An apparatus for a base station (BS), the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: The BS decodes the UE capability message received from the UE, the UE capability message including: One or more RRM AI model IDs of the radio resource management (RRM) artificial intelligence (AI) model supported by the UE; Each RRM AI model is configured to be mapped to the transmit beam pattern of the cell used for RRM; At the BS, auxiliary information for the UE from the serving cell of the BS is encoded, wherein the auxiliary information includes: The RRM AI model ID used for the RRM AI model of the BS; and The BS is used for the SSB index beam mapping of the RRM AI model; and RRM is performed on the UE, which is configured to use the RRM AI model with the RRM AI model ID for the BS having the SSB index beam mapping.

15. The apparatus of claim 14, wherein the one or more processors are further configured to: At the BS, the beam transmission patterns from one or more neighboring cells are decoded; At the BS, the beam transmission mode is associated with an existing RRM AI model ID; and Based on the SSB index mapped to the beam transmission pattern, the RRM AI model ID is encoded for transmission to the UE, so that the UE can use the RRM AI model with the RRM AI model ID to perform RRM on the one or more neighboring cells.

16. The apparatus of claim 14, wherein the one or more processors are further configured to: At the BS, the beam transmission patterns from one or more neighboring cells are decoded; At the BS, it is determined that the beam transmission mode is not associated with the existing RRM AI model; The beam transmission pattern is transmitted to the server so that the new RRM AI model can be trained. Associate the new RRM AI model with the new RRM AI model ID; as well as Based on the SSB index mapped to the beam transmission pattern, the new RRM AI model ID is encoded for transmission to the user equipment (UE) so that the UE can use the new RRM AI model with the new RRM AI model ID to perform RRM on the one or more neighboring cells.

17. The apparatus of claim 14, wherein the one or more processors are further configured to: The training message is encoded at the BS for transmission to one or more neighboring cells, thereby initiating the training of a new RRM AI model for the one or more neighboring cells.

18. The apparatus of claim 17, wherein the one or more processors are further configured to: The new RRM AI model is trained via the air interface with one or more neighboring cells; or The new RRM AI model is trained via a wired link with one or more neighboring cells; or The new RRM AI model is trained via a wired link to a server configured to train the RRM AI model.

19. The apparatus of claim 14, wherein the one or more processors are further configured to: The training data is encoded at the BS for transmission to the UE, thereby training the RRM AI model for the BS.

20. The apparatus of claim 14, wherein each RRM AI model is associated with a Synchronization Signal Block (SSB) index mapped to a beam transmission pattern for the cell used for RRM, and the beam transmission pattern is associated with a plurality of pilot beams, wherein each pilot beam has an SSB beam index.

21. The apparatus of claim 20, wherein the one or more processors are further configured to: The beam transmission patterns of the multiple neighboring cells are mapped to the single RRM AI model by mapping the pilot beams of multiple neighboring cells to the input of a single RRM AI model with a single RRM AI model ID, so that the multiple neighboring cells can use the single RRM AI model, wherein each of the multiple neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

22. An apparatus for a user equipment (UE), the apparatus comprising: One or more processors coupled to the memory, said one or more processors being configured to: The UE capability message is encoded for transmission to a serving cell with a base station (BS), the UE capability message including one or more RRM AI model identifiers (IDs) of one or more RRM AI models available for radio resource management (RRM) measurements at the UE, wherein each RRM AI model is configured to be mapped to a transmission beam pattern of the cell for RRM. The auxiliary information from the serving cell is decoded at the UE, wherein the auxiliary information includes: RRM AI model ID for one or more neighboring cells; and For the transmission beam patterns of the one or more neighboring cells, such that the transmission beam patterns can be mapped to the RRM AI model; and The RRM AI model, which maps to the transmit beam pattern associated with the RRM AI model ID of the one or more neighboring cells, is used to perform RRM for the one or more neighboring cells.

23. The apparatus of claim 22, wherein the one or more processors are further configured to: The training information from the BS is decoded at the UE, wherein the training information includes a new RRM AI model ID for the new RRM AI model; and Receive the new RRM AI model trained for the cell from the server, so that the UE can: RRM is performed on the cell using the new RRM AI model that has the new RRM AI model ID associated with the cell.

24. The apparatus of claim 22, wherein the one or more processors are further configured to: Receive a new RRM AI model trained for the cell from the server, enabling the UE to: RRM is performed on the cell using the new RRMAI model of the transmit beam pattern mapped to the new RRM AI model ID associated with the cell; and The UE capability message is encoded for transmission to the BS, the UE capability message including the new RRM AI model ID of the new RRM AI model, to notify the BS of the UE's updated capabilities.

25. The apparatus of claim 22, wherein the one or more processors are further configured to: Operating the RRM model timer; and When the RRM AI model timer expires, a refresh of one or more RRM AI models is performed at one or more of the UE or servers.

26. The apparatus of claim 25, wherein the one or more processors are further configured to: Train one or more refreshed RRM AI models via the air interface with the BS or one or more neighboring cells; or The server receives one or more refreshed RRM AI models, the server being configured to train one or more refreshed RRM AI models via wired links with the one or more neighboring cells.

27. The apparatus of claim 22, wherein the one or more processors are further configured to: The training data received from the BS is decoded at the UE to train the RRM AI model for the BS.

28. The apparatus of claim 22, wherein each RRM AI model is associated with a Synchronization Signal Block (SSB) index mapped to a beam transmission pattern for the cell used for RRM, and the beam transmission pattern is associated with a plurality of pilot beams, wherein each pilot beam has an SSB beam index.

29. The apparatus of claim 28, wherein the one or more processors are further configured to: The beam transmission patterns of the multiple neighboring cells are mapped to the single RRM AI model by mapping the pilot beams of multiple neighboring cells to the input of a single RRM AI model with a single RRM AI model ID, so that the multiple neighboring cells can use the single RRM AI model, wherein each of the multiple neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

30. A method for mapping a radio resource management (RRM) artificial intelligence (AI) model to a transmit beam pattern for a cell used in RRM, the method comprising: At the base station (BS), the UE capability message received from the user equipment (UE) is decoded, the UE capability message including: One or more RRM AI model IDs of the radio resource management (RRM) artificial intelligence (AI) model supported by the UE; Each RRM AI model is configured to be mapped to the transmit beam pattern of the cell used for RRM; At the BS, auxiliary information for the UE from the serving cell of the BS is encoded, wherein the auxiliary information includes: The RRM AI model ID used for the RRM AI model of the BS; and The BS is used for the SSB index beam mapping of the RRM AI model; and RRM is performed on the UE, which is configured to use the RRM AI model with the RRM AI model ID for the BS having the SSB index beam mapping.

31. The method according to claim 30, further comprising: At the BS, the beam transmission patterns from one or more neighboring cells are decoded; At the BS, the beam transmission mode is associated with an existing RRM AI model ID; as well as Based on the SSB index mapped to the beam transmission pattern, the RRM AI model ID is encoded for transmission to the UE, so that the UE can use the RRM AI model with the RRM AI model ID to perform RRM on the one or more neighboring cells.

32. The method according to claim 30, further comprising: At the BS, the beam transmission patterns from one or more neighboring cells are decoded; At the BS, it is determined that the beam transmission mode is not associated with the existing RRM AI model; The beam transmission pattern is transmitted to the server so that the new RRM AI model can be trained. Associate the new RRM AI model with the new RRM AI model ID; as well as Based on the SSB index mapped to the beam transmission pattern, the new RRM AI model ID is encoded for transmission to the user equipment (UE) so that the UE can use the new RRM AI model with the new RRM AI model ID to perform RRM on the one or more neighboring cells.

33. The method according to claim 30, further comprising: The training message is encoded at the BS for transmission to one or more neighboring cells, thereby initiating the training of a new RRM AI model for the one or more neighboring cells.

34. The method according to claim 33, further comprising: The new RRM AI model is trained via the air interface with one or more neighboring cells; or The new RRM AI model is trained via a wired link with one or more neighboring cells; or The new RRM AI model is trained via a wired link to a server configured to train the RRM AI model.

35. The method according to claim 30, further comprising: The training data is encoded at the BS for transmission to the UE, thereby training the RRM AI model for the BS.

36. The method of claim 30, wherein each RRM AI model is associated with a Synchronization Signal Block (SSB) index mapped to a beam transmission pattern for the cell used for RRM, and the beam transmission pattern is associated with a plurality of pilot beams, wherein each pilot beam has an SSB beam index.

37. The method of claim 30, further comprising: The beam transmission patterns of the multiple neighboring cells are mapped to the single RRM AI model by mapping the pilot beams of multiple neighboring cells to the input of a single RRM AI model with a single RRM AI model ID, so that the multiple neighboring cells can use the single RRM AI model, wherein each of the multiple neighboring cells uses the same transmission codebook pattern, and the pilot beams are configured to enable physical RRM measurements to be performed.

38. An apparatus configured to cause a user equipment (UE) to perform any of the methods described according to claims 30 to 37.

39. A baseband processor configured to perform the method according to one or more of claims 30 to 37.

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