Beam measurement accuracy test metrics for artificial intelligence / machine language (ai / ml) based beam management

Abstract

A method of measuring beam ID and RSRP test metrics for AI beam management for a UE is disclosed. The method includes identifying an optimal measured beam of a plurality of beams having a beam ID with a measured RSRP value above a selected threshold, and using an artificial intelligence (AI) model to predict RSRP values and beam IDs for a set of beams of the plurality of beams, selecting one or more optimal predicted beams of the set of beams based on the predicted RSRP values beam IDs. A margin tolerance box is defined based on a predetermined ΔRSRP value and a predetermined Δbeam ID prediction value. The AI model is determined to operate within acceptable parameters when the measured RSRP values and beam IDs place at least one of the optimal measured beam and the optimal predicted beams in the same margin tolerance box.

Description

Technical Field

[0001] Embodiments of the present invention relate to wireless communication, including apparatus, systems, and methods for measuring the performance of beam management AI / ML models in wireless communication systems. Background Technology

[0002] The use of wireless communication systems is growing rapidly. In recent years, wireless devices, such as smartphones and tablets, have become increasingly complex and 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 user 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. Research into new radio access technologies began in 2015, 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. Furthermore, NR allows for more flexible scheduling compared to current LTE. Therefore, ongoing development of NR is striving to leverage the higher throughput potentially achievable at higher frequencies. Attached Figure Description

[0005] 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.

[0006] Figure 1B Examples of base stations and access points communicating with user equipment (UE) devices according to some implementation schemes are illustrated.

[0007] Figure 2 Example block diagrams of base stations according to some implementation schemes are shown.

[0008] Figure 3 Example block diagrams of servers according to some implementation schemes are shown.

[0009] Figure 4 Exemplary block diagrams of a UE according to some implementation schemes are shown.

[0010] Figure 5 Exemplary block diagrams of cellular communication circuits according to some implementation schemes are shown.

[0011] Figure 6 Examples of baseband processor architectures for UEs according to some implementation schemes are illustrated.

[0012] Figure 7 Example block diagrams illustrating the interface of a baseband circuit according to some implementation schemes are shown.

[0013] Figure 8 Examples of control plane protocol stacks based on some implementation schemes are shown.

[0014] Figure 9 Examples of user plane protocol stacks based on some implementation schemes are shown.

[0015] Figure 10 Example components of the core network according to some implementation schemes are shown.

[0016] Figure 11 This is an example illustration of beamforming transmission between a base station and a UE according to some implementation schemes.

[0017] Figure 12 Example AI / ML inference for beam management is illustrated according to some implementation schemes.

[0018] Figure 13 Example illustrations are provided for measuring RSRP inference accuracy and beam ID accuracy according to some implementation schemes, where the UE passes the test metric of the third option.

[0019] Figure 14 Example illustrations are provided for measuring RSRP inference accuracy and beam ID accuracy according to some implementation schemes, where the UE passes the test metrics of the first option and the third option.

[0020] Figure 15 Example illustrations are provided for measuring RSRP inference accuracy and beam ID accuracy according to some implementation schemes, where the UE passes the test metrics of the second option.

[0021] Figure 16 Another example illustration illustrates the measurement of RSRP inference accuracy and beam ID accuracy according to some implementation schemes, where the UE passes the test metrics of the second option.

[0022] Figure 17Example illustrations are provided for measuring RSRP inference accuracy and beam ID inference accuracy using a KPI according to some implementation schemes, which can simultaneously capture similarity across both the RSRP and beam ID domains.

[0023] Figure 18 Example illustrations are provided for measuring RSRP inference accuracy and beam ID inference accuracy according to some implementation schemes, where KPI tests are performed using margin boxes.

[0024] Figure 19 A flowchart illustrating an example method for measuring beam identification (ID) and reference signal received power (RSRP) test metrics for artificial intelligence (AI) beam management at the user equipment (UE) according to some implementation schemes is provided.

[0025] 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

[0026] the term The following is a glossary of terms used in this disclosure: Storage medium or memory—any of various types of nontransitory memory devices or storage devices. The term "memory medium" 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 medium" 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.

[0027] 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.

[0028] 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 "configurable logic units."

[0029] 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.

[0030] 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, the Internet of Things, 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.

[0031] Base station—The term “base station” has the full range of its common meaning and includes at least a wireless communication station installed in a fixed location and used to communicate with a UE as part of a wireless telephone system or radio system, including but not limited to next-generation node B (gNB or gNodeB) and NG-RAN nodes in NR.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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” may mean within 0.1% of a specified or expected value, while in various other implementations, the threshold may be, for example, 2%, 3%, 5%, etc., depending on the expectations or settings of the specific application.

[0037] 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).

[0038] 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 "having 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. Typically, the circuit forming the structure corresponding to "configured" can include hardware circuitry.

[0039] 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.

[0040] The exemplary embodiments can be further understood with reference to the following description and related figures, wherein the same elements have the same reference numerals. The exemplary embodiments relate to apparatus, systems, and methods for reducing energy consumption of network components, such as base stations in wireless communication systems.

[0041] Example implementations are described regarding communication between a next-generation node B (gNB) and user equipment (UE). However, references to gNB or UE are provided for illustrative purposes only. The example implementations can be used with any electronic component capable of establishing a connection to the network and configured with hardware, software, and / or firmware to support reduced energy consumption of network components in a wireless communication system. Therefore, gNB or UE as described herein is used to refer to any suitable type of electronic component.

[0042] An example implementation is also described with reference to fifth-generation (5G) new radio (NR). However, references to 5G NR networks are provided for illustrative purposes only. The example implementation can be used with any suitable type of network.

[0043] Throughout this specification, various information elements (IEs) are referred to by specific names. It should be understood that these names are merely examples, and the IEs carrying the information referenced throughout this specification may be referenced by various entities under other names.

[0044] 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 1AThe system described herein is merely one example of a possible system, and the features of this disclosure can be implemented in any of a variety of systems as needed.

[0045] 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.

[0046] 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.

[0047] 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".

[0048] 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.

[0049] 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-106N and similar devices over a geographical area via one or more cellular communication standards.

[0050] Therefore, although base station 102A can act as such Figure 1A The illustrated UEs 106A-106N are "serving cells," 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-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 a service area size. For example, Figure 1A The illustrated base stations 102A-102B may be macro cells, while base station 102N may be a micro cell. Other configurations are also possible.

[0051] 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 gNB may be connected to a legacy evolved packet core (EPC) network and / or to an NR core (NRC) network. Furthermore, a gNB 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 gNBs.

[0052] 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.

[0053] In some implementations, base station 102A may select a paging configuration and a PEI configuration for UE 106. Base station 102A may encode the paging configuration and PEI configuration and send them to UE 106 as part of the registration process. Using the paging configuration, UE 106 may determine which PO and PF to monitor during the paging cycle. Using the PEI configuration, UE 106 may determine the radio frame carrying the relevant PEI.

[0054] 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.

[0055] 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.

[0056] 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).

[0057] 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.

[0058] Figure 2 Block diagram of a base station (gNB) 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 those addresses into locations in memory (e.g., memory 260 and read-only memory (ROM) 250) or into other circuitry or devices.

[0059] Base station 102 may include at least one network port 270. Network port 270 may be configured to be coupled to a network and provide access rights as described above. Figure 1A and Figure 2 The network described herein includes multiple devices such as UE device 106.

[0060] Network port 270 (or an additional network port) may also be configured, or alternatively configured, to be coupled 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).

[0061] In some implementations, base station 102 may be a next-generation base station, such as a 5G New Radio (5G NR) base station, or a “gNB”. In such implementations, base station 102 may be connected 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, UEs capable of operating according to 5G NR may be connected to one or more TRPs within one or more gNBs.

[0062] 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.

[0063] 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.).

[0064] As further described herein, base station 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 a FPGA (Field-Programmable Gate Array), or as an ASIC (Application-Specific Integrated Circuit), or a combination thereof. Alternatively (or otherwise), the processor 204 of base station 102, in conjunction with one or more of other components 230, 232, 234, 240, 250, 260, 270, may be configured to implement or support some or all of the features described herein.

[0065] 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.

[0066] 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.

[0067] 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 those addresses into locations in memory (e.g., memory 364 and read-only memory (ROM) 354) or into other circuitry or devices.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] Figure 4 Block diagram of User Equipment (UE) Figure 4A 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 implementations, 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, an unmanned aerial vehicle (UAV), a UAV controller (UAC), and / or a combination of devices, as well as 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, the set of components 400 may be implemented as individual components or groups of components for various purposes. The set of components 400 may be (e.g., communicatively; directly or indirectly) coupled to various other circuitry of communication device 106.

[0073] 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.

[0074] Cellular communication circuitry 430 may be coupled (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 as 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 as 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.

[0075] 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 specific 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.

[0076] 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 discrete 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.

[0077] 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) cards 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 functions, 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.

[0078] 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 remain in standby while 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.

[0079] As shown in the figure, the SOC 400 may include a processor 402 and display circuitry 404. The processor executes program instructions for 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.

[0080] 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, processor 402 of communication device 106 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 in addition), 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 in addition), 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.

[0081] 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.

[0082] 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.

[0083] Figure 5 Block diagram of cellular communication circuit Figure 5 Exemplary simplified block diagrams of cellular communication circuits according to some implementation schemes are shown. It should be noted that... Figure 5The 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.

[0084] The cellular communication circuit 530 may (e.g., communicatively; directly or indirectly) be coupled to one or more antennas, such as ( Figure 4 Antennas 435a-435b 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).

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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 NSA NR 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 in addition), 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 in addition), processor 512 may be configured to implement some or all of the features described herein by combining one or more of other components 530, 532, 534, 535, 550, 570, 572, 335a, 335b, and 336.

[0089] 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.

[0090] 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 in addition), 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 in addition), 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.

[0091] 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.

[0092] 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 type of UE as needed.

[0093] 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 of the illustrated 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 components described below 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).

[0094] 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.

[0095] 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-604D) may process various radio control functions that enable communication with one or more radio networks via RF circuitry 606. In other embodiments, some or all of the functions of the baseband processors 604A-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, and radio frequency shifting. In some embodiments, the modulation / demodulation circuitry of the baseband circuitry 604 may include Fast Fourier Transform (FFT), pre-decoding, or constellation mapping / demapping functionality. In some embodiments, the encoding / decoding circuitry of the 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 functions may be included in other embodiments.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 circuitry 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.

[0107] 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 cascaded, tunable delay element, a phase detector, a charge pump, and a set of D-type flip-flops. 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] In some implementations, the PMC 612 manages the power supplied to the baseband circuitry 604. Specifically, the PMC 612 controls power selection, voltage scaling, battery charging, or DC-DC conversion. The PMC 612 is typically included when the device 600 can be battery powered, 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.

[0112] Although Figure 6A PMC 612 is shown coupled only to the baseband circuit 604, but in other embodiments, the PMC 612 may be coupled additionally or alternatively 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.

[0113] In some implementations, the PMC 612 may control or otherwise become part of various power-saving mechanisms of the device 600. For example, if the 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, it may enter a state called Discontinuous Receive Mode (DRX) after a period of inactivity. During this state, the device 600 may power down for short intervals, thereby saving power.

[0114] If there is no data traffic activity during the extended period, device 600 may transition to the RRC_Idle state, in which it disconnects from the network and does not perform operations such as channel quality feedback or handover. Device 600 enters an ultra-low power state and performs paging, where it also periodically wakes up to listen to the network, and then shuts down at least part of the device's power again. Device 600 may not receive data in this state. To receive data, it will transition back to the RRC_Connected state.

[0115] 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.

[0116] 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 of various systems as needed.

[0117] As discussed above, Figure 6 The baseband circuit 604 may include processors 604A-604E and a memory 604G utilized by the processors. Each of the processors 604A-604E may respectively include a memory interface 704A-704E for transferring / receiving data to / from the memory 604G.

[0118] The baseband circuit 604 may further include: one or more interfaces for communicatively coupling to other circuits / devices, such as a memory interface 712 (e.g., an interface for transferring data to / receiving data from a memory external to the baseband circuit 604); and an application circuit interface 714 (e.g., for...). Figure 6 The application circuit 602 is an interface for transmitting data / receiving data from the application circuit; the RF circuit interface 716 (e.g., for transmitting data to / from the application circuit). Figure 6 RF circuit 606 is an interface for transmitting / receiving data from / from the RF circuit; wireless hardware connectivity interface 718 (e.g., for connecting to / from near field communication (NFC) components, Bluetooth). ® Components (e.g., Bluetooth) ® Low power consumption, Wi-Fi ® Interfaces for transmitting / receiving data from components and other communication components); and power management interface 720 (e.g., an interface for transmitting or receiving power or control signals to / from the PMC 612).

[0119] Figure 8 Control plane protocol stack Figure 8 This is an example of a control plane protocol stack according to some implementation schemes. In this implementation scheme, control plane 800 is shown as a communication protocol stack between UE 106a (or alternatively, UE 106b), RAN node 102A (or alternatively, RAN node 102B) and Mobility Management Entity (MME) 621.

[0120] PHY layer 801 can transmit or receive information used by MAC layer 802 through one or more air interfaces. PHY layer 801 can further perform link adaptive or adaptive modulation and decoding (AMC), power control, cell search (e.g., for initial synchronization and handover purposes), and other measurements used by higher layers (such as RRC layer 805). PHY layer 801 can also further perform: error detection of the transport channel, forward error correction (FEC) decoding / decoding of the transport channel, modulation / demodulation of the physical channel, interleaving, rate matching, mapping to the physical channel, and multiple-input multiple-output (MIMO) antenna processing.

[0121] MAC layer 802 can perform the following: mapping between logical channels and transport channels, multiplexing MAC service data units (SDUs) from one or more logical channels onto a transport block (TB) to be delivered to the PHY via the transport channel, demultiplexing MAC SDUs from a transport block (TB) delivered from the PHY via the transport channel onto one or more logical channels, multiplexing MAC SDUs onto a TB, scheduling information reporting, error correction via Hybrid Automatic Repeat Request (HARQ), and logical channel prioritization.

[0122] RLC layer 803 can operate in multiple modes, including Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). RLC layer 803 can perform the transmission of higher-layer protocol data units (PDUs), error correction via Automatic Repeat Request (ARQ) for AM data transmission, and concatenation, segmentation, and reassembly of RLC SDUs for UM and AM data transmission. RLC layer 803 can also re-segment RLC data PDUs used for AM data transmission, reorder RLC data PDUs used for UM and AM data transmission, detect duplicate data used for UM and AM data transmission, discard RLC SDUs used for UM and AM data transmission, detect protocol errors used for AM data transmission, and perform RLC re-establishment.

[0123] PDCP layer 804 can perform header compression and decompression of IP data, maintain PDCP sequence numbers (SNs), perform sequential delivery of upper-layer PDUs during lower-layer re-establishment, eliminate duplication of lower-layer SDUs during lower-layer re-establishment for radio bearers mapped on RLC AM, encrypt and decrypt control plane data, perform integrity protection and integrity verification of control plane data, control timer-based data discarding, and perform security operations (e.g., encryption, decryption, integrity protection, integrity verification, etc.).

[0124] The main services and functions of RRC layer 805 may include broadcasting system information (e.g., included in the Master Information Block (MIB) or System Information Block (SIB) related to the Non-Access Stratum (NAS), broadcasting system information related to the Access Stratum (AS), paging, establishment, maintenance, and release of RRC connections between the UE and E-UTRAN (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), establishment, configuration, maintenance, and release of point-to-point radio bearers, security functions including key management, mobility between Radio Access Technologies (RATs), and measurement configuration for UE measurement reporting. The MIB and SIB may include one or more Information Elements (IEs), each of which may include a separate data field or data structure.

[0125] UE 601 and RAN node 102A can use the Uu interface (e.g., the LTE-Uu interface) to exchange control plane data via a protocol stack including PHY layer 801, MAC layer 802, RLC layer 803, PDCP layer 804 and RRC layer 805.

[0126] The Non-Access Stratum (NAS) protocol 806 forms the highest layer of the control plane between UE 601 and MME 621. NAS protocol 806 supports the mobility and session management procedures of UE 601 to establish and maintain IP connectivity between UE 601 and P-GW 623.

[0127] The S1 Application Protocol (S1-AP) layer 815 supports the functions of the S1 interface and includes the Basic Procedure (EP). The EP is the interaction unit between RAN node 102A and network 100. S1-AP layer services can include two sets: UE-associated services and non-UE-associated services. These services perform functions including, but not limited to: E-UTRAN Radio Access Bearer (E-RAB) management, UE capability indication, mobility, NAS signaling transmission, RAN Information Management (RIM), and configuration transmission.

[0128] The Flow Control Transmission Protocol (SCTP) layer (optionally referred to as the SCTP / IP layer) 814 may be based in part on the IP protocol supported by the IP layer 813 to ensure reliable delivery of signaling messages between the RAN node 102A and the MME 621. The L2 layer 812 and the L1 layer 811 may refer to the communication links (e.g., wired or wireless) used by the RAN node and the MME to exchange information.

[0129] RAN node 102A and MME 621 can use the S1-MME interface to exchange control plane data through a protocol stack including L1 layer 811, L2 layer 812, IP layer 813, SCTP layer 814 and S1-AP layer 815.

[0130] Figure 9 User plane protocol stack Figure 9 This is an example of a user plane protocol stack according to some implementation schemes. In this implementation, user plane 900 is shown as a communication protocol stack between UE 106A (or alternatively, UE 106B or 106N), RAN node 102A (or alternatively, RAN node 102B), S-GW 622, and P-GW 623. User plane 900 may utilize at least some of the same protocol layers as control plane 800. For example, UE 601 and RAN node 102A may use a Uu interface (e.g., an LTE-Uu interface) to exchange user plane data via a protocol stack including PHY layer 801, MAC layer 802, RLC layer 803, and PDCP layer 804.

[0131] The General Packet Radio Service (GPRS) tunneling protocol for the user plane (GTP-U) layer 904 can be used to carry user data within the GPRS core network and between the radio access network and the core network. For example, the transmitted user data can be packets in any of the IPv4, IPv6, or PPP formats. The UDP and IP Security (UDP / IP) layer 903 provides checksums for data integrity, port numbers for addressing different functions at the source and destination, and encryption and authentication for selected data streams. RAN node 102A and S-GW 622 can exchange user plane data using the S1-U interface via a protocol stack including L1 layer 811, L2 layer 812, UDP / IP layer 903, and GTP-U layer 904. S-GW 622 and P-GW 623 can exchange user plane data using the S5 / S8a interface via a protocol stack including L1 layer 811, L2 layer 812, UDP / IP layer 903, and GTP-U layer 904. (As described above...) Figure 8 The NAS protocol discussed here supports the mobility and session management process of UE 106 to establish and maintain IP 813 connectivity between UE 106 and P-GW 623.

[0132] For the remainder of this disclosure, references to base station (gNB) and user equipment (UE) are assumed to refer to base station (gNB) 102 and user equipment (UE) 106, even if specific reference numerals may be omitted.

[0133] Figure 10 Core network Figure 10Example architectures of system 1000, including core network (CN) 1020, are illustrated according to various implementation schemes. CN 1020 may be the core network of a 5G system (which may be referred to as 5GC). System 1000 is shown to include UE 1001, which may be the same as or similar to UE 106A, 106B, or 106N previously discussed; (R)AN 1010, which may be the same as or similar to BS 102A or 102N previously discussed; and data network (DN) 1003, which may be, for example, operator services, Internet access, or third-party services; and CN 1020. CN 1020 may include multiple network functions, including Authentication Server Function (AUSF) 1022; Access and Mobility Management Function (AMF) 1021; Session Management Function (SMF) 1024; Network Exposure Function (NEF) 1023; Policy Control Function (PCF) 1026; Network Repository Function (NRF) 1025; Unified Data Management (UDM) 1027; Application Function (AF) 1028; User Plane Function (UPF) 1002; and Network Slice Selection Function (NSSF) 1029. In some cases, these network functions may be implemented as virtualization software-based functions / services.

[0134] UPF 1002 can act as an anchor point for mobility within and between RATs, an external Packet Data Unit (PDU) session point interconnected with DN 1003, and a branch point supporting multihomed PDU sessions. A PDU session is a logical connection between the UE and the DN. UPF 1002 can also perform packet routing and forwarding, packet inspection, user plane portion of policy rules, lawful packet interception (user plane (UP) collection), traffic usage reporting, quality of service (QoS) processing on the user plane (e.g., packet filtering, gating, UL / DL rate enforcement), uplink traffic verification (e.g., Service Data Flow (SDF) to QoS flow mapping), transport level packet marking in uplink and downlink, and downlink packet buffering and downlink data notification triggering. UPF 1002 may include an uplink classifier to support traffic flow routing to the data network. DN 1003 may represent various network operator services, Internet access, or third-party services. DN 1003 may include or be similar to the previously discussed application server 104. UPF 1002 can interact with SMF1024 via the N4 reference point between SMF 1024 and UPF 1002.

[0135] Location management function 1030 can be used to receive location management information from UE 106 or base station 102 and use that information to determine the location of the UE. Alternatively, the UE can provide its location to LMF 1030 based on signals received from three or more base stations 102N, using information such as GPS information and triangulation information.

[0136] AUSF 1022 can store authentication data for UE 1001 and handle authentication-related functionality. AUSF 1022 can facilitate a common authentication framework for various access types. AUSF 1022 can communicate with AMF 1021 via the N12 reference point between AMF 1021 and AUSF 1022; and can communicate with UDM 1027 via the N13 reference point between UDM 1027 and AUSF 1022. Additionally, AUSF 1022 can present an interface based on Nausf services.

[0137] AMF 1021 is responsible for registration management (e.g., registering UE 1001, etc.), connection management, reachability management, mobility management, lawful interception of AMF-related events, and access authentication and authorization. AMF 1021 can be the termination point of the N11 reference point between AMF 1021 and SMF 1024. AMF 1021 provides transport for SM messages between UE 1001 and SMF 1024 and acts as a transparent proxy for routing SM messages. AMF 1021 can also be used between UE 1001 and SMSF (… Figure 10 AMF 1021 provides transmission of Short Message Service (SMS) messages between (not shown). AMF 1021 can act as a Security Anchor Function (SEAF), which may include interaction with AUSF 1022 and UE 1001, and reception of an intermediate key established due to the UE 1001 authentication process. In the case of authentication using the Universal User Identity Module (USIM), AMF 1021 may retrieve security material from AUSF 1022. AMF 1021 may also include a Security Context Management (SCM) function, which receives a key from the SEAF for deriving a network-specific access key. Furthermore, AMF 1021 may be the termination point of the RAN Control Plane (CP) interface, which may include or be the N2 reference point between (R)AN 1010 and AMF 1021, and AMF 1021 may be the termination point of NAS (N1) signaling, performing NAS encryption and integrity protection.

[0138] AMF 1021 can also support NAS signaling with UE 1001 via a Non-3GPP Interoperability Function (N3IWF) interface. N3IWF can be used to provide access to untrusted entities. N3IWF can be the termination point of the N2 interface between the control plane (R)AN 1010 and AMF 1021, and can be the termination point of the N3 reference point between the user plane (R)AN 1010 and UPF 1002. Therefore, AMF 1021 can process N2 signaling for PDU sessions from SMF 1024 and AMF 1021, encapsulate / decapsulate packets for IPSec and N3 tunneling, mark N3 user plane packets in the uplink, and perform QoS corresponding to the N3 packet markings, while taking into account the QoS requirements associated with such markings received via N2. The N3IWF can also relay uplink and downlink control plane non-access stratum (NAS) signaling between UE 1001 and AMF 1021 via the N1 reference point between UE 1001 and AMF 1021, and relay uplink and downlink user plane packets between UE 1001 and UPF 1002. The N3IWF also provides a mechanism for establishing an Internet Protocol Security (IPsec) tunnel using UE 1001. AMF 1021 can present an interface based on Namf services and can be the N14 reference point between two AMF 1021s, as well as the AMF 1021 and the 5G Equipment Identity Register (5G-EIR). Figure 10 The endpoint of the N17 reference point (not shown).

[0139] UE 1001 may need to register with AMF 1021 to receive network services. Registration Management (RM) is used to register UE 1001 with or deregister it from the network (e.g., AMF 1021) and to establish a UE context in the network (e.g., AMF 1021). UE 1001 can operate in RM-REGISTERED or RM-DEREGISTERED states. In RM-DEREGISTERED state, UE 1001 does not register with the network, and the UE context in AMF 1021 does not maintain valid location or routing information for UE 1001; therefore, AMF 1021 cannot reach UE 1001. In RM-REGISTERED state, UE 1001 registers with the network, and the UE context in AMF 1021 can maintain valid location or routing information for UE 1001; therefore, AMF 1021 can reach UE 1001. In RM-REGISTERED state, UE 1001 can perform mobility registration update procedures, periodic registration update procedures triggered by the expiration of periodic update timers (e.g., notifying the network that UE 1001 is still active), and registration update procedures to update UE capability information or renegotiate protocol parameters with the network, etc.

[0140] The AMF 1021 can store one or more RM contexts for UE 1001, where each RM context is associated with a specific access to the network. The RM context can be a data structure, database object, etc., indicating or storing registration status and periodic update timers, particularly for each access type. The AMF 1021 can also store 5GC Mobility Management (MM) contexts that are the same as or similar to the Evolved Packet Services (EPS) Mobility Management (E) MM contexts discussed earlier. In various implementations, the AMF 1021 can store UE 1001's CE Mode B restriction parameters in the associated MM context or Registration Management (RM) context. The AMF 1021 can also derive values ​​from UE usage setting parameters already stored in the UE context (and / or MM / RM context) when needed.

[0141] Connection Management (CM) can be used to establish and release signaling connections between UE 1001 and AMF 1021 via the N1 interface. Signaling connections are used to enable NAS signaling exchange between UE 1001 and CN 1020, and include signaling connections between the UE and AN (e.g., RRC connections for non-3GPP access or UE-N3IWF connections) and N2 connections between the AN (e.g., AN 1010) and AMF 1021 for UE 1001. UE 1001 can operate in one of two CM states (CM-IDLE mode or CM-CONNECTED mode). When UE 1001 operates in CM-IDLE state / mode, UE 1001 may not have a NAS signaling connection established with AMF 1021 via the N1 interface, and (R)AN 1010 signaling connections (e.g., N2 and / or N3 connections) may exist for UE 1001. When UE 1001 operates in CM-CONNECTED state / mode, UE 1001 may have a NAS signaling connection established with AMF 1021 via the N1 interface, and may have a (R)AN 1010 signaling connection (e.g., N2 and / or N3 connection) for UE 1001. Establishing an N2 connection between (R)AN 1010 and AMF 1021 allows UE 1001 to transition from CM-IDLE mode to CM-CONNECTED mode, and UE 1001 can transition from CM-CONNECTED mode to CM-IDLE mode when the N2 signaling between (R)AN 1010 and AMF 1021 is released.

[0142] SMF 1024 is responsible for Session Management (SM), session establishment, modification, and publication (including tunnel maintenance between UPF and AN nodes); UE IP address allocation and management (including optional authorization); selection and control of UP functions; configuration of UPF traffic redirection to route traffic to the correct destination; termination of interfaces toward policy control functions; policy enforcement and QoS control portions; lawful interception (for SM events and interfaces with the LI system); termination of the SM portion of NAS messages; downlink data notification; initiation of AN-specific SM information transmitted to the AN via N2 through AMF; and determination of the SSC mode of the session. SM may refer to the management of PDU sessions, and a PDU session or "session" may refer to the PDU connectivity service that provides or enables PDU exchange between UE 1001, identified by the Data Network Name (DNN), and Data Network (DN) 1003. A PDU session can be established upon request from UE 1001, modified upon request from both UE 1001 and CN 1020, and released upon request from both UE 1001 and CN 1020 using NAS SM signaling exchanged via the N1 reference point between UE 1001 and SMF1024. Upon request from the application server, CN 1020 can trigger a specific application within UE 1001. In response to receiving a trigger message, UE 1001 can pass the trigger message (or relevant portions / information of the trigger message) to one or more identified applications within UE 1001. The identified application within UE 1001 can establish a PDU session to a specific data network name (DNN). SMF 1024 can check whether the UE 1001 request matches the user subscription information associated with UE 1001. In this regard, SMF 1024 can retrieve and / or request to receive update notifications regarding SMF1024 level subscription data from UDM 1027.

[0143] The SMF 1024 may include the following roaming functionalities: handling local execution to apply QoS SLAB Virtual Public Land Mobile Network (VPLMN); charging data collection and charging interface (VPLMN); lawful interception (for SM events and interfaces with the LI system, in the VPLMN); and support for interaction with external DNs to transmit signaling for PDU session authorization / authentication via external DNs. In roaming scenarios, an N16 reference point between two SMF 1024s may be included in System 1000, which may be located between another SMF 1024 in the visited network and an SMF 1024 in the home network. Additionally, the SMF 1024 may present an interface based on NSMF services.

[0144] The NEF 1023 provides components for securely exposing services and capabilities provided by 3GPP network functions to third parties, internal exposure / re-exposure, application functions (e.g., AF 1028), edge computing, or fog computing systems. In such implementations, the NEF 1023 can authenticate, authorize, and / or restrict AFS. The NEF 1023 can also translate information exchanged with AF 1028 and information exchanged with internal network functions. For example, the NEF 1023 can translate between AF service identifiers and internal SCC information. The NEF 1023 can also receive information from other network functions (NFs) based on their exposure capabilities. This information can be stored as structured data at the NEF 1023 or stored at a data storage NF using a standardized interface. The stored information can then be re-exposed by the NEF 1023 to other NFs and AFs, and / or used for other purposes such as analysis. Additionally, the NEF 1023 can present an interface based on Nnef services.

[0145] The NRF 1025 supports service discovery, receiving NF discovery requests from NF instances and providing information about discovered NF instances to them. The NRF 1025 also maintains information about available NF instances and the services they support. As used herein, terms such as "instantiation" can refer to the creation of an instance, and "instance" can refer to the concrete occurrence of an object, which may occur, for example, during the execution of program code. Additionally, the NRF 1025 can present an interface based on Nnrf services.

[0146] The PCF 1026 can provide policy rules to control plane functions for enforcement and supports a unified policy framework to manage network behavior. The PCF 1026 can also implement a front-end (FE) to access subscription information related to policy decisions in the UDR of the UDM 1027. The PCF 1026 can communicate with the AMF 1021 via an N15 reference point, which can include the PCF 1026 in the visited network and the AMF 1021 in roaming scenarios. PCF 1026 can communicate with AF 1028 via the NS reference point between PCF 1026 and AF 1028; and communicate with SMF 1024 via the N7 reference point between PCF 1026 and SMF 1024. System 1000 and / or CN 1020 may also include an N24 reference point between PCF 1026 (in the home network) and PCF 1026 in the visited network. In addition, PCF 1026 may present an interface based on NPCF services.

[0147] UDM 1027 can process subscription-related information to support network entities in handling communication sessions and can store subscription data for UE 1001. For example, subscription data can be communicated between UDM 1027 and AMF 1021 via an NS reference point between UDM 1027 and AMF. UDM 1027 may include two parts: Application FE and UDR ( Figure 10 (FE and UDR are not shown). The UDR may store subscription data and policy data of UDM 1027 and PCF 1026, and / or structured data for exposure of NEF 1023, as well as application data (including PFD for application detection, application request information of multiple UE 1001s). An interface based on the Nadr service may be presented by UDR 221 to allow UDM 1027, PCF 1026, and NEF 1023 to access a specific set of stored data, and to read, update (e.g., add, modify), delete, and subscribe to notifications of relevant data changes in the UDR. The UDM may include UDM-FE, which is responsible for handling credentials, location management, subscription management, etc. Several different front-ends may serve the same user in different transactions. The UDM-FE accesses subscription information stored in the UDR and performs authentication credential processing, user identification processing, access authorization, registration / mobility management, and subscription management. The UDR may interact with SMF 1024 via the N10 reference point between UDM 1027 and SMF 1024. The UDM 1027 also supports SMS management, with SMS-FE implementing similar application logic as previously discussed. Additionally, the UDM 1027 can present an interface based on Nudm services.

[0148] AF 1028 can provide application influence on traffic routing, provide access to the NCE, and interact with the policy framework for policy control. The NCE can be a mechanism allowing CN 1020 and AF 1028 to provide information to each other via NEF 1023, which can be used in edge computing implementations. In such implementations, network operators and third-party services can be hosted near the UE 1001 access point to achieve efficient service delivery through reduced end-to-end latency and load on the transport network. For edge computing implementations, 5GC can select UPF 1002 near UE 1001 and perform traffic redirection from UPF 502 to DN 1003 via the N6 interface. This can be based on UE subscription data, UE location, and information provided by AF 1028. In this way, AF 1028 can influence UPF (re)selection and traffic routing. Based on operator deployment, when AF 1028 is considered a trusted entity, the network operator can allow AF 1028 to interact directly with the relevant NF. In addition, the AF 1028 can present an interface based on Naf services.

[0149] NSSF 1029 can select a set of network slice instances to serve UE 501. If needed, NSSF 1029 can also determine the allowed network slice selection assistance information (NSSAI) and the mapping to the subscribed individual NSSAI (S-NSSAI). NSSF 1029 can also determine the set of AMFs, or a list of candidate AMFs 1021, for serving UE 1001 based on appropriate configuration and possibly by querying NRF 1025. The selection of a set of network slice instances for UE 1001 can be triggered by AMF 1021 registered with UE 1001 interacting with NSSF 1029, which can result in a change to AMF 1021. NSSF 1029 can interact with AMF 1021 via the N22 reference point between AMF 1021 and NSSF 1029; and via the N31 reference point (…). Figure 10 (Not shown) communicates with another NSSF 1029 in the visited network. Additionally, the NSSF 1029 can present an interface based on the Nnssf service.

[0150] As previously discussed, CN 1020 may include a Short Message Service Function (SMSF), which is responsible for SMS subscription checks and verification, as well as relaying SM messages to and / or from UE 1001 or other entities such as SMS-GMSC / IWMSC / SMS routers. SMS may also interact with AMF 1021 and UDM 1027 to notify UE 1001 of its availability for SMS transmission (e.g., setting a UE unreachable flag and notifying UDM 1027 when UE 1001 is available for SMS).

[0151] CN 1020 may also include Figure 10 Other elements not shown include data storage systems / architecture, 5G-EIR, Secure Edge Protection Agent (SEPP), etc. Data storage systems may include Structured Data Storage Network Function (SDSF), Unstructured Data Storage Function (UDSF), etc. Any network function (NF) can be transmitted via any NF and UDSF ( Figure 10 The N18 reference point (not shown) is used to store unstructured data in or retrieve unstructured data from the UDSF (e.g., UE context). Individual NFs can share a UDSF to store their respective unstructured data, or each NF can have its own UDSF located at or near its location. Additionally, the UDSF can present an interface based on Nudsf services (…). Figure 10(Not shown). 5G-EIR can be an NF that checks the status of a Permanent Equipment Identifier (PEI) to determine whether a specific piece of equipment / entity should be blacklisted from the network; and SEPP can be a non-transparent agent that performs topology hiding, message filtering, and policing on the control plane interface between PLMNs.

[0152] Furthermore, there can be more reference points and / or service-based interfaces between NF services; however, for clarity, Figure 10 These interfaces and reference points are omitted. In one example, CN 1020 may include an Nx interface, which is an inter-CN interface between the Mobility Management Entity (MME) and AMF 1021 to enable interoperability between CN 1020 and CNs in the 4G system. Other example interfaces / reference points may include an interface based on N5G-EIR services presented by 5G-EIR, reference point N27 between the NRF in the visited network and the NRF in the home network; and reference point N31 between the NSSF in the visited network and the NSSF in the home network.

[0153] Figure 11 Beam management in 4G and 5G systems In 4G LTE systems, the UE is configured to periodically monitor the radio link to perform channel measurements and obtain power and channel quality data on the radio link with its serving base station (eNB). This method enables the UE to determine whether the network can reach it with acceptable link quality. If the link quality is found to be too low relative to a selected threshold set by the network, the UE reports a radio link failure (RLF) to the network and triggers a higher-layer reconnection process. This process includes initiating a new cell reselection procedure. The cell reselection process can be time-consuming, resulting in occasional interruptions to the data link between the UE and the base station, and typically leading to a reduction in overall data rates.

[0154] Advances in 5G New Radio (NR) include the entry into millimeter-wave (mmWave) bands, known as Frequency Range 2 (FR2). This higher frequency range offers significantly greater potential bandwidth compared to the bands used in 3GPP 4G LTE, enabling greater data throughput. However, signals transmitted in higher frequency bands can behave very differently than those transmitted in 4G LTE bands, at least in part due to the characteristics of the Earth's atmosphere, which can absorb a significant amount of signal energy in the FR2 band. To overcome signal loss in the FR2 band, 5G NR radio links have been redesigned to include beamforming, where signals are transmitted and received in a relatively narrow beam. The beam allows a higher portion of the transmitted energy to be received at the receiver. However, the narrow beam width typically requires beam scanning, where multiple beams are periodically transmitted in different directions to enable the formation of a radio link between base station 102 and UE 106 using at least one of the beams. Figure 11 Example illustrations of beam scanning and beamforming transmission between base station 102 and UE 106 according to some embodiments are provided. Base station 106 may initially be unaware of the location of UE 102 relative to the base station. Therefore, the base station can use beamforming with an antenna array to transmit signals in n different transmit (Tx) beams, each pointing in a different direction. Similarly, the UE can use beamforming with multiple antennas to form multiple receive (Rx) beams pointing in different directions. UE 106 can then perform multiple measurements on the Rx beams to identify which Tx beam / Rx beam pair has the maximum power and signal quality.

[0155] The use of multiple distinct TX and Rx beams introduces the need for efficient beam management, where the UE and base station periodically identify the optimal beam for communication at any given point in time. Beam management in 5G can be quite complex, involving the use of Layer 1 (PHY) and Layer 2 (Media Access Control (MAC)) procedures to establish and maintain optimal beam pairs between the base station and the UE, or between two UEs, to achieve good connectivity. A beam pair may include a transmit beam and a corresponding receive beam in one link direction. Some elements of beam management may include: beam scanning for selecting the optimal beam during initial access by the UE and base station; beam measurement and determination, where the UE measures the beam strength of one or more beams by measuring received power signals such as the Reference Signal Received Power (RSRP) for each beam to determine which beam has the highest RSRP; beam reporting, which may be initially performed using the Random Access Channel (RACH) procedure, followed by periodic measurement reporting while the UE is in a connected mode with the base station; beam recovery when the signal in a beam pair drops below a threshold level; and beam handover to intra-cell or inter-cell beams using Layer 3 (L3) measurements of neighboring cells and / or L1 / L2 triggered mobility (LTM).

[0156] To improve the quality of beam management in 5G systems, artificial intelligence (AI) and machine learning (ML) models can be used to assist UE 106, base station 102 (RAN 1010), and core network 1020 in performing beam management. To this end, 3GPP Technical Report (TR) 38.843 V18.0.0 (2023-12) is a case study providing a 3GPP framework for air interface use cases utilizing AI / ML technologies. This case study includes protocols for identifying key performance indicators (KPIs) and test metrics for beam management use cases that can be used with AI / ML.

[0157] AI / ML model development The development of AI / ML models can be divided into four main phases: training, simulation (validation), deployment, and inference. The following paragraphs briefly describe the main tasks involved in each phase.

[0158] Training Phase: In this phase, the AI ​​model is trained on the dataset. This involves feeding the model input data and corresponding correct output labels, allowing the model to learn patterns and relationships within the data. Training typically involves optimizing algorithms to tune the model's parameters to minimize error.

[0159] Simulation Phase: In this phase, the trained model is extensively tested to ensure it performs well on previously unseen data. This phase involves using validation datasets to evaluate performance metrics such as accuracy, precision, and recall. Simulation helps identify any issues with the model's generalization and performance before deployment.

[0160] Deployment Phase: Once the model has been trained and successfully simulated, it is ready for deployment. Deployment involves integrating the model into a production environment where it can predict or classify new, unseen data. This may involve creating an application programming interface (API) or integrating the model into applications or systems that will use it.

[0161] Inference Phase: In this phase, the deployed model is used to make predictions or classifications on real-world data (referred to as "Scenes" in this paper). A Scene typically refers to a specific situation or problem domain in which an AI / ML model is applied or evaluated. Scenes help define the context in which the AI / ML model is deployed. The AI / ML model takes input data, processes that input data, and produces an output called inference, such as a predicted RSRP or predicted beam ID for a beam (auxiliary).

[0162] AI / ML model monitoring Monitoring and evaluation: Monitoring entities such as UE 106, base station 102 or network 1020 can continuously monitor various factors such as data characteristics, system performance metrics or environmental conditions.

[0163] Decision making: Based on the monitored factors, the monitoring entity decides whether to switch to a different machine learning model that is more suitable for the current conditions or task, uses transfer learning to fine-tune the current model to better match the environmental conditions, or indicates that it needs to fall back to a non-AI-based positioning.

[0164] AI model switching refers to the process of dynamically selecting or switching between different machine learning models or algorithms based on certain conditions or criteria. This method is typically used in adaptive systems, where the optimal model for a specific task may change over time or in different contexts. AI model switching may include the following.

[0165] Model selection: The monitored entity selects the most suitable model from a set of predefined models or algorithms. This selection can be based on factors such as accuracy, efficiency, or robustness.

[0166] Model fine-tuning: Once a model is selected, it can be further trained on task-specific datasets. This is called fine-tuning. Fine-tuning a pre-trained model reduces the initial amount of training required for the model while ensuring that the model is trained for the specific task it will be used for. This allows the model to be trained more generally for multiple specific tasks. Fine-tuning a pre-trained model can be optional, depending on how much the initial training differs from the model's end use.

[0167] Adaptation: Once a new model is selected and optionally fine-tuned, the monitored entity adapts its operations to use the newly selected model for prediction or decision-making.

[0168] Novel Solution from 3GPP Release 18 Case Study It should be understood that the accuracy of inferences made by AI / ML models is limited by the accuracy of the monitored factors, such as data characteristics, system performance metrics, or environmental conditions.

[0169] This disclosure provides novel and non-obvious technical solutions applicable to research cases identified above. For example, this disclosure provides unique technical solutions relating to (1) how to define key performance indicators (KPIs) for beam monitoring that take into account both RSRP accuracy and beam identification (ID) accuracy, while providing flexibility to reduce false measurements. It should be understood that the technical solutions provided herein may be incorporated into future specifications, including 3GPP Release 19.

[0170] Figures 12 to 16 The current problem with KPI measurement Figure 12An example inference process 1200 for beam management, as outlined in 3GPP TR 38.843 V18.0.0 (2023-12), is illustrated. Measurements can be performed on multiple beams (referred to as set B). In one example, measurements can be performed by the UE to identify the RSRP of each of the N beams received at the UE, where N is a positive integer. The value of N can be a subset of the total number of beams in the codebook. For example, N could be 12 beams measured from 64 beams in the codebook. These multiple measurements can be used as input to an AI / ML model 1202. Furthermore, beam ID information for each of the N measured beams can also be provided as input to the AI / ML model 1202. The AI / ML model can be configured to output a predicted RSRP and beam ID for beam set A. In one example, set A beams can be the complete beam codebook, such as 64 beams. Alternatively, it can be a selected subset of the complete codebook, such as 32 beams. The predicted RSRP can be compared with the measured RSRP to determine an RSRP accuracy metric. Additionally, the predicted beam ID can be compared with the actual beam ID to determine a beam prediction accuracy metric.

[0171] In Section 6.3.1 of 3GPP TR 38.843, beam prediction accuracy metrics, measured as a percentage, may include: - Top-1 (%): The percentage of "the strongest beam in the Top-1 prediction"; - Top-K / 1(%): The percentage of "the strongest beam in the Top-1 predictions is one of the top K predictions", where K is a positive integer; or - Top-1 / K(%): The percentage of "Top-1 predicted beam is the strongest beam among the top K strongest beams".

[0172] AI / ML model 1202 can be trained to output the probability that each beam in set A is the Top-1 beam and provide a predicted L1-RSRP for each beam.

[0173] As used herein, an ideal beam is a beam measured under substantially ideal conditions, such as measuring a beam in a predetermined test using specialized test equipment in a dedicated environment where unwanted noise or signals are limited.

[0174] The Top-1 Ideal Auxiliary Tx Beam is defined as follows: Option A (baseline), the Top-1 Ideal Auxiliary Tx Beam is the Tx beam that produces the maximum L1-RSRP across all Tx and Rx beams; Option B, the Top-1 Ideal Auxiliary Tx Beam is the Tx beam that produces the maximum L1-RSRP when combined with the specific Rx beam to be reported across all Tx beams.

[0175] The Top-1 ideal auxiliary Tx beam with predicted RSRP and beam ID is output from the AI / ML model 1202. The measured RSRP of the ideal auxiliary Tx beam can be compared with the predicted RSRP of the same beam ID (e.g., the ideal beam ID) to provide an RSRP accuracy output 1204. RSRP accuracy can be the first option for a KPI of the AI / ML model. Beam prediction accuracy can be the second option for a KPI of the AI / ML model. For RSRP prediction accuracy, the test passes validation if the predicted RSRP value of the strongest beam is within the tolerance margin of the measured RSRP value from the conventional strongest beam (otherwise, the test fails). For beam prediction accuracy, the test passes validation if the strongest beam ID from the conventional or measured beam is among the top K predicted beam IDs.

[0176] In the third option, the success rate of correctly predicting the beam with the largest RSRP among the top K predicted beams is greater than the RSRP of the strongest beam minus (-)x dB, where x is a real number. The value of x can be determined by considering the relevant measurement accuracy.

[0177] For RSRP prediction accuracy, the test passes validation if the predicted RSRP value of the strongest beam is within the tolerance margin of the RSRP value of the strongest beam from conventional or measured sources; otherwise, the test fails. For beam prediction accuracy, the test passes validation for the AI / ML model if the strongest beam ID from conventional or measured sources is among the top K predicted beam IDs.

[0178] In summary, the following options have been discussed: - Option 1: RSRP accuracy; - Option 2: Beam prediction accuracy (described above); and - Option 3: The maximum RSRP among the top K predicted beams is greater than the RSRP-x dBm of the strongest beam.

[0179] Based on the definitions of KPIs and test metrics, a UE's success in beam management testing can depend on which of the above options is being used. That is, a UE may pass the test under option 1 and option 3 test metrics, but it may fail the test under option 2 test metrics.

[0180] Figure 13 An example illustration is provided where the UE passes option 3 because the best beam among the top 4 AI / ML predicted beams (e.g., star-shaped) has a higher RSRP than the best conventional / measured beam (ideal beam) (e.g., circular). The UE fails option 2 test metric because the ideal beam ID is not among the top 4 predicted beam IDs.

[0181] Figure 14 Another example illustration is provided, in which the UE passes the test metrics under options 3 and 1, but fails the test metric for option 2 due to beam ID accuracy. Figure 14 In another example illustrated, in the first scan, the conventional beam passes all three tests because it is within the RSRP of the first four beams and its beam ID accuracy is the same as one of the first k beams. However, in the second scan, the conventional beam may fail because its RSRP is now lower than that of the first four beams. Figure 15 In this case, the UE can use option 2 because the conventional measurement beam (ideal beam) has the same beam ID as one of the first four beams. However, the predicted RSRP of the first four predicted beams is much lower than the RSRP of the measurement beam. Therefore, it fails under the metrics of options 1 and 3.

[0182] In Option 2 test metrics, determining the beam ID that the AI / ML prediction is accurate does not necessarily mean that the RSRP accuracy of Options 1 and 3 is met. Figure 16 The problem is illustrated below. The best conventional measurement beam ID matches one of the predicted beam IDs, and therefore the beam prediction accuracy test for option 2 will pass. However, since the RSRP of the best predicted beam is significantly lower than that of the conventional / measurement beam, the RSRP prediction accuracy of the AI / ML model may be poor.

[0183] According to some implementation plans, such as Figures 13 to 16 As illustrated, the RSRP prediction accuracy that the current option might produce can be addressed by configuring the UE to transmit a predicted beam with optimal beam prediction accuracy to network 1020. The network can then be configured to transmit a reference signal (via base station 102) on the beam ID of the predicted beam. The UE can then perform new measurements (not using the prediction from AI / ML model 1202). However, this may come at the cost of performing additional measurements.

[0184] Figures 17 to 18 Define a new test metric In another implementation, a test metric can be defined that considers both RSRP accuracy and beam ID accuracy, providing flexibility for a predetermined amount of accuracy threshold. The test metric can be a KPI that simultaneously captures similarity across both the RSRP and beam ID domains. In one example, this can be achieved by defining a margin tolerance box (or K boxes for the first K beams), such as... Figure 17 As illustrated. Parameters These represent the one-dimensional tolerance margins in each of the RSRP accuracy domain and the beam ID accuracy domain, respectively, to form a two-dimensional tolerance margin box. In one implementation, a similar tolerance margin can be defined for each of the first k beams. A similar margin tolerance box for the value. The use of tolerance boxes is achieved by making... The values ​​can be programmably defined to provide flexible programmable schemes. Each tolerance box can have a tolerance box around which the optimal conventional beam and / or optimal prediction beam is defined. Values ​​similar to or more lenient than those in the past.

[0185] In one implementation, the beam ID of the predicted beam from the AI / ML model output and the beam ID from the conventionally measured beam can be grouped in pairs. In one implementation, The values ​​can be fixed. If the grouping pairs fall within the tolerance box, the UE using the AI / ML model will pass the test. In one example, the margin can be made more aggressive for optimal beams (such as optimal measured beam and optimal predicted beam), such as... Figure 17 As illustrated. For Figure 17 The second optimal beam margin Margin comparable to the first optimal beam More lenient (larger). A second optimal beam can have a larger margin relative to the first optimal beam, either in the x-direction (e.g., beam ID accuracy), the y-direction (e.g., RSRP accuracy), or both. In another example, a majority vote can be used to determine whether a beam within the margin box passes or fails.

[0186] In another example, a UE is considered to have passed the test in the first measurement only if it also passes the test in the second measurement. If beam ID accuracy passes both tests, the test is considered passed, even if RSRP accuracy does not pass both tests. For example, beam ID accuracy may pass both tests, but RSRP accuracy may only pass one. In this example, since RSRP can change significantly over a relatively short period, the UE can be considered to have passed both parts of the test.

[0187] Figure 18 An example of a test performed using a margin box is provided, where a first margin box 1802 is illustrated around the optimal conventional / measurement beam 1804. However, the optimal predicted beam 1806 does not fall within the first margin box 1802. Therefore, the first test fails. However, due to the existence of If the value is zero (e.g., the first optimal conventional beam 1804 and the optimal predicted beam 1806 are in the same beam ID), then the second test can be performed. In the second test, the RSRP changes, and the conventional measurement beam 1808 does indeed fall into the second margin box 1810 along with the optimal predicted beam 1806. Therefore, the second round of testing is successful.

[0188] According to some embodiments, an apparatus for a UE 106 is disclosed. The apparatus includes one or more processors 402, 604 coupled to a memory, the processors being configured to: identify one or more optimal measurement beams among a plurality of beams having a Measurement Reference Signal Received Power (RSRP) value above a selected threshold, wherein each beam among the plurality of beams has a beam identifier (ID). An AI / ML model 1202 can be used to predict RSRP values ​​and beam ID values ​​for a set of beams among the plurality of beams.

[0189] In one implementation, the multiple measurement beams may be the same as the beam set predicted by AI / ML model 1202. Alternatively, the multiple measurement beams may be a subset of the beam set predicted by AI / ML model 1202. For example, as previously discussed, the multiple measurement beams may be a subset, such as 12 measurement beams measured at UE 106. These 12 measurement beams may be inputs to AI / ML model 1202. AI / ML model 1202 may then output predicted RSRP values ​​and beam ID values ​​for the beam set (such as the beam set of a complete codebook).

[0190] The one or more processors can also be configured to select one or more optimal predicted beams from the beam set based on predicted RSRP values ​​and predicted beam IDs for the beam set. A margin tolerance box 1802 can be defined based on a predetermined ΔRSRP value and a predetermined Δbeam ID predicted value. When, based on the predicted RSRP value and predicted beam ID, the measured RSRP value and beam ID cause at least one optimal measured beam from one or more optimal measured beams to fall into the same margin tolerance box as at least one optimal predicted beam from one or more optimal predicted beams, it can be determined that the AI / ML model 1202 is operating within an acceptable threshold. As previously discussed, in Figure 17 and Figure 18 An example of a margin tolerance frame is shown in the figure.

[0191] In some implementations, the AI ​​model 1202 may be located at the UE 106 or at the base station 102 of the serving cell of the UE 106. The predetermined ΔRSRP value and the predetermined Δbeam ID prediction value may be received from the base station 102, from the network 1020, or may be predefined at the UE 106.

[0192] In some implementations, one or more processors 402, 604 are further configured to determine that the AI ​​model 1202 operates within an acceptable threshold when the optimal measurement beam with the highest measured RSRP value among one or more optimal measurement beams is in the same margin tolerance box as the optimal prediction beam with the highest RSRP value in the beam set.

[0193] In some implementations, one or more processors 402, 604 are further configured to determine that the AI ​​model 1202 operates within an acceptable threshold when the optimal measurement beam having the highest measured RSRP value among one or more optimal measurement beams is in the same margin tolerance box as the optimal predicted beam having the predicted beam ID that is closest to the optimal measurement beam in the beam set.

[0194] In some implementations, one or more processors 402, 604 are further configured to define a first margin tolerance frame based on a first predetermined ΔRSRP value and a first predetermined Δbeam ID prediction value; and to define a second margin tolerance frame based on a second predetermined ΔRSRP value and a second predetermined Δbeam ID prediction value; and to define an nth margin tolerance frame based on an nth predetermined ΔRSRP value and an nth predetermined Δbeam ID prediction value, where n is a positive integer greater than 2.

[0195] In some embodiments, one or more processors are further configured to define a second margin tolerance box using one or more of the following: a second predetermined ΔRSRP value that is different from the first predetermined ΔRSRP value or a second predetermined Δbeam ID prediction value that is different from the first Δbeam ID prediction value; or a second predetermined ΔRSRP value that is the same as the first predetermined ΔRSRP value or a second predetermined Δbeam ID prediction value that is the same as the first Δbeam ID prediction value. For example, margin box 1802 may be the same size as margin box 1810. Alternatively, margin box 1810 may have a different size than margin box 1802.

[0196] In some embodiments, one or more processors are further configured to define the nth margin tolerance box using one or more of the following: an nth predetermined ΔRSRP value different from the first predetermined ΔRSRP value or an nth predetermined Δbeam ID prediction value different from the first Δbeam ID prediction value; or an nth predetermined ΔRSRP value identical to the first predetermined ΔRSRP value or an nth predetermined Δbeam ID prediction value identical to the first Δbeam ID prediction value. In some embodiments, the nth margin tolerance box has at least one dimension greater than the second margin tolerance box, and the second margin tolerance box has at least one dimension greater than the first margin tolerance box.

[0197] In some implementations, one or more processors 402, 604 are further configured to associate a second margin tolerance frame with a measurement beam having a second highest measured RSRP value among one or more optimal measurement beams and a prediction beam having a second highest RSRP value in the beam set; and to determine that the AI ​​model operates within an acceptable threshold when the measurement beam having the second highest RSRP value and the prediction beam having a second highest RSRP value in the beam set are within the second margin tolerance frame.

[0198] In some implementations, one or more processors 402, 604 are further configured to associate the nth margin tolerance frame with the measurement beam having the nth highest RSRP value among the measurement RSRP values ​​of one or more optimal measurement beams and the prediction beam having the nth highest RSRP value in the beam set; and to determine that the AI ​​model operates within an acceptable threshold when the measurement beam having the nth highest RSRP value and the prediction beam having the nth highest RSRP value in the beam set are within the second margin tolerance frame.

[0199] In some implementations, one or more processors 402, 604 are further configured to determine that the AI ​​model operates within an acceptable threshold when, in multiple measurements, the measured RSRP value and beam ID, based on the predicted RSRP value and predicted beam ID, cause at least one of the one or more optimal measured beams to fall into the same margin tolerance box as at least one of the one or more optimal predicted beams. Each measurement in the multiple measurements may include at least one of a different measurement for the measured RSRP value or a new predicted RSRP value output from the AI ​​model.

[0200] In some implementations, one or more processors 402, 604 are further configured to perform a second measurement if: the beam ID of at least one of the one or more optimal measurement beams is the same as the predicted beam ID of at least one of the one or more optimal predicted beams in the beam set; and in the first measurement, the measured RSRP of at least one of the one or more optimal measurement beams and the predicted RSRP of at least one of the one or more optimal predicted beams in the beam set are outside the tolerance frame. One or more processors may also be configured to determine that the AI ​​model operates within an acceptable threshold when, based on the predicted RSRP value and the predicted beam ID, the measured RSRP value and beam ID from the second measurement place at least one of the one or more optimal measurement beams and at least one of the one or more optimal predicted beams within the same margin tolerance frame.

[0201] Figure 19 Flowchart for measuring the performance of beam management AI / ML models Figure 19 A flowchart illustrating an example of a method 1900 for measuring beam identification (ID) and reference signal received power (RSRP) test metrics for artificial intelligence (AI) beam management at a user equipment (UE) according to some implementation schemes.

[0202] Figure 19 The methods shown can be used in conjunction with any of the systems, methods, or apparatuses illustrated in the figures, as well as other devices. In various embodiments, some of the method elements shown may be performed concurrently in a different order than those shown, or may be omitted. Additional method elements may also be performed as needed.

[0203] According to the implementation, method 1900 for measuring beam identification (ID) and reference signal received power (RSRP) test metrics for artificial intelligence (AI) beam management of user equipment (UE) includes identifying one or more optimal measurement beams among a plurality of beams that have a measured reference signal received power (RSRP) value above a selected threshold, wherein each beam among the plurality of beams has a beam identification (ID), as shown in box 1902. Method 1900 also includes using an artificial intelligence (AI) model to predict RSRP values ​​and beam IDs for a set of beams among the plurality of beams, as shown in box 1904. As shown in box 1906, one or more optimal predicted beams in a set of beams can be selected based on the predicted RSRP values ​​and predicted beam IDs of the set of beams. The AI / ML model is configured to output a set of predicted beams, wherein each predicted beam includes: a predicted L1-RSRP value; and a predicted beam ID. Method 1900 also defines a margin tolerance box based on a predetermined ΔRSRP value and a predetermined Δ beam ID prediction value, as shown in box 1908. Method 1900 further includes determining that the AI ​​model operates within an acceptable threshold when, based on the predicted RSRP value and the predicted beam ID, the measured RSRP value and beam ID place at least one of the one or more optimal measured beams within the same margin tolerance box as at least one of the one or more optimal predicted beams, as shown in box 1910.

[0204] Embodiments of systems, apparatus and methods The following embodiments relate to specific technical implementations and point out specific features, elements, or actions that may be used or otherwise combined in implementing such implementations.

[0205] Example 1 relates to a method for measuring beam identification (ID) and reference signal received power (RSRP) test metrics for artificial intelligence (AI) beam management of a user equipment (UE), the method comprising: identifying one or more optimal measurement beams among a plurality of beams having measurement reference signal received power (RSRP) values ​​above a selected threshold, wherein each of the beams among the plurality of beams has a beam identification (ID); using an artificial intelligence (AI) model to predict RSRP values ​​and beam IDs for a set of beams among the plurality of beams; selecting one or more optimal predicted beams in the set of beams based on the predicted RSRP values ​​and predicted beam IDs of the set of beams; defining a margin tolerance frame based on a predetermined ΔRSRP value and a predetermined Δbeam ID prediction value; and determining that the AI ​​model operates within an acceptable threshold when, based on the predicted RSRP values ​​and predicted beam IDs, the measured RSRP values ​​and beam IDs place at least one optimal measurement beam among the one or more optimal measurement beams in the same margin tolerance frame as at least one optimal predicted beam among the one or more optimal predicted beams.

[0206] Example 2 relates to the method according to Example 1, the method further comprising determining that the AI ​​model operates within an acceptable threshold when the optimal measurement beam with the highest measured RSRP value among one or more optimal measurement beams and the optimal prediction beam with the highest RSRP value in the beam set are in the same margin tolerance frame.

[0207] Example 3 relates to the method according to Example 1, the method further comprising determining that the AI ​​model operates within an acceptable threshold when the optimal measurement beam having the highest measured RSRP value among one or more optimal measurement beams and the optimal predicted beam having the predicted beam ID of the beam ID closest to the optimal measurement beam in the beam set are in the same margin tolerance frame.

[0208] Example 4 relates to the method according to Example 1, the method further comprising defining a first margin tolerance frame based on a first predetermined ΔRSRP value and a first predetermined Δbeam ID prediction value; defining a second margin tolerance frame based on a second predetermined ΔRSRP value and a second predetermined Δbeam ID prediction value; and defining an nth margin tolerance frame based on an nth predetermined ΔRSRP value and an nth predetermined Δbeam ID prediction value, where n is a positive integer greater than 2.

[0209] Example 5 relates to the method according to Example 4, the method further comprising defining a second margin tolerance frame using one or more of the following: a second predetermined ΔRSRP value that is different from a first predetermined ΔRSRP value or a second predetermined Δbeam ID prediction value that is different from a first Δbeam ID prediction value; or a second predetermined ΔRSRP value that is the same as a first predetermined ΔRSRP value or a second predetermined Δbeam ID prediction value that is the same as a first Δbeam ID prediction value.

[0210] Example 6 relates to the method according to Example 4, the method further comprising defining the nth margin tolerance box using one or more of the following: an nth predetermined ΔRSRP value different from a first predetermined ΔRSRP value or an nth predetermined Δbeam ID prediction value different from a first Δbeam ID prediction value; or an nth predetermined ΔRSRP value identical to a first predetermined ΔRSRP value or an nth predetermined Δbeam ID prediction value identical to a first Δbeam ID prediction value.

[0211] Example 7 relates to a method according to any one of Examples 4 to 6, wherein the nth margin tolerance box has at least one dimension greater than the second margin tolerance box, and the second margin tolerance box has at least one dimension greater than the first margin tolerance box.

[0212] Example 8 relates to the method according to Example 4, the method further comprising associating a second margin tolerance frame with a measurement beam having a second highest measured RSRP value among one or more optimal measurement beams and a prediction beam having a second highest RSRP value in the beam set; and determining that the AI ​​model operates within an acceptable threshold when the measurement beam having the second highest RSRP value and the prediction beam having the second highest RSRP value in the beam set are located within the second margin tolerance frame.

[0213] Example 9 relates to the method according to Example 4, the method further comprising: associating an nth margin tolerance frame with a measurement beam having the nth highest RSRP value among one or more optimal measurement beams and a predicted beam having the nth highest RSRP value in a beam set; and determining that the AI ​​model is operating within an acceptable threshold when the measurement beam having the nth highest RSRP value and the predicted beam having the nth highest RSRP value in a beam set are within a second margin tolerance frame.

[0214] Example 10 relates to the method according to Example 1, the method further comprising: determining that the AI ​​model is operating within an acceptable threshold when, in multiple measurements, based on the predicted RSRP value and the predicted beam ID, the measured RSRP value and beam ID cause at least one of one or more optimal measured beams to be within the same margin tolerance box as at least one of one or more optimal predicted beams, wherein each of the multiple measurements includes at least one of a different measurement for the measured RSRP value or a new predicted RSRP value output from the AI ​​model.

[0215] Example 11 relates to the method according to Example 10, wherein multiple measurements include performing a second measurement if: the beam ID of at least one of the one or more optimal measurement beams is the same as the predicted beam ID of at least one of the one or more optimal predicted beams in the beam set; and in the first measurement, the measured RSRP of at least one of the one or more optimal measurement beams and the predicted RSRP of at least one of the one or more optimal predicted beams in the beam set are outside the tolerance frame. The method of Example 10 further includes determining that the AI ​​model operates within an acceptable threshold when, based on the predicted RSRP value and the predicted beam ID, the measured RSRP value and beam ID from the second measurement place at least one of the one or more optimal measurement beams and at least one of the one or more optimal predicted beams within the same margin tolerance frame.

[0216] 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.

[0217] 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.

[0218] 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 method implementations described herein, or any subset of any method implementations described herein, or any combination of such subsets). The device may be implemented in any of the various forms.

[0219] 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 of the methods described herein for operating the UE can serve as the basis for a corresponding method for operating the base station.

[0220] 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 of measuring beam identification (ID) and reference signal received power (RSRP) test metrics for artificial intelligence (AI) beam management of a user equipment (UE), the method comprising: identifying one or more optimal measured beams of a plurality of beams having measured reference signal received power (RSRP) values above a selected threshold, wherein each of the beams of the plurality of beams has a beam identification (ID); using an artificial intelligence (AI) model to predict RSRP values and beam IDs for a set of beams of the plurality of beams; selecting one or more optimal predicted beams of the set of beams based on the predicted RSRP values and predicted beam IDs of the set of beams; defining a margin tolerance box based on a predetermined ARSRP value and a predetermined Abeam ID prediction value; and determining that the AI model operates within an acceptable threshold when the measured RSRP values and the beam IDs of the one or more optimal measured beams and the one or more optimal predicted beams are in the same margin tolerance box based on the predicted RSRP values and the predicted beam IDs.

2. The method of claim 1, further comprising determining that the AI model operates within the acceptable threshold when an optimal measured beam having a highest measured RSRP value of the RSRP values of the one or more optimal measured beams is in the same margin tolerance box as an optimal predicted beam of the set of beams having a highest RSRP value.

3. The method of claim 1, further comprising determining that the AI model operates within the acceptable threshold when an optimal measured beam having a highest measured RSRP value of the RSRP values of the one or more optimal measured beams is in the same margin tolerance box as an optimal predicted beam of the set of beams having a predicted beam ID closest to a beam ID of the optimal measured beam.

4. The method of claim 1, further comprising: defining a first margin tolerance box based on a first predetermined ARSRP value and a first predetermined Abeam ID prediction value; defining a second margin tolerance box based on a second predetermined ARSRP value and a second predetermined Abeam ID prediction value; and defining an nth margin tolerance box based on an nth predetermined ARSRP value and an nth predetermined Abeam ID prediction value, wherein n is a positive integer greater than 2.

5. The method of claim 4, further comprising: defining the second margin tolerance box with one or more of: a second predetermined ARSRP value different from the first predetermined ARSRP value or a second predetermined Abeam ID prediction value different from the first Abeam ID prediction value; or a second predetermined ARSRP value same as the first predetermined ARSRP value or a second predetermined Abeam ID prediction value same as the first Abeam ID prediction value.

6. The method of claim 4, further comprising: defining the nth margin tolerance box with one or more of: ​ ​ an nth predetermined ARSRP value different from the first predetermined ARSRP value or an nth predetermined Abeam ID prediction value different from the first Abeam ID prediction value; or an nth predetermined ARSRP value same as the first predetermined ARSRP value or an nth predetermined Abeam ID prediction value same as the first Abeam ID prediction value.

7. The method of any one of claims 4-6, wherein the nth margin tolerance box has at least one dimension greater than the second margin tolerance box, and the second margin tolerance box has at least one dimension greater than the first margin tolerance box.

8. The method of claim 4, further comprising: associating the second margin tolerance box with a measurement beam having a second highest measured RSRP value of the RSRP values of the one or more optimal measurement beams and a predicted beam of the beam set having a second highest RSRP value of the beam set; and determining that the AI model is operating within an acceptable threshold when the measurement beam having the second highest RSRP value and the predicted beam of the beam set having the second highest RSRP value of the beam set are within the second margin tolerance box.

9. The method of claim 4, further comprising: associating the nth margin tolerance box with a measurement beam having an nth highest RSRP value of the measured RSRP values of the one or more optimal measurement beams and a predicted beam having an nth highest RSRP value of the beam set; and determining that the AI model is operating within an acceptable threshold when the measurement beam having the nth highest RSRP value and the predicted beam of the beam set having the nth highest RSRP value are within the second margin tolerance box.

10. The method of claim 1, further comprising: determining that the AI model is operating within an acceptable threshold when, based on the predicted RSRP values and the predicted beam IDs, the measured RSRP values and the beam IDs place at least one of the one or more optimal measurement beams and at least one of the one or more optimal predicted beams in the same margin tolerance box in a plurality of measurements, wherein each of the plurality of measurements includes at least one of a different measurement for the measured RSRP values or a new predicted RSRP value output from the AI model.

11. The method of claim 10, wherein the plurality of measurements includes performing a second measurement when: the beam ID of the at least one of the one or more optimal measurement beams is a same beam ID as the predicted beam ID of the at least one of the one or more optimal predicted beams in the set of beams; and the measured RSRP of the at least one of the one or more optimal measurement beams and the predicted RSRP value of the at least one of the one or more optimal predicted beams of the beam set are outside a tolerance box in a first measurement; and and determining that the AI model is operating within an acceptable threshold when, based on the predicted RSRP values and the predicted beam IDs, the measured RSRP values and the beam IDs place at least one of the one or more optimal measured beams in the same margin tolerance box as at least one of the one or more optimal predicted beams.

12. An apparatus of a user equipment (UE), the apparatus comprising: one or more processors coupled to a memory, the one or more processors configured to: identify one or more optimal measured beams of a plurality of beams having measured reference signal received power (RSRP) values above a selected threshold, wherein each of the beams of the plurality of beams has a beam identification (ID); use an artificial intelligence (AI) model to predict RSRP values and beam IDs for a set of beams of the plurality of beams; select one or more optimal predicted beams of the set of beams based on predicted RSRP values and predicted beam IDs of the set of beams; define a margin tolerance box based on a predetermined ARSRP value and a predetermined Abeam ID prediction value; and determine that the AI model is operating within an acceptable threshold when, based on the predicted RSRP values and the predicted beam IDs, the measured RSRP values and the beam IDs place at least one of the one or more optimal measured beams in the same margin tolerance box as at least one of the one or more optimal predicted beams.

13. The apparatus of claim 12, wherein the AI model is located at the UE or at a base station of a serving cell of the UE.

14. The apparatus of claim 12, wherein the predetermined ARSRP value and the predetermined Abeam ID prediction value are received from a base station.

15. The apparatus of claim 12, wherein the one or more processors are further configured to determine that the AI model is operating within the acceptable threshold when an optimal measured beam having a highest measured RSRP value of the RSRP values of the one or more optimal measured beams is in the same margin tolerance box as an optimal predicted beam of the set of beams having a highest RSRP value.

16. The apparatus of claim 12, wherein the one or more processors are further configured to determine that the AI model is operating within the acceptable threshold when an optimal measured beam having a highest measured RSRP value of the RSRP values of the one or more optimal measured beams is in the same margin tolerance box as an optimal predicted beam of the set of beams having a predicted beam ID closest to a beam ID of the optimal measured beam.

17. The apparatus of claim 12, wherein the one or more processors are further configured to: define a first margin tolerance box based on a first predetermined ARSRP value and a first predetermined Abeam ID prediction value; define a second margin tolerance box based on a second predetermined ARSRP value and a second predetermined Abeam ID prediction value; and define an n-th margin tolerance box based on an n-th predetermined ARSRP value and an n-th predetermined Abeam ID prediction value, where n is a positive integer greater than 2.

18. The apparatus of claim 17, wherein the one or more processors are further configured to: define the second margin tolerance box with one or more of: a second predetermined ARSRP value different from the first predetermined ARSRP value or a second predetermined Abeam ID prediction value different from the first Abeam ID prediction value; or a second predetermined ARSRP value same as the first predetermined ARSRP value or a second predetermined Abeam ID prediction value same as the first Abeam ID prediction value.

19. The apparatus of claim 17, wherein the one or more processors are further configured to: define the n-th margin tolerance box with one or more of: an n-th predetermined ARSRP value different from the first predetermined ARSRP value or an n-th predetermined Abeam ID prediction value different from the first Abeam ID prediction value; or an n-th predetermined ARSRP value same as the first predetermined ARSRP value or an n-th predetermined Abeam ID prediction value same as the first Abeam ID prediction value.

20. The apparatus of any one of claims 17-19, wherein the n-th margin tolerance box has at least one dimension greater than the second margin tolerance box and the second margin tolerance box has at least one dimension greater than the first margin tolerance box.

21. The apparatus of claim 17, wherein the one or more processors are further configured to: associate the second margin tolerance box with a measured beam having a second highest measured RSRP value of the RSRP values of the one or more optimal measured beams and a predicted beam of the beam set having a second highest RSRP value of the beam set; and determine that the Al model operates within an acceptable threshold when the measured beam having the second highest RSRP value and the predicted beam of the beam set having the second highest RSRP value of the beam set are within the second margin tolerance box.

22. The apparatus of claim 17, wherein the one or more processors are further configured to: associate the n-th margin tolerance box with a measured beam having an n-th highest RSRP value of the measured RSRP values of the one or more optimal measured beams and a predicted beam of the beam set having an n-th highest RSRP value; and determine that the Al model operates within an acceptable threshold when the measured beam having the n-th highest RSRP value and the predicted beam of the beam set having the n-th highest RSRP value are within the second margin tolerance box.

23. The apparatus of claim 12, wherein the one or more processors are further configured to: determining that the AI model is operating within an acceptable threshold when, based on the predicted RSRP values and the predicted beam IDs, the measured RSRP values and the beam IDs place at least one of the one or more optimal measurement beams and at least one of the one or more optimal predicted beams in the same margin tolerance bin, wherein each of the multiple measurements includes at least one of a different measurement for the measured RSRP values or a new predicted RSRP value output from the AI model.

24. The apparatus of claim 23, wherein the one or more processors are further configured to perform a second measurement when: the beam ID of the at least one of the one or more optimal measurement beams is a same beam ID as the predicted beam ID of the at least one of the one or more optimal predicted beams in the set of beams; and the measured RSRP of the at least one of the one or more optimal measurement beams and the predicted RSRP value of the at least one of the one or more optimal predicted beams in the beam set are outside a tolerance bin in a first measurement; and the one or more processors are further configured to: determine that the AI model is operating within the acceptable threshold when, based on the predicted RSRP values and the predicted beam IDs, the measured RSRP values and the beam IDs from the second measurement place at least one of the one or more optimal measurement beams and at least one of the one or more optimal predicted beams in the same margin tolerance bin.

25. A baseband processor configured to cause a user equipment (UE) to perform any of the methods of claims 1-11.

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

27. A baseband processor configured to cause a base station to perform any of the operations described herein.

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

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