Post deployment artificial intelligence / machine learning (ai / ML) functionality and drift validation

Abstract

A method of compensating for drift in an Al model at a UE is disclosed. The method comprises computing monitoring metrics for the Al model located at the UE. The monitoring metrics can be sent to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model. A new Al model can be implemented at the UE to replace the Al model when the performance drift is determined to be greater than a threshold level. New monitoring metrics can be computed at the UE for the new Al model located at the UE. The monitoring metrics can be sent to the network to enable the network to perform network conformance testing on the new monitoring metrics. An activation message is decoded at the UE to activate the new Al model at the UE.

Description

POST DEPLOYMENT ARTIFICIAL INTELLIGENCE / MACHINE LEARNING (AI / ML) FUNCTIONALITY AND DRIFT VALIDATIONFIELD

[0001] Embodiments of the invention relate to wireless communications, including apparatuses, systems, and methods for compensating for drift in an artificial intelligence (Al) model at a user equipment (UE).DESCRIPTION OF THE RELATED ART

[0002] Wireless communication systems are rapidly growing in usage. In recent years, wireless devices such as smart phones and tablet computers have become increasingly sophisticated. In addition to supporting telephone calls, many mobile devices now provide access to the internet, email, text messaging, and navigation using the global positioning system (GPS) and are capable of operating sophisticated applications that utilize these functionalities.

[0003] Long Term Evolution (LTE) has been the technology of choice for the majority of wireless network operators worldwide, providing mobile broadband data and high-speed Internet access to their subscriber base. LTE was first proposed in 2004 and was first standardized in 2008. Since then, as usage of wireless communication systems has expanded exponentially, demand has risen for wireless network operators to support a higher capacity for a higher density of mobile broadband users. In 2015, a study of a new radio access technology began and, in 2017, a first release of Fifth Generation New Radio (5G NR) was standardized.

[0004] 5G-NR, also simply referred to as NR, provides, as compared to LTE, a higher capacity for a higher density of mobile broadband users, while also supporting device-to-device, ultra-reliable, and massive machine type communications with lower latency and / or lower battery consumption. Further, NR may allow for more flexible scheduling as compared to current LTE. Consequently, efforts are being made in ongoing developments of NR to take advantage of higherthroughputs possible at higher frequencies.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] A better understanding of the present subject matter can be obtained when the following detailed description of various embodiments is considered in conjunction with the following drawings, in which:

[0006] FIG. 1 illustrates an example wireless communication system according to some embodiments.

[0007] FIG. 1 B illustrates an example of a base station and an access point in communication with a user equipment (UE) device, according to some embodiments.

[0008] FIG. 2 illustrates an example block diagram of a base station, according to some embodiments.

[0009] FIG. 3 illustrates an example block diagram of a server according to some embodiments.

[0010] FIG. 4 illustrates an example block diagram of a UE according to some embodiments.

[0011] FIG. 5 illustrates an example block diagram of cellular communication circuitry, according to some embodiments.

[0012] FIG. 6 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.

[0013] FIG. 7 illustrates an example block diagram of an interface of baseband circuitry according to some embodiments.

[0014] FIG. 8 illustrates an example of a control plane protocol stack in accordance with some embodiments.

[0015] FIG. 9 illustrates an example of a user plane protocol stack in accordance with some embodiments.

[0016] FIG. 10 illustrates example components of a core network in accordancewith some embodiments.

[0017] FIG. 11 illustrates an example validation loop procedure for an updated AI / ML model that has been trained and successfully emulated, and is ready for deployment, according to some embodiments.

[0018] FIG. 12 illustrates an example online training procedure for fine-tuning an AI / ML model or functionality at the UE to provide reinforcement learning for the AI / ML model according to some embodiments.

[0019] FIG. 13 illustrates an example online training procedure for fine-tuning an AI / ML model or functionality at an OTT server to provide reinforcement learning for the AI / ML model according to some embodiments.

[0020] FIG. 14 illustrates a flow chart of an example of a method of compensating for drift in an artificial intelligence (Al) model at a user equipment (UE), according to some embodiments.

[0021] FIGs. 15A and 15B illustrate a flow chart of an example of a method of fine-tuning an A) model at a UE to provide reinforcement learning, according to some embodiments.

[0022] FIGs. 16A and 16B illustrate a flow chart of an example of a method 1600 of fine-tuning an Al model at a server for a UE to provide reinforcement learning, according to some embodiments.

[0023] FIG. 17 illustrates a flow chart of an example of a method of compensating for drift at a network for an Al model located at a UE, according to some embodiments.

[0024] FIGs. 18A and 18B illustrate a flow chart of an example of a method 1500 of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) from a network to provide reinforcement learning, according to some embodiments.

[0025] While the features described herein may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the drawings and detailed description thereto are notintended to be limiting to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the subject matter as defined by the appended claims.DETAILED DESCRIPTIONTerms

[0026] The following is a glossary of terms used in this disclosure:

[0027] Memory Medium or Memory - Any of various types of non-transitory memory devices or storage devices. The term “memory medium” is intended to include an installation medium, e.g., a CD-ROM, floppy disks, or tape device; a computer system memory or random-access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; a non-volatile memory such as a Flash, magnetic media, e.g., a hard drive, or optical storage; registers, or other similar types of memory elements, etc. The memory medium may include other types of non-transitory memory as well or combinations thereof. In addition, the memory medium may be located in a first computer system in which the programs are executed, or may be located in a second different computer system which connects to the first computer system over a network, such as the Internet. In the latter instance, the second computer system may provide program instructions to the first computer for execution. The term “memory medium” may include two or more memory mediums which may reside in different locations, e.g., in different computer systems that are connected over a network. The memory medium may store program instructions (e.g., embodied as computer programs) that may be executed by one or more processors.

[0028] Carrier Medium - a memory medium as described above, as well as a physical transmission medium, such as a bus, network, and / or other physical transmission medium that conveys signals such as electrical, electromagnetic, or digital signals.

[0029] Programmable Hardware Element includes various hardware devices comprising multiple programmable function blocks connected via a programmableinterconnect. Examples include FPGAs (Field Programmable Gate Arrays), PLDs (Programmable Logic Devices), FPOAs (Field Programmable Object Arrays), and CPLDs (Complex PLDs). The programmable function blocks may range from fine grained (combinatorial logic or look up tables) to coarse grained (arithmetic logic units or processor cores). A programmable hardware element may also be referred to as "reconfigurable logic”.

[0030] Computer System (or Computer) - any of various types of computing or processing systems, including a personal computer system (PC), mainframe computer system, workstation, network appliance, Internet appliance, personal digital assistant (PDA), television system, grid computing system, or other device or combinations of devices. 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.

[0031] User Equipment (UE) (or “UE Device”) - any of various types of computer systems devices which are mobile or portable and which performs wireless communications. Examples of UE devices include mobile telephones or smart phones (e.g., iPhone™, Android™-based phones), portable gaming devices (e.g., Nintendo DS™, PlayStation Portable™, Gameboy Advance™, iPhone™), laptops, wearable devices (e.g., smart watch, smart glasses), PDAs, portable Internet devices, Internet of Things, music players, data storage devices, other handheld devices, unmanned aerial vehicles (UAVs) (e.g., drones), UAV controllers (UACs), and so forth. In general, the term “UE” or “UE device” can be broadly defined to encompass any electronic, computing, and / or telecommunications device (or combination of devices) which is easily transported by a user and capable of wireless communication.

[0032] Base Station - The term “Base Station” has the full breadth of its ordinary meaning, and at least includes a wireless communication station installed at a fixed location and used to communicate with UEs as part of a wireless telephone system or radio system, including but not limited Next Generation Node-Bs (gNB or gNodeB) in NR and NG-RAN nodes.

[0033] Processing Element (or Processor) - refers to various elements or combinations of elements that are capable of performing a function in a device, such as a user equipment or a cellular network device. Processing elements may include, for example: processors and associated memory, portions or circuits of individual processor cores, entire processor cores, processor arrays, circuits such as an ASIC (Application Specific Integrated Circuit), programmable hardware elements such as a field programmable gate array (FPGA), as well any of various combinations of the above.

[0034] Channel - a medium used to convey information from a sender (transmitter) to a receiver. It should be noted that since characteristics of the term “channel” may differ according to different wireless protocols, the term “channel” as used herein may be considered as being used in a manner that is consistent with the standard of the type of device with reference to which the term is used. In some standards, channel widths may be variable (e.g., depending on device capability, band conditions, etc.). For example, LTE may support scalable channel bandwidths from 1.4 MHz to 20MHz. 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 may be 22 MHz wide while Bluetooth channels may 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, e.g., different channels for uplink or downlink and / or different channels for different uses such as data, control information, etc.

[0035] Band - The term "band" has the full breadth of its ordinary meaning, and at least includes a section of spectrum (e.g., radio frequency spectrum) in which channels are used or set aside for the same purpose.

[0036] Automatically - refers to an action or operation performed by a computer system (e.g., software executed by the computer system) or device (e.g., circuitry, programmable hardware elements, ASICs, etc.), without user input directly specifying or performing the action or operation. Thus, the term "automatically" is in contrast to an operation being manually performed or specifiedby the user, where the user provides input to directly perform the operation. An automatic procedure may be initiated by input provided by the user, but the subsequent actions that are performed “automatically” are not specified by the user, i.e., are not performed “manually”, where the user specifies each action to perform. For example, a user filling out an electronic form by selecting each field and providing input specifying information (e.g., by typing information, selecting check boxes, radio selections, etc.) is filling out the form manually, even though the computer system will update the form in response to the user actions. The form may be automatically filled out by the computer system where the computer system (e.g., software executing on the computer system) analyzes the fields of the form and fills in the form without any user input specifying the answers to the fields. As indicated above, the user may invoke the automatic filling of the form, but is not involved in the actual filling of the form (e.g., the user is not manually specifying answers to fields but rather they are being automatically completed). The present specification provides various examples of operations being automatically performed in response to actions the user has taken.

[0037] Approximately - refers to a value that is almost correct or exact. For example, approximately may refer to a value that is within 1 to 10 percent of the exact (or desired) value. It should be noted, however, that the actual threshold value (or tolerance) may be application dependent. For example, in some embodiments, “approximately” may mean within 0.1 % of some specified or desired value, while in various other embodiments, the threshold may be, for example, 2%, 3%, 5%, and so forth, as desired or as set by the particular application.

[0038] Concurrent - refers to parallel execution or performance, where tasks, processes, or programs are performed in an at least partially overlapping manner. For example, concurrency may be implemented using “strong” or strict parallelism, where tasks are performed (at least partially) in parallel on respective computational elements, or using “weak parallelism”, where the tasks are performed in an interleaved manner, e.g., by time multiplexing of execution threads.

[0039] Various components may be described as “configured to” perform a taskor tasks. In such contexts, “configured to” is a broad recitation generally meaning “having structure that” performs the task or tasks during operation. As such, the component can be configured to perform the task even when the component is not currently performing that task (e.g., a set of electrical conductors may be configured to electrically connect a module to another module, even when the two modules are not connected). In some contexts, “configured to” may be a broad recitation of structure generally meaning “having circuitry that” performs the task or tasks during operation. As such, the component can be configured to perform the task even when the component is not currently on. In general, the circuitry that forms the structure corresponding to “configured to” may include hardware circuits.

[0040] Various components may be described as performing a task or tasks, for convenience in the description. Such descriptions should be interpreted as including the phrase “configured to.” Reciting a component that is configured to perform one or more tasks is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation for that component.

[0041] The example embodiments may be further understood with reference to the following description and the related appended drawings, wherein like elements are provided with the same reference numerals. The example embodiments relate to apparatuses, systems and method for reducing energy usage by network components, e.g., base stations in wireless communication systems.

[0042] The example embodiments are described with regard to communication between a Next Generation Node B (gNB) and a user equipment (UE). However, reference to a gNB or a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and / or firmware to support for reducing energy usage by network components in wireless communication systems. Therefore, the gNB or UE as described herein is used to represent any appropriate type of electronic component.

[0043] The example embodiments are also described with regard to a fifth generation (5G) New Radio (NR). However, reference to a 5G NR network ismerely provided for illustrative purposes. The example embodiments may be utilized with any appropriate type of network.

[0044] Throughout this description various information elements (lEs) are referred to by specific names. It should be understood that these names are only examples and the lEs carrying the information referred to throughout this description may be referred to by other names by various entities.Figures 1 A and 1 B: Communication Systems

[0045] FIG. 1 A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of FIG. 1 A is merely one example of a possible system, and that features of this disclosure may be implemented in any of various systems, as desired.

[0046] As shown, the example wireless communication system includes a base station 102A which communicates over a transmission medium with one or more user devices 106A, 106B, etc., through 106N. Each of the user devices may be referred to herein as a “user equipment” (UE). Thus, the user devices 106 are referred to as UEs or UE devices.

[0047] The base station (BS) 102A may be a base transceiver station (BTS) or cell site (a “cellular base station”) and may include hardware that enables wireless communication with the UEs 106A through 106N.

[0048] The communication area (or coverage area) of the base station may be referred to as a “cell.” The base station 102A and the UEs 106 may be configured to communicate over the transmission medium using any of various radio access technologies (RATs), also referred to as wireless communication technologies, or telecommunication standards, such as GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces), LTE, LTE-Advanced (LTE-A), 5G new radio (5G NR), HSPA, 3GPP2 CDMA2000 (e.g., 1 xRTT, 1 xEV-DO, HRPD, eHRPD), etc. Note that if the base station 102A is implemented in the context of LTE, also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, it may alternately be referred to as an 'eNodeB' or ‘eNB’. Note that ifthe base station 102A is implemented in the context of 5G NR, it may alternately be referred to as ‘gNodeB’ or ‘gNB’.

[0049] As shown, the base station 102A may also be equipped to communicate with a network 100 (e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN), and / or the Internet, among various possibilities). Thus, the base station 102A may facilitate communication between the user devices and / or between the user devices and the network 100. In particular, the cellular base station 102A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and / or data services.

[0050] Base station 102A and other similar base stations (such as base stations 102B...102N) operating according to the same or a different cellular communication standard may thus be provided as a network of cells, which may provide continuous or nearly continuous overlapping service to UEs 106A-N and similar devices over a geographic area via one or more cellular communication standards.

[0051] Thus, while base station 102A may act as a “serving cell” for UEs 106A- N as illustrated in FIG. 1A, each UE 106 may also be capable of receiving signals from (and possibly within communication range of) one or more other cells (which might be provided by base stations 102B-N and / or any other base stations), which may be referred to as “neighboring cells”. Such cells may also be capable of facilitating communication between user devices and / or between user devices and the network 100. Such cells may include “macro” cells, “micro” cells, “pico” cells, and / or cells which provide any of various other granularities of service area size. For example, base stations 102A-B illustrated in FIG. 1 A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.

[0052] In some embodiments, base station 102A may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “gNB”. In some embodiments, a gNB may be connected to a legacy evolved packet core (EPC)network and / or to a NR core (NRC) network. In addition, a gNB cell may include one or more transition and reception points (TRPs). In addition, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more gNBs.

[0053] Note that a UE 106 may be capable of communicating using multiple wireless communication standards. For example, the UE 106 may be configured to communicate using a wireless networking (e.g., Wi-Fi) and / or peer-to-peer wireless communication protocol (e.g., Bluetooth, Wi-Fi peer-to-peer, etc.) in addition to 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., 1 xRTT, 1xEV-DO, HRPD, eHRPD), etc.). The UE 106 may also or alternatively be configured to communicate using one or more global navigational satellite systems (GNSS, e.g., 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, if desired. Other combinations of wireless communication standards (including more than two wireless communication standards) are also possible.

[0054] In some embodiments, the base station 102A may select a paging configuration and a PEI configuration for UEs 106. The base station 102A may encode and transmit the paging configuration and the PEI configuration to UEs 106 as part of a registration process. Using the paging configuration, UEs 106 can determine which PO and PF to monitor in a paging cycle. Using the PEI configuration, UEs 106 can determine the radio frame that carries relevant PEI.

[0055] FIG. 1 B illustrates user equipment 106 (e.g., one of the devices 106A through 106N) in communication with a base station 102 and an access point 112, according to some embodiments. The UE 106 may be a device with both cellular communication capability and non-cellular communication capability (e.g., Bluetooth, Wi-Fi, and so forth) such as a mobile phone, a hand-held device, a computer or a tablet, or virtually any type of wireless device.

[0056] The UE 106 may include a processor that is configured to executeprogram instructions stored in memory. The UE 106 may perform any of the method embodiments described herein by executing such stored instructions. Alternatively, or in addition, the UE 106 may include a programmable hardware element such as an FPGA (field-programmable gate array) that is configured to perform any of the method embodiments described herein, or any portion of any of the method embodiments described herein.

[0057] The UE 106 may include one or more antennas for communicating using one or more wireless communication protocols or technologies. In some embodiments, the UE 106 may be configured to communicate using, for example, CDMA2000 (1 xRTT / 1 xEV-DO I HRPD I eHRPD), LTE / LTE- Advanced, or 5G NR using a single shared radio and / or GSM, LTE, LTE-Advanced, or 5G NR using the single shared radio. The shared radio may couple to a single antenna, or may couple to multiple antennas (e.g., for MIMO) for performing wireless communications. In general, a radio may include any combination of a baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc.), ordigital processing circuitry (e.g., for digital modulation as well as other digital processing). Similarly, the radio may implement one or more receive and transmit chains using the aforementioned hardware. For example, the UE 106 may share one or more parts of a receive and / or transmit chain between multiple wireless communication technologies, such as those discussed above.

[0058] In some embodiments, the UE 106 may include separate transmit and / or receive chains (e.g., including separate antennas and other radio components) for each wireless communication protocol with which it is configured to communicate. As a further possibility, the UE 106 may include one or more radios which are shared between multiple wireless communication protocols, and one or more radios which are used exclusively by a single wireless communication protocol. For example, the UE 106 might include a shared radio for communicating using either of LTE or 5G NR (or LTE or IxRTTor LTE or GSM), and separate radios for communicating using each of Wi-Fi and Bluetooth. Other configurations are also possible.FIG. 2: Block Diagram of a Base Station (gNB)

[0059] FIG. 2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of FIG. 2 is merely one example of a possible base station. As shown, the base station 102 may include processor(s) 204 which may execute program instructions for the base station 102. The processor(s) 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from the processor(s) 204 and translate those addresses to locations in memory (e.g., memory 260 and read only memory (ROM) 250) or to other circuits or devices.

[0060] The base station 102 may include at least one network port 270. The network port 270 may be configured to couple to a telephone network and provide a plurality of devices, such as UE devices 106, access to a network as described above in FIGs. 1 A and 2.

[0061] The network port 270 (or an additional network port) may also or alternatively be configured to couple to a cellular network, e.g., a core network of a cellular service provider. The core network may provide mobility related services and / or other services to a plurality of devices, such as UE devices 106. In some cases, the network port 270 may couple to a telephone network via the core network, and / or the core network may provide a telephone network (e.g., among other UE devices serviced by the cellular service provider).

[0062] In some embodiments, base station 102 may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “gNB”. In such embodiments, base station 102 may be connected to a legacy evolved packet core (EPC) network and / or to a NR core (NRC) network. In addition, base station 102 may be considered a 5G NR cell and may include one or more transition and reception points (TRPs). In addition, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more gNBs.

[0063] The base station 102 may include at least one antenna 234, and possibly multiple antennas. The at least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UEdevices 106 via radio 230. The antenna 234 communicates with the radio 230 via communication chain 232. Communication chain 232 may be a receive chain, a transmit chain or both. The radio 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.

[0064] The base station 102 may be configured to communicate wirelessly using multiple wireless communication standards. In some instances, the base station 102 may include multiple radios, which may enable the base station 102 to communicate according to multiple wireless communication technologies. For example, as one possibility, the base station 102 may include an LTE radio for performing communication according to LTE as well as a 5G NR radio for performing communication according to 5G NR. In such a case, the base station 102 may be capable of operating as both an LTE base station and a 5G NR base station. As another possibility, the base station 102 may include a multi-mode radio which is capable of performing communications 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.).

[0065] As described further subsequently herein, the base station 102 may include hardware and software components for implementing or supporting implementation of features described herein. The processor 204 of the base station 102 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, the processor 204 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit), or a combination thereof. Alternatively (or in addition) the processor 204 of the base station 102, in conjunction with one or more of the other components 230, 232, 234, 240, 250, 260, 270 may be configured to implement or support implementation of part or all of the features described herein.

[0066] In addition, as described herein, processor(s) 204 may be comprised ofone or more processing elements. In other words, one or more processing elements may be included in processor(s) 204. Thus, processor(s) 204 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor(s) 204. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 204.

[0067] Further, as described herein, radio 230 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in radio 230. Thus, radio 230 may include one or more integrated circuits (ICs) that are configured to perform the functions of radio 230. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of radio 230.FIG. 3: Block Diagram of a Server

[0068] FIG. 3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of FIG. 3 is merely one example of a possible server. As shown, the server 104 may include processor(s) 344 which may execute program instructions for the server 104. The processor(s) 344 may also be coupled to memory management unit (MMU) 374, which may be configured to receive addresses from the processor(s) 344 and translate those addresses to locations in memory (e.g., memory 364 and read only memory (ROM) 354) or to other circuits or devices.

[0069] The server 104 may be configured to provide a plurality of devices, such as base station 102, and UE devices 106 access to network functions, e.g., as further described herein.

[0070] In some embodiments, the server 104 may be part of a radio access network, such as a 5G New Radio (5G NR) radio access network. In some embodiments, the server 104 may be connected to a legacy evolved packet core (EPC) network and / or to a NR core (NRC) network.

[0071] As described herein, the server 104 may include hardware and softwarecomponents for implementing or supporting implementation of features described herein. The processor 344 of the server 104 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, the processor 344 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit), or a combination thereof. Alternatively (or in addition) the processor 344 of the server 104, in conjunction with one or more of the other components 354, 364, and / or 374 may be configured to implement or support implementation of part or all of the features described herein.

[0072] In addition, as described herein, processor(s) 344 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor(s) 344. Thus, processor(s) 344 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor(s) 344. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 344.FIG. 4: Block Diagram of a User Equipment (UE)

[0073] FIG. 4 illustrates an example simplified block diagram of a communication device 106, according to some embodiments. It is noted that the block diagram of the communication device of FIG. 4 is only one example of a possible communication device. According to embodiments, 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, notebook, or portable computing device), a tablet, an unmanned aerial vehicle (UAV), a UAV controller (UAC) and / or a combination of devices, among other devices. As shown, the communication device 106 may include a set of components 400 configured to perform corefunctions. For example, this set of components may be implemented as a system on chip (SOC), which may include portions for various purposes. Alternatively, this set of components 400 may be implemented as separate components or groups of components for the various purposes. The set of components 400 may be coupled (e.g., communicatively; directly or indirectly) to various other circuits of the communication device 106.

[0074] For example, the communication device 106 may include various types of memory (e.g., including NAND flash 410), an input / output interface such as connector l / F 420 (e.g., for connecting to a computer system; dock; charging station; input devices, such as a microphone, camera, keyboard; output devices, such as speakers; etc.), the display 460, which 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 a network interface card, e.g., for Ethernet.

[0075] The cellular communication circuitry 430 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 as shown. The short to medium range wireless communication circuitry 429 may also couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 437 and 438 as shown. Alternatively, the short to medium range wireless communication circuitry 429 may couple (e.g., communicatively; directly or indirectly) to the antennas 435 and 436 in addition to, or instead of, coupling (e.g., communicatively; directly or indirectly) to the antennas 437 and 438. The 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.

[0076] In some embodiments, as further described below, cellular communication circuitry 430 may include dedicated receive chains (including and / or coupled to, e.g., communicatively; directly or indirectly, dedicated processorsand / or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR). In addition, in some embodiments, cellular communication circuitry 430 may include a single transmit chain that may be switched between radios dedicated to specific RATs. For example, a first radio may be dedicated to a first RAT, e.g., LTE, and may be in communication with a dedicated receive chain and a transmit chain shared with an additional radio, e.g., a second radio that may be dedicated to a second RAT, e.g., 5G NR, and may be in communication with a dedicated receive chain and the shared transmit chain.

[0077] The communication device 106 may also include and / or be configured for use with one or more user interface elements. The user interface elements may include any of various elements, such as display 460 (which may be a touchscreen display), a keyboard (which may be a discrete keyboard or may be implemented as part of a touchscreen display), a mouse, a microphone and / or speakers, one or more cameras, one or more buttons, and / or any of various other elements capable of providing information to a user and / or receiving or interpreting user input.

[0078] The communication device 106 may further include one or more smart cards 445 that include SIM (Subscriber Identity Module) functionality, such as one or more UICC(s) (Universal Integrated Circuit Card(s)) cards 445. Note that the term “SIM” or “SIM entity” is intended to include any of various types of SIM implementations or SIM functionality, such as the one or more UICC(s) cards 445, one or more eUlCCs, one or more eSIMs, either 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 may be embedded, e.g., may be soldered onto a circuit board in the UE 106, or each SIM 410 may be implemented as a removable smart card. Thus, the SIM(s) may be one or more removable smart cards (such as UICC cards, which are sometimes referred to as “SIM cards”), and / or the SIMs 410 may be one or more embedded cards (such as embedded UICCs (eUlCCs), which are sometimes referred to as “eSIMs” or “eSIM cards”). In some embodiments (such as when the SIM(s) include an eUlCC), one or more of the SIM(s) may implement embedded SIM (eSIM) functionality; in such anembodiment, a single one of the SIM(s) may execute multiple SIM applications. Each of the SIMs may include components such as a processor and / or a memory; instructions for performing SIM / eSIM functionality may be stored in the memory and executed by the processor. In some embodiments, the UE 106 may include a combination of removable smart cards and fixed / non-removable smart cards (such as one or more elllCC cards that implement eSIM functionality), as desired. For example, the UE 106 may comprise two embedded SIMs, two removable SIMs, or a combination of one embedded SIMs and one removable SIMs. Various other SIM configurations are also contemplated.

[0079] As noted above, in some embodiments, the UE 106 may include two or more SIMs. The inclusion of two or more SIMs in the UE 106 may allow the UE 106 to support two different telephone numbers and may allow the UE 106 to communicate on corresponding two or more respective networks. For example, a first SIM may support a first RAT such as LTE, and a second SIM 410 support a second RAT such as 5G NR. Other implementations and RATs are of course possible. In some embodiments, when the UE 106 comprises two SIMs, the UE 106 may support Dual SIM Dual Active (DSDA) functionality. The DSDA functionality may allow the UE 106 to be simultaneously connected to two networks (and use two different RATs) at the same time, or to simultaneously maintain two connections supported by two different SIMs using the same or different RATs on the same or different networks. The DSDA functionality may also allow the UE 106 to simultaneously receive voice calls or data traffic on either phone number. In certain embodiments the voice call may be a packet switched communication. In other words, the voice call may be received using voice over LTE (VoLTE) technology and / or voice over NR (VoNR) technology. In some embodiments, the UE 106 may support Dual SIM Dual Standby (DSDS) functionality. The DSDS functionality may allow either of the two SIMs in the UE 106 to be on standby waiting for a voice call and / or data connection. In DSDS, when a call / data is established on one SIM, the other SIM is no longer active. In some embodiments, DSDx functionality (either DSDA or DSDS functionality) may be implemented with a single SIM (e.g., a eUlCC) that executes multiple SIM applications for differentcarriers and / or RATs.

[0080] As shown, the SOC 400 may include processor(s) 402, which may execute program instructions for the communication device 106 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460. The processor(s) 402 may also be coupled to memory management unit (MMU) 440, which may be configured to receive addresses from the processor(s) 402 and translate those addresses to locations in memory (e.g., memory 406, read only memory (ROM) 450, NAND flash memory 410) and / or to other circuits or devices, such as the display circuitry 404, short to medium range wireless communication circuitry 429, cellular communication circuitry 430, connector l / F 420, and / or display 460. The MMU 440 may be configured to perform memory protection and page table translation or set up. In some embodiments, the MMU 440 may be included as a portion of the processor(s) 402.

[0081] As described herein, the communication device 106 may include hardware and software components for implementing the above features for a communication device 106 to communicate a scheduling profile for power savings to a network. The processor 402 of the communication device 106 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 402 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 402 of the communication device 106, in conjunction with one or more of the other components 400, 404, 406, 410, 420, 429, 430, 440, 445, 450, 460 may be configured to implement part or all of the features described herein.

[0082] In addition, as described herein, processor 402 may include one or more processing elements. Thus, processor 402 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor 402. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 402.

[0083] Further, as described herein, cellular communication circuitry 430 and short to medium range wireless communication circuitry 429 may each include one or more processing elements. In other words, one or more processing elements may be included in cellular communication circuitry 430 and, similarly, one or more processing elements may be included in short to medium range wireless communication circuitry 429. Thus, cellular communication circuitry 430 may include one or more integrated circuits (ICs) that are configured to perform the functions of cellular communication circuitry 430. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of cellular communication circuitry 430. Similarly, the short to medium range wireless communication circuitry 429 may include one or more ICs that are configured to perform the functions of short to medium range wireless communication circuitry 429. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of short to medium range wireless communication circuitry 429.FIG. 5: Block Diagram of Cellular Communication Circuitry

[0084] FIG. 5 illustrates an example simplified block diagram of cellular communication circuitry, according to some embodiments. It is noted that the block diagram of the cellular communication circuitry of FIG. 5 is only one example of a possible cellular communication circuit. According to embodiments, cellular communication circuitry 530, which may be cellular communication circuitry 430, may be included in a communication device, such as communication device 106 described above. As noted above, 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, notebook, or portable computing device), a tablet and / or a combination of devices, among other devices.

[0085] The cellular communication circuitry 530 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas435a-b and 436 as shown (in FIG. 4). In some embodiments, cellular communication circuitry 530 may include dedicated receive chains (including and / or coupled to, e.g., communicatively; directly or indirectly, dedicated processors and / or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR). For example, as shown in FIG. 5, cellular communication circuitry 530 may include a modem 510 and a modem 520. Modem 510 may be configured for communications according to a first RAT, e.g., such as LTE or LTE-A, and modem 520 may be configured for communications according to a second RAT, e.g., such as 5G NR.

[0086] As shown, modem 510 may include one or more processors 512 and a memory 516 in communication with processors 512. Modem 510 may be in communication with a 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 receive circuitry (RX) 532 and transmit circuitry (TX) 534. In some embodiments, receive circuitry 532 may be in communication with downlink (DL) front end 550, which may include circuitry for receiving radio signals via antenna 335a.

[0087] Similarly, modem 520 may include one or more processors 522 and a memory 526 in communication with processors 522. Modem 520 may be in communication with an 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 receive circuitry 542 and transmit circuitry 544. In some embodiments, receive circuitry 542 may be in communication with DL front end 560, which may include circuitry for receiving radio signals via antenna 335b.

[0088] In some embodiments, a switch 570 may couple transmit circuitry 534 to uplink (UL) front end 572. In addition, switch 570 may couple transmit circuitry 544 to UL front end 572. UL front end 572 may include circuitry for transmitting radio signals via antenna 336. Thus, when cellular communication circuitry 530 receives instructions to transmit according to the first RAT (e.g., as supported via modem 510), switch 570 may be switched to a first state that allows modem 510 to transmit signals according to the first RAT (e.g., via a transmit chain that includes transmitcircuitry 534 and UL front end 572). Similarly, when cellular communication circuitry 530 receives instructions to transmit according to the second RAT (e.g., as supported via modem 520), switch 570 may be switched to a second state that allows modem 520 to transmit signals according to the second RAT (e.g., via a transmit chain that includes transmit circuitry 544 and UL front end 572).

[0089] As described herein, the modem 510 may include hardware and software components for implementing the above features or for time division multiplexing UL data for NSA NR operations, as well as the various other techniques described herein. The processors 512 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 512 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 512, in conjunction with one or more of the other components 530, 532, 534, 535, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[0090] In addition, as described herein, processors 512 may include one or more processing elements. Thus, processors 512 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 512. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processors 512.

[0091] The processors 522 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 522 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 522, in conjunction with one or more of the other components 540, 542, 544, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[0092] In addition, as described herein, processors 522 may include one or more processing elements. Thus, processors 522 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 522. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processors 522.FIG. 6: Block Diagram of a Baseband Processor Architecture for a UE

[0093] FIG. 6 illustrates example components of a device 600 in accordance with some embodiments. It is noted that the device of FIG. 6 is merely one example of a possible system, and that features of this disclosure may be implemented in any of various UEs, as desired.

[0094] In some embodiments, the 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 coupled together at least as shown. The components of the illustrated device 600 may be included in a UE 106 or a RAN node 102A. In some embodiments, the device 600 may include less elements (e.g., a RAN node may not utilize application circuitry 602, and instead include a processor / controller to process IP data received from an EPC). In some embodiments, the device 600 may include additional elements such as, for example, memory / storage, display, camera, sensor, or input / output (I / O) interface. In other embodiments, the components described below may be included in more than one device (e.g., said circuitries may be separately included in more than one device for Cloud-RAN (C- RAN) implementations).

[0095] The application circuitry 602 may include one or more application processors. For example, the application circuitry 602 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, etc.). The processors may be coupled with or may include memory / storage and may beconfigured to execute instructions stored in the memory / storage to enable various applications or operating systems to run on the device 600. In some embodiments, processors of application circuitry 602 may process IP data packets received from an EPC.

[0096] The baseband circuitry 604 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The baseband circuitry 604 may include one or more baseband processors or control logic to process baseband signals received from a receive signal path of the RF circuitry 606 and to generate baseband signals for a transmit signal path of the RF circuitry 606. Baseband processing circuity 604 may interface with the application circuitry 602 for generation and processing of the baseband signals and for controlling operations of the RF circuitry 606. For example, in some embodiments, the 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 baseband processor(s) 604D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G), sixth generation (6G), etc.). The baseband circuitry 604 (e.g., one or more of baseband processors 604A-D) may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry 606. In other embodiments, some or all of the functionality of baseband processors 604A-D may be included in modules stored in the memory 604G and executed via a Central Processing Unit (CPU) 604E. The radio control functions may include, but are not limited to, signal modulation / demodulation, encoding / decoding, radio frequency shifting, etc. In some embodiments, modulation / demodulation circuitry of the baseband circuitry 604 may include Fast- Fourier Transform (FFT), precoding, or constellation mapping / demapping functionality. In some embodiments, 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. Embodiments of modulation / demodulation and encoder / decoder functionality are not limited to these examples and may include other suitable functionality in other embodiments.

[0097] In some embodiments, the baseband circuitry 604 may include one or more audio digital signal processor(s) (DSP) 604F. The audio DSP(s) 604F may be include elements for compression / decompression and echo cancellation and may include other suitable processing elements in other embodiments. Components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on a same circuit board in some embodiments. In some embodiments, some or all of the constituent components of the baseband circuitry 604 and the application circuitry 602 may be implemented together such as, for example, on a system on a chip (SOC).

[0098] In some embodiments, the baseband circuitry 604 may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry 604 may support communication with an evolved universal terrestrial radio access network (EUTRAN) or other wireless metropolitan area networks (WMAN), a wireless local area network (WLAN), a wireless personal area network (WPAN). Embodiments in which the baseband circuitry 604 is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.

[0099] RF circuitry 606 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry 606 may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. RF circuitry 606 may include a receive signal path which may include circuitry to down-convert RF signals received from the FEM circuitry 608 and provide baseband signals to the baseband circuitry 604. RF circuitry 606 may also include a transmit signal path which may include circuitry to up-convert baseband signals provided by the baseband circuitry 604 and provide RF output signals to the FEM circuitry 608 for transmission.

[0100] In some embodiments, the receive signal path of the RF circuitry 606 may include mixer circuitry 606a, amplifier circuitry 606b and filter circuitry 606c. In some embodiments, the transmit signal path of the RF circuitry 606 may include filter circuitry 606c and mixer circuitry 606a. RF circuitry 606 may also includesynthesizer circuitry 606d for synthesizing a frequency for use by the mixer circuitry 606a of the receive signal path and the transmit signal path. In some embodiments, the mixer circuitry 606a of the receive signal path may be configured to down-convert RF signals received from the FEM circuitry 608 based on the synthesized frequency provided by synthesizer circuitry 606d. The amplifier circuitry 606b may be configured to amplify the down-converted signals and the filter circuitry 606c may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signals to generate output baseband signals. Output baseband signals may be provided to the baseband circuitry 604 for further processing. In some embodiments, the output baseband signals may be zero-frequency baseband signals, although this is not a necessity. In some embodiments, mixer circuitry 606a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.

[0101] In some embodiments, the mixer circuitry 606a of the transmit signal path may be configured to up-convert input baseband signals based on the synthesized frequency provided by the synthesizer circuitry 606d to generate RF output signals for the FEM circuitry 608. The baseband signals may be provided by the baseband circuitry 604 and may be filtered by filter circuitry 606c.

[0102] In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of 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 circuitry 606a of the receive signal path and the mixer circuitry 606a may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may be configured for super-heterodyne operation.

[0103] In some embodiments, the output baseband signals and the inputbaseband signals may be analog baseband signals, although the scope of the embodiments is not limited in this respect. In some alternate embodiments, the output baseband signals and the input baseband signals may be digital baseband signals. In these alternate embodiments, the RF circuitry 606 may include analog- to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 604 may include a digital baseband interface to communicate with the RF circuitry 606.

[0104] In some dual-mode embodiments, a separate radio IC circuitry may be provided for processing signals for each spectrum, although the scope of the embodiments is not limited in this respect.

[0105] In some embodiments, the synthesizer circuitry 606d may be a fractional-N synthesizer or a fractional N / N+1 synthesizer, although the scope of the embodiments is not limited in this respect as other types of frequency synthesizers may be suitable. For example, synthesizer circuitry 606d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.

[0106] The synthesizer circuitry 606d may be configured to synthesize an output frequency for use by the mixer circuitry 606a of the RF circuitry 606 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 606d may be a fractional N / N+1 synthesizer.

[0107] In some embodiments, frequency input may be provided by a voltage controlled oscillator (VCO), although that is not a necessity. Divider control input may be provided by either the baseband circuitry 604 or the applications processor 602 depending on the desired output frequency. In some embodiments, a divider control input (e.g., N) may be determined from a look-up table based on a channel indicated by the applications processor 602.

[0108] Synthesizer circuitry 606d of the RF circuitry 606 may include a divider, a delay-locked loop (DLL), a multiplexer and a phase accumulator. In some embodiments, the divider may be a dual modulus 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 either N or N+1 (e.g., based on a carry out) to provide a fractional division ratio. In some example embodiments, the DLL may include a set of cascaded, tunable, delay elements, a phase detector, a charge pump and a D-type flip-flop. In these embodiments, the delay elements may be configured to break a VCO period up into Nd equal packets of phase, where Nd is the number of delay elements in the delay line. In this way, the DLL provides negative feedback to help ensure that the total delay through the delay line is one VCO cycle.

[0109] 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 divider circuitry to generate multiple signals at the carrier frequency with multiple different phases with respect to each other. In some embodiments, the output frequency may be a LO frequency (fLO). In some embodiments, the RF circuitry 606 may include an IQ / polar converter.

[0110] FEM circuitry 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 the amplified versions of the received signals to the RF circuitry 606 for further processing. FEM circuitry 608 may also include a transmit signal path which may include circuitry configured to amplify signals for transmission provided by the RF circuitry 606 for transmission by one or more of the one or more antennas 610. In various embodiments, the amplification through the transmit or receive signal paths may be done solely in the RF circuitry 606, solely in the FEM 608, or in both the RF circuitry 606 and the FEM 608.

[0111] In some embodiments, the FEM circuitry 608 may include a TX / RX switch to switch between transmit mode and receive mode operation. The FEM circuitry may include a receive signal path and a transmit signal path. The receive signal path of the FEM circuitry may include an LNA to amplify received RF signals and provide the amplified received RF signals as an output (e.g., to the RF circuitry606). The transmit signal path of the FEM circuitry 608 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 606), and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 610).

[0112] In some embodiments, the PMC 612 may manage power provided to the baseband circuitry 604. In particular, the PMC 612 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. The PMC 612 may often be included when the device 600 is capable of being powered by a battery, for example, when the device is included in a UE. The PMC 612 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.

[0113] While FIG. 6 shows the PMC 612 coupled only with the baseband circuitry 604, in other embodiments the PMC 612 may be additionally or alternatively coupled with, and perform similar power management operations for, other components such as, but not limited to, application circuitry 602, RF circuitry 606, or FEM 608.

[0114] In some embodiments, the PMC 612 may control, or otherwise be part of, various power saving mechanisms of the device 600. For example, if the device 600 is in a radio resource control_Connected (RRC_Connected) state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the device 600 may power down for brief intervals of time and thus save power.

[0115] If there is no data traffic activity for an extended period of time, then the device 600 may transition off to an RRCJdle state, where it disconnects from the network and does not perform operations such as channel quality feedback, handover, etc. The device 600 goes into a very low power state and it performs paging where, again, it periodically wakes up to listen to the network and then powers down at least portions of the device again. The device 600 may not receive data in this state. In order to receive data, it will transition back to anRRC_Connected state.

[0116] An additional power saving mode may allow a device to be unavailable to the network for periods longer than a paging interval (ranging from seconds to a few hours). During this time, the device is totally unreachable to the network and may power down completely. Any data sent during this time incurs a large delay and it is assumed the delay is acceptable.FIG. 7: Block Diagram of an Interface of Baseband Circuitry

[0117] FIG. 7 illustrates example interfaces of baseband circuitry in accordance with some embodiments. It is noted that the baseband circuitry of FIG. 7 is merely one example of a possible circuitry, and that features of this disclosure may be implemented in any of various systems, as desired.

[0118] As discussed above, the baseband circuitry 604 of FIG. 6 may comprise processors 604A-604E and a memory 604G utilized by said processors. Each of the processors 604A-604E may include a memory interface, 704A-704E, respectively, to send / receive data to / from the memory 604G.

[0119] The baseband circuitry 604 may further include one or more interfaces to communicatively couple to other circuitries / devices, such as a memory interface 712 (e.g., an interface to send / receive data to / from memory external to the baseband circuitry 604), an application circuitry interface 714 (e.g., an interface to send / receive data to / from the application circuitry 602 of FIG. 6), an RF circuitry interface 716 (e.g., an interface to send / receive data to / from RF circuitry 606 of FIG. 6), a wireless hardware connectivity interface 718 (e.g., an interface to send / receive data to / from Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components), and a power management interface 720 (e.g., an interface to send / receive power or control signals to / from the PMC 612.FIG. 8: Control Plane Protocol Stack

[0120] FIG. 8 is an illustration of a control plane protocol stack in accordance with some embodiments. In this embodiment, a control plane 800 is shown as a communications protocol stack between the UE 106a (or alternatively, the UE 106b), the RAN node 102A (or alternatively, the RAN node 102B), and the mobility management entity (MME) 621 .

[0121] The PHY layer 801 may transmit or receive information used by the MAC layer 802 over one or more air interfaces. The PHY layer 801 may further perform link adaptation or adaptive modulation and coding (AMC), power control, cell search (e.g., for initial synchronization and handover purposes), and other measurements used by higher layers, such as the RRC layer 805. The PHY layer 801 may still further perform error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, modulation / demodulation of physical channels, interleaving, rate matching, mapping onto physical channels, and Multiple Input Multiple Output (MIMO) antenna processing.

[0122] The MAC layer 802 may perform mapping between logical channels and transport channels, multiplexing of MAC service data units (SDUs) from one or more logical channels onto transport blocks (TB) to be delivered to PHY via transport channels, de-multiplexing MAC SDUs to one or more logical channels from transport blocks (TB) delivered from the PHY via transport channels, multiplexing MAC SDUs onto TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), and logical channel prioritization.

[0123] The RLC layer 803 may operate in a plurality of modes of operation, including: Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). The RLC layer 803 may execute transfer of upper layer protocol data units (PDUs), error correction through automatic repeat request (ARQ) for AM data transfers, and concatenation, segmentation and reassembly of RLC SDUs for UM and AM data transfers. The RLC layer 803 may also execute re-segmentation of RLC data PDUs for AM data transfers, reorder RLC data PDUs for UM and AM data transfers, detect duplicate data for UM and AM data transfers,discard RLC SDUs for UM and AM data transfers, detect protocol errors for AM data transfers, and perform RLC re-establishment.

[0124] The PDCP layer 804 may execute header compression and decompression of IP data, maintain PDCP Sequence Numbers (SNs), perform insequence delivery of upper layer PDUs at re-establishment of lower layers, eliminate duplicates of lower layer SDUs at re-establishment of lower layers for radio bearers mapped on RLC AM, cipher and decipher control plane data, perform integrity protection and integrity verification of control plane data, control timerbased discard of data, and perform security operations (e.g., ciphering, deciphering, integrity protection, integrity verification, etc.).

[0125] The main services and functions of the RRC layer 805 may include broadcast of system information (e.g., included in Master Information Blocks (MIBs) or System Information Blocks (SIBs) related to the non-access stratum (NAS)), broadcast of system information related to the access stratum (AS), paging, establishment, maintenance and release of an RRC connection 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, inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting. Said MIBs and SIBs may comprise one or more information elements (lEs), which may each comprise individual data fields or data structures.

[0126] The UE 601 and the RAN node 102A may utilize a Uu interface (e.g., an LTE-Uu interface) to exchange control plane data via a protocol stack comprising the PHY layer 801 , the MAC layer 802, the RLC layer 803, the PDCP layer 804, and the RRC layer 805.

[0127] The non-access stratum (NAS) protocols 806 form the highest stratum of the control plane between the UE 601 and the MME 621. The NAS protocols 806 support the mobility of the UE 601 and the session management procedures to establish and maintain IP connectivity between the UE 601 and the P-GW 623.

[0128] The S1 Application Protocol (S1 -AP) layer 815 may support the functions of the S1 interface and comprise Elementary Procedures (EPs). An EP is a unit of interaction between the RAN node 102A and the network 100. The S1 -AP layer services may comprise two groups: 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 transport, RAN Information Management (RIM), and configuration transfer.

[0129] The Stream Control Transmission Protocol (SCTP) layer (alternatively referred to as the SCTP / IP layer) 814 may ensure reliable delivery of signaling messages between the RAN node 102A and the MME 621 based, in part, on the IP protocol, supported by the IP layer 813. The L2 layer 812 and the L1 layer 81 1 may refer to communication links (e.g., wired or wireless) used by the RAN node and the MME to exchange information.

[0130] The RAN node 102A and the MME 621 may utilize an S1 -MME interface to exchange control plane data via a protocol stack comprising the L1 layer 81 1 , the L2 layer 812, the IP layer 813, the SCTP layer 814, and the S1 -AP layer 815.FIG. 9: User Plane Protocol Stack

[0131] FIG. 9 is an illustration of an example of a user plane protocol stack in accordance with some embodiments. In this embodiment, a user plane 900 is shown as a communications protocol stack between the UE 106A (or alternatively, the UE 106B or 106N), the RAN node 102A (or alternatively, the RAN node 102B), the S-GW 622, and the P-GW 623. The user plane 900 may utilize at least some of the same protocol layers as the control plane 800. For example, the UE 601 and the RAN node 102A may utilize a Uu interface (e.g., an LTE-Uu interface) to exchange user plane data via a protocol stack comprising the PHY layer 801 , the MAC layer 802, the RLC layer 803, the PDCP layer 804.

[0132] The General Packet Radio Service (GPRS) Tunneling Protocol for the user plane (GTP-U) layer 904 may be used for carrying user data within the GPRScore network and between the radio access network and the core network. The user data transported can be packets in any of IPv4, IPv6, or PPP formats, for example. The UDP and IP security (UDP / IP) layer 903 may provide checksums for data integrity, port numbers for addressing different functions at the source and destination, and encryption and authentication on the selected data flows. The RAN node 102A and the S-GW 622 may utilize an S1 -U interface to exchange user plane data via a protocol stack comprising the L1 layer 81 1 , the L2 layer 812, the UDP / IP layer 903, and the GTP-U layer 904. The S-GW 622 and the P-GW 623 may utilize an S5 / S8a interface to exchange user plane data via a protocol stack comprising the L1 layer 811 , the L2 layer 812, the UDP / IP layer 903, and the GTP- U layer 904. As discussed above with respect to FIG. 8, NAS protocols support the mobility of the UE 106 and the session management procedures to establish and maintain IP 813 connectivity between the UE 106 and the P-GW 623.

[0133] 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 though specific reference numerals may be omitted.FIG. 10: Core Network

[0134] FIG. 10 illustrates an example architecture of a system 1000 including a core network (CN) 1020 in accordance with various embodiments. The CN 1020 may be a core network for a 5G System (which may be referred to as a 5GC). The system 1000 is shown to include a UE 1001 , which may be the same or similar to the UEs 106A, 106B, or 106N discussed previously; a (R)AN 1010, which may be the same or similar to the BSs 102A or 102N discussed previously; and a data network (DN) 1003, which may be, for example, operator services, Internet access, or 3rd party services; and a CN 1020. The CN 1020 may include a number of network functions including an Authentication Server Function (AUSF) 1022; an Access and Mobility Management Function (AMF) 1021 ; a Session Management Function (SMF) 1024; a Network Exposure Function (NEF) 1023; a Policy Control Function (PCF) 1026; a Network Repository Function (NRF) 1025; a Unified DataManagement (UDM) 1027; an Application Function (AF) 1028; a User Plane Function (UPF) 1002; and a Network Slice Selection Function (NSSF) 1029. These network functions may be implemented, in some cases, as virtualized software based functions / services.

[0135] The UPF 1002 may act as an anchor point for intra-RAT and inter-RAT mobility, an external packet data unit (PDU) session point of interconnect to DN 1003, and a branching point to support mufti-homed PDU session. A PDU session is a logical connection between the UE and the DN. The UPF 1002 may also perform packet routing and forwarding, perform packet inspection, enforce the user plane part of policy rules, lawfully intercept packets (user plane (UP) collection), perform traffic usage reporting, perform quality of service (QoS) handling for a user plane (e.g., packet filtering, gating, UL / DL rate enforcement), perform Uplink Traffic verification (e.g., Service Data Flows (SDF) to QoS flow mapping), transport level packet marking in the uplink and downlink, and perform downlink packet buffering and downlink data notification triggering. UPF 1002 may include an uplink classifier to support routing traffic flows to a data network, The DN 1003 may represent various network operator services, Internet access, or third party services. DN 1003 may include, or be similar to, application server 104 discussed previously. The UPF 1002 may interact with the SMF 1024 via an N4 reference point between the SMF 1024 and the UPF 1002.

[0136] A location management function 1030 can be used to receive location management information from the UE 106 or base station 102 and use the information to determine a location of the UE. Alternatively, the UE may provide the location of the UE to the LMF 1030 using information such as GPS information and triangulation information based on signals received from 3 or more base stations 102N.

[0137] The AUSF 1022 may store data for authentication of UE 1001 and handle authentication-related functionality, The AUSF 1022 may facilitate a common authentication frame work for various access types. The AUSF 1022 may communicate with the AMF 1021 via an N12 reference point between the AMF 1021 and the AUSF 1022; and may communicate with the UDM 1027 via an N13reference point between the UDM 1027 and the AUSF 1022. Additionally, the AUSF 1022 may exhibit an Nausf service-based interface.

[0138] The AMF 1021 may be responsible for registration management (e.g., for registering UE 1001 , etc.), connection management, reachability management, mobility management, and lawful interception of AMF-related events, and access authentication and authorization. The AMF 1021 may be a termination point for the an N1 1 reference point between the AMF 1021 and the SMF 1024. The AMF 1021 may provide transport for SM messages between the UE 1001 and the SMF 1024, and act as a transparent proxy for routing SM messages. AMF 1021 may also provide transport for Short Message Service (SMS) messages between UE 1001 and an SMSF (not shown by FIG. 10). AMF 1021 may act as a security anchor function (SEAF), which may include interaction with the AUSF 1022 and the UE 1001 , receipt of an intermediate key that was established as a result of the UE 1001 authentication process. Where Universal Subscriber Identity Module (USIM) based authentication is used, the AMF 1021 may retrieve the security material from the AUSF 1022. AMF 1021 may also include a Security Context Management (SCM) function, which receives a key from the SEAF that it uses to derive accessnetwork specific keys. Furthermore, AMF 1021 may be a termination point of a RAN control plane (CP) interface, which may include or be an N2 reference point between the (R)AN 1010 and the AMF 1021 ; and the AMF 1021 may be a termination point of NAS (Nl) signaling, and perform NAS ciphering and integrity protection.

[0139] AMF 1021 may also support NAS signaling with a UE 1001 over a non- 3GPP Inter-Working Function (N3IWF) interface. The N3IWF may be used to provide access to untrusted entities. N3IWF may be a termination point for the N2 interface between the (R)AN 1010 and the AMF 1021 for the control plane, and may be a termination point for the N3 reference point between the (R)AN 1010 and the UPF 1002 for the user plane. As such, the AMF 1021 may handle N2 signaling from the SMF 1024 and the AMF 1021 for PDU sessions and encapsulate / de- encapsulate packets for IPSec and N3 tunneling, mark N3 user-plane packets in the uplink, and enforce QoS corresponding to N3 packet marking while consideringQoS requirements associated with such marking received over N2. N3IWF may also relay uplink and downlink control plane non-access stratum (NAS) signaling between the UE 1001 and AMF 1021 via an N1 reference point between the UE 1001 and the AMF 1021 , and relay uplink and downlink user-plane packets between the UE 1001 and UPF 1002. The N3IWF also provides mechanisms for internet protocol security (IPsec) tunnel establishment with the UE 1001. The AMF 1021 may exhibit an Namf service based interface, and may be a termination point for an N14 reference point between two AMFs 1021 and an N17 reference point between the AMF 1021 and a 5G Equipment Identity Register (5G-EIR) (not shown by FIG. 10).

[0140] The UE 1001 may need to register with the AMF 1021 in order to receive network services. Registration Management (RM) is used to register or deregister the UE 1001 with the network (e.g., AMF 1021 ), and establish a UE context in the network (e.g., AMF 1021 ). The UE 1001 may operate in an RM-REGISTERED state or an RM-DEREGISTERED state. In the RM-DEREGISTERED state, the UE 1001 is not registered with the network, and the UE context in AMF 1021 holds no valid location or routing information for the UE 1001 so the UE 1001 is not reachable by the AMF 1021. In the RM REGISTERED state, the UE 1001 is registered with the network, and the UE context in AMF 1021 may hold a valid location or routing information for the UE 1001 so the UE 1001 is reachable by the AMF 1021. In the RM-REGISTERED state, the UE 1001 may perform mobility registration update procedures, perform periodic registration update procedures triggered by expiration of the periodic update timer (e.g., to notify the network that the UE 1001 is still active), and perform a Registration Update procedure to update UE capability information or to re-negotiate protocol parameters with the network, among others.

[0141] The AMF 1021 may store one or more RM contexts for the UE 1001 , where each RM context is associated with a specific access to the network. The RM context may be a data structure, database object, etc. that indicates or stores, inter glia, a registration state per access type and the periodic update timer. The AMF 1021 may also store a 5GC mobility management (MM) context that may bethe same or similar to the evolved packet services (EPS) Mobility Management (E)MM context discussed previously. In various embodiments, the AMF 1021 may store a CE mode B Restriction parameter of the UE 1001 in an associated MM context or registration management (RM) context. The AMF 1021 may also derive the value, when needed, from the UE's usage setting parameter already stored in the UE context (and / or MM / RM context).

[0142] Connection Management (CM) may be used to establish and release a signaling connection between the UE 1001 and the AMF 1021 over the N1 interface. The signaling connection is used to enable NAS signaling exchange between the UE 1001 and the CN 1020, and comprises both the signaling connection between the UE and the AN (e.g., RRC connection or UE-N3IWF connection for non-3GPP access) and the N2 connection for the UE 1001 between the AN (e.g., AN 1010) and the AMF 1021. The UE 1001 may operate in one of two CM states, CM-IDLE mode or CM-CONNECTED mode. When the UE 1001 is operating in the CM-IDLE state / mode, the UE 1001 may have no NAS signaling connection established with the AMF 1021 over the N1 interface, and there may be (R)AN 1010 signaling connection (e.g., N2 and / or N3 connections) for the UE 1001. When the UE 1001 is operating in the CM-CONNECTED state / mode, the UE 1001 may have an established NAS signaling connection with the AMF 1021 over the Nl interface, and there may be a (R)AN 1010 signaling connection (e.g., N2 and / or N3 connections) for the UE 1001. Establishment of an N2 connection between the (R)AN 1010 and the AMF 1021 may cause the UE 1001 to transition from CM-IDLE mode to CM-CONNECTED mode, and the UE 1001 may transition from the CM-CONNECTED mode to the CM-IDLE mode when N2 signaling between the (R)AN 1010 and the AMF 1021 is released.

[0143] The SMF 1024 may be responsible for session management (SM) session establishment, modify and release, including tunnel maintain between UPF and AN node); UE IP address allocation and management (including optional authorization); selection and control of UP function; configuring traffic steering at UPF to route traffic to proper destination; termination of interfaces toward policy control functions; controlling part of policy enforcement and QoS; lawful intercept(for SM events and interface to LI system); termination of SM parts of NAS messages; downlink data notification; initiating AN specific SM information, sent via AMF over N2 to AN; and determining SSC mode of a session. SM may refer to management of a PDU session, and a PDU session or "session" may refer to a PDU connectivity service that provides or enables the exchange of PDUs between a UE 1001 and a data network (DN) 1003 identified by a Data Network Name (DNN). PDU sessions may be established upon UE 1001 request, modified upon UE 1001 and CN 1020 request, and released upon UE 1001 and CN 1020 request using NAS SM signaling exchanged over the N1 reference point between the UE 1001 and the SMF 1024. Upon request from an application server, the CN 1020 may trigger a specific application in the UE 1001. In response to receipt of the trigger message, the UE 1001 may pass the trigger message (or relevant parts / information of the trigger message) to one or more identified applications in the UE 1001. The identified application(s) in the UE 1001 may establish a PDU session to a specific data network name (DNN). The SMF 1024 may check whether the UE 1001 requests are compliant with user subscription information associated with the UE 1001. In this regard, the SMF 1024 may retrieve and / or request to receive update notifications on SMF 1024 level subscription data from the UDM 1027.

[0144] The SMF 1024 may include the following roaming functionality: handling local enforcement to apply QoS SLAB virtual Public Land Mobile Network (VPLMN); charging data collection and charging interface (VPLMN); lawful intercept (in VPLMN for SM events and interface to LI system); and support for interaction with external DN for transport of signaling for PDU session authorization / authentication by external DN. An N16 reference point between two SMFs 1024 may be included in the system 1000, which may be between another SMF 1024 in a visited network and the SMF 1024 in the home network in roaming scenarios. Additionally, the SMF 1024 may exhibit the Nsmf service-based interface.

[0145] The NEF 1023 may provide means for securely exposing the services and capabilities provided by 3GPP network functions for third party, internalexposure / re-exposure, Application Functions (e.g., AF 1028), edge computing or fog computing systems, etc. In such embodiments, the NEF 1023 may authenticate, authorize, and / or throttle the AFS. NEF 1023 may also translate information exchanged with the AF 1028 and information exchanged with internal network functions. For example, the NEF 1023 may translate between an AF- Service-ldentifier and an internal SCC information. NEF 1023 may also receive information from other network functions (NFs) based on exposed capabilities of other network functions. This information may be stored at the NEF 1023 as structured data, or at a data storage NF using standardized interfaces. 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 analytics. Additionally, the NEF 1023 may exhibit an Nnef service-based interface.

[0146] The NRF 1025 may support service discovery functions, receive NF discovery requests from NF instances, and provide the information of the discovered NF instances to the NF instances. NRF 1025 also maintains information of available NF instances and their supported services. As used herein, the terms "instantiate," "instantiation," and the like may refer to the creation of an instance, and an "instance" may refer to a concrete occurrence of an object, which may occur, for example, during execution of program code. Additionally, the NRF 1025 may exhibit the Nnrf service based interface.

[0147] The PCF 1026 may provide policy rules to control plane function(s) to enforce them, and may also support unified policy framework to govern network behavior, The PCF 1026 may also implement a front end (FE) to access subscription information relevant for policy decisions in a UDR of the UDM 1027. The PCF 1026 may communicate with the AMF 1021 via an N15 reference point between the PCF 1026 and the AMF 1021 , which may include a PCF 1026 in a visited network and the AMF 1021 in case of roaming scenarios. The PCF 1026 may communicate with the AF 1028 via an NS reference point between the PCF 1026 and the AF 1028; and with the SMF 1024 via an N7 reference point between the PCF 1026 and the SMF 1024, The system 1000 and / or CN 1020 may also include an N24 reference point between the PCF 1026 (in the home network) anda PCF 1026 in a visited network, Additionally, the PCF 1026 may exhibit an Npcf service-based interface.

[0148] The UDM 1027 may handle subscription-related information to support the network entities' handling of communication sessions, and may store subscription data of UE 1001. For example, subscription data may be communicated between the UDM 1027 and the AMF 1021 via an NS reference point between the UDM 1027 and the AMF. The UDM 1027 may include two parts, an application FE and a UDR (the FE and UDR are not shown by FIG. 10). The UDR may store subscription data and policy data for the UDM 1027 and the PCF 1026, and / or structured data for exposure and application data (including PFDs for application detection, application request information for multiple UEs 1001 ) for the NEF 1023. The Nadr service-based interface may be exhibited by the UDR 221 to allow the UDM 1027, PCF 1026, and NEF 1023 to access a particular set of the stored data, as well as to read, update (e.g., add, modify), delete, and subscribe to notification of relevant data changes in the UDR. The UDM may include a UDM- FE, which is in charge of processing credentials, location management, subscription management and so on. 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 handling, access authorization, registration / mobility management, and subscription management. The UDR may interact with the SMF 1024 via an NI0 reference point between the UDM 1027 and the SMF 1024. UDM 1027 may also support SMS management, wherein an SMS-FE implements the similar application logic as discussed previously. Additionally, the UDM 1027 may exhibit the Nudm service based interface.

[0149] The AF 1028 may provide application influence on traffic routing, provide access to the NOE, and interact with the policy framework for policy control. The NCE may be a mechanism that allows the GN 1020 and AF 1028 to provide information to each other via NEF 1023, which may be used for edge computing implementations. In such implementations, the network operator and third party services may be hosted close to the UE 1001 access point of attachment to achievean efficient service delivery through the reduced end-to-end latency and load on the transport network. For edge computing implementations, the 5GC may select a UPF 1002 close to the UE 1001 and execute traffic steering from the UPF 502 to DN 1003 via the N6 interface. This may be based on the UE subscription data, UE location, and information provided by the AF 1028. In this way, the AF 1028 may influence UPF (re)selection and traffic routing. Based on operator deployment, when AF 1028 is considered to be a trusted entity, the network operator may permit AF 1028 to interact directly with relevant NFs. Additionally, the AF 1028 may exhibit an Naf service-based interface.

[0150] The NSSF 1029 may select a set of network slice instances serving the UE 501. The NSSF 1029 may also determine allowed Network Slice Selection Assistance Information (NSSAI) and the mapping to the subscribed single NSSAI (S-NSSAI) is, if needed. The NSSF 1029 may also determine the AMF set to be used to serve the UE 1001 , or a list of candidate AMF(s) 1021 based on a suitable configuration and possibly by querying the NRF 1025. The selection of a set of network slice instances for the UE 1001 may be triggered by the AMF 1021 with which the UE 1001 is registered by interacting with the NSSF 1029, which may lead to a change of AMF 1021. The NSSF 1029 may interact with the AMF 1021 via an N22 reference point between AMF 1021 and NSSF 1029; and may communicate with another NSSF 1029 in a visited network via an N31 reference point (not shown by FIG. 10). Additionally, the NSSF 1029 may exhibit an Nnssf service-based interface.

[0151] As discussed previously, the CN 1020 may include a short message service function (SMSF), which may be responsible for SMS subscription checking and verification, and relaying SM messages to / from the UE 1001 to / from other entities, such as an SMS-GMSC / IWMSC / SMS-router. The SMS may also interact with AMF 1021 and UDM 1027 for a notification procedure that the UE 1001 is available for SMS transfer (e.g., set a UE not reachable flag, and notifying UDM 1027 when UE 1001 is available for SMS).

[0152] The CN 1020 may also include other elements that are not shown by FIG. 10, such as a Data Storage system / architecture, a 5G-EIR, a Security EdgeProtection Proxy (SEPP), and the like. The Data Storage system may include a Structured Data Storage Network Function (SDSF), air Unstructured Data Storage Function (UDSF), and / or the like. Any network function (NF) may store and retrieve unstructured data into / from the UDSF (e.g., UE contexts), via N18 reference point between any NF and the UDSF (not shown by FIG. 10), Individual NFs may share a UDSF for storing their respective unstructured data or individual NFs may each have their own UDSF located at or near the individual NFs. Addition- ally, the UDSF may exhibit an Nudsf service-based interface (not shown by FIG. 10). The 5G-EIR may be an NF that checks the status of permanent equipment identifier (PEI) for determining whether particular equipment / entities are blacklisted from the network; and the SEPP may be a non-transparent proxy that performs topology hiding, message filtering, and policing on inter-PLMN control plane interfaces.

[0153] Additionally, there may be many more reference points and / or servicebased interfaces between the NF services in the NFs; however, these interfaces and reference points have been omitted from FIG. 10 for clarity. In one example, the CN 1020 may include an Nx interface, which is an inter-CN interface between a mobility management entity (MME) and the AMF 1021 in order to enable interworking between CN 1020 and a CN in a 4G system. Other example interfaces / reference points may include an N5G-EIR service-based interface exhibited by a 5G-EIR, an N27 reference point between the NRF in the visited network and the NRF in the home network; and an N31 reference point between the NSSF in the visited network and the NSSF in the home network.AI / ML Model Development

[0154] The development of the AI / ML models may comprise four main phases: a training, emulation (validation), deployment, and inference phase. The main task involved in each phase are briefly described in the proceeding paragraphs.

[0155] Training Phase: In this phase, the Al model is trained on a dataset. This involves feeding the model with input data and corresponding correct output labels, allowing the model to learn patterns and relationships within the data. Trainingtypically involves optimization algorithms to adjust the model's parameters to minimize errors.

[0156] Emulation Phase: In the emulation phase, the trained model is tested extensively to ensure it performs well on data it hasn't seen before. This phase involves evaluating the model's performance metrics such as accuracy, precision, recall, etc., using validation datasets. Emulation helps identify any issues with the model's generalization and performance before deployment.

[0157] Deployment Phase: Once the model has been trained and successfully emulated, it's ready for deployment. Deployment involves integrating the model into a production environment where it can make predictions or classifications on new, unseen data. This may involve creating application programming interfaces (APIs) or integrating the model into applications or systems where it will be used.

[0158] Inference Phase: In this phase, the deployed model is used to make predictions or classifications on real-world data referred to herein as a “scenario.” A scenario typically refers to a specific situation or problem domain in which an AI / ML model is applied or evaluated. Scenarios help frame the context in which AI / ML model is are deployed. The AI / ML model takes input data, processes it, and produces an output referred to as an inference, e.g., a predicted RSRP for a beam (assisted), or a predicted beam ID.AI / ML Model Monitoring

[0159] Monitoring and Evaluation: A monitoring entity, such as a UE 106, base station 102, or network 1020, can continuously monitor various factors such as data characteristics, system performance metrics, or environmental conditions.

[0160] Decision Making: Based on the monitored factors, the monitoring entity decides whether to switch to a different machine learning model that is better suited for the current conditions or task, finetune the current model using transfer learning to better match the environmental conditions or indicate the need to fall back to non-AI based positioning.

[0161] Al 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 approach is often used in adaptive systems where the optimal model for a particular task may change over time or in different contexts. Al model switching may include the following.

[0162] Model Selection: The monitoring entity selects the most appropriate model from a set of pre-defined models or algorithms. This selection can be based on factors such as accuracy, efficiency, or robustness.

[0163] Model finetuning: Once a model is selected, the model can be further trained on a dataset that is specific to a task. This is known as finetuning. Finetuning a pre-trained model can reduce the amount of initial training for the model, while ensuring the model is trained for the specific task for which it will be used. This enables models to be trained more generally for multiple specific tasks. Finetuning a pre-trained model may be optional, depending on how different the initial training is from the end use of the model.

[0164] Adaptation: Once a new model is selected and optionally finetuned, the monitoring entity adapts its operation to use the newly chosen model for making predictions or decisions.Novel Solutions to 3GPP Release 18 Case Study

[0165] It will be appreciated that the accuracy of the inferences made by AI / ML models are limited to the accuracy of the monitored factors, such as the data characteristics, system performance metrics, or environmental conditions.

[0166] In 3GPP, when AI / ML models have passed RAN4 testing and have already been deployed in the field, the UE performance and behavior can only be guaranteed under the scenarios / configurations defined during testing. However, when radio propagation conditions are drifting from nominal and / or scenario / configurations (under testing environment), a change to an updated AI / ML model should be employed.

[0167] However, there are currently no tests defined that would be conducted for guaranteeing performance for the updated models since the granularity of conditions / scenarios is not yet defined and it would be impractical to test for all possible scenarios / configurations. There may be performance degradation at the UE and / or network if the new AI / ML model is not well verified. Consequently, postdeployment validation of the functionality of AI / ML models becomes an issue.

[0168] In legacy 3GPP systems, the change of modem software is typically rare. Software updates are often prompted by algorithmic fixes or updates. However, in AI / ML-based 3GPP implementations, the adjustment, enhancement, or modification of the AI / ML model is a significantly more complicated process. In addition, changes to AI / ML models or changes caused by Al can become quite challenging to trace the reasons behind the change due to the inherent explainability issues of AI / ML and its subsequent impacts.

[0169] A series of modifications to AI / ML functionality over a UE, base station, or network’s lifetime, post-deployment, can potentially lead to the following issues: (1 ) Integration of a new updated AI / ML model onto the device without complete validation, can result in inaccurate results; and (2) Modification, updating, or fine- tuning of an AI / ML model, can lead to degraded performance under certain conditions, even if improvements occur under other conditions.

[0170] Therefore, it becomes essential to study the adaptability and flexibility of AI / ML based functionalities for post-deployment validation / testing of AI / ML features. The present disclosure provides novel and non-obvious technical solutions applicable to the study cases identified above. For example, the present disclosure provides unique technical solutions on (1 ) compensating for drift in an artificial intelligence (Al) model at a user equipment (UE); and (2) fine-tuning an artificial intelligence (Al) model at a user equipment (UE) to provide reinforcement learning. It will be appreciated that the technical solutions provided herein may be incorporated into future specifications, including 3GPP Release 19.FIG. 1 1 : Validation Loop of Updated AI / ML Model

[0171] FIG. 11 illustrates an example validation loop procedure for an updated AI / ML model that has been trained and successfully emulated, and is ready for deployment. The procedure shown in FIG. 1 1 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the procedure elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional procedure elements may also be performed as desired.

[0172] In accordance with an embodiment, the validation loop procedure 1 100 is a life cycle management (LCM) procedure for an updated AI / ML model to be inserted into a 5G system (5GS) 1000. In a first step, the procedure comprises computing monitoring metrics at a UE 106 for an artificial intelligence I machine learning (AI / ML) model 1 102. The monitoring metrics can be encoded, at the UE 106, for transmission to the network 1020 (via a RAN 1010, such as base station 102). The network 1020 can then perform LCM functionality management based on the monitoring metrics received from the UE.

[0173] If performance drift in the monitoring metrics is detected at the network 1020, the network can implement a transfer of a new AI / ML model / Functionality to the UE 106. Alternatively, the network can signal the UE to switch to a different AI / ML model that may be stored at the UE or base station 102. Previous UE capability signaling to the network can enable the network to know which AI / ML models are available at the network. New AI / ML model(s) may be transferred from the network to the UE via the base station 102 (e.g. RAN 1010). Alternatively, an over-the-top (OTT) server can be used to send the new AI / ML model(s) to the UE. The UE can also autonomously perform an AI / ML model update.

[0174] In a third step of the procedure 1 100, the UE 106 can perform post deployment validation before deploying the updated AI / ML model. The UE can compute monitoring metrics for the updated AI / ML model to enable the UE to monitor the performance of the new model. In one embodiment, the UE can send the updated monitoring metrics to the network 1020. The network can then perform LCM functionality management using the updated monitoring metrics.

[0175] The network 1020 may have preset or dynamic thresholds for the AI / ML models. The network can compare the updated monitoring metrics, which can also be referred to as key performance indicators (KPIs), with the preset or dynamic thresholds and determine if the new AI / ML model able to be activated in the 5GS 1000. This can be referred to as network conformance testing. If the monitoring metrics pass the threshold levels (e.g. the AI / ML model passes network conformance testing), then the network can activate the AI / ML model in the network, shown as LCM activation in step 5a of the procedure 1100. The activation / deployment of the new AI / ML model may also be performed at the UE, as shown in step 5b, in which case the UE can send a message to the network indicating that the new AI / ML model as been activated.

[0176] If the monitoring metrics of the new AI / ML model fail the test (e.g. are less than the preset or dynamic thresholds, then the UE can continue to operate in fallback mode I legacy mode, as shown in step 5c, which has previously passed conformance testing with the network. The fallback mode may involve operating the UE and network with the previous AI / ML model. Alternatively, the fallback / legacy mode may involve operating the UE and network without the use of any AI / ML model (or without the use of the specific AI / ML model that did not pass network conformance testing) until an AI / ML model can be found that passes conformance testing with the network. The procedure 1100 can be repeated by signaling another model or functionality, returning to step 3, until the monitoring metrics pass 3GPP network requirements, such as radio access network 4 (RAN4) requirements.

[0177] In another embodiment, the UE device vendor can test and confirm any changes, updates or modifications to the AI / ML functionalities / features used against 3GPP AI / ML requirements, such as RAN4 requirements. 3GPP AI / ML specifications are set forth in 3GPP Technical Report (TR) 38.843 V18.0.0 (January 2024). RAN4 requirements are still being developed and may be further developed and implemented in 3GPP Rel. 19 and later releases. Any changes in AI / ML functionalities or features can be tested as pre-deployment tests. The modifications to the AI / ML functionalities and features can become part of the UEcapabilities for an AI / ML model or functionality and UE operating conditions under different scenarios. For example, UE capabilities can include information such as: (UE capability: Model 1 : {Configurationl / scenariol }, {Configuration / scenario 2} , Model 2: {Configuration / Scenario 1}, etc. ).

[0178] Before the network 1020 signals a modification to an AI / ML functionality, the network can first check the UE capability to determine if the UE supports that AI / ML functionality. As previously discussed, the UE capability can be previously signaled to the network. However, the scalability and applicability of this approach is challenging. The granularity of radio conditions and scenarios could be quite fine (many scenarios to be tested). Testing over all possible scenarios / configurations could be challenging.FIG. 12: Fine-tuning of the AI / ML Model at the UE

[0179] FIG. 12 illustrates an example online training procedure 1200 for fine- tuning an AI / ML model 1202 or functionality at the UE 106 to provide reinforcement learning for the AI / ML model. The procedure 1200 shown in FIG. 12 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the procedure elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional procedure elements may also be performed as desired.

[0180] In a first step of the procedure 1200 illustrated in the example of FIG. 12, the UE 106 can compute monitoring metrics for a specific AI / ML model 1202 or functionality and can send those monitoring metrics to the network 1020. The network can then perform LCM functionality management and determine if drift is detected by comparing the monitoring metrics received from the UE with previously received monitoring metrics and calculating a change between the monitoring metrics and the previously received monitoring metrics. If the change is greater than a threshold, the network can determine that a drift in performance is detected, as shown at step 2. If the drift in performance is significant, the network mayreplace the AI / ML model 1202 with a different AI / ML model. However, if performance drift detected is less significant, the network can initiate retraining of the AI / ML model as shown in step 3. Alternatively, the UE 106 can autonomously initiate retraining of the AI / ML model. In a fourth step, the UE can request data collection, either at the UE or from the network, for retraining and fine-tuning the AI / ML model. The network can then assist the UE in collecting data for retraining the AI / ML model. For example, the network may send a specific type of reference signal that the UE can measure and use as data for retraining the AI / ML model. During retraining fine-tunes, the UE may fallback to legacy functionality without the use of the specific AI / ML model that is being retrained. In a fifth step, the UE can collect data used for training the AI / ML model. The data may be measured by the UE or received from the network via the RAN 1010 (e.g. base station 102).

[0181] The procedure 1200 continues with the UE 106 performing fine-tuning of the AI / ML model 1202 at the UE. After fine tuning is done, the UE can compute updated monitoring metrics with the fine-tuned AI / ML model and send the metrics to the network 1020. In a sixth step, the network can use the metrics to determine if the fine-tuned AI / ML model passes network threshold levels for the AI / ML model. This iterative procedure can continue until the monitoring metrics pass the network threshold levels and the updated model is ready to be deployed again at the UE. The updated AI / ML model can then be activated at the UE. The UE can store and update the AI / ML model for the specific AI / ML functionality and the scenario / configuration, as previously discussed. The UE can add to its capability list, with a new model ID, or a note of a fine-tuned model ID. For example, one example of a capability list can comprise: (model ID (fine-tuned) -> (configuration / scenario). The UE can signal a new model ID to the network. The network then knows which UE model to activate given a similar configuration / scenario.FIG. 13: Fine-tuning of the AI / ML Model at an OTT server

[0182] FIG. 13 illustrates an example online training procedure 1300 for fine-tuning an AI / ML model 1302 or functionality at an OTT server 1304 to provide reinforcement learning for the AI / ML model. The procedure 1300 shown in FIG. 13 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the procedure elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional procedure elements may also be performed as desired.

[0183] In a first step of the procedure 1300 illustrated in the example of FIG. 13, the UE 106 can compute monitoring metrics for the AI / ML model 1302 and sends the monitoring metrics to the network 1020. The network can then perform LCM functionality management and determine if drift is detected by comparing the monitoring metrics received from the UE with previously received monitoring metrics and calculating a change in the metrics. If the change is greater than a threshold, the network can determine that a drift in performance is detected, as shown at step 2. If the drift in performance is significant, the network may replace the AI / ML model 1302 with a different AI / ML model. However, if performance drift detected is less significant, the network can initiate retraining of the AI / ML model as shown in step 3. Alternatively, the UE can also be configured to autonomously initiate retraining without receiving a message from the network.

[0184] In a fourth step of the procedure 1300, the UE 106 can send a request for data collection for retraining and fine-tuning the AI / ML model 1302 to the network 1020. The network can then assist the UE in collecting data for retraining the AI / ML model. For example, the network may send a specific type of reference signal that the UE can measure and use as data for retraining the AI / ML model. The UE can then collect the data, as shown in step 5. The data may be measured at the UE or received from another source, such as the network 1020 via a RAN 1010 (e.g. base station 102).

[0185] In the example embodiment of procedure 1300, the UE 106 can send the data collected for training, such as the measured data, to an over-the-top (OTT) server 1304 in communication with the UE 106. The OTT can then use the data to fine-tune the AI / ML model 1302, as shown at step 6. If fine-tuning the AI / MLmodel uses a significant amount of processing power, it can be beneficial for the UE to communicate the training data to the OTT server and allow the training to take place there. The fine tuning may continue until the fine-tuned AI / ML model is capable of passing a test, such as using the network or RAN 4 threshold levels to determine that the AI / ML model is ready for deployment in the 5GS 1000. Once fine-tuning is complete, the fine-tuned AI / ML model 1302 can be passed from the OTT server 1304 to the UE 106, as shown in step 7. The network can send a message to the UE to perform data collection for monitoring and metric computation of the AI / ML model, as in step 8. The monitoring metrics can then be sent from the UE to the network, as shown in step 9. The network can determine if the fine-tuned AI / ML model meets the threshold levels, such as RAN 4 requirements at step 10. If the AI / ML model monitoring metrics are above the threshold level, the fine-tuned AI / ML model passes and updating is complete and the NW can send a message to activate the tine-tuned AI / ML model at the UE. The UE can then store and update the AI / ML model 1302 for that AI / ML functionality AI / ML and scenario / configuration. The UE can send an updated model logical ID to the network to identify the new, fine-tuned model ID. The model ID can be assigned / decided from the network based on the current configuration / scenario.FIG. 14: Flow Chart for compensating for drift in Al model at a UE

[0186] FIG. 14 illustrates a flow chart of an example of a method 1400 of compensating for drift in an artificial intelligence (Al) model at a user equipment (UE), according to some embodiments.

[0187] The method shown in FIG. 14 may be used in conjunction with any of the systems, methods, or devices illustrated in the figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[0188] In accordance with an embodiment, a method 1400, compensating for drift in an artificial intelligence (Al) model at a user equipment (UE) is disclosed.The method 1400 comprises computing monitoring metrics for the Al model located at the UE, as shown block 1402. The monitoring metrics are encoded at the UE for transmission to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model, as shown in block 1404. A new Al model is implemented at the UE to replace the Al model when the performance drift is determined to be greater than a threshold level, as shown in block 1406. New monitoring metrics are computed for the new Al model located at the UE, as shown in block 1408. The new monitoring metrics are encoded at the UE for transmission to the network to enable the network to perform network conformance testing on the new monitoring metrics to determine that the new monitoring metrics pass thresholds of the network conformance testing, as shown in block 1410. An activation message, received at the UE from the network is decoded, instructing the UE to activate the new Al model when the network conformance testing indicates the new monitoring metrics pass the thresholds of the network conformance testing, as shown in block 1412. The new Al model is then activated at the UE in response to receiving the activation message from the network, as shown in block 1414.

[0189] The method 1400 can further comprise encoding, for transmission to the network, a UE capability message that includes Al models stored at the UE. The UE capability message can include one or more configurations or scenarios that are associated with each model, comprising: UE capability: Model 1 : {Configuration / scenariol }, {Configuration / scenario 2} ,... {Configuration / scenario N}, Model 2: {Gonfiguration / Scenario 1}, {Configuration / scenario 2} ,... {Configuration / scenario N}, ... Model N: {Configuration / Scenario 1 },{Configuration / scenario 2} ,... {Configuration / scenario N}, where N is a positive integer.

[0190] In some embodiments, implementing a new Al model comprises receiving the new Al model from one of the network and a server at the UE. The server can be an over-the-top (OTT) server in communication with the UE.

[0191] In some embodiments, implementing a new Al model comprises activating the new Al model that is stored on the UE to replace the Al model.

[0192] The method 1400 can further comprise decoding, at the UE, a new Al model identification received from the network indicating the new Al model for the UE to implement at the UE.

[0193] A legacy mode message that is received at the UE from the network can be decoded at the UE to instruct the UE to perform in a legacy mode when the network conformance testing indicates the new monitoring metrics do not pass the thresholds of the network conformance testing; and operate the UE in a legacy mode.

[0194] In some embodiments, operating the UE in a legacy mode comprises: operating the UE with a previous Al model that was used prior to the new Al model; or operating the UE without the Al model until the new Al model passes the network conformance testing and the UE receives the activation message.FIGs. 15A, 15B: Flow Chart for Fine-Tuning an Al Model at a UE

[0195] FIGs. 15A and 15B illustrate a flow chart of an example of a method 1500 of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) to provide reinforcement learning, according to some embodiments.

[0196] The method shown in FIGs. 15A and 15B may be used in conjunction with any of the systems, methods, or devices illustrated in the figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[0197] In accordance with an embodiment, a method 1500, of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) to provide reinforcement learning is disclosed. The method 1500 comprises computing monitoring metrics for the Al model located at the UE, as shown in block 1502. The UE can encode monitoring metrics at the UE for transmission to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model having a change greater than a drift threshold, as shown in block 1504. The UE can decode an Al model retrainingmessage received from the network when the change is greater than the drift threshold, as shown in block 1506. The UE can encode a request for data collection for transmission to the network, as shown in block 1508. The UE can decode a data collection for training the Al model, as shown in block 1510. The UE can perform finetune training of the Al model at the UE based using the data collection to form a finetuned Al model, as shown in block 1512. The UE can compute finetuned monitoring metrics for the finetuned Al model located at the UE, as shown in block 1514.

[0198] FIG. 15B continues the method 1500, with encoding the finetuned monitoring metrics at the UE for transmission to the network to enable the network to perform network conformance testing on the finetuned monitoring metrics to determine that the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, as shown in block 1516. The UE can decode an activation message received at the UE from the network instructing the UE to activate the finetuned Al model when the network conformance testing indicates the finetuned monitoring metrics pass the one or more thresholds of the network conformance testing, as shown in block 1518. The UE can then activate the finetuned Al model at the UE in response to receiving the activation message from the network, as shown in block 1520.

[0199] In some embodiments, the UE is configured to autonomously initiate retraining of the Al model when the Al model retraining message is received at the UE indicating that the Al model has the change greater than the drift threshold.

[0200] The method 1500 can further comprise storing the finetuned Al model at the UE with an ID associated with a new configuration or a finetuned configuration and sending the ID for the finetuned Al model to the network in a UE capability message.

[0201] The method 1500 can further comprise encoding, for transmission to the network, a UE capability message that includes Al models stored at the UE.

[0202] In some embodiments, the UE capability message includes one or more configurations or scenarios that are associated with each model, comprising: UEcapability: Model 1 (finetuned): {Configuration / scenariol }, {Configuration / scenario 2} {Configuration / scenario N}, Model 2 (finetuned): {Configuration / Scenario 1}, {Configuration / scenario 2} {Configuration / scenario N}, ... Model N (finetuned): {Configuration / Scenario 1 }, {Configuration / scenario 2} ,... {Configuration / scenario N}, where N is a positive integer.

[0203] The method 1500 can further comprise operating the UE in a legacy mode when the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, wherein operating the UE in the legacy mode comprises: operating the UE with a previous Al model that was used prior to the finetuned Al model; or operating the UE without the Al model until the finetuned Al model passes the network conformance testing and the UE receives the activation message.FIGs. 16A, 16B: Flow Chart for Fine-Tuning an Al model at a server for a UE

[0204] FIGs. 16A and 16B illustrate a flow chart of an example of a method 1600 of fine-tuning an artificial intelligence (Al) model at a server for a user equipment (UE) to provide reinforcement learning, according to some embodiments.

[0205] The method shown in FIGs. 16A and 16B may be used in conjunction with any of the systems, methods, or devices illustrated in the figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[0206] In accordance with an embodiment, a method 1600, of fine-tuning an artificial intelligence (Al) model at a server for a user equipment (UE) to provide reinforcement learning is disclosed. The method 1600 comprises computing monitoring metrics for the Al model located at the UE, as shown in block 1602. The UE can encode the monitoring metrics at the UE for transmission to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model having a changegreater than a drift threshold, as shown in block 1604. The UE can decode an Al model retraining message received from the network when the change is greater than the drift threshold, as shown in block 1606. The UE can encode a request for data collection for transmission to the network, as shown in block 1608. The UE can decode a data collection for training the Al model, as shown in block 1610. The UE can send the data collection to the server having an Al model, as shown in block 1612. The UE can perform fine-tune training of the Al model at the server based using the data collection to form a finetuned Al model, as shown in block 1614.

[0207] Continuing in FIG. 16B, the UE can receive the finetuned Al model from the server, as shown in block 1616. The UE can compute fine-tuned monitoring metrics for the fine-tuned Al model located at the UE, as shown in block 1618. The UE can encode the fine-tuned monitoring metrics for transmission to the network to enable the network to perform network conformance testing on the finetuned monitoring metrics to determine that the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, as shown in block 1620. The UE can decode an activation message received from the network instructing the UE to activate the finetuned Al model when the network conformance testing indicates the finetuned monitoring metrics pass the one or more thresholds of the network conformance testing, as shown in block 1622. The UE can activate the finetuned Al model at the UE in response to receiving the activation message from the network, as shown in block 1624.

[0208] In some embodiments, the UE is configured to autonomously initiate retraining of the Al model when the Al model retraining message is received at the UE indicating that the Al model has the change greater than the drift threshold.

[0209] The method 1600 can further comprise storing the finetuned Al model at the UE with an ID associated with a new configuration or a finetuned configuration and sending the ID for the finetuned Al model to the network in a UE capability message.

[0210] The method 1600 can further comprise encoding, for transmission to thenetwork, a UE capability message that includes Al models stored at the UE.

[0211] In some embodiments, the UE capability message includes one or more configurations or scenarios that are associated with each model, comprising: UE capability: Model 1 (finetuned): {Configuration / scenariol }, {Configuration / scenario 2} ,... {Configuration / scenario N}, Model 2 (finetuned): {Gonfiguration / Scenario 1}, {Configuration / scenario 2} ,... {Configuration / scenario N}, ... Model N (finetuned): {Configuration / Scenario 1 }, {Configuration / scenario 2} ,... {Configuration / scenario N}, where N is a positive integer.

[0212] The method 1600 can further comprise operating the UE in a legacy mode when the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, wherein operating the UE in the legacy mode comprises: operating the UE with a previous Al model that was used prior to the finetuned Al model; or operating the UE without the Al model until the finetuned Al model passes the network conformance testing and the UE receives the activation message.FIG. 17: Flow Chart for compensating for drift at a network for an Al model located at a UE

[0213] FIG. 17 illustrates a flow chart of an example of a method 1700 of compensating for drift at a network for an artificial intelligence (Al) model located at a user equipment (UE), according to some embodiments.

[0214] The method shown in FIG. 17 may be used in conjunction with any of the systems, methods, or devices illustrated in the figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[0215] In accordance with an embodiment, a method 1700, of compensating for drift at a network for an artificial intelligence (Al) model located at a user equipment (UE) is disclosed. The method comprises decoding, at the network, monitoring metrics received from the UE for the Al model located at the UE, as shown in block1702. The network can determine if performance drift has occurred in the Al model based on determining a difference in the monitoring metrics with previously received monitoring metrics received at the network for the Al model, as shown in block 1704. The network can determine that a new Al model for the UE is needed to replace the Al model when the performance drift is determined to be greater than a threshold level, as shown in block 1706. New monitoring metrics can be decoded at the network that were computed at the UE for the new Al model located at the UE, as shown in block 1708. The network can perform network conformance testing on the new monitoring metrics to determine that the new monitoring metrics pass thresholds of the network conformance testing, as shown in block 1710. The network can encode an activation message for transmission to the UE from the network instructing the UE to activate the new Al model when the network conformance testing indicates the new monitoring metrics pass the thresholds of the network conformance testing to enable the UE to activate the new Al model at the UE in response to receiving the activation message from the network, as shown in block 1712.FIGs. 18A, 18B: Flow Chart for Fine-Tuning an Al Model at a UE From a Network

[0216] FIGs. 18A and 18B illustrate a flow chart of an example of a method 1500 of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) from a network to provide reinforcement learning, according to some embodiments.

[0217] The method shown in FIGs. 18A and 18B may be used in conjunction with any of the systems, methods, or devices illustrated in the figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[0218] In accordance with an embodiment, a method 1800, of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) from a network to provide reinforcement learning is disclosed. The method 1800 comprises decoding, at the network, monitoring metrics computed at the UE for the Al model located at theUE, as shown in block 1804. The network can determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model having a change greater than a drift threshold, as shown in block 1806. The network can encode an Al model retraining message for transmission to the UE when the change is greater than the drift threshold, as shown in block 1808. The network can decode a request for data collection received from the UE, as shown in block 1810. The network can encode a data collection for training the Al model to enable the UE to perform finetune training of the Al model at the UE based using the data collection to form a finetuned Al model, as shown in block 1812.

[0219] Continuing in FIG. 18B, the method 1800 further comprises decoding, at the network, finetuned monitoring metrics received from the UE that were computed at the UE for the finetuned Al model located at the UE, as shown in block 1814. The network can perform conformance testing, at the network, on the finetuned monitoring metrics to determine that the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, as shown in block 1816. The network can encode an activation message for transmission to the UE from the network instructing the UE to activate the finetuned Al model when the network conformance testing indicates the finetuned monitoring metrics pass the one or more thresholds of the network conformance testing to enable the UE to activate the finetuned Al model at the UE in response to receiving the activation message from the network, as shown in block 1818.

[0220] Embodiments of the present disclosure may be realized in any of various forms. For example, some embodiments may be realized as a computer- implemented method, a computer readable memory medium, or a computer system. Other embodiments may be realized using one or more custom-designed hardware devices such as ASICs. Still other embodiments may be realized using one or more programmable hardware elements such as FPGAs.

[0221] In some embodiments, a non-transitory computer-readable memory medium may be configured so that it stores program instructions and / or data,where the program instructions, if executed by a computer system, cause the computer system to perform a method, e.g., any of the method embodiments described herein, or, any combination of the method embodiments described herein, or, any subset of any of the method embodiments described herein, or, any combination of such subsets.

[0222] In some embodiments, a device (e.g., a UE 106) may be configured to include a processor (or a set of processors) and a memory medium, where the memory medium stores program instructions, where the processor is configured to read and execute the program instructions from the memory medium, where the program instructions are executable to implement any of the various method embodiments described herein (or, any combination of the method embodiments described herein, or, any subset of any of the method embodiments described herein, or, any combination of such subsets). The device may be realized in any of various forms.

[0223] Any of the methods described herein for operating a user equipment (UE) may be the basis of a corresponding method for operating a base station, by interpreting each message / signal X received by the UE in the downlink as message / signal X transmitted by the base station, and each message / signal Y transmitted in the uplink by the UE as a message / signal Y received by the base station.

[0224] Although the embodiments above have been described in considerable detail, numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

Claims

CLAIMSWhat is claimed is:1 . A method of compensating for drift in an artificial intelligence (Al) model at a user equipment (UE), the method comprising: computing monitoring metrics for the Al model located at the UE; encoding the monitoring metrics at the UE for transmission to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model; implementing a new Al model at the UE to replace the Al model when the performance drift is determined to be greater than a threshold level; computing new monitoring metrics for the new Al model located at the UE; encoding the new monitoring metrics at the UE for transmission to the network to enable the network to perform network conformance testing on the new monitoring metrics to determine that the new monitoring metrics pass thresholds of the network conformance testing; decoding an activation message received at the UE from the network instructing the UE to activate the new Al model when the network conformance testing indicates the new monitoring metrics pass the thresholds of the network conformance testing; and activating the new Al model at the UE in response to receiving the activation message from the network.

2. The method of claim 1 , further comprising encoding, for transmission to the network, a UE capability message thatincludes Al models stored at the UE.

3. The method of claim 2, wherein the UE capability message includes one or more configurations or scenarios that are associated with each model, comprising: UE capability: Model 1 : {Configuration / scenariol}, {Configuration / scenario 2} {Configuration / scenario N}, Model 2: {Configuration / Scenario 1}, {Configuration / scenario 2} {Configuration / scenario N}, ... Model N: {Configuration / Scenario 1}, {Configuration / scenario 2} ,... {Configuration / scenario N}, where N is a positive integer.

4. The method of claim 1 , wherein implementing a new Al model comprises receiving the new Al model from one of the network and a server at the UE.

5. The method of claim 4, wherein the server is an over-the-top (OTT) server in communication with the UE.

6. The method of claim 1 , wherein implementing a new Al model comprises activating the new Al model that is stored on the UE to replace the Al model.

7. The method of claim 1 , further comprising decoding, at the UE, a new Al model identification received from the network indicating the new Al model for the UE to implement at the UE.

8. The method of claim 1 , further comprising: decoding a legacy mode message received at the UE from the network instructing the UE to perform in a legacy mode when the network conformance testing indicates the new monitoring metrics do not pass the thresholds of the network conformance testing; andoperating the UE in a legacy mode.

9. The method of claim 8, wherein operating the UE in a legacy mode comprises: operating the UE with a previous Al model that was used prior to the new Al model; or operating the UE without the Al model until the new Al model passes the network conformance testing and the UE receives the activation message.

10. A method of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) to provide reinforcement learning, the method comprising: computing monitoring metrics for the Al model located at the UE; encoding, at the UE, the monitoring metrics at the UE for transmission to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model having a change greater than a drift threshold; decoding, at the UE, an Al model retraining message received from the network when the change is greater than the drift threshold; encoding, at the UE, a request for data collection for transmission to the network; decoding, at the UE, a data collection for training the Al model; finetune training the Al model at the UE based using the data collection to form a finetuned Al model; computing finetuned monitoring metrics for the finetuned Al model located at the UE; encoding the finetuned monitoring metrics at the UE fortransmission to the network to enable the network to perform network conformance testing on the finetuned monitoring metrics to determine that the finetuned monitoring metrics pass one or more thresholds of the network conformance testing; decoding an activation message received at the UE from the network instructing the UE to activate the finetuned Al model when the network conformance testing indicates the finetuned monitoring metrics pass the one or more thresholds of the network conformance testing; and activating the finetuned Al model at the UE in response to receiving the activation message from the network.11 . The method of claim 10, wherein the UE is configured to autonomously initiate retraining of the Al model when the Al model retraining message is received at the UE indicating that the Al model has the change greater than the drift threshold.

12. The method of claim 10, further comprising storing the finetuned Al model at the UE with an ID associated with a new configuration or a finetuned configuration and sending the ID for the finetuned Al model to the network in a UE capability message.

13. The method of claim 10, further comprising encoding, for transmission to the network, a UE capability message that includes Al models stored at the UE.

14. The method of claim 13, wherein the UE capability message includes one or more configurations or scenarios that are associated with each model, comprising: UE capability: Model 1 (finetuned): {Configuration / scenariol}, {Configuration / scenario 2} ,... {Configuration / scenario N}, Model 2 (finetuned):{Configuration / scenario N}, ... Model N (finetuned): {Configuration / Scenario 1}, {Configuration / scenario 2} ,... {Configuration / scenario N}, where N is a positive integer.

15. The method of claim 10, further comprising operating the UE in a legacy mode when the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, wherein operating the UE in the legacy mode comprises: operating the UE with a previous Al model that was used prior to the finetuned Al model; or operating the UE without the Al model until the finetuned Al model passes the network conformance testing and the UE receives the activation message.

16. A method of fine-tuning an artificial intelligence (Al) model at a server for a user equipment (UE) to provide reinforcement learning, the method comprising: computing monitoring metrics for the Al model located at the UE; encoding, at the UE, the monitoring metrics at the UE for transmission to a network to enable the network to determine if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model having a change greater than a drift threshold; decoding, at the UE, an Al model retraining message received from the network when the change is greater than the drift threshold; encoding, at the UE, a request for data collection for transmission to the network; decoding, at the UE, a data collection for training the Al model; sending the data collection to the server having an Almodel; finetune training the Al model at the server based using the data collection to form a finetuned Al model; receiving, at the UE, the finetuned Al model from the server; computing, at the UE, finetuned monitoring metrics for the finetuned Al model located at the UE; encoding, at the UE, the finetuned monitoring metrics for transmission to the network to enable the network to perform network conformance testing on the finetuned monitoring metrics to determine that the finetuned monitoring metrics pass one or more thresholds of the network conformance testing; decoding, at the UE, an activation message received from the network instructing the UE to activate the finetuned Al model when the network conformance testing indicates the finetuned monitoring metrics pass the one or more thresholds of the network conformance testing; and activating the finetuned Al model at the UE in response to receiving the activation message from the network.

17. The method of claim 16, wherein the UE is configured to autonomously initiate retraining of the Al model when the Al model retraining message is received at the UE indicating that the Al model has the change greater than the drift threshold.

18. The method of claim 16, further comprising storing the finetuned Al model at the UE with an ID associated with a new configuration or a finetuned configuration and sending the ID for the finetuned Al model to the network in a UE capability message.

19. The method of claim 16, further comprising encoding, for transmission to the network, a UE capability message thatincludes Al models stored at the UE.

20. The method of claim 19, wherein the UE capability message includes one or more configurations or scenarios that are associated with each model, comprising: UE capability: Model 1 (finetuned): {Configuration / scenariol}, {Configuration / scenario 2}{Configuration / scenario N}, Model 2 (finetuned): {Configuration / Scenario 1}, {Configuration / scenario 2} {Configuration / scenario N}, ... Model N (finetuned): {Configuration / Scenario 1}, {Configuration / scenario 2} ,... {Configuration / scenario N}, where N is a positive integer.21 . The method of claim 16, further comprising operating the UE in a legacy mode when the finetuned monitoring metrics pass one or more thresholds of the network conformance testing, wherein operating the UE in the legacy mode comprises: operating the UE with a previous Al model that was used prior to the finetuned Al model; or operating the UE without the Al model until the finetuned Al model passes the network conformance testing and the UE receives the activation message.

22. A method of compensating for drift at a network for an artificial intelligence (Al) model located at a user equipment (UE), the method comprising: decoding, at the network, monitoring metrics received from the UE for the Al model located at the UE, determining, at the network, if performance drift has occurred in the Al model based on determining a difference in the monitoring metrics with previously received monitoring metrics received at the network for the Al model; determining a new Al model for the UE to replace the Almodel when the performance drift is determined to be greater than a threshold level; computing new monitoring metrics for the new Al model located at the UE; decoding new monitoring metrics at the network, received from the UE, that were computed at the UE for the new Al model located at the UE; performing network conformance testing on the new monitoring metrics to determine that the new monitoring metrics pass thresholds of the network conformance testing; and encoding an activation message for transmission to the UE from the network instructing the UE to activate the new Al model when the network conformance testing indicates the new monitoring metrics pass the thresholds of the network conformance testing to enable the UE to activate the new Al model at the UE in response to receiving the activation message from the network.

23. A method of fine-tuning an artificial intelligence (Al) model at a user equipment (UE) from a network to provide reinforcement learning, the method comprising: decoding, at the network, monitoring metrics computed at the UE for the Al model located at the UE; determining, at the network, if performance drift has occurred in the Al model based on previously received monitoring metrics for the Al model having a change greater than a drift threshold; encoding, at the network, an Al model retraining message for transmission to the UE when the change is greater than the drift threshold; decoding, at the network, a request for data collection received from the UE;encoding, at the network, a data collection for training the Al model to enable the UE to perform finetune training of the Al model at the UE based using the data collection to form a finetuned Al model; decoding, at the network, finetuned monitoring metrics received from the UE that were computed at the UE for the finetuned Al model located at the UE; performing network conformance testing, at the network, on the finetuned monitoring metrics to determine that the finetuned monitoring metrics pass one or more thresholds of the network conformance testing; and encoding an activation message for transmission to the UE from the network instructing the UE to activate the finetuned Al model when the network conformance testing indicates the finetuned monitoring metrics pass the one or more thresholds of the network conformance testing to enable the UE to activate the finetuned Al model at the UE in response to receiving the activation message from the network.

24. A baseband processor configured to cause a user equipment (UE) to perform any of the methods of claims 1 to 21 .

25. A user equipment (UE) configured to perform any of the operations described in claims 1 to 21.

26. A network configured to perform any of the operations described in claims 22 to 23.

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

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