Ai / ML based RSRP measurement prediction using a latent variable in wireless communication networks

Abstract

Apparatuses, systems, and methods for performing prediction of a frequency layer measurement using an artificial intelligence (AI) / machine learning (ML) models are described. Systems may acquire reference signal received power (RSRP) measurements for a first frequency layer, use an artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurements, and use an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements for a second frequency layer from the latent variable.

Description

P70400W01ARTIFICIAL INTELLEGENCE(AI) / MACHINE LEARNING(ML) BASED RADIO RESOURCE MANAGEMENT (RRM) MEASUREMENT PREDICTION USING A LATENT VARIABLE IN WIRELESS COMMUNCATION NETWORKSFIELD

[0001] The invention relates to wireless communications, and more particularly to apparatuses, systems, and methods for predicting radio resource management (RRM) measurements for a frequency layer from a latent variable using artificial intelligence (AI) / machine learning (ML) models.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. Additionally, there exist numerous different wireless communication technologies and standards.

[0003] Long Term Evolution (LTE), also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, 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. Thus, in 2015 study of a new radio access technology began and, in 2017, a first release of the Third Generation Partnership Project (3 GPP) Fifth Generation New Radio (5G NR) was standardized. 5th generation mobile networks or 5th generation wireless systems, referred to as 3GPP NR (otherwise known as 5G-NR or NR-5G for 5G New Radio, also simply referred to as NR). NR proposes a higher capacity for a higher density of mobile broadband users, also supporting device-to-device, ultrareliable, and massive machine communications, as well as lower latency and lower battery consumption, than LTE standards.

[0004] 5G-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 typeP70400W01communications with lower latency and / or lower battery consumption. Further, NR may allow for more flexible UE scheduling as compared to current LTE. Consequently, efforts are being made in ongoing developments of 5G-NR to take advantage of higher throughputs possible at higher frequencies.

[0005] One aspect of wireless communication systems, e.g., systems for NR cellular wireless communications, is measurement of reference signals, including synchronization signals.SUMMARY

[0006] Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for using artificial intelligence (AI) / machine learning (ML) model for predicting radio resource management (RRM) measurements from a latent variable using artificial intelligence (AI) / machine learning (ML) models.

[0007] Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for a device configured for communicating in a wireless communication network, comprising: one or more processors, coupled to a memory, configured to transmit a measurement configuration, wherein the measurement configuration specifies reference signal received power (RSRP) measurements specified in the measurement configuration; receive a latent variable corresponding to the specified measurements; and use an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable.

[0008] Other embodiments relate to a user equipment comprising: one or more processors, coupled to a memory, configured to: receive a measurement configuration; acquire reference signal received power (RSRP) measurements specified in the measurement configuration; use an artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement; and use an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable.

[0009] The techniques described herein may be implemented in and / or used with a number of different types of devices, including but not limited to base stations, access points, cellular phones, tablet computers, wearable computing devices, portable media players, vehicles, and any of various other computing devices.

[0010] This Summary is intended to provide a brief overview of some of the subject matter described in this document. Accordingly, it will be appreciated that the above-described features areP70400W01merely examples and should not be construed to narrow the scope or spirit of the subject matter described herein in any way. Other features, aspects, and advantages of the subject matter described herein will become apparent from the following Detailed Description, Figures, and Claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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:

[0012] Figure 1A illustrates an example wireless communication system according to some embodiments.

[0013] Figure IB illustrates an example of a base station and an access point in communication with a user equipment (UE) device, according to some embodiments.

[0014] Figure 2 illustrates an example block diagram of a base station, according to some embodiments.

[0015] Figure 3 illustrates an example block diagram of a server according to some embodiments.

[0016] Figure 4 illustrates an example block diagram of a UE according to some embodiments.

[0017] Figure 5 illustrates an example block diagram of cellular communication circuitry, according to some embodiments.

[0018] Figure 6A illustrates an example of a 5G network architecture that incorporates both 3GPP (e.g., cellular) and non-3GPP (e.g., non-cellular) access to the 5G CN, according to some embodiments.

[0019] Figure 6B illustrates an example of a 5G network architecture that incorporates both dual 3GPP (e.g., LTE and 5G NR) access and non-3GPP access to the 5G CN, according to some embodiments.

[0020] Figure 7 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.

[0021] Figure 8 illustrates an example of a device in accordance with some embodiments.

[0022] Figure 9 illustrates an example baseband circuitry in accordance with some embodiments.

[0023] Figure 10 illustrates an example of a control plane protocol stack in accordance with some embodiments.P70400W01

[0024] Figure 11 illustrates an example of a UE within a heterogenous network, according to some embodiments.

[0025] Figures 12A-12B illustrate example timing diagrams including a set of measurement gaps and corresponding measurements, according to some embodiments.

[0026] Figure 13 illustrates an example timing diagrams including a set of measurement gaps and corresponding measurements, according to some embodiments.

[0027] Figures 14A-14B illustrates an example of a user equipment (UE) collecting measurements, according to some embodiments.

[0028] Figures 15A-15B illustrates an example of a user equipment (UE) collecting measurements, according to some embodiments.

[0029] Figure 16 illustrates an AI / ML-based model for predicting radio resource management (RRM) measurements for sites at one frequency layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, according to some embodiments.

[0030] Figure 17 illustrates utilizing AI / ML-based models for predicting radio resource management (RRM) measurements for sites at one frequency layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, according to some embodiments.

[0031] Figure 18 illustrates an example deployment of AI / ML-based models for predicting radio resource management (RRM) measurements for sites at one frequency layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, according to some embodiments.

[0032] Figure 19 illustrates a block diagram of an example of a method to predict a frequency layer measurement using artificial intelligence (AI) / machine learning (ML) models, according to some embodiments.

[0033] Figure 20 illustrates a block diagram of an example of a method to predict a frequency layer measurement using artificial intelligence (AI) / machine learning (ML) models, according to some embodiments.

[0034] 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 not intended 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 andP70400W01scope of the subject matter as defined by the appended claims.DETAILED DESCRIPTION

[0035] The following is a glossary of terms used in this disclosure:

[0036] 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.

[0037] 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.

[0038] Programmable Hardware Element - includes various hardware devices comprising multiple programmable function blocks connected via a programmable interconnect. 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”.

[0039] 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 leastP70400W01one processor that executes instructions from a memory medium.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.P70400W01

[0044] 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.

[0045] Wi-Fi - The term " Wi-Fi" (or WiFi) has the full breadth of its ordinary meaning, and at least includes a wireless communication network or RAT that is serviced by wireless LAN (WLAN) access points and which provides connectivity through these access points to the Internet. Most modem Wi-Fi networks (or WLAN networks) are based on IEEE 802.11 standards and are marketed under the name “Wi-Fi”. A Wi-Fi (WLAN) network is different from a cellular network.

[0046] 3GPP Access - refers to accesses (e.g., radio access technologies) that are specified by the Third Generation Partnership Project (3GPP) standards. These accesses include, but are not limited to, GSM / GPRS, LTE, LTE-A, and / or 5G NR. In general, 3GPP access refers to various types of cellular access technologies.

[0047] Non-3GPP Access - refers any accesses (e.g., radio access technologies) that are not specified by 3GPP standards. These accesses include, but are not limited to, WiMAX, CDMA2000, Wi-Fi, WLAN, and / or fixed networks. Non-3GPP accesses may be split into two categories, "trusted" and "untrusted": Trusted non-3GPP accesses can interact directly with an evolved packet core (EPC) and / or a 5G core (5GC) whereas untrusted non-3GPP accesses interwork with the EPC / 5GC via a network entity, such as an Evolved Packet Data Gateway and / or a 5G NR gateway. In general, non-3GPP access refers to various types on non-cellular access technologies.

[0048] 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 specified by 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 can 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 fieldsP70400W01but 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.

[0049] 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.

[0050] 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.

[0051] Various components may be described as “configured to” perform a task or 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. Tn general, the circuitry that forms the structure corresponding to “configured to” may include hardware circuits.

[0052] 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.

[0053] 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.

[0054] 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 isP70400W01merely 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.

[0055] The example embodiments are also described with regard to a fifth generation (5G) New Radio (NR). However, reference to a 5G NR network is merely provided for illustrative purposes. The example embodiments may be utilized with any appropriate type of network.

[0056] Throughout this description various information elements (IES) are referred to by specific names. It should be understood that these names are only examples and the IEs carrying the information referred to throughout this description may be referred to by other names by various entities.Figures 1 A and IB: Communication Systems

[0057] Figure 1A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of Figure 1A is merely one example of a possible system, and that features of this disclosure may be implemented in any of various systems, as desired.

[0058] As shown, the example wireless communication system includes a base station 102A which communicates over a transmission medium with one or more user equipment 106 A, 106B, etc., through 106N devices. Each of the user devices may be referred to herein as a “user equipment” (UE). Thus, the user equipment 106 devices are referred to as UEs or UE devices.

[0059] 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.

[0060] 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„ IxRTT, IxEV-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 beP70400W01referred to as an 'eNodeB' or ‘eNB’. Note that if the base station 102A is implemented in the context of 5G NR, it may alternately be referred to as ‘gNodeB’ or ‘gNB’.

[0061] As shown, the base station 102 A may also be equipped to communicate with a network (NW) 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 102 A may facilitate communication between the user devices and / or between the user devices and the network 100. In particular, the cellular base station 102 A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and / or data services.

[0062] 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.

[0063] Thus, while base station 102A may act as a “serving cell” for UEs 106A-N as illustrated in Figure 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 Figure 1 A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.

[0064] 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.

[0065] 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., IxRTT, IxEV-DO, HRPD, eHRPD), etc.). The UE 106 mayP70400W01also 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.

[0066] In some embodiments, the base station 102A may select a paging configuration and a permanent equipment identifier (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.

[0067] Figure IB illustrates user equipment 106 (e.g., one of the UEs 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.

[0068] The UE 106 may include a processor that is configured to execute program 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.

[0069] 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 (1xRTT / 1xEV-DO / HRPD / 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.), or digital 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.P70400W01

[0070] 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.Figure 2: Block Diagram of a Base Station (gNB)

[0071] Figure 2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of Figure 3 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.

[0072] 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 UEs 106, access to the telephone network as described above in Figures 1 and 2.

[0073] 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 UEs 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).

[0074] 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.

[0075] The base station 102 may include at least one antenna 234, and possibly multiple antennas.P70400W01The at least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UEs 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.

[0076] 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.).

[0077] 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.

[0078] In addition, as described herein, processor(s) 204 may be comprised of one 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.

[0079] 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, radioP70400W01230 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.Figure 3: Block Diagram of a Server

[0080] Figure 3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of Figure 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.

[0081] The server 104 may be configured to provide a plurality of devices, such as base station 102 and UEs 106 access to network functions, e.g., as further described herein.

[0082] 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.

[0083] As described herein, the server 104 may include hardware and software components 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.

[0084] 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.P70400W01Figure 4: Block Diagram of a User Equipment (UE)

[0085] Figure 4 illustrates an example simplified block diagram of a communication device 405, according to some embodiments. It is noted that the block diagram of the communication device of Figure 4 is only one example of a possible communication device. According to embodiments, communication device 405 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 405 may include a set of components 400 configured to perform core functions. For example, this set of components may be implemented as a system on 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 405.

[0086] For example, the communication device 405 may include various types of memory (e.g., including NAND flash 410), an input / output interface such as connector I / 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 405, 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 405 may include wired communication circuitry (not shown), such as a network interface card, e.g., for Ethernet.

[0087] 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.P70400W01

[0088] 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 processors and / 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.

[0089] The communication device 405 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.

[0090] The communication device 405 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 eUICCs, 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 (UICC) may be implemented as a removable smart card. Thus, the STM(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 (UICCs) may be one or more embedded cards (such as embedded UICCs (eUICCs), which are sometimes referred to as “eSIMs” or “eSIM cards”). In some embodiments (such as when the SIM(s) include an eUICC), one or more of the SIM(s) may implement embedded SIM (eSIM) functionality; in such an embodiment, 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 eUICC cards that implement eSIM functionality), as desired. For example, the UE 106 may comprise two embedded SIMs, twoP70400W01removable SIMs, or a combination of one embedded SIMs and one removable SIMs. Various other SIM configurations are also contemplated.

[0091] 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 (UICC) 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 (DSD A) functionality. The DSD A 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. Tn 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 eUICC) that executes multiple SIM applications for different carriers and / or RATs.

[0092] As shown, the SOC 400 may include processor(s) 402, which may execute program instructions for the communication device 405 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460. The processors) 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 I / 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.

[0093] As noted above, the communication device 405 may be configured to communicate using wireless and / or wired communication circuitry. The communication device 405 may be configured to perform methods for predicting radio resource management (RRM) measurements for one frequencyP70400W01layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, as further described herein.

[0094] As described herein, the communication device 405 may include hardware and software components for implementing the above features for a communication device 405 to communicate a scheduling profile for power savings to a network. The processor 402 of the communication device 405 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 405, 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.

[0095] 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.

[0096] 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.Figure 5: Block Diagram of Cellular Communication Circuitry

[0097] Figure 5 illustrates an example simplified block diagram of cellular communication circuitry, according to some embodiments. It is noted that the block diagram of the cellularP70400W01communication circuitry of Figure 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 405 described above. As noted above, communication device 405 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.

[0098] The cellular communication circuitry 530 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435a-b and 436 as shown (in Figure 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 Figure 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.

[0099] 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.

[0100] 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.

[0101] 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 transmit circuitryP70400W01534 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).

[0102] In some embodiments, the cellular communication circuitry 530 may be configured to perform methods for predicting radio resource management (RRM) measurements for one frequency layer from other frequency layer measurements using artificial intelligence (Al)Zmachine learning (ML) models, as further described herein.

[0103] 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 non-standalone (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, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[0104] 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.

[0105] As described herein, the modem 520 may include hardware and software components for implementing the above features for performing methods for predicting radio resource management (RRM) measurements for one frequency layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, as further described herein. 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, 335 and 336 may be configured to implement part or all of the features described herein.P70400W01

[0106] 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.Figures 6A, 6B, and 7: 5G Core Network Architecture - Interworking with Wi-Fi

[0107] In some embodiments, the 5G core network (CN) may be accessed via (or through) a cellular connection / interface (e.g., via a 3GPP communication architecture / protocol) and a non-cellular connection / interface (e.g., a non-3GPP access architecture / protocol such as Wi-Fi connection). Figure 6A illustrates an example of a 5G network architecture that incorporates both 3GPP (e.g., cellular) and non-3GPP (e.g., non-cellular) access to the 5G CN, according to some embodiments. As shown, a user equipment device (e.g., such as UE 106) may access the 5G CN through both a radio access network (RAN, e.g., such as gNB 604, which may be a base station 102) and an access point, such as AP 612. The AP 612 may include a connection to the Internet 600 as well as a connection to a non-3GPP inter-working function (N3IWF) 603 network entity. The N3IWF may include a connection to a core access and mobility management function (AMF) 605 of the 5G CN. The AMF 605 may include an instance of a 5G mobility management (5G MM) function associated with the UE 106. In addition, the RAN (e.g., gNB 604) may also have a connection to the AMF 605. Thus, the 5G CN may support unified authentication over both connections as well as allow simultaneous registration for UE 106 access via both gNB 604 and AP 612. As shown, the AMF 605 may include one or more functional entities associated with the 5G CN (e.g., network slice selection function (NSSF) 620, short message service function (SMSF) 622, application function (AF) 624, unified data management (UDM) 626, policy control function (PCF) 628, and / or authentication server function (AUSF) 630). Note that these functional entities may also be supported by a session management function (SMF) 606a and an SMF 606b of the 5G CN. The AMF 605 may be connected to (or in communication with) the SMF 606a. Further, the gNB 604 may in communication with (or connected to) a user plane function (UPF) 608a that may also be communication with the SMF 606a. Similarly, the N3IWF 603 may be communicating with a UPF 608b that may also be communicating with the SMF 606b. Both UPFs may be communicating with the data network (e.g., DN 610a and 610b) and / or the Internet 600 and Internet Protocol (IP) Multimedia Subsystem / IP Multimedia Core Network Subsystem (IMS) core network 610.

[0108] Figure 6B illustrates an example of a 5G network architecture that incorporates both dual 3GPP (e.g., LTE and 5G NR) access and non-3GPP access to the 5G CN, according to someP70400W01embodiments. As shown, a user equipment device (e.g., such as UE 106) may access the 5G CN through both a radio access network (RAN, e.g., such as gNB 604 or eNB 602, which may be a base station 102) and an access point, such as AP 612. The AP 612 may include a connection to the Internet 600 as well as a connection to the N3IWF 603 network entity. The N3IWF may include a connection to the AMF 605 of the 5G CN. The AMF 605 may include an instance of the 5G MM function associated with the UE 106. In addition, the RAN (e.g., gNB 604) may also have a connection to the AMF 605. Thus, the 5G CN may support unified authentication over both connections as well as allow simultaneous registration for UE 106 access via both gNB 604 and AP 612. In addition, the 5G CN may support dual-registration of the UE on both a legacy network (e.g., LTE via eNB 602) and a 5G network (e.g., via gNB 604). As shown, the eNB 602 may have connections to a mobility management entity (MME) 642 and a serving gateway (SGW) 644. The MME 642 may have connections to both the SGW 644 and the AMF 605. In addition, the SGW 644 may have connections to both the SMF 606a and the UPF 608a. As shown, the AMF 605 may include one or more functional entities associated with the 5G CN (e.g., NSSF 620, SMSF 622, AF 624, UDM 626, PCF 628, and / or AUSF 630). Note that UDM 626 may also include a home subscriber server (HSS) function and the PCF may also include a policy and charging rules function (PCRF). Note further that these functional entities may also be supported by the SMF606a and the SMF 606b of the 5G CN. The AMF 605 may be connected to (or in communication with) the SMF 606a. Further, the gNB 604 may in communication with (or connected to) the UPF 608a that may also be communication with the SMF 606a. Similarly, the N3IWF 603 may be communicating with a UPF 608b that may also be communicating with the SMF 606b. Both UPFs may be communicating with the data network (e.g., DN 610a and 610b) and / or the Internet 600 and IMS core network 610.

[0109] Note that in various embodiments, one or more of the above-described network entities may be configured to perform methods for predicting radio resource management (RRM) measurements for one frequency layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, as further described herein.

[0110] Figure 7 illustrates an example of a baseband processor architecture for a UE (e.g., such as UE 106), according to some embodiments. The baseband processor architecture 700 described in Figure 7 may be implemented on one or more radios (e.g., radios 429 and / or 430 described above) or modems (e.g., modems 510 and / or 520) as described above. As shown, the non-access stratum (NAS) 710 may include a 5G NAS 720 and a legacy NAS 750. The legacy NAS 750 may include a communication connection with a legacy access stratum (AS) 770. The 5G NAS 720 may include communication connections with both a 5G AS 740 and a non-3GPP AS 730 and Wi-Fi AS 732. The 5G NAS 720 may include functional entities associated with both access stratums. Thus, the 5G NASP70400W01720 may include multiple 5G MM entities 726 and 728 and 5G session management (SM) entities 722 and 724. The legacy NAS 750 may include functional entities such as short message service (SMS) entity 752, evolved packet system (EPS) session management (ESM) entity 754, session management (SM) entity 756, EPS mobility management (EMM) entity 758, and mobility management (MM) / GPRS mobility management (GMM) entity 760. In addition, the legacy AS 770 may include functional entities such as LTE AS 772, UMTS AS 774, and / or GSM / GPRS AS 776.

[0111] Thus, the baseband processor architecture 700 allows for a common 5G-NAS for both 5G cellular and non-cellular (e.g., non-3GPP access). The baseband processor architecture 700 can be in communication with one or more UICC(s) 745. Note that as shown, the 5G MM may maintain individual connection management and registration management state machines for each connection. Additionally, a device (e.g., UE 106) may register to a single PLMN (e.g., 5G CN) using 5G cellular access as well as non-cellular access. Further, it may be possible for the device to be in a connected state in one access and an idle state in another access and vice versa. Finally, there may be common 5G-MM procedures (e.g., registration, de-registration, identification, authentication, as so forth) for both accesses.

[0112] Note that in various embodiments, one or more of the above-described functional entities of the 5G NAS and / or 5G AS may be configured to perform methods for predicting radio resource management (RRM) measurements for one frequency layer from other frequency layer measurements using artificial intelligence (AI) / machine learning (ML) models, as further described herein.Figures 8 and 9: Device components

[0113] Figure 8 illustrates example components of a device 800 in accordance with some embodiments. In some embodiments, the device 800 may include application circuitry 802, baseband circuitry 804, Radio Frequency (RF) circuitry 806, front-end module (FEM) circuitry 808, one or more antennas 810, and power management circuitry (PMC) 812 coupled together at least as shown. The components of the illustrated device 800 may be included in a UE or a RAN node. In some embodiments, the device 800 may include less elements (e.g., a RAN node may not utilize application circuitry 802, and instead include a processor / controller to process IP data received from an EPC). In some embodiments, the device 800 may include additional elements such as, for example, memory / storage, display, camera, sensor, or input / output (RO) 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).P70400W01

[0114] The application circuitry 802 may include one or more application processors. For example, the application circuitry 802 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processors) 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 be configured to execute instructions stored in the memory / storage to enable various applications or operating systems to run on the device 800. In some embodiments, processors of application circuitry 802 may process IP data packets received from an EPC.

[0115] The baseband circuitry 804 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The baseband circuitry 804 may include one or more baseband processors or control logic to process baseband signals received from a receive signal path of the RF circuitry 806 and to generate baseband signals for a transmit signal path of the RF circuitry 806. Baseband processing circuity 804 may interface with the application circuitry 802 for generation and processing of the baseband signals and for controlling operations of the RF circuitry 806. For example, in some embodiments, the baseband circuitry 804 may include a third generation (3G) baseband processor 804A, a fourth generation (4G) baseband processor 804B, a fifth generation (5G) baseband processor 804C, or other baseband processor(s) 804D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G), si8h generation (6G), etc.). The baseband circuitry 804 (e.g., one or more of baseband processors 804A-D) may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry 806. In other embodiments, some or all of the functionality of baseband processors 804A-D may be included in modules stored in the memory 804G and executed via a Central Processing Unit (CPU) 804E. 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 804 may include Fast-Fourier Transform (FFT), precoding, or constellation mapping / demapping functionality. In some embodiments, encoding / decoding circuitry of the baseband circuitry 804 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.

[0116] In some embodiments, the baseband circuitry 804 may include one or more audio digital signal processor(s) (DSP) 804F. The audio DSP(s) 804F 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 singleP70400W01chip, 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 804 and the application circuitry 802 may be implemented together such as, for example, on a system on a chip (SOC).

[0117] In some embodiments, the baseband circuitry 804 may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry 804 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 804 is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.

[0118] RF circuitry 806 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry 806 may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. RF circuitry 806 may include a receive signal path which may include circuitry to down-convert RF signals received from the FEM circuitry 808 and provide baseband signals to the baseband circuitry 804. RF circuitry 806 may also include a transmit signal path which may include circuitry to up-convert baseband signals provided by the baseband circuitry 804 and provide RF output signals to the FEM circuitry 808 for transmission.

[0119] In some embodiments, the receive signal path of the RF circuitry 806 may include mixer circuitry 806a, amplifier circuitry 806b and filter circuitry 806c. In some embodiments, the transmit signal path of the RF circuitry 806 may include filter circuitry 806c and mixer circuitry 806a. RF circuitry 806 may also include synthesizer circuitry 806d for synthesizing a frequency for use by the mixer circuitry 806a of the receive signal path and the transmit signal path. In some embodiments, the mixer circuitry 806a of the receive signal path may be configured to down-convert RF signals received from the FEM circuitry 808 based on the synthesized frequency provided by synthesizer circuitry 806d. The amplifier circuitry 806b may be configured to amplify the down-converted signals and the filter circuitry 806c 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 804 for further processing. In some embodiments, the output baseband signals may be zero-frequency baseband signals, although this is not a requirement. In some embodiments, mixer circuitry 806a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.

[0120] In some embodiments, the mixer circuitry 806a of the transmit signal path may beP70400W01configured to up-convert input baseband signals based on the synthesized frequency provided by the synthesizer circuitry 806d to generate RF output signals for the FEM circuitry 808. The baseband signals may be provided by the baseband circuitry 804 and may be filtered by filter circuitry 806c.

[0121] In some embodiments, the mixer circuitry 806a of the receive signal path and the mixer circuitry 806a 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 806a of the receive signal path and the mixer circuitry 806a 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 806a of the receive signal path and the mixer circuitry 806a may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuitry 806a of the receive signal path and the mixer circuitry 806a of the transmit signal path may be configured for super-heterodyne operation.

[0122] In some embodiments, the output baseband signals and the input baseband 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 806 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 804 may include a digital baseband interface to communicate with the RF circuitry 806.

[0123] 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.

[0124] In some embodiments, the synthesizer circuitry 806d may be a fractional-N synthesizer or a fractional N / N+l 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 806d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.

[0125] The synthesizer circuitry 806d may be configured to synthesize an output frequency for use by the mixer circuitry 806a of the RF circuitry 806 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 806d may be a fractional N / N+l synthesizer.

[0126] In some embodiments, frequency input may be provided by a voltage controlled oscillator (VCO), although that is not a requirement. Divider control input may be provided by either the baseband circuitry 804 or the applications circuitry 802 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 onP70400W01a channel indicated by the applications circuitry 802.

[0127] Synthesizer circuitry 806d of the RF circuitry 806 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+l (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.

[0128] In some embodiments, synthesizer circuitry 806d 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 806 may include an IQ / polar converter.

[0129] FEM circuitry 808 may include a receive signal path which may include circuitry configured to operate on RF signals received from one or more antennas 810, amplify the received signals and provide the amplified versions of the received signals to the RF circuitry 806 for further processing. FEM circuitry 808 may also include a transmit signal path which may include circuitry configured to amplify signals for transmission provided by the RF circuitry 806 for transmission by one or more of the one or more antennas 810. In various embodiments, the amplification through the transmit or receive signal paths may be done solely in the RF circuitry 806, solely in the FEM 808, or in both the RF circuitry 806 and the FEM 808.

[0130] In some embodiments, the FEM circuitry 808 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 circuitry 806). The transmit signal path of the FEM circuitry 808 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 806), and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 810).P70400W01

[0131] In some embodiments, the PMC 812 may manage power provided to the baseband circuitry 804. In particular, the PMC 812 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. The PMC 812 may often be included when the device 800 is capable of being powered by a battery, for example, when the device is included in a UE. The PMC 812 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.

[0132] While Figure 8 shows the PMC 812 coupled only with the baseband circuitry 804. However, in other embodiments, the PMC 812 may be additionally or alternatively coupled with, and perform similar power management operations for, other components such as, but not limited to, application circuitry 802, RF circuitry 806, or FEM 808.

[0133] In some embodiments, the PMC 812 may control, or otherwise be part of, various power saving mechanisms of the device 800. For example, if the device 800 is in an 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 800 may power down for brief intervals of time and thus save power.

[0134] If there is no data traffic activity for an extended period of time, then the device 800 may transition off to an RRC_Idle state, where it disconnects from the network and does not perform operations such as channel quality feedback, handover, etc. The device 800 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 again. The device 800 may not receive data in this state, in order to receive data, it will transition back to an RRC_Connected state.

[0135] 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.

[0136] Processors of the application circuitry 802 and processors of the baseband circuitry 804 may be used to execute elements of one or more instances of a protocol stack. For example, processors of the baseband circuitry 804, alone or in combination, may be used to execute Layer 3, Layer 2, or Layer 1 functionality, while processors of the application circuitry 804 may utilize data (e.g., packet data) received from these layers and further execute Layer 4 functionality (e.g., transmission communication protocol (TCP) and user datagram protocol (UDP) layers). As referred to herein, Layer 3 may comprise a radio resource control (RRC) layer, described in further detail below. As referred to herein, Layer 2 may comprise a medium access control (MAC) layer, a radio link control (RLC) layer, and a packetP70400W01data convergence protocol (PDCP) layer, described in further detail below. As referred to herein, Layer 1 may comprise a physical (PHY) layer of a UE / RAN node, described in further detail below.

[0137] Figure 9 illustrates example interfaces of baseband circuitry in accordance with some embodiments. As discussed above, the baseband circuitry 804 of Figure 8 may comprise processors 804A-804E and a memory 804G utilized by said processors. Each of the processors 804A-804E may include a memory interface, 904A-904E, respectively, to send / receive data to / from the memory 804G.

[0138] The baseband circuitry 804 may further include one or more interfaces to communicatively couple to other circuitries / devices, such as a memory interface 912 (e.g., an interface to send / receive data to / from memory external to the baseband circuitry 804), an application circuitry interface 914 (e.g., an interface to send / receive data to / from the application circuitry 802 of FIG. 8), an RF circuitry interface 916 (e.g., an interface to send / receive data to / from RF circuitry 806 of Figure 8), a wireless hardware connectivity interface 918 (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 920 (e.g., an interface to send / receive power or control signals to / from the PMC 812.Figure 10: Control Plane Protocol Stack

[0139] Figure 10 is an illustration of a control plane protocol stack in accordance with some embodiments. In one embodiment, a control plane 1000 may be a communications protocol stack between one or more UEs such as, for example, UE 801, and / or one or more RAN nodes 811, and a mobility management entity (MME) 821.

[0140] The PHY layer 1001 may transmit or receive information used by the MAC layer 1002 over one or more air interfaces. The PHY layer 1001 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 1005. The PHY layer 1001 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.

[0141] The MAC layer 1002 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,P70400W01multiplexing MAC SDUs onto TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), and logical channel prioritization.

[0142] The RLC layer 1003 may operate in a plurality of modes of operation, including: Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). The RLC layer 1003 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 1003 may also execute resegmentation 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.

[0143] The PDCP layer 1004 may execute header compression and decompression of IP data, maintain PDCP Sequence Numbers (SNs), perform in-sequence delivery of upper layer PDUs at reestablishment 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 timer-based discard of data, and perform security operations (e.g., ciphering, deciphering, integrity protection, integrity verification, etc.).

[0144] The main services and functions of the RRC layer 1005 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 (IES), which may each comprise individual data fields or data structures.

[0145] In one example, a UE (e.g., UE 106A-N) and a RAN node (e.g., base station 102) 811 may utilize a Uu interface (e.g., an LTE-Uu interface) to exchange control plane data via a protocol stack comprising the PHY layer 1001, the MAC layer 1002, the RLC layer 1003, the PDCP layer 1004, and the RRC layer 1005.

[0146] The non-access stratum (NAS) protocols 1006 form the highest stratum of the control plane between the UE (e.g., UE 106A-N) 801 and an MME 821. The NAS protocols 1006 support theP70400W01mobility of the UE (e.g., UE 106A-N) 801and the session management procedures to establish and maintain IP connectivity between the UE (e.g., UE 106A-N) 801and a P-GW.

[0147] The SI Application Protocol (Sl-AP) layer 1015 may support the functions of the SI interface and comprise Elementary Procedures (EPs). An EP is a unit of interaction between a RAN node (e.g., base station 102)81 land the CN. The Sl-AP layer 1015 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.

[0148] The Stream Control Transmission Protocol (SCTP) layer (alternatively referred to as the SCTP / IP layer) 1014 may ensure reliable delivery of signaling messages between the RAN node (e.g., base station 102) 81 land a MME821 based, in part, on the IP protocol, supported by the IP layer 1013. The L2 layer 1012 and the LI layer 1011 may refer to communication links (e.g., wired or wireless) used by the RAN node (e.g., base station 102) and the MME to exchange information.

[0149] The RAN node (e.g., base station 102) 811and the MME 821may utilize an Sl-MME interface to exchange control plane data via a protocol stack comprising the LI layer 1011, the L2 layer 1012, the IP layer 1013, the SCTP layer 1014, and the Sl-AP layer 1015.11-12B: Radio Resource Management (RRM) Measurements bv User Equipment within a Heterogenous Network

[0150] As described above, for example, with respect to Figure 1A, a UE 106 may be capable of receiving signals from and transmitting signals to base stations 102A-N, which may include one or more of macro cells, micro cells, or pico cells. Cells providing various service area sizes may form a heterogeneous network. Figure 11 illustrates an example of a UE 106 within a heterogenous network 1100, according to some embodiments. Figure 11 illustrates various base stations 102A-G which may serve as cells. Cells within a heterogeneous network may be distinguished according to allocated Physical Cell IDs (PCIs). Figure 11 illustrates where each base station 102A-N includes multiple transmission (TX) beams and UE 106 includes multiple receiving (RX) beams and further illustrates where multiple frequency layers, FRi, FR2, and FR3 may be utilized. Further, Figure 11 illustrates where a Linkxcorresponds to an RX beam and a TX beam pair, a serving cell, and a frequency layer. Thus, the total number potential links in a heterogenous network is given by: K = NTXX NRXX NceltX FRlayer, where, NTXis the number TX beams for a Cell, NRXis the number of RX beams for a UE, Nceu is the number of cells within a coverage area, and FRiayeris the number of frequency layers. In the example illustrated in Figure 11, Ncell= 7 and FRlayer= 3. In someP70400W01examples implementations NTXmay be equal to 64 and NRXmay be equal to 4 or 8. In other implementations NTXand NRXmay have different values.

[0151] UE 106 and base stations 102A-G may utilize a Physical Downlink Shared Channel (PDSCH), and a Physical Downlink Control Channel (PDCCH) for DL transmissions. Further, UE 106 and base stations 102A-G may utilize a Physical Uplink Shared Channel (PUSCH), a Physical Uplink Control Channel (PUCCH), and a Sounding Reference Signal (SRS) for UL transmissions. UE 106 and base stations 102A-G may utilize an individual per-channel beam indication framework in intra-band (i.e., within the same frequency band, but different channels) communications. For example, a transmission configuration indication (TCI) signaling framework provides where a beam for a target channel / signal (e.g., PDSCH, PDCCH, CSI-RS) to be received by the UE 106 can be indicated by a TCI. In the case of UL transmissions, a BS can signal in the PDCCH a downlink control information (DCI). The DCI can include information used for scheduling uplink data transmitted from the UE 106 in the PUSCH to the BS 102. The DCI can also include information for the UE to adjust uplink power in the PUSCH and PUCCH for power control.

[0152] UE 106 may perform radio resource management (RRM) measurements of various reference signals, including for example, an LP-SS (low power synchronization signal), a SSB, i.e., a Synchronization Signal / PBCH block (SSB) which includes a PSS (Primary SS), a SSS (Secondary SS), Physical Broadcast Channel (PBCH) and a demodulation reference signal (DMRS), or a Channel State Information Reference Signal (CSI-RS). RRM measurements may be used to compare the quality of links. That is, for example, comparisons based on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Interference Noise Ratio (SINR) of a SSB may be used to evaluate the quality of a particular link. For example, RSRP and / or RSRQ may be used to determine a best beam pair for a current serving cell. That is, a beam pair with the highest RSRP measurement may be considered the best beam pair. Further, the RSRP of a current serving cell’s best beam may be compared to a RSRP of a neighbor cell’s best beam. Further, comparisons of RRM measurements may be used to determine whether a change in a TX beam and RX beam pair is needed. It should be noted that a UE reports beam level RRM measurements to a NW and a NW may make a handover decision based on reported measurements.

[0153] A measurement gap is a designated time where a UE can perform RRM measurements and does not transmit or receive data. For example, during measurement gaps, measurements may be performed on SSBs. A measurement gap length (MGL) provides the duration / length of a measurement gap. In one example, the MGL may be set to one of 1.5, 3, 3.5, 4, 5.5, and 6 ms. A measurement gap repetition period (MGRP) provides the periodicity at which a measurement gap repeats. In one example, the MGRP may be set to one of 20, 40, 80, and 160 ms. Figures 12A-12BP70400W01are example timing diagrams illustrating a set of measurement gaps which may be used by a UE to perform RRM measurements. In the example illustrated in Figure 12A, MGL is set to 6 ms and MGRP is set to 20 ms. Figure 12A further illustrates a SSB-based RRM Measurement Timing Configuration (SMTC) window. A SMTC window provides the timing of a cell’s SSBs and a UE measures SSBs that fall within a configured SMTC window.

[0154] As illustrated in Figure 12A, the SMTC window is contained within a measurement gap. A network configures a SMTC window based on SSB burst periodicity. In some examples, SSB burst periodicity can have values 5, 10, 20, 40, 80, and 160 ms. It should be noted that a UE may require RF re-tuning time to perform RRM measurements. For example, a RF re -tuning time may be provided at the beginning and end of a MG. In some examples, a RF re -tuning time of 0.5 ms may be provided for earner frequency measurements in a first frequency range and 0.25 ms for a second frequency range. In the example illustrated in Figure 12A, for the MGL of 6 ms, a RF retuning time of 0.5 ms is provided, which allows 5 ms for actual measurements. NR provides various defined configurations for the MGL, MGRP and the SMTC window.

[0155] As described above, in order to determine RSRP, UE performs measurements across: different sites (same frequency layer); different frequency layers (inter-frequency); and different TX / RX beams. As provided above, the total number potential links in a heterogenous network is given by: K = NTXx NRXx Nceu x FRtayerand each link may correspond to a potential measurement. For example, in the case of heterogenous network 1100 where, Nceu = 7, FRiayer= 3, NTX= 64, and NRX= 8, the number of potential beam level measurements is 10,752. Figure 12B illustrates an example where beam level measurements to be performed are allocated to the set of measurement gaps in Figure 12A. The example illustrated in Figure 12B provides where for each of three cells, Celli-Celh, and a frequency layer, FR1-FR3, a TX beam sweep measurement is performed for three RX beams, RXi- RX3. That is, in the case of heterogenous network 1100 where,Ceii = 7. FRiayer= 3, NTX= 64, and NRX= 8, the example of Figure 12B provides where 576 (i.e., 64x9) of the possible 10,752 measurements are performed. As described above, comparisons of RRM measurements may be used to determine whether a change in a TX beam and RX beam pair is needed and a NW may make a handover decision based on reported measurements. Thus, beam level measurements performed during an allocated set of measurement gaps may be used to determine whether a change in a TX beam and RX beam pair is needed and for a handover decision.

[0156] Beam level measurements utilize DL / UL resources and impact throughput. That is, as more beam level measurements are performed, more DL / UL resources are utilized and throughput is decreased. Measurement overhead may be specified by a total measurement gap time divided by the length of a corresponding time period. In the example illustrated in Figure 12A, the measurementP70400W01overhead is (9*6) / ((8*26)+6) = 0.252. That is, in the example illustrated in Figure 12A, for approximately 25% of the time period, the UE does not transmit or receive data. As such, measurement overhead may occupy significant DL / UL resources and have a substantial impact on throughput. As provided above, the MGL, a MGRP, and SSB burst periodicity may be set and as such measurement overhead may be configured by setting MGL, a MGRP, and SSB. For example, the number of real-time measurements performed may be reduced by decreasing MGL and increasing MGRP. However, reducing the number of real-time measurements performed may impact performance with respect to, for example, determining a whether a change in a TX beam and RX beam pair is needed and for a handover decision.

[0157] According to the techniques herein, the number of real-time measurements performed may be reduced while mitigating the impact on performance. For example, according to the techniques herein, MGL may be reduced and / or MGRP may be increased without significantly impacting performance. Further, according to the techniques herein, the number of real-time measurements required may be reduced, thereby saving UE power. That is, for example, the measurement reporting of a UE may be reduced. Further, according to the techniques herein, reducing measurement samples allows for the RRM measurement interval to be relaxed to KxSMTC periodicity, where K is a RRM scaling factor. That is, for example, compared to NR, the measurement interval for SMTC can be enlarged from 160ms to 3* 160ms = 480ms (e.g., K=3). Further, according to the techniques herein, beam search overhead may be reduced. Reducing measurement overhead according to one or more of the techniques herein may lead to reduced scheduling restrictions, results in throughput increases, reduced measurement restrictions, L3 / L1 measurement delay reduction, e.g., by minimizing the Rx beam sweep set, and / or a potential decrease of MGL, which leads to increases in throughput.

[0158] As described in further detail below, in one example, according to the techniques described herein, measurement overhead may be reduce while mitigating the impact on performance by utilizing an artificial intelligence (AI) / machine learning (ML)-based model to predict RSRP measurements across different frequency layers. In one example, an artificial intelligence (AI) / machine learning (ML)-based model can be used to predict the RSRP for one frequency layer from the other frequency layer measurements. In this manner, according to the techniques herein, measurement gaps may be significantly reduced.Figure 13: Radio Resource Management (RRM) Measurement Overhead Reduction

[0159] As provided above, Figure 12B illustrates an example where beam level measurements toP70400W01be performed are allocated to the set of measurement gaps illustrated in Figure 12 A. Similarly, Figure 13 illustrates where beam level measurements to be performed are allocated to the set of measurement gaps. Figure 13 illustrates an example where, according to the techniques herein, compared to the example illustrated in Figure 12B, the number measurements performed is reduced and measurement overhead is reduced. That is, in Figure 13, MGL is reduced and measurements are skipped. It should be noted that, in one example, the MGL may be reduced based on a partial TX beam sweep measurement being performed. That is, it may take less time to perform a partial TX beam sweep measurement. In the example illustrated in Figure 13, the measurement overhead is (4*3) / ((8*23)+3) = 0.064, which is significantly less than the 0.252 measurement overhead for the example illustrated in Figures 12A-12B and may result in a throughput increase of -30%. As further illustrated in Figure 13, according to the techniques herein, skipped measurements may be predicted. That is, for example, the example illustrated in Figure 13 may provide where 128 (i.e., 32x4) measurements are performed and from these 128 measurements one or more of the 448 (i.e., 576-128) skipped measurement in the example of Figure 12B may be predicted. Thus, if one or more of the 448 skipped measurements are able to be predicted accurately, measurement overhead may be reduced without a significant degradation in performance.

[0160] In one example, according to the techniques herein, as described in further detail below, measurements may be predicted by utilizing an artificial intelligence (Al)Zmachine learning (ML)-based model.Figures 14A-14B, 15A-15B: Training an Artificial intelligence (AI) / Machine learning (ML) based model mav be used to predict RSRP measurements

[0161] Figures 14A-15B illustrate an example of a layout of a deployment. The example illustrated in Figures 14A-15B illustrates Sites, which may corresponds to a Next Generation Node B (gNB), and a user equipment (UE) in a hexagonal deployment. It should be noted that in other examples, other types of deployments may be utilized. With respect to Figures 14A-15B, RSRP measurements for a UE at a location for a frequency layer for a number of N cells may be specified using a vector. For example, referring to the example illustrated in Figure 14A, a vector xt= (%ii, xi2,, xiN) may be specified, where i identifies a UE at a location and N is equal the number of sites. Inx;, each xinis a function of f(PL(FRk, lxly'), SSF^k'), where PL is a path loss function for a frequency layer k and a location lx., ly. of the UE i and SSFkprovides the small scale fading. The example in Figure 14A provides where xt= xisitel, xisite4, xisite6) for a frequency layer k. The example in Figure 14B provides where the location of the UE is moved from lx., ly. to lx, lyjP70400W01and a new set of corresponding measurements are acquired, i.e., Xj = (x7Sitei,xjsite4> Xjsiteb) for the frequency layer k. It should be noted that xtand Xj are correlated to due spatial consistency. As such, based on xtand Xj if measurements xu= (,xuSitel>xuSite4, xuSite6) for frequency layer k are acquired, the location of lxu, lyuof the UE may be predicted. In this manner, a database of samples may be acquired by placing a UE in random locations and acquiring measurements which capture the characteristics of the environment.

[0162] Figures 15A-15B illustrate examples where the UE acquires measurements for noncollated sites, compared to the sites in Figures 14A-14B, at a frequency layer m. That is, in Figure 15A, the UEis placed at Zx.y. and acquires yt= yisite3>yisite >yisite7) for a frequency layer k and in Figure 15B, the UE is placed at lXj, lyjand acquires yj = (yjsite^^yjsites^jsite?') for a frequency layer k. It should be noted that similar to xtand Xj, samples ytand y7will be correlated due to spatial consistency. Samples x;and y, will be uncorrelated. However, samples xtand yi have a common latent variable lr., lv.. In this manner, if the latent variable can be inferred from x data, then y can be estimated from a measurement x. That is, for example, using a database of x data, if measurements xu= xuSitel, xuSite4, xuSite6) for the frequency layer k are acquired, the location of lxu, lyu °f the UE may be predicted and further the predicted location lxu, lyumay be used to predict yu= yusite3> yusites, yusite7^ using a database of y data. Thus, according to the techniques herein, a database of x data and y data may be built by acquiring RSRP measurement at various locations for respective frequency layers and non-collocated sites. As described in further detail below, the database may provide training samples for estimating or predicting a y measurement from an x measurement. That is, assuming there is a set B of frequency layers / cells where measurements are acquired and a set A of frequency layers that need to be predicted, a function f: R\B\XNR\A\XN(N sites per layer) may be found based on a training set of possibly noisy measurements. That is, training data (X4, Ex), ••• (Xn, Yn) may be used to determine the inference Y = f X).

[0163] In this manner, according to the techniques herein, during a measurement campaign, a set of predefined reference points lN= (lxN, lyN) may be used to collect RSRP values from n neighboring cells. A reference fingerprint s = [s1(•••,£„] is a vector of RSRP samples across different cells. A series of reference fingerprints may be collected at each reference point and stored in a database along with the physical coordinates (lxn, lyn). The fingerprint database may be referred to as a radio map. Vectors of RSRP measurements may correspond to different combinations of frequency layers and cells. After the measurement campaign, in a prediction phase, the reference database may be exploited in order to obtain a RSRP estimate given a new fingerprint s', measured at the unknown location.P70400W01

[0164] In one example, rather than exploiting the database, during a deployment, for estimating a new fingerprint s', which may require storing the database at various locations in a NW, including at a UE, according to the techniques herein, an artificial intelligence (AI) / machine learning (ML)-based model may be utilized. An AI / ML model may comprise a number of non-linear transformation units, i.e. neurons and a sufficiently large set of free parameters, i.e., interconnection weights. In one example, an AI / ML model may (1) exploit the fingerprint database in order to approximate a function that maps the fingerprints (Fl) from the high dimensional signal space to coordinates in the plane by interpolating the collected data and (2) map the coordinates to RSRP estimates at F2 (database). Further, an AI / ML model can merge steps (1) and (2) for direct RSRP estimation without computing the coordinates. That is, according to the techniques herein an AI / ML model may be used to predict an RSRP directly from an RSRP measurement. Using the AI / ML model may compress the memory requirements. For example, according to simulation results, using an AI / ML model may reduce memory requirement by approximately 80% compared to using a database.

[0165] It should be noted that in statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y. In any nonparametric regression, the conditional expectation of a variable Y relative to a variable X can be written as:= tn(X) where m is an unknown function. That is, given a random pair (X, Y) G RdX R, the function f0(x) = E(y|X = x) is called the regression function (of Y on X). The basic goal in non-parametric regression is to construction an estimator f of f0without assuming a specific parametric form for f0, and instead only assuming f0is smooth in some way.

[0166] A kernel function is a similarity function that takes as input vectors in original feature space and calculates a modified inner product in a higher-dimensional space. That is, for example, a AI / L function approximation may be specified as follows:E(T|X = x)'Li=1Kn(x ~xj)

[0167] Thus, according to the techniques herein, for an s vector stored in the database and a target RSRP stored in the database, for an s' vector, weighs in a kernel function, i.e., K / (may be used to predict an RSRP.Figures 16, 17: Al / L-based models for predicting radio resource management (RRM) measurements for sites at one frequency layer from other frequency layer measurementsP70400W01

[0168] As described above, in some cases, it can be assumed that the input x (RSRP values at FR1) and the target y (RSRP values at FR2 ) are related to the same latent variable z (e.g., related to the user positions). Tn one example, according to the techniques herein, an appropriate transformation of the raw measurement x may be determined to efficiently leam a reliable predictor for y. This may be described as finding a mapping F from x E Rnto a latent variable z e Rkk, such that F x -> z, n > k.Then the optimal predictor can be defined by a function of z E Rkwith parameter 0' denoted by fg' z) such that fg'(z) = argmaxyp(y\z, 9'').

[0169] Figure 16 illustrates an A I / M L-based model for predicting radio resource management (RRM) measurements for sites at one frequency layer from other frequency layer measurements using artificial intelligence (Al)Zmachine learning (ML) models, according to some embodiments. The AI / ML-based model in Figure 16 includes probabilistic encoder 1602 which performs a probabilistic encoding function P(Z|X), where P(Z|X) provides the probability distribution of Z for each X and probabilistic decoder 1606 which performs a probabilistic decoding function P(Y|Z), where P(Y|Z) is the conditional probability of Y given Z. Further, the AI / ML-based model in Figure 16 includes decoder 1604 which performs a decoding function P(Z|X) and, as described below, may be used during a first phase to tune probabilistic encoding function P(Z|X), such that P(Z|X) encodes / compresses input data X such that Z may be decoded / decompressed with an acceptable amount of loss. Further, in Figure 16 variable z is sampled from the probability distribution provided by probabilistic encoder 1602. Each of 1602, 1604, and 1606 may include neural networks. Further, input X may include a multi-dimensional tensor of measurements, for example, RSRPs for various locations, frequency layer, beam pairs, and / or cells, as described above.

[0170] In the example illustrated in Figure 16, for the input x (e.g., RSRP values at FR1), probabilistic encoder 1602, which may include a variational autoencoder maps x to latent variable z. In one example, the input x may be mapped into a distribution and z can be sampled from the distribution. In one example, the relationship between the data input x and the latent encoding vector z can be fully defined by: (1) Likelihood:( |z) and (2) Posterior: qg(z|x). As such, by learning good values for 0 and < > probabilistic encoder 1602 and decoder 1604 may be tuned in order to encode the input x such that in can be reconstructed.

[0171] In the example illustrated in Figure 16, there are N observed datapoints xtand each datapoint is generated by a (local) latent random variable zt. Each xt(RSRP) will depend on zfin a complex, non-linear way. In practice, this dependency will be parameterized by a deep neural network with parameters 0. The neural network qg(z|x) can be varied in a number of ways (number of layers, type of non-linearities, number of hidden units, etc.). A function qg(z|x) may be learned that mapsP70400W01each xtto an appropriate variational parameters, (p,, crf), mean and variance, which would represent that there are good values of zt. For example, the mean and variance of a gaussian distribution could be encoded in ztspace. The function q0(z\x) is parameterized by a global parameter 6 shared by all the datapoints.

[0172] In some cases, it may be assumed that the xtare images, so that the model is a generative model of images. Once a good value of 0 and < > have been learned, images can be generated from the model as follows:Sample z according to the prior p(z).Sample x according to the likelihood (x z)

[0173] That is, according to the techniques herein, a generative model may be developed for Xj. In this case, each image is being represented by a latent code z and that code gets mapped to images using the likelihood, which depends on the 6 that has been learned. The likelihood is interpreted as the decoder in this context, where the role of the decoder is to decode z into x. It should be noted, that since this is a probabilistic model, there is uncertainty about the z that encodes a given datapoint x.

[0174] Once good values have been learned, the following may be performed:Start with a given image x;Use the function q0(z|x) to encode x as z;Use the model likelihood (x|z) to decode z and get a reconstructed image x'

[0175] That is, in this manner, referring to Figure 16, during a first phase, each of probabilistic encoder 1602 and decoder 1604 may be tuned based on whether X’ is an acceptable reconstruction of input X.

[0176] Further, in another example, two autoencoders may be trained. That is, a pair of probabilistic encoder / decoder for measured values x may be trained and a pair of encoder / decoder for predicted values y may be trained. Figure 17 illustrates an example where two autoencoders are trained. That is, in Figure 17, training data for x and y may be collected, for example, as described above. As described above, by collecting RSRP measurements across different cells and frequency layers, a mapping from those measurements to a location latent space may be inferred. Tn Figure 17, the training data for x may be used to train encoder 1702 and decoder 1704 for encoding x data to a latent space z and the training data for y may be used to train encoder 1706 and decoder 1708 for encoding y data to a latent space z. That is, neural networks for each of the encoders and decoders may be tuned learning good values of 6 and < >. The trained encoder 1702 and decoder 1708 may beP70400W01deployed such that y can be predicted by x. That is, the output latent space of a variational autoencoder for x can be used as the input to the decoder for y. It should be noted that in one example, the lower-dimensional space is stochastic, i.e., the encoder outputs the parameters of a gaussian distribution and this mapping may be learned through the variational auto-encoders. As such, higher dimension input across spatial dimension (Tx beams, Rx beams) can also be compressed through auto-encoders.Figures 18: AI / ML-based models for predicting radio resource management (RRM) measurements at one frequency layer from other frequency layer measurements within a network

[0177] Figure 18 illustrates an example of AI / ML model for predicting radio resource management (RRM) measurements for sites at one frequency layer from other frequency layer measurements, according to some embodiments. In the example illustrated in Figure 18, RRM measurement unit 1810 of UE 106 collects RRM measurements. For example, RSRP measurements for sites at a first frequency layer may be acquired. For example, NW 100 may signal a UE to measure RSRP across different cells and frequency layers. That is, NW 100 may transmit a measurement configuration which specific measurements to be acquired. These measurements may correspond to an x or s. as described above. In some case, prior to sending a measurement configuration, NW 100 may determine the capabilities of UE 106.

[0178] As illustrated in Figure 18, UE 106 includes a AI / ML encoder 1820. AI / ML encoder 1820 represents an example of a AI / ML model that may generate a latent variable from the acquired measurements. For example, AI / ML encoder 1820 may include an autoencoder and / or a variational autoencoder that generates z from x. AI / ML encoder 1820 may include a probabilistic encoder P(Z|X), for example, as described above. As further, illustrated in Figure 18, each of UE 106 and NW 100 include AI / ML decoder 1830. AI / ML decoder 1830 represents an example of a AI / ML model that may predict measurements from a latent variable. For example, AI / ML decoder 1830 may include an autodecoder, including a variational autodecoder, that generates y from z. For example, AI / ML decoder 1830 may include a probabilistic decoder P(Y|X), for example, as described above.

[0179] In one example, a measurement configuration may specify measurements the UE 106 is to predict. Further, in one example, the predicted measurements are a multi-dimensional tensor, and rather than send the predicted measurements to the NW 100, UE 106 may send the latent variable. NW 100 can reconstruct the predicted measurements. That is, by using AI / ML decoder 1830 NW 100 can reconstruct predicted measurements across different cells / frequencies and Tx beams to recover target Y. Tn this manner, UE skip performing measurement and reporting measurements for target Y. As described above, this reduces measurement overhead and improves throughput. Furthermore, if targetP70400W01Y is accurately predicted, performance may not be significantly degraded.

[0180] In this manner, UE 106 represents an example of a device configured to receive a measurement configuration; acquire reference signal received power (RSRP) measurements specified in the measurement configuration; use an artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement; and use an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable

[0181] In this manner, NW 106 represents an example of a device configured to transmit a measurement configuration, wherein the measurement configuration specifies reference signal received power (RSRP) measurements specified in the measurement configuration; receive a latent variable corresponding to the specified measurements; and use an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable.Figures 19 and 20: Prediction of a frequency layer measurement measurements

[0182] Figure 19 illustrates a block diagram of an example of a method 1900 to perform prediction of a frequency layer measurement using an AI / ML-based model, according to some embodiments. The method shown in Figure 19 may be used in conjunction with any of the systems, methods, or devices shown in the Figures, among other devices. That is, Figure 19 provides an example of a procedure for prediction of a frequency layer measurement which may be performed, for example, by a UE. 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. As shown, this method may operate as follows.

[0183] A UE 106, or other device, receives a measurement configuration at 1902. For example, a UE 106 receive a measurement configuration including measurements to be acquired and / or measurements to be predicted, as described above. The UE 106 acquires reference signal received power (RSRP) measurements corresponding to the measurement configuration at 1904. For example, UE 106 acquires a reference signal received power (RSRP) measurement, as described above. UE 106 generates a latent variable from the acquired reference signal received power (RSRP) measurements using a AI / ML based encoder at 1906. For example, UE 106 may utilize a AI / ML-based model to map measurements x to a latent variable z, as described above. UE 106 predicts reference signal received power (RSRP) measurements from the latent variable using a AI / ML based decoder at 1908. For example, UE 106 may utilize a AI / ML-based model to predict measurements yP70400W01from a latent variable z, as described above. UE 106 may transmit the latent variable to a NW and / or a server at 1910.

[0184] Figure 20 illustrates a block diagram of an example of a method 2000 to perform prediction of a frequency layer measurement using an AI / ML-based model, according to some embodiments. The method shown in Figure 20 may be used in conjunction with any of the systems, methods, or devices shown in the Figures, among other devices. That is, Figure 20 provides an example of a procedure for prediction of a frequency layer measurement which may be performed, for example, by a NW, and / or components thereof. 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. As shown, this method may operate as follows.

[0185] A NW 100, including components thereof, transmits a measurement configuration at 2002. For example, NW 100 transmits a measurement configuration including measurements to be acquired and / or measurements to be predicted, as described above. NW 100 receives a latent variable at 2004. For example, an AI / ML-based model may be used to map measurements x to a latent variable z, as described above. NW 100 predicts reference signal received power (RSRP) measurements from the latent variable using a AI / ML based decoder at 2006. For example, NW 100 may utilize a AI / ML- based model to predict measurements y from a latent variable z, as described above.

[0186] In some examples, the reference signal received power (RSRP) measurements specified in the measurement configuration include measurements for first frequency layer.

[0187] In some examples, predicted reference signal received power (RSRP) measurements correspond to a second frequency layer.

[0188] In some examples, the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes an autoencoder.

[0189] In some examples, the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes a variational autoencoder.

[0190] In some examples, the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes a probabilistic encoder.

[0191] In some examples, the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes an autodecoder.

[0192] In some examples, the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a variationalP70400W01autodecoder.

[0193] In some examples, the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a probabilistic decoder.

[0194] In some examples, an artificial intelligence (AI) / machine learning (ML) model utilizes a database UE radio maps.

[0195] In some examples, a UE radio map includes reference fingerprint for a set of reference locations over a localization area.

[0196] The present disclosure contemplates that, in some embodiments, data used by prediction of a frequency layer measurement processes includes publicly available data. To protect user privacy, data may be anonymized, aggregated, and / or otherwise processed to remove or to the degree possible limit any individual identification. As discussed herein, entities that collect, share, and / or otherwise utilize such data should obtain user consent prior to and / or provide transparency when collecting such data. Furthermore, the present disclosure contemplates that the entities responsible for the use of data, including, but not limited to data used in association with prediction of a frequency layer measurement processes, should attempt to comply with well-established privacy policies and / or privacy practices.

[0197] For example, such entities may implement and consistently follow policies and practices recognized as meeting or exceeding industry standards and regulatory requirements for developing and / or training prediction of a frequency layer measurement processes. In doing so, attempts should be made to ensure all intellectual property rights and privacy considerations are maintained. Training should include practices safeguarding training data, such as personal information, through sufficient protections against misuse or exploitation. Such policies and practices should cover all stages of the prediction of a frequency layer measurement processes development, training, and use, including data collection, data preparation, model training, model evaluation, model deployment, and ongoing monitoring and maintenance. Transparency and accountability should be maintained throughout. Such policies should be easily accessible by users and should be updated as the collection and / or use of data changes. User data should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection and sharing should occur through transparency with users and / or after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such data and ensuring that others with access to the data adhere to their privacy policies and procedures. Further, such entities should subject themselves to evaluation by third parties toP70400W01certify, as appropriate for transparency purposes, their adherence to widely accepted privacy policies and practices. In addition, policies and / or practices should be adapted to the particular type of data being collected and / or accessed and tailored to a specific use case and applicable laws and standards, including jurisdiction-specific considerations.

[0198] In some embodiments, prediction of a frequency layer measurement processes may utilize models that may be trained (e.g., supervised learning or unsupervised learning) using various training data, including data collected using a user device. Such use of user-collected data may be limited to operations on the user device. For example, the training of the model can be done locally on the user device so no part of the data is sent to another device. In other implementations, the training of the model can be performed using one or more other devices (e.g., server(s)) in addition to the user device but done in a privacy preserving manner, e.g., via multi-party computation as may be done cryptographically by secret sharing data or other means so that the user data is not leaked to the other devices.

[0199] In some embodiments, the trained model can be centrally stored on the user device or stored on multiple devices, e.g., as in federated learning. Such decentralized storage can similarly be done in a privacy preserving manner, e.g., via cryptographic operations where each piece of data is broken into shards such that no device alone (i.e., only collectively with another device(s)) or only the user device can reassemble or use the data. In this manner, a pattern of behavior of the user or the device may not be leaked, while taking advantage of increased computational resources of the other devices to train and execute the ML model. Accordingly, user-collected data can be protected. In some implementations, data from multiple devices can be combined in a privacy-preserving manner to train an ML model.

[0200] In some embodiments, the present disclosure contemplates that data used for prediction of a frequency layer measurement processes may be kept strictly separated from platforms where the prediction of a frequency layer measurement processes are deployed and / or used to interact with users and / or process data. In such embodiments, data used for offline training of prediction of a frequency layer measurement processes may be maintained in secured datastores with restricted access and / or not be retained beyond the duration necessary for training purposes. In some embodiments, the prediction of a frequency layer measurement processes may utilize a local memory cache to store data temporarily during a user session. The local memory cache may be used to improve performance of prediction of a frequency layer measurement processes. However, to protect user privacy, data stored in the local memory cache may be erased after the user session is completed. Any temporary caches of data used for online learning or inference may be promptly erased after processing. All data collection, transfer, and / or storage should use industry-standard encryptionP70400W01and / or secure communication.

[0201] In some embodiments, as noted above, techniques such as federated learning, differential privacy, secure hardware components, homomorphic encryption, and / or multi-party computation among other techniques may be utilized to further protect personal information data during training and / or use of the prediction of a frequency layer measurement processes. The prediction of a frequency layer measurement processes should be monitored for changes in underlying data distribution such as concept drift or data skew that can degrade performance of prediction of a frequency layer measurement processes over time.

[0202] In some embodiments, the prediction of a frequency layer measurement processes are trained using a combination of offline and online training. Offline training can use curated datasets to establish baseline model performance, while online training can allow prediction of a frequency layer measurement to continually adapt and / or improve. The present disclosure recognizes the importance of maintaining strict data governance practices throughout this process to ensure user privacy is protected.

[0203] In some embodiments, prediction of a frequency layer measurement processes may be designed with safeguards to maintain adherence to originally intended purposes, even as prediction of a frequency layer measurement processes adapt based on new data. Any significant changes in data collection and / or applications of prediction of a frequency layer measurement process use may (and in some cases should) be transparently communicated to affected stakeholders and / or include obtaining user consent with respect to changes in how user data is collected and / or utilized.

[0204] Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively restrict and / or block the use of and / or access to data. That is, the present disclosure contemplates that hardware and / or software elements can be provided to prevent or block access to data. For example, in the case of some services, the present technology should be configured to allow users to select to “opt in” or “opt out” of participation in the collection of data during registration for services or anytime thereafter. In another example, the present technology should be configured to allow users to select not to provide certain data for training prediction of a frequency layer measurement processes and / or for use as input during the inference stage of such systems. In yet another example, the present technology should be configured to allow users to be able to select to limit the length of time data is maintained or entirely prohibit the use of their data for use by prediction of a frequency layer measurement processes. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user can be notified when their data is being input into the prediction of a frequency layer measurement processes for training or inference purposes, and / orP70400W01reminded when prediction of a frequency layer measurement processes generate outputs or make decisions based on their data.

[0205] The present disclosure recognizes prediction of a frequency layer measurement processes should incorporate explicit restrictions and / or oversight to mitigate against risks that may be present even when such systems having been designed, developed, and / or operated according to industry best practices and standards. For example, outputs may be produced that could be considered erroneous, harmful, offensive, and / or biased; such outputs may not necessarily reflect the opinions or positions of the entities developing or deploying these systems. Furthermore, in some cases, references to or failures to cite third-party products and / or services in the outputs should not be construed as endorsements or affiliations by the entities providing prediction of a frequency layer measurement processes. Generated content can be filtered for potentially inappropriate or dangerous material prior to being presented to users, while human oversight and / or ability to override or correct erroneous or undesirable outputs can be maintained as a failsafe.

[0206] The present disclosure further contemplates that users of prediction of a frequency layer measurement processes should refrain from using the services in any manner that infringes upon, misappropriates, or violates the rights of any party. Furthermore, the temporal prediction of beam pair quality changes processes should not be used for any unlawful or illegal activity, nor to develop any application or use case that would commit or facilitate the commission of a crime, or other tortious, unlawful, or illegal act including misinformation, disinformation, misrepresentations (e.g., deepfakes), deception, impersonation, and propaganda. The predicting prediction of a frequency layer measurement processes should not violate, misappropriate, or infringe any copyrights, trademarks, rights of privacy and publicity, trade secrets, patents, or other proprietary or legal rights of any party, and appropriately attribute content as required. Further, prediction of a frequency layer measurement processes should not interfere with any security, digital signing, digital rights management, content protection, verification, or authentication mechanisms. The prediction of a frequency layer measurement processes should not misrepresent machine-generated outputs as being human-generated.

[0207] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so as to minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

[0208] 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-P70400W01readable 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.

[0209] 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.

[0210] In some embodiments, a device (e.g., a UE 106) may be configured to include a processor (or a set of processors) including one or more baseband processors and one or more application 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.

[0211] 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.

[0212] 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

P70400W01CLAIMSWhat is claimed is:

1. An apparatus of a user equipment (UE) configured to perform prediction of a frequency layer measurement using an artificial intelligence (AI) / machine learning (ML) model, the apparatus comprising:one or more processors configured to:receive a measurement configuration;acquire reference signal received power (RSRP) measurements specified in the measurement configuration;use an artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement; anduse an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable.

2. The apparatus of claim 1, wherein the one or more processors are further configured to transmit the latent variable.

3. The apparatus of any of claims 1 and 2, wherein the reference signal received power (RSRP) measurements specified in the measurement configuration include measurements for a first frequency layer.

4. The apparatus of any of claims 1-3, wherein predicted reference signal received power (RSRP) measurements correspond to a second frequency layer.

5. The apparatus of any of claims 1-4, wherein the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes an autoencoder.

6. The apparatus of any of claims 1-5, wherein the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes a variational autoencoder.P70400W017. The apparatus of any of claims 1-6, wherein the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes a probabilistic encoder.

8. The apparatus of any of claims 1-7, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes an autodecoder.

9. The apparatus of any of claims 1-8, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a variational autodecoder.

10. The apparatus of any of claims 1-9, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a probabilistic decoder.

11. The apparatus of any of claims 1-10, wherein an artificial intelligence (AI) / machine learning (ML) model utilizes a database of UE radio maps.

12. The apparatus of claim 11, wherein a UE radio map includes reference fingerprint for a set of reference locations over a localization area.

13. An apparatus of a communications network (NW) configured to perform prediction of a frequency layer measurement using an artificial intelligence (AI) / machine learning (ML) model, the apparatus comprising:one or more processors configured to:transmit a measurement configuration, wherein the measurement configuration specifies reference signal received power (RSRP) measurements specified in the measurement configuration;receive a latent variable corresponding to the specified measurements; and use an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable.P70400W0114. The apparatus of claim 13, wherein the reference signal received power (RSRP) measurements specified in the measurement configuration include measurements for a first frequency layer.

15. The apparatus of any of claims 13 and 14, wherein predicted reference signal received power (RSRP) measurements correspond to a second frequency layer.

16. The apparatus of any of claims 13-15, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes an autodecoder.

17. The apparatus of any of claims 13-16, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a variational autodecoder.

18. The apparatus of any of claims 13-17, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a probabilistic decoder.

19. The apparatus of any of claims 13-18, wherein the artificial intelligence (AI) / machine learning (ML) model utilizes a database of UE radio maps.

20. The apparatus of claim 19, wherein a UE radio map includes reference fingerprint for a set of reference locations over a localization area.

21. A method to perform prediction of a frequency layer measurement using an artificial intelligence (AI) / machine learning (ML) model, the method comprising:acquiring reference signal received power (RSRP) measurements for a first frequency layer; using an artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurements; andusing an artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements for a second frequency layer from the latent variable.

22. The method of claim 21, wherein the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes an autoencoder.P70400W0123. The method of any of claims 21 and 22, wherein the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes a variational autoencoder.

24. The method of any of claims 21-23, wherein the artificial intelligence (AI) / machine learning (ML) model to generate a latent variable from the acquired measurement includes a probabilistic encoder.

25. The method of any of claims 21-24, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes an autodecoder.

26. The method of any of claims 21-25, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a variational autodecoder.

27. The method of any of claims 21-26, wherein the artificial intelligence (AI) / machine learning (ML) model to predict reference signal received power (RSRP) measurements from the latent variable includes a probabilistic decoder.

28. The method of any of claims 21-27, wherein an artificial intelligence (AI) / machine learning (ML) model utilizes a database of UE radio maps.

29. The method of claim 28, wherein a UE radio map includes reference fingerprint for a set of reference locations over a localization area.

30. A device configured for communicating in a wireless communication network, comprising:one or more processors, coupled to a memory, configured to perform any of the methods of claims 21-29.

31. The device of claim 30, wherein the device includes a user equipment (UE).

32. The device of claim 30, wherein the device includes a base station (BS).P70400W0133. The device of claim 30, wherein the device includes a network entity.

34. A non-transitory computer program product, comprising computer instructions which, when executed by one or more processors, perform any of the methods of claims 21-29.

35. A baseband processor configured to cause a user equipment (UE) to perform any of the methods of claims 21-29.

36. A baseband processor configured to cause a base station to perform one or more of the methods of claims 21-29.

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