Beam management with cell optimized beam patterns and reinforcement learnining

Abstract

An apparatus of a user equipment one or more processors coupled to a memory and configured to: dynamically determine and enhance a set B of transmission (Tx) beams based on data collected from a network across user equipment (UE) distributions from a particular site; dynamically determine and enhance a set B of reception (Rx) beams for a UE based on data collected from one or more of the network or the UE; determine a Tx and Rx beam pair from the set B of Tx beams and the set B of Rx beams representing a true optimal Tx / Rx beam pair using one or more artificial intelligence (Al) models, wherein the one or more Al model comprise one or more two-sided Al models; and perform channel-based beam forming based on the Tx and Rx beam pair, wherein the channel-based beam forming adapts weight coefficients using reinforcement learning.

Description

Client Ref. No. P67055WO1 BEAM MANAGEMENT WITH CELL OPTIMIZED BEAM PATTERNS AND REINFORCEMENT LEARNING FIELD

[0001] Embodiments of the invention relate to wireless communications, including apparatuses, systems, and methods for beam management with cell optimized beam patterns and reinforcement learning, in wireless communication systems. DESCRIPTION OF THE RELATED ART

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

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

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

[0005] 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 type communications with lower latency and / or lower battery consumption. Further, NR may allow for more flexible UE scheduling as compared to current LTE.Client Ref. No. P67055WO1 Consequently, efforts are being made in ongoing developments of 5G-NR to take advantage of higher throughputs possible at higher frequencies.

[0006] One aspect of wireless communication systems, including, for example, systems for NR cellular wireless communications, Wi-Fi networks, and terrestrial / non-terrestrial wireless communications systems, is the transmission and measurement of reference signals to compensate for time and frequency offsets. SUMMARY

[0007] Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for an apparatus of a user equipment, the apparatus comprising one or more processors, coupled to a memory, configured to: dynamically determine and enhance a set B of transmission (Tx) beams based on data collected from a network across user equipment (UE) distributions from a particular site; dynamically determine and enhance a set B of reception (Rx) beams for a UE based on data collected from one or more of the network or the UE; determine a Tx and Rx beam pair from the set B of Tx beams and the set B of Rx beams representing a true optimal Tx / Rx beam pair using one or more artificial intelligence (AI) models, wherein the one or more AI model comprise one or more two-sided AI models; and perform channel-based beam forming based on the Tx and Rx beam pair, wherein the channel-based beam forming adapts weight coefficients using reinforcement learning.

[0008] 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, internet of things (IOT) and any of various other computing devices.

[0009] 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 are merely 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 DRAWINGSClient Ref. No. P67055WO1

[0010] 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:

[0011] FIG.1A illustrates an example wireless communication system according to some embodiments.

[0012] FIG. 1B illustrates an example of a base station and an access point in communication with a user equipment (UE) device, according to some embodiments.

[0013] FIG.2 illustrates an example block diagram of a base station, according to some embodiments.

[0014] FIG. 3 illustrates an example block diagram of a server according to some embodiments.

[0015] FIG. 4 illustrates an example block diagram of a UE according to some embodiments.

[0016] FIG.5 illustrates an example block diagram of cellular communication circuitry, according to some embodiments.

[0017] FIG. 6 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.

[0018] FIG.7 illustrates an example block diagram of an interface of baseband circuitry according to some embodiments.

[0019] FIG. 8 illustrates an example of a current approach to artificial intelligence / machine learning (AI / ML) beam management using a codebook-based design in accordance with some embodiments.

[0020] FIG.9A illustrates an example of site-specific online fine-tuning for multiple-input multiple-output (MIMO) beamforming in accordance with some embodiments.

[0021] FIG.9B illustrates a general framework for beam management with cell optimized beam patterns in accordance with some embodiments.

[0022] FIG.9C illustrates an example of site-specific online fine-tuning for multiple-input single-output (MISO) beamforming in accordance with some embodiments.

[0023] FIG.10 illustrates an example of the training procedure and signaling process for AI / ML beam management in accordance with some embodiments.

[0024] FIG. 11 illustrates an example of the deployment phase (inference) for AI / ML beam management in accordance with some embodiments.

[0025] FIG. 12 illustrates an example of the inference signaling procedure for AI / ML beam management in accordance with some embodiments.Client Ref. No. P67055WO1

[0026] FIG.13 illustrates a flow chart of a method for AI / ML beam management with cell optimized beam patterns and reinforcement learning by a UE in accordance with some embodiments.

[0027] FIG.14 illustrates a flow chart of a method for AI / ML beam management with cell optimized beam patterns and reinforcement learning by a network in accordance with some embodiments.

[0028] 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 and scope of the subject matter as defined by the appended claims. DETAILED DESCRIPTION Terms

[0029] The following is a glossary of terms used in this disclosure:

[0030] 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.Client Ref. No. P67055WO1

[0031] 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.

[0032] 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”.

[0033] Computer System (or Computer) – any of various types of computing or processing systems, including a personal computer system (PC), mainframe computer system, workstation, network appliance, Internet appliance, personal digital assistant (PDA), television system, grid computing system, or other device or combinations of devices. In general, the term "computer system" can be broadly defined to encompass any device (or combination of devices) having at least one processor that executes instructions from a memory medium.

[0034] 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.

[0035] 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) in NR. A “Base Station” is a network component of a wireless network while a UE is not.Client Ref. No. P67055WO1

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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 will update the formClient Ref. No. P67055WO1 in response to the user actions. The form may be automatically filled out by the computer system where the computer system (e.g., software executing on the computer system) analyzes the fields of the form and fills in the form without any user input specifying the answers to the fields. As indicated above, the user may invoke the automatic filling of the form but is not involved in the actual filling of the form (e.g., the user is not manually specifying answers to fields but rather they are being automatically completed). The present specification provides various examples of operations being automatically performed in response to actions the user has taken.

[0040] 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.

[0041] 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.

[0042] 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. In general, the circuitry that forms the structure corresponding to “configured to” may include hardware circuits.

[0043] Various components may be described as performing a task or tasks, for convenience in the description. Such descriptions should be interpreted as including theClient Ref. No. P67055WO1 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.

[0044] 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.

[0045] The example embodiments are described with regard to communication between a network and / or base station such as, for example, base station, and a user equipment (UE). However, reference to a base station or a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and / or firmware to support for reducing energy usage by network components in wireless communication systems. Therefore, the base station or UE as described herein is used to represent any appropriate type of electronic component.

[0046] The example embodiments are also described with regard to a fifth generation (5G) New Radio (NR) network that may configure a UE to support for reducing energy usage by network components in wireless communication systems. 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.

[0047] 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. FIGS.1A and 1B: Communication Systems

[0048] FIG. 1A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of FIG. 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.

[0049] As shown, the example wireless communication system includes a base station 102A, which communicates over a transmission medium with one or more user devices such as, for example UE 106A, 106B, etc., through 106N. Each of the user devices may beClient Ref. No. P67055WO1 referred to herein as a “user equipment” (UE). Thus, the user devices are referred to as UEs or UE devices.

[0050] 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.

[0051] 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., 1xRTT, 1xEV-DO, HRPD, eHRPD), etc. Note that if the base station 102A is implemented in the context of LTE, also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, it may alternately be referred to as an 'eNodeB' or ‘eNB’. Note that if the base station 102A is implemented in the context of 5G NR, it may alternately be referred to as ‘gNodeB’ or ‘gNB’.

[0052] As shown, the base station 102A may also be equipped to communicate with a network 100 (e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN), and / or the Internet, among various possibilities). Thus, the base station 102A may facilitate communication between the user devices and / or between the user devices and the network 100. In particular, the cellular base station 102A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and / or data services.

[0053] 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. It should be noted that UEs 106A- N may be reference individually or collectively as UE 106.

[0054] Thus, while base station 102A may act as a “serving cell” for UEs 106A-N as illustrated in FIG. 1A, each UE 106 may also be capable of receiving signals from (and possibly within communication range of) one or more other cells (which might be provided by base stations 102B-N and / or any other base stations), which may be referred to as “neighboring cells”. Such cells may also be capable of facilitating communication betweenClient Ref. No. P67055WO1 user devices and / or between user devices and the network 100. Such cells may include “macro” cells, “micro” cells, “pico” cells, and / or cells which provide any of various other granularities of service area size. For example, base stations 102A-B illustrated in FIG.1A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.

[0055] In some embodiments, base station 102A may be a next generation base station, e.g., a 5G New Radio (5G NR) base station such as, for example, a gNB. In some embodiments, a base station may be connected to a legacy evolved packet core (EPC) network and / or to a NR core (NRC) network. In addition, a base station 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 base stations.

[0056] 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., 1xRTT, 1xEV- DO, HRPD, eHRPD), etc.). The UE 106 may also or alternatively be configured to communicate using one or more global navigational satellite systems (GNSS, e.g., GPS or GLONASS), one or more mobile television broadcasting standards (e.g., ATSC-M / H or DVB-H), and / or any other wireless communication protocol, if desired. Other combinations of wireless communication standards (including more than two wireless communication standards) are also possible.

[0057] FIG. 1B illustrates UE 106 (e.g., one of the UE’s 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.

[0058] 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-programmableClient Ref. No. P67055WO1 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.

[0059] 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.

[0060] 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 1xRTTor LTE or GSM), and separate radios for communicating using each of Wi-Fi and Bluetooth. Other configurations are also possible. FIG.2: Block Diagram of a Base Station

[0061] FIG.2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of FIG. 2 is merely one example of a possible base station. As shown, the base station 102 may include processor(s) 204 which may execute program instructions for the base station 102. The processor(s) 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from the processor(s) 204 and translate those addresses to locations in memory (e.g., memory 260 and read only memory (ROM) 250) or to other circuits or devices.Client Ref. No. P67055WO1

[0062] The base station 102 may include at least one network port 270. The network port 270 may be configured to couple to a telephone network and provide a plurality of devices, such as UE 106 of FIG.1B, access to the telephone network as described above in Figures 1 and 2.

[0063] The network port 270 (or an additional network port) may also or alternatively be configured to couple to a cellular network, e.g., a core network of a cellular service provider. The core network may provide mobility related services and / or other services to a plurality of devices, such as UE 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).

[0064] In some embodiments, base station 102 may be a next generation base station, e.g., a 5G New Radio (5G NR) base station. 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 base stations.

[0065] The base station 102 may include at least one antenna 234, and possibly multiple antennas. The at least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UE 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.

[0066] 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 wirelessClient Ref. No. P67055WO1 communication technologies (e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc.).

[0067] 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.

[0068] 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.

[0069] Further, as described herein, radio 230 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in radio 230. Thus, radio 230 may include one or more integrated circuits (ICs) that are configured to perform the functions of radio 230. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of radio 230.

[0070] In some embodiments, the base station 102, and / or processors 204 thereof, can be capable of and configured to determine, for a user equipment, a paging configuration that reduces energy usage by network components, e.g., base station 102, in wireless communication systems. FIG.3: Block Diagram of a Server

[0071] FIG.3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of FIG.3 is merely one example of a possible server. As shown, the server 104 may include processor(s) 344 which may execute programClient Ref. No. P67055WO1 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.

[0072] The server 104 may be configured to provide a plurality of devices, such as base station 102, and UE 106 access to network functions, e.g., as further described herein.

[0073] 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.

[0074] 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.

[0075] In addition, as described herein, processor(s) 344 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor(s) 344. Thus, processor(s) 344 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor(s) 344. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 344. FIG.4: Block Diagram of a User Equipment (UE)

[0076] FIG.4 illustrates an example simplified block diagram of a UE 106, according to some embodiments. It is noted that the block diagram of the communication device of FIG. 4 is only one example of a possible communication device. According to embodiments, UE 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computingClient Ref. No. P67055WO1 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 UE 106 may include a set of components 400 configured to perform core functions. For example, this set of components may be implemented as a system on 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 UE 106.

[0077] For example, the UE 106 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 UE 106, and cellular communication circuitry 430 such as for 5G NR, LTE, GSM, etc., and short to medium range wireless communication circuitry 429 (e.g., Bluetooth™ and WLAN circuitry). In some embodiments, UE 106 may include wired communication circuitry (not shown), such as a network interface card, e.g., for Ethernet.

[0078] 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.

[0079] 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 firstClient Ref. No. P67055WO1 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.

[0080] The UE 106 may also include and / or be configured for use with one or more user interface elements. The user interface elements may include any of various elements, such as display 460 (which may be a touchscreen display), a keyboard (which may be a discrete keyboard or may be implemented as part of a touchscreen display), a mouse, a microphone and / or speakers, one or more cameras, one or more buttons, and / or any of various other elements capable of providing information to a user and / or receiving or interpreting user input.

[0081] The UE 106 may further include one or more smart cards 445 that include SIM (Subscriber Identity Module) functionality, such as one or more UICC(s) (Universal Integrated Circuit Card(s)) cards 445. Note that the term “SIM” or “SIM entity” is intended to include any of various types of SIM implementations or SIM functionality, such as the one or more UICC(s) cards 445, one or more 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 may be implemented as a removable smart card. Thus, the SIM(s) may be one or more removable smart cards (such as UICC cards, which are sometimes referred to as “SIM cards”), and / or the SIMs 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, two removable SIMs, or a combination of one embedded SIMs and one removable SIMs. Various other SIM configurations are also contemplated.Client Ref. No. P67055WO1

[0082] 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 support a second RAT such as 5G NR. Other implementations and RATs are of course possible. In some embodiments, when the UE 106 comprises two SIMs, the UE 106 may support Dual SIM Dual Active (DSDA) functionality. The DSDA functionality may allow the UE 106 to be simultaneously connected to two networks (and use two different RATs) at the same time, or to simultaneously maintain two connections supported by two different SIMs using the same or different RATs on the same or different networks. The DSDA functionality may also allow the UE 106 to simultaneously receive voice calls or data traffic on either phone number. In certain embodiments the voice call may be a packet switched communication. In other words, the voice call may be received using voice over LTE (VoLTE) technology and / or voice over NR (VoNR) technology. In some embodiments, the UE 106 may support Dual SIM Dual Standby (DSDS) functionality. The DSDS functionality may allow either of the two SIMs in the UE 106 to be on standby waiting for a voice call and / or data connection. In DSDS, when a call / data is established on one SIM, the other SIM is no longer active. In some embodiments, DSDx functionality (either DSDA or DSDS functionality) may be implemented with a single SIM (e.g., a eUICC) that executes multiple SIM applications for different carriers and / or RATs.

[0083] As shown, the SOC 400 may include processor(s) 402, which may execute program instructions for the UE 106 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460. The processor(s) 402 may also be coupled to memory management unit (MMU) 440, which may be configured to receive addresses from the processor(s) 402 and translate those addresses to locations in memory (e.g., memory 406, read only memory (ROM) 450, NAND flash memory 410) and / or to other circuits or devices, such as the display circuitry 404, short to medium range wireless communication circuitry 429, cellular communication circuitry 430, connector 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.

[0084] As described herein, the UE 106 may include hardware and software components for implementing the above features for a UE 106 to communicate a scheduling profile forClient Ref. No. P67055WO1 power savings to a network. The processor 402 of the UE 106 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 402 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 402 of the UE 106, in conjunction with one or more of the other components 400, 404, 406, 410, 420, 429, 430, 440, 445, 450, 460 may be configured to implement part or all of the features described herein.

[0085] 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.

[0086] 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.

[0087] In some embodiments, the UE 106 and / or the processors 402 thereof can be configured to and / or capable of identifying, at the UE 106, paging configurations that reduce energy consumption at the UE 106 and the base station 102. FIG.5: Block Diagram of Cellular Communication CircuitryClient Ref. No. P67055WO1

[0088] FIG. 5 illustrates an example simplified block diagram of cellular communication circuitry, according to some embodiments. It is noted that the block diagram of the cellular communication circuitry of FIG.5 is only one example of a possible cellular communication circuit. According to embodiments, cellular communication circuitry 530, which may be cellular communication circuitry 430, may be included in a communication device, such as UE 106 described above. As noted above, UE 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device), a tablet and / or a combination of devices, among other devices.

[0089] 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 FIG. 4). In some embodiments, cellular communication circuitry 530 may include dedicated receive chains (including and / or coupled to, e.g., communicatively; directly or indirectly. dedicated processors and / or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR). For example, as shown in FIG.5, cellular communication circuitry 530 may include a modem 510 and a modem 520. Modem 510 may be configured for communications according to a first RAT, e.g., such as LTE or LTE-A, and modem 520 may be configured for communications according to a second RAT, e.g., such as 5G NR.

[0090] 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.

[0091] 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.Client Ref. No. P67055WO1

[0092] 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 circuitry 534 and UL front end 572). Similarly, when cellular communication circuitry 530 receives instructions to transmit according to the second RAT (e.g., as supported via modem 520), switch 570 may be switched to a second state that allows modem 520 to transmit signals according to the second RAT (e.g., via a transmit chain that includes transmit circuitry 544 and UL front end 572).

[0093] As described herein, the modem 510 may include hardware and software components for implementing the above features or for time division multiplexing UL data for NSA NR operations, as well as the various other techniques described herein. The processors 512 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 512 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 512, in conjunction with one or more of the other components 530, 532, 534, 535, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[0094] 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.

[0095] 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 otherClient Ref. No. P67055WO1 components 540, 542, 544, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[0096] In addition, as described herein, processors 522 may include one or more processing elements. Thus, processors 522 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 522. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processors 522. FIG.6: Block Diagram of a Baseband Processor Architecture for a UE

[0097] FIG.6 illustrates example components of a device 600 in accordance with some embodiments. It is noted that the device of FIG. 6 is merely one example of a possible system, and that features of this disclosure may be implemented in any of various UEs, as desired.

[0098] In some embodiments, the device 600 may include application circuitry 602, baseband circuitry 604, Radio Frequency (RF) circuitry 606, front-end module (FEM) circuitry 608, one or more antennas 610, and power management circuitry (PMC) 612 coupled together at least as shown. The components of the illustrated device 600 may be included in a UE 106 or a base station 102A such as, for example a RAN node. In some embodiments, the device 600 may include less elements (e.g., a RAN node may not utilize application circuitry 602, and instead include a processor / controller to process IP data received from an EPC). In some embodiments, the device 600 may include additional elements such as, for example, memory / storage, display, camera, sensor, or input / output (I / O) interface. In other embodiments, the components described below may be included in more than one device (e.g., said circuitries may be separately included in more than one device for Cloud-RAN (C-RAN) implementations).

[0099] The application circuitry 602 may include one or more application processors. For example, the application circuitry 602 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, etc.). The processors may be coupled with or may include memory / storage and may be configured to execute instructions stored in the memory / storage to enable various applications or operating systems to run on the device 600. In some embodiments, processors of application circuitry 602 may process IP data packets received from an EPC.Client Ref. No. P67055WO1

[0100] The baseband circuitry 604 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The baseband circuitry 604 may include one or more baseband processors or control logic to process baseband signals received from a receive signal path of the RF circuitry 606 and to generate baseband signals for a transmit signal path of the RF circuitry 606. Baseband processing circuity 604 may interface with the application circuitry 602 for generation and processing of the baseband signals and for controlling operations of the RF circuitry 606. For example, in some embodiments, the baseband circuitry 604 may include a third generation (3G) baseband processor 604A, a fourth generation (4G) baseband processor 604B, a fifth generation (5G) baseband processor 604C, or other baseband processor(s) 604D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G), sixth generation (6G), etc.). The baseband circuitry 604 (e.g., one or more of baseband processors 604A-D) may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry 606. In other embodiments, some or all of the functionality of baseband processors 604A-D may be included in modules stored in the memory 604G and executed via a Central Processing Unit (CPU) 604E. The radio control functions may include, but are not limited to, signal modulation / demodulation, encoding / decoding, radio frequency shifting, etc. In some embodiments, modulation / demodulation circuitry of the baseband circuitry 604 may include Fast-Fourier Transform (FFT), precoding, or constellation mapping / demapping functionality. In some embodiments, encoding / decoding circuitry of the baseband circuitry 604 may include convolution, tail-biting convolution, turbo, Viterbi, or Low-Density Parity Check (LDPC) encoder / decoder functionality. Embodiments of modulation / demodulation and encoder / decoder functionality are not limited to these examples and may include other suitable functionality in other embodiments.

[0101] In some embodiments, the baseband circuitry 604 may include one or more audio digital signal processor(s) (DSP) 604F. The audio DSP(s) 604F may be include elements for compression / decompression and echo cancellation and may include other suitable processing elements in other embodiments. Components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on a same circuit board in some embodiments. In some embodiments, some or all of the constituent components of the baseband circuitry 604 and the application circuitry 602 may be implemented together such as, for example, on a system on a chip (SOC).Client Ref. No. P67055WO1

[0102] In some embodiments, the baseband circuitry 604 may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry 604 may support communication with an evolved universal terrestrial radio access network (EUTRAN) or other wireless metropolitan area networks (WMAN), a wireless local area network (WLAN), a wireless personal area network (WPAN). Embodiments in which the baseband circuitry 604 is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.

[0103] RF circuitry 606 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry 606 may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. RF circuitry 606 may include a receive signal path which may include circuitry to down-convert RF signals received from the FEM circuitry 608 and provide baseband signals to the baseband circuitry 604. RF circuitry 606 may also include a transmit signal path which may include circuitry to up-convert baseband signals provided by the baseband circuitry 604 and provide RF output signals to the FEM circuitry 608 for transmission.

[0104] In some embodiments, the receive signal path of the RF circuitry 606 may include mixer circuitry 606a, amplifier circuitry 606b and filter circuitry 606c. In some embodiments, the transmit signal path of the RF circuitry 606 may include filter circuitry 606c and mixer circuitry 606a. RF circuitry 606 may also include synthesizer circuitry 606d for synthesizing a frequency for use by the mixer circuitry 606a of the receive signal path and the transmit signal path. In some embodiments, the mixer circuitry 606a of the receive signal path may be configured to down-convert RF signals received from the FEM circuitry 608 based on the synthesized frequency provided by synthesizer circuitry 606d. The amplifier circuitry 606b may be configured to amplify the down-converted signals and the filter circuitry 606c may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signals to generate output baseband signals. Output baseband signals may be provided to the baseband circuitry 604 for further processing. In some embodiments, the output baseband signals may be zero-frequency baseband signals, although this is not a necessity. In some embodiments, mixer circuitry 606a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.Client Ref. No. P67055WO1

[0105] In some embodiments, the mixer circuitry 606a of the transmit signal path may be configured to up-convert input baseband signals based on the synthesized frequency provided by the synthesizer circuitry 606d to generate RF output signals for the FEM circuitry 608. The baseband signals may be provided by the baseband circuitry 604 and may be filtered by filter circuitry 606c.

[0106] In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for image rejection (e.g., Hartley image rejection). In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may be configured for super-heterodyne operation.

[0107] 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 606 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 604 may include a digital baseband interface to communicate with the RF circuitry 606.

[0108] 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.

[0109] In some embodiments, the synthesizer circuitry 606d may be a fractional-N synthesizer or a fractional N / N+1 synthesizer, although the scope of the embodiments is not limited in this respect as other types of frequency synthesizers may be suitable. For example, synthesizer circuitry 606d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.

[0110] The synthesizer circuitry 606d may be configured to synthesize an output frequency for use by the mixer circuitry 606a of the RF circuitry 606 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 606d may be a fractional N / N+1 synthesizer.Client Ref. No. P67055WO1

[0111] In some embodiments, frequency input may be provided by a voltage-controlled oscillator (VCO), although that is not a necessity. Divider control input may be provided by either the baseband circuitry 604 or the applications processor 602 depending on the desired output frequency. In some embodiments, a divider control input (e.g., N) may be determined from a look-up table based on a channel indicated by the applications processor 602.

[0112] Synthesizer circuitry 606d of the RF circuitry 606 may include a divider, a delay- locked loop (DLL), a multiplexer and a phase accumulator. In some embodiments, the divider may be a dual modulus divider (DMD), and the phase accumulator may be a digital phase accumulator (DPA). In some embodiments, the DMD may be configured to divide the input signal by either N or N+1 (e.g., based on a carry out) to provide a fractional division ratio. In some example embodiments, the DLL may include a set of cascaded, tunable, delay elements, a phase detector, a charge pump and a D-type flip-flop. In these embodiments, the delay elements may be configured to break a VCO period up into Nd equal packets of phase, where Nd is the number of delay elements in the delay line. In this way, the DLL provides negative feedback to help ensure that the total delay through the delay line is one VCO cycle.

[0113] In some embodiments, synthesizer circuitry 606d may be configured to generate a carrier frequency as the output frequency, while in other embodiments, the output frequency may be a multiple of the carrier frequency (e.g., twice the carrier frequency, four times the carrier frequency) and used in conjunction with quadrature generator and divider circuitry to generate multiple signals at the carrier frequency with multiple different phases with respect to each other. In some embodiments, the output frequency may be a LO frequency (fLO). In some embodiments, the RF circuitry 606 may include an IQ / polar converter.

[0114] FEM circuitry 608 may include a receive signal path which may include circuitry configured to operate on RF signals received from one or more antennas 610, amplify the received signals and provide the amplified versions of the received signals to the RF circuitry 606 for further processing. FEM circuitry 608 may also include a transmit signal path which may include circuitry configured to amplify signals for transmission provided by the RF circuitry 606 for transmission by one or more of the one or more antennas 610. In various embodiments, the amplification through the transmit or receive signal paths may be done solely in the RF circuitry 606, solely in the FEM 608, or in both the RF circuitry 606 and the FEM 608.Client Ref. No. P67055WO1

[0115] In some embodiments, the FEM circuitry 608 may include a TX / RX switch to switch between transmit mode and receive mode operation. The FEM circuitry may include a receive signal path and a transmit signal path. The receive signal path of the FEM circuitry may include an LNA to amplify received RF signals and provide the amplified received RF signals as an output (e.g., to the RF circuitry 606). The transmit signal path of the FEM circuitry 608 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 606), and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 610).

[0116] In some embodiments, the PMC 612 may manage power provided to the baseband circuitry 604. In particular, the PMC 612 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. The PMC 612 may often be included when the device 600 is capable of being powered by a battery, for example, when the device is included in a UE. The PMC 612 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.

[0117] While FIG.6 shows the PMC 612 coupled only with the baseband circuitry 604, in other embodiments the PMC 612 may be additionally or alternatively coupled with, and perform similar power management operations for, other components such as, but not limited to, application circuitry 602, RF circuitry 606, or FEM 608.

[0118] In some embodiments, the PMC 612 may control, or otherwise be part of, various power saving mechanisms of the device 600. For example, if the device 600 is in a radio resource control_Connected (RRC_Connected) state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the device 600 may power down for brief intervals of time and thus save power.

[0119] If there is no data traffic activity for an extended period of time, then the device 600 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 600 goes into a very low power state and it performs paging where, again, it periodically wakes up to listen to the network and then powers down at least portions of the device again. The device 600 may not receive data in this state. In order to receive data, it will transition back to an RRC_Connected state.

[0120] 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 downClient Ref. No. P67055WO1 completely. Any data sent during this time incurs a large delay and it is assumed the delay is acceptable.

[0121] Processors of the application circuitry 602 and processors of the baseband circuitry 604 may be used to execute elements of one or more instances of a protocol stack. Accordingly, the baseband circuitry 604 can be used to encode a message for transmission between a UE and a base station, or decode a message received between a UE and a base station. These examples are not intended to be limiting. The baseband circuitry can be used as previously described. FIG.7: Block Diagram of an Interface of Baseband Circuitry

[0122] FIG. 7 illustrates example interfaces of baseband circuitry in accordance with some embodiments. It is noted that the baseband circuitry of FIG.7 is merely one example of a possible circuitry, and that features of this disclosure may be implemented in any of various systems, as desired.

[0123] As discussed above, the baseband circuitry 604 of FIG. 6 may comprise processors 604A-604E and a memory 604G utilized by said processors. Each of the processors 604A-604E may include a memory interface, 704A-704E, respectively, to send / receive data to / from the memory 604G.

[0124] The baseband circuitry 604 may further include one or more interfaces to communicatively couple to other circuitries / devices, such as a memory interface 712 (e.g., an interface to send / receive data to / from memory external to the baseband circuitry 604), an application circuitry interface 714 (e.g., an interface to send / receive data to / from the application circuitry 602 of FIG. 6), an RF circuitry interface 716 (e.g., an interface to send / receive data to / from RF circuitry 606 of FIG. 6), a wireless hardware connectivity interface 718 (e.g., an interface to send / receive data to / from Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components), and a power management interface 720 (e.g., an interface to send / receive power or control signals to / from the PMC 612.

[0125] In modern wireless communication systems, particularly in 5G networks, beam management plays a crucial role in ensuring efficient and effective communication between network nodes and user equipment (UE). Traditional approaches to beam management have relied on predetermined sets of beams and codebook-based beamforming designs. These methods typically use a set of wide beams (Set B) for initial access and a set of narrow beams (Set A) for fine-tuning.Client Ref. No. P67055WO1

[0126] However, these conventional approaches face several limitations. They require extensive measurements across both Set A and Set B beams, leading to significant overhead. The predefined beam sets may not be optimal for specific deployment scenarios or user distributions, potentially resulting in suboptimal performance. Moreover, the current methods often rely on sub-optimal beam search procedures that may not achieve the best possible transmit (Tx) and receive (Rx) beam pairing.

[0127] As wireless networks become more complex and dynamic, there is a growing need for more adaptive and efficient beam management techniques that can optimize performance based on specific deployment scenarios and changing user distributions.

[0128] As described herein, mechanism of the illustrated embodiments introduces an innovative approach to beam management using artificial intelligence and machine learning (AI / ML) techniques with cell-optimized beam patterns. This operation dynamically computes and optimizes both transmit (Tx) and receive (Rx) beam sets based on data collected from the network across user distributions from particular sites. It employs a two-sided AI / ML model that uses reinforcement learning to adapt weight coefficients, aiming to find solutions close to the true optimal Tx / Rx beam pair.

[0129] The illustrated embodiments significantly reduce measurement overhead by requiring measurements only on the optimized Set B of Tx and Rx beams, eliminating the need for Set A beam measurements. It also introduces channel-based beamforming and site-specific online fine-tuning, allowing for better adaptation to specific deployment scenarios. This approach has the potential to improve the efficiency, adaptability, and overall performance of beam management in advanced wireless communication systems.

[0130] To address these challenges, the mechanisms of the illustrated embodiments provide for artificial intelligence and machine learning (AI / ML) beam management with cell optimized beam patterns and reinforcement learning. This approach allows both the network and the UE to optimize beam patterns based on current conditions and predictions. The system dynamically computes and optimizes sets of transmit (Tx) and receive (Rx) beams based on data collected across user distributions from particular sites. By employing a two- sided AI / ML model with reinforcement learning, the system can adapt weight coefficients to find solutions close to the true optimal Tx / Rx beam pair. This adaptive system significantly reduces measurement overhead by eliminating the need for Set A beam measurements and introduces channel-based beamforming for better adaptation to specific deployment scenarios. By dynamically adjusting to network conditions and user distributions, the mechanisms of the illustrated embodiments aim to improve overall system performance,Client Ref. No. P67055WO1 increase efficiency, and enhance the adaptability of beam management in advanced wireless communication systems. FIG.8: Current AI / ML Beam Management Approach (Codebook Based)

[0131] FIG. 8 illustrates an example of a current approach to artificial intelligence / machine learning (AI / ML) beam management use case, which is based on a codebook design. FIG.8 shows the interaction between a network node (NW) and a user equipment (UE) during a beam management process.

[0132] FIG.8 depicts the NW transmitting beams from a set B of transmit (Tx) beams. The UE can sweep its own receive (Rx) beams, denoted as Rx set of N beams, where N is a positive integer, to determine a quasi-optimum reference signal received power (RSRP). This procedure continues with the NW transmitting another beam from set B, and the UE again sweeping its Rx beams to determine the quasi-optimum RSRP. This cycle repeats until the NW has swept all the beams in set B.

[0133] It should be noted that there may be significant loss of signal power if the UE determines RSRP with a random Rx beam. Also, computation of RSRP with the optimum Rx beam can require search over multiple Tx beams (e.g. N Rx beams times the set of beams in the Set B of Tx beams). The collected RSRP measurements, denoted as^^, ^^, … . , ^^, serve as input to the AI / ML model. These measurements are represented as^ in FIG.8.

[0134] The AI / ML model, which may be transferred to the UE from the network (e.g. base station 102) or an over-the-top (OTT) server, processes these measurements Z. The output of the AI / ML model is the predicted fine Tx beam, which is then indicated to the UE. In the final step, the NW transmits the predicted fine beam, and the UE sweeps its N beams to find the best Rx beam.

[0135] FIG.8 illustrates that the set B is a subset of set A of Tx beams, indicating that set B contains fewer, likely wider beams compared to the more numerous, narrower beams in set A.

[0136] The current approach to AI / ML beam management, as illustrated in FIG.8, faces several challenges and limitations. The optimum receive (Rx) beams found through the sequential procedure, which selects the best beam for each transmit (Tx) beam, may not be the best Rx beam overall. Finding the true best Tx / Rx beam pair would require an exhaustive search, which is impractical in real-world scenarios.Client Ref. No. P67055WO1

[0137] For training purposes, the current approach uses a set of measurements with a set A, resulting in a supervised learning problem. This leads to substantial data collection overhead. Furthermore, during the inference phase, the exact association between set A and set B must be maintained, complicating the design and limiting the generalizability of the AI / ML model.

[0138] The set B of beams may not be optimal for the current deployment scenario and user distributions. In certain situations, some beams in set B could be blocked, degrading the performance of the AI / ML model. Additionally, the current technical report (TR) for the AI / ML interface lacks a metric to evaluate the "goodness" of the beams in set B. The QoS or other measurements to know the quality of the beams in set B may not be known.

[0139] Due to varying propagation conditions for particular sites or environments, a predefined set B is unlikely to lead to optimal performance. Optimizing set B through data collection from a specific site and subsequent learning could improve performance, even when set B belongs to a predefined codebook.

[0140] To address these limitations, a new approach to AI / ML beam management with cell optimized beam patterns is proposed. This approach aims to overcome the challenges of the current system and provide more efficient and adaptive beam management.

[0141] In some examples, the proposed system dynamically computes and optimizes set B based on data collected from the network node (NW) across user equipment (UE) distributions from a particular site. This involves online site-specific learning and reinforcement learning. Additionally, a set B for Rx beams is introduced, which is also dynamically computed and optimized based on collected data. This allows UEs to have different set B patterns depending on their current location.

[0142] The proposed system employs a machine learning approach to solve an optimization problem for Tx / Rx beam pairing, aiming to find a solution close to the true optimal Tx / RX beam pair. In contrast to the current approach that uses measurements at both set A and set B Tx beams for training, the proposed system only needs measurements at set B of Tx and set B of Rx beams. There is no set A beam measurement set.

[0143] The learning is done by formulating an optimization problem whose maximization drives the adaptation of weight coefficients in a reinforcement learning fashion. Furthermore, the proposed approach introduces a two-sided AI / ML model, expanding on the current one-sided model. Finally, the proposal shifts from the current codebook-based beamforming design to a channel-based beamforming approach.Client Ref. No. P67055WO1 FIG.9A: Site Specific Online Fine-Tuning MIMO Beamforming

[0144] FIG.9A illustrates an example diagram of site-specific online fine-tuning process for multiple-input multiple-output (MIMO) beamforming in the proposed AI / ML beam management system. FIG. 9A depicts a network node (NW) connected to multiple userequipment (UE) devices (UE1, UE2, ..., UEN) through their respective channels (ℎ^, ℎ^,ℎ^), with a set of V Tx beams to be optimized, W is a set of B of Rx beams to be optimized, ^^^is a measurement of fine Tx beam functions (to be optimized), ^^^is a measurement of fine Rx beam functions (to be optimized), and ^^is a generated fine Rx beam, and ^^is a generated fine Tx beam.

[0145] In short, the operations of FIG. 9A involve the following operations. ^^^is a generator function that takes measurements ^ as input and produces fine Tx beams (^^^, ^^^, ^^^) for each UE. This function is to be optimized. ^^^is the generator^ as input and produces fine Rx beams (^^^, ^^^, ^^^) for each UE. This function is also to be optimized. ^ represents the measurements on Set B, calculatedas ^^ = |^^^(^^^^ ^)|^, for each UE, where ^ is the channel matrix.

[0146] The system evaluates the performance of the fine beams by computing ^^,= |^^^^ ^^|^for each UE, which represents the signal quality using the fine beams. An optimization routine aims to maximize an objective function !, which balances the expected signal quality of fine beams and wide beams. The optimization process updates thebeamforming matrices ("^, "^) and beam generation functions ("^^ for ^^^, "# for ^^^)iteratively. This creates a closed-loop system that continuously optimizes beam patterns based on specific cell conditions and user distributions.

[0147] Said differently, the NW uses a beamforming matrix ^ for transmission of wide beams, while the UEs use beamforming matrices ^ for reception. The process involves dynamically computing fine transmit (Tx) beams ^^^, ^^^, ^^^for each UE using a generator function ^^^, and fine receive (Rx) beams ^^^, ^^^, ^^^using a generator function ^^^. That is, the generator function ^^^takes the measurements ^ and produces fine Rx beams ^^for the UE. The Δ^^^, which is applied to the ^^^function, may be used to improve its performance in generating the fine Rx beam

[0148] These functions take measurements ^ as input, where ^ represents the received signal strength for each combination of Tx and Rx beams. For each UE, ^^= |^^^(^^^^^)|^, where ^^is the channel matrix for the $%ℎ UE, such as for example, ^^,^^, ^ , where N is a positive integer greater than 1. The system also computes, for theClient Ref. No. P67055WO1 coarse beams, ^^^={*∈^,&…,' ,( -. / 0}^^for each UE such as, for example, ^^^, ^^^, ^^. Thesystem evaluates the performance of the fine beams by computing ^^, = |^^^^ ^^|^for each UE. An optimization routine is used to maximize an objective function ! (e.g., ! optimization routine) defined in equation 1: ^^^ ^{23&,24' ,5(,6}! = 789∈^:|^^^^^| ; + (1 − 7){*∈ &^,…',(,}?^^^^^? (1),

[0149] beams andwide beams. The optimization process updates the beamforming matrices ("^, "^) andbeam generation functions ("^^, "#) iteratively.

[0150] FIG. 9A also illustrates the distinction between the initial access phase using coarse beams and the fine beam phase, illustrating the progression from wide beam measurements to fine beam optimization in the proposed system. FIG.9B: General Framework for BM With Cell Optimized Beam Patterns Management

[0151] FIG. 9B illustrates an example of a general framework for beam management with cell optimized beam patterns in accordance with some embodiments. FIG.9B depicts the interaction between a network node (NW) and multiple user equipment (UE) devices, showing how beam patterns are optimized using artificial intelligence and machine learning (AI / ML) techniques.

[0152] Similar to 9A, FIG.9B depicts a network node (NW) connected to multiple userequipment (UE) devices (UE1, UE2, ..., UEN) through their respective channels (ℎ^, ℎ^,ℎ^), where ^ is a set of Tx beams to be optimized, W is a set of B of Rx beams to be optimized, ^^^is a measurement of fine Tx beam functions (to be optimized), ^^^is a measurement of fine Rx beam functions (to be optimized), and ^^is a generated fine Rx beam, and ^^is a generated fine Tx beam.

[0153] The process involves measurements on Set B, represented by ^(^, ^), which aretaken across UEs and serve as input to both ^^^and ^^^functions. ^^^generates fine Tx beams (^^^, ^^^, ^^^) for each UE, while ^^^generates fine Rx beams (^^) for the UEs.

[0154] The optimization routine @ evaluates the performance of the beam managementstrategy using ^(^, ^, ^AB, ^^^) and produces updates to improve the system, including "^and Δ^^, for the NW side, and "^ and Δ^^^for the UE side. FIG. 9B depicts the transmission of computed fine beams and subsequent measurements, illustrating the iterative nature of the optimization process.Client Ref. No. P67055WO1 FIG.9C: General Framework for BM With Cell Optimized Beam Patterns Management

[0155] FIG.9C illustrates an example of site-specific online fine-tuning for multiple-input single-output (MISO) beamforming in accordance with some embodiments.

[0156] Similar to FIG. 9A and 9B, FIG. 9C depicts a network node (NW) connected to multiple user equipment (UE) devices (UE1, UE2, ..., UEN) through their respectivechannels (ℎ^, ℎ^, ℎ^). The NW employs a pilot transmit (Tx) codebook with wide beams,represented by the matrix ^. This Tx codebook is used to sweep signals across the UEs.Each UE makes power measurements ^^, ^^, … , ^ based on these sweeps, where ^ =|ℎ^^ |^for the $%ℎ UE.

[0157] A beam generator function ^(^) takes these measurements (e.g., ^^, ^^, … , ^ )as input and produces fine Tx beams ^^, ^^, , , , , ^^), for each UE. The system thenevaluates the performance of these fine beams by computing signal quality metrics ^^, ^^, ^ .

[0158] An optimization routine aims to maximize an objective function J, defined in equation 2 as: &^ ^{C',,6(! = 89∈^:|ℎ ^(ℎ |V) ^; (2),

[0159] which represents the expected signal quality using the fine beams. The design parameters of the Tx codebook and beam generator functions are denoted as E ={^FG^H9IJ , ^FG^H9IJ}.

[0160] The optimization process updates these parameters using a derivative-free optimization method, represented by the equation 3 as: E^K^ = LME^, ! (E^ , E^N^ … )O = E^ + {∆^, ∆Q} (3),

[0161] This approach is necessary because the channel coefficients are unknown, making it impossible to compute the derivatives of @ with respect to the design parameters directly. FIG.9C also provides that derivative-free optimization methods such as coordinate descent (multilevel coordinated search), Nelder-Mead method, particle swarm optimization, and simulated annealing can be used. Alternatively, reinforcement learning (RL) techniques may be applied. The site-specific online fine-tuning process for MISO beamforming allows the system to continuously adapt its beamforming strategies based on the specificClient Ref. No. P67055WO1 conditions of each cell and the distribution of users within it, potentially leading to improved performance in varying network scenarios. FIG.10 : Training Procedure and Signaling for AI / ML Beam Management

[0162] FIG.10 illustrates an example of the training procedure and signaling process for AI / ML beam management in accordance with some embodiments.

[0163] FIG.10 illustrates an example signaling flow of signaling 1000 between a network node (NW) and a user equipment (UE) for the training procedure of AI / ML beam management with cell optimized beam patterns. The signaling 1000 shown in FIG.10 may be used in conjunction with any of the systems, methods, and / or devices described in this disclosure. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows:

[0164] At step 1, the process begins with the network (NW) sweeping a set B of transmit (Tx) beams. Concurrently, the UE sweeps the set B of receive (Rx) beams.

[0165] In step 2, the UE reports measurements ^ on set B beams to the NW.

[0166] At step 3, the NW determine and / or compute a fine Tx beam from the measurements ^ using its generator function ^AB.

[0167] In step 4, the UE computes a fine Rx beam from the measurements Z using its generator function ^RB. The UE may also determine and / or compute the reference signal received power (RSRP) with the fine Tx / Rx beam pair and reports the RSRP to the NW.

[0168] In step 5, the NW can adjust the set B of Tx beams (^), the set B of Rx beams (^), and the beam generator functions (^AB and ^RB) based on the reported RSRP and other performance metrics.

[0169] The operations of signaling of steps 1-5 may then repeat, creating a continuous loop for ongoing optimization of the beam management strategy.

[0170] More specifically, as described herein, the operations used during training may begin with the network node (NW) and user equipment (UE) initializing coarse wide beam codebooks ^ and ^ for transmission and reception, respectively. The NW then sweeps the wide beams in ^, while the UE uses its sensing beams in ^ to make power measurementsfor each beam pair in (^, ^), denoted as ^. These wide beam measurements ^ serve asinput to both the UE's reception weight generator function ^RB() and the NW's transmission weight generator function ^AB(). ^RB() generates a fine beam reception weight for the UEClient Ref. No. P67055WO1 ^^(e.g., fine Rx beam ^^), while ^AB () produces a fine beam transmission weight for the NW ^^(e.g., fine beam ^^).

[0171] A function of the measurements ^, specifically ^^S = {*∈ &^,…',(,}^S, is used as input to the optimization routine for initial access. The NW then transmits using the fine beam ^^, and the UE receives with the fine Rx beam ^^.

[0172] A power measurement ^^is taken at each UE and reported back to the NW. The optimization routine uses these reported measurements as inputs to compute a cost function @ based on the current parameter settings. Using a derivative-free optimization method, the design parameters, denoted for the training as E ={^FG^H9IJ , QFG^H9IJ , ^FG^H9IJ , ^FG^H9IJ}, are updated according to the equation E^K^ = E^ +{∆^, ∆Q, ∆^, ∆#}^

[0173] Following this optimization, the NW updates its beamforming (BF) Tx wide beamcodebook and beam generator using {∆^, ∆Q}^. The NW can also send updatingparameters{∆^, ∆#}^ for the Rx wide beam codebook and Rx beam generator to the UE.The entire procedure then repeats these operations, creating an iterative process for continuous improvement of the beam management strategy. FIG.11: Deployment Phase (Inference) for AI / ML Beam Management

[0174] FIG.11 illustrates an example diagram 1100 of a deployment phase, or inference stage, for AI / ML beam management with cell optimized beam patterns.

[0175] FIG.11 depicts three main elements: a network node (NW) such as, for example base station 102, a user equipment (UE) such as, for example UE 106), and a server 1110. The NW and UE are shown to contain the optimized beam patterns and generator functions developed during the training phase.

[0176] Specifically, the base station 102 is represented with ^ and ^AB, where ^ is the optimized set B of transmit (Tx) beams, and ^AB is the fine Tx beam generator function. The UE 106 is shown with ^ and ^RB, where ^ is the optimized set B of receive (Rx) beams, and ^RB is the fine Rx beam generator function.

[0177] FIG. 11 illustrates the communication between the NW and UE, showing the transmission of the fine Tx beam (^^^) from the NW (e.g., base station 102) to the UE 106, and the UE's 106 reception using its fine Rx beam (^^^). The UE performs powermeasurements ^, denoted a ^ = |^^^(T)|^, where T is the received signal matrix.

[0178] The server 1110 is depicted, connected to both the NW (e.g., base station 102)Client Ref. No. P67055WO1 and the UE 106. It should be noted that training can also take place on server 1110, with the resulting models subsequently transferred to the NW and UE for deployment. Thus, FIG. 11 illustrates the deployment configuration of the AI / ML beam management system, showing how the optimized beam patterns and generator functions are utilized in real-world operation. It emphasizes the system's ability to leverage pre-trained models for efficient beam management in varying network conditions. FIG.12: Inference Signaling Procedure for AI / ML Beam Management

[0179] FIG.12 illustrates an example of inference signaling procedure for AI / ML beam management in accordance with some embodiments.

[0180] FIG.12 illustrates an example signaling flow of method 1200 between a network node (NW) and a user equipment (UE) for the inference phase of AI / ML beam management with cell optimized beam patterns. The method 1200 shown in FIG. 12 may be used in conjunction with any of the systems, methods, and / or devices described in this disclosure. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows:

[0181] At step 1, the method 1200 begins with the network (NW) sweeping a set B of transmit (Tx) beams. Concurrently, the UE sweeps the set B of receive (Rx) beams.

[0182] In step 2, the UE reports measurements ^ on set B beams to the NW.

[0183] At step 3, the NW determines and / or computes a fine Tx beam from the measurements Z using its generator function ^AB.

[0184] In step 4, the UE determines and / or computes a fine Rx beam from the measurements ^ using its generator function ^RB. The UE may also determine and / or compute the reference signal received power (RSRP) with the fine Tx / Rx beam pair and reports the RSRP to the NW.

[0185] More specifically, the method 1200 may begin with the network node (NW) sweeping the wide beams in the trained set ^. Simultaneously, the user equipment (UE) uses its sensing beams in the trained set ^ to make power measurements for each beampair in (^, ^). These measurements are collectively denoted as ^.

[0186] Using these measurements ^, the UE employs its trained generator function #() to estimate the optimum reception beam ^^. This step allows the UE to adapt its receiving beam pattern to the current channel conditions.Client Ref. No. P67055WO1

[0187] The UE then sends the measurements ^ to the NW. Upon receiving these measurements, the NW uses its own trained generator function Q() to estimate the optimum transmission beam ^^. This enables the NW to adapt its transmitting beam pattern based on the current channel state.

[0188] Thus, FIG.12 illustrates how the AI / ML beam management system operates by utilizing the optimized beam patterns and generator functions developed during the training phase to quickly adapt to current channel conditions and maintain optimal communication links. FIG.13: Flow Chart of AI / ML beam management with cell optimized beam patterns

[0189] FIG. 13 illustrates a flow chart of a method 1300 for performing artificial intelligence / machine learning (AI / ML) beam management with cell optimized beam patterns in accordance with some embodiments. The method 1300 shown in FIG.13 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the 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.

[0190] In some embodiments, the method 1300 may comprise dynamically determining and enhancing a set B of transmission (Tx) beams based on data collected from a network across user equipment (UE) distributions from a particular site, based on block 1310.

[0191] The method 1300 may comprise dynamically determining and enhancing a set B of reception (Rx) beams for a UE based on data collected from one or more of the networks or the UE, as in block 1320.

[0192] The method 1300 may comprise determining a Tx and Rx beam pair from the set B of Tx beams and the set B of Rx beams representing a true optimal Tx / Rx beam pair using one or more artificial intelligence (AI) models, wherein the one or more AI model comprise one or more two-sided AI models, as in block 1330.

[0193] The method 1300 may comprise performing channel-based beam forming based on the Tx and Rx beam pair, wherein the channel-based beam forming adapts weight coefficients using reinforcement learning, as in block 1340.

[0194] In some embodiments, the set B of Tx beams is smaller than a set A of Tx beams. In some embodiments, the set B of Rx beams is smaller than a set A of Rx beams. In some embodiments, dynamically determining and enhancing the set B of Tx beams is based onClient Ref. No. P67055WO1 online site-specific learning. In some embodiments, dynamically determining and enhancing the set B of Tx beams is based on reinforcement learning.

[0195] In some embodiments, the UE has different set B patterns for the set B of Rx beams depending on a current site where the UE is located.

[0196] In some embodiments, the method 1300 further comprises performing measurements only at the set B of Tx beams and the set B of Rx beams. In some embodiments, no measurements are performed at a set A of beams.

[0197] In some embodiments, the one or more AI models comprise a first AI model at the UE for selecting beams from the set B of Rx beams.

[0198] In some embodiments, the one or more two-sided AI models comprise a second AI model at the network for selecting beams from the set B of Tx beams.

[0199] In some embodiments, the method 1300 further comprises formulating an objective function that maximizes a weighted sum of: a first term representing an expected beam forming gain using fine beams, and a second term representing an expected maximum beam forming gain using wide beams, wherein the objective function is based on beam generator functions, beam codebooks, fine beams, and wide beams.

[0200] In some embodiments, the determining the Tx and Rx beam pair further comprising using derivative-free optimization operations.

[0201] In some embodiments, the derivative-free optimization operations comprise a coordinate descent.

[0202] In some embodiments, the derivative-free optimization operations comprise a multilevel coordinated search.

[0203] In some embodiments, the derivative-free optimization operations comprise a Nelder-Mead method.

[0204] In some embodiments, the derivative-free optimization operations comprise particle swarm optimization.

[0205] In some embodiments, the derivative-free optimization methods comprise simulated annealing.

[0206] In some embodiments, the method 1300 further comprises decoding, from the network, swept transmissions from the set B of Tx beams; and sweeping, by the UE, the set B of Rx beams.

[0207] In some embodiments, the method 1300 further comprises encoding, for transmission to the network, measurements on the set B of Tx beams and the set B of Rx beams.Client Ref. No. P67055WO1

[0208] In some embodiments, the method 1300 further comprises decoding, from the network, information about a fine Tx beam determined by the network based on the measurements; and determining, by the UE, a fine Rx beam from the measurements.

[0209] In some embodiments, the method 1300 further comprises determining, by the UE, a Reference Signal Received Power (RSRP) with the fine Tx beam and the fine Rx beam and reporting the RSRP to the network.

[0210] In some embodiments, the method 1300 further comprises initializing a coarse wide beam codebook V for transmission and a coarse wide beam codebook W for reception, where V is a positive integer.

[0211] In some embodiments, the method 1300 further comprises sweeping, by the network, wide beams in the coarse wide beam codebook V; using, by the UE, sensing beams in the coarse wide beam codebook W to make a power measurement for each beam pair in (V,W); and denoting the power measurements as Z, where W is a positive integer.

[0212] In some embodiments, the method 1300 further comprises using the power measurements Z as input to a Rx reception weight generator function that generates a fine beam reception weight to be used at the UE; and sending the power measurements Z to the network to serve as input to a transmission weight generator function that generates a fine beam transmission weight to be used at the network, where Z is a positive integer

[0213] In some embodiments, an apparatus is disclosed that is configured to cause a user equipment (UE), having a processor coupled to memory, to perform any of the operations of the method 1300. FIG.14: Flow Chart of AI / ML beam management with cell optimized beam patterns

[0214] FIG. 14 illustrates a flow chart of a method 1400 for performing artificial intelligence / machine learning (AI / ML) beam management with cell optimized beam patterns by a network in accordance with some embodiments. The method 1300 shown in FIG.14 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[0215] In some embodiments, the method 1400 may comprise dynamically determining and enhancing a set B of transmission (Tx) beams based on data collected across one or more user equipment (UE) distributions from a particular site, as in block 1410.Client Ref. No. P67055WO1

[0216] The method 1400 may comprise sweeping the set B of Tx beams across one or more UEs, as in block 1420. The method 1400 may comprise decode, from the one or more UEs, measurement data on the set B of Tx beams and a set B of reception (Rx) beams, wherein no set A beam measurement set is used, as in block 1430.

[0217] The method 1400 may comprise determining, using a generator function, a fine Tx beam for each of the one or more UEs based on the measurement data, as in block 1440.

[0218] The method 1400 may comprise performing channel-based beam forming using the fine Tx beams, as in block 1450.

[0219] The method 1400 may comprise adjusting (adapting) weight coefficients for beam forming based on maximization of an optimization function, where the adjusting is performed using reinforcement learning, as in block 1460.

[0220] The method 1400 may comprise updating the set B of Tx beams and the generator function based on the adjusted (adapted) weight coefficients, as in block 1470.

[0221] The method 1400 may comprise perform a two-sided artificial intelligence (AI) model in conjunction with the one or more UEs for beam management, as in block 1480.

[0222] In some embodiments, an apparatus is disclosed that is configured to cause a base station, having a processor coupled to memory, to perform any of the operations of the method 1400.

[0223] Embodiments of the present disclosure may be realized in any of various forms. For example, some embodiments may be realized as a computer-implemented method, a computer readable memory medium, or a computer system. Other embodiments may be realized using one or more custom-designed hardware devices such as ASICs. Still other embodiments may be realized using one or more programmable hardware elements such as FPGAs.

[0224] 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.

[0225] In some embodiments, a device (e.g., a UE 106) may be configured to include a processor (or a set of processors) and a memory medium, where the memory mediumClient Ref. No. P67055WO1 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.

[0226] 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.

[0227] 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.

[0228] The present disclosure contemplates that, in some embodiments, data used by AI / ML beam management with cell optimized beam patterns processes include 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 AI / ML beam management with cell optimized beam patterns processes, should attempt to comply with well-established privacy policies and / or privacy practices.

[0229] 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 AI / ML beam management with cell optimized beam patterns 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 AI / ML beam management with cell optimized beam patterns processes development, training, and use, including data collection, data preparation, model training, modelClient Ref. No. P67055WO1 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 to certify, 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.

[0230] In some embodiments, AI / ML beam management with cell optimized beam patterns 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.

[0231] 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.Client Ref. No. P67055WO1

[0232] In some embodiments, the present disclosure contemplates that data used for AI / ML beam management with cell optimized beam patterns processes may be kept strictly separated from platforms where the AI / ML beam management with cell optimized beam patterns processes are deployed and / or used to interact with users and / or process data. In such embodiments, data used for offline training of the AI / ML beam management with cell optimized beam patterns 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 AI / ML beam management with cell optimized beam patterns 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 the AI / ML beam management with cell optimized beam patterns 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 encryption and / or secure communication.

[0233] 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 AI / ML beam management with cell optimized beam patterns processes. The AI / ML beam management with cell optimized beam patterns processes should be monitored for changes in underlying data distribution such as concept drift or data skew that can degrade performance of the AI / ML beam management with cell optimized beam patterns processes over time.

[0234] In some embodiments, the AI / ML beam management with cell optimized beam patterns 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 the AI / ML beam management with cell optimized beam patterns processes 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.

[0235] In some embodiments, the AI / ML beam management with cell optimized beam patterns processes may be designed with safeguards to maintain adherence to originally intended purposes, even as the AI / ML beam management with cell optimized beam patterns processes adapt based on new data. Any significant changes in data collection and / orClient Ref. No. P67055WO1 applications of AI / ML beam management with cell optimized beam patterns 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.

[0236] 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 the AI / ML beam management with cell optimized beam patterns 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 the AI / ML beam management with cell optimized beam patterns 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 AI / ML beam management with cell optimized beam patterns processes for training or inference purposes, and / or reminded when the AI / ML beam management with cell optimized beam patterns processes generate outputs or make decisions based on their data.

[0237] The present disclosure recognizes AI / ML beam management with cell optimized beam patterns and reinforcement learning 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 the AI / ML beam management with cell optimized beam patterns processes. Generated content can be filtered for potentially inappropriate or dangerous material prior to being presented toClient Ref. No. P67055WO1 users, while human oversight and / or ability to override or correct erroneous or undesirable outputs can be maintained as a failsafe.

[0238] The present disclosure further contemplates that users of the AI / ML beam management with cell optimized beam patterns and reinforcement learning processes should refrain from using the services in any manner that infringes upon, misappropriates, or violates the rights of any party. Furthermore, the AI / ML beam management with cell optimized beam patterns 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 AI / ML beam management with cell optimized beam patterns 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, the AI / ML beam management with cell optimized beam patterns processes should not interfere with any security, digital signing, digital rights management, content protection, verification, or authentication mechanisms. The AI / ML beam management with cell optimized beam patterns processes should not misrepresent machine-generated outputs as being human-generated.

Claims

Client Ref. No. P67055WO1 CLAIMS What is claimed is:

1. A method of performing artificial intelligence / machine learning (AI / ML) beam management with cell optimized beam patterns, the method comprising: dynamically determining and enhancing a set B of transmission (Tx) beams based on data collected from a network across user equipment (UE) distributions from a particular site; dynamically determining and enhancing a set B of reception (Rx) beams for a UE based on data collected from one or more of the network or the UE; determining a Tx and Rx beam pair from the set B of Tx beams and the set B of Rx beams representing a true optimal Tx / Rx beam pair using one or more artificial intelligence (AI) models, wherein the one or more AI model comprise one or more two-sided AI models; and performing channel-based beam forming based on the Tx and Rx beam pair, wherein the channel-based beam forming adapts weight coefficients using reinforcement learning.

2. The method of claim 1, wherein the set B of Tx beams is smaller than a set A of Tx beams.

3. The method of claim 1, wherein the set B of Rx beams is smaller than a set A of Rx beams.

4. The method of claim 1, wherein dynamically determining and enhancing the set B of Tx beams is based on online site-specific learning.

5. The method of claim 1, wherein dynamically determining and enhancing the set B of Tx beams is based on reinforcement learning.

6. The method of claim 1, wherein the UE has different set B patterns for the set B of Rx beams depending on a current site where the UE is located.Client Ref. No. P67055WO1 7. The method of claim 1, further comprising performing measurements only at the set B of Tx beams and the set B of Rx beams.

8. The method of claim 7, wherein no measurements are performed at a set A of beams.

9. The method of claim 1, wherein the one or more AI models comprise a first AI model at the UE for selecting beams from the set B of Rx beams.

10. The method of claim 1, wherein the one or more two-sided AI models comprise a second AI model at the network for selecting beams from the set B of Tx beams.

11. The method of claim 1, further comprising formulating an objective function that maximizes a weighted sum of: a first term representing an expected beam forming gain using fine beams, and a second term representing an expected maximum beam forming gain using wide beams, wherein the objective function is based on beam generator functions, beam codebooks, fine beams, and wide beams.

12. The method of claim 1, wherein determining the Tx and Rx beam pair further comprising using derivative-free optimization operations.

13. The method of claim 12, wherein the derivative-free optimization operations comprise a coordinate descent.

14. The method of claim 12, wherein the derivative-free optimization operations comprise a multilevel coordinated search.

15. The method of claim 12, wherein the derivative-free optimization operations comprise a Nelder-Mead method.

16. The method of claim 12, wherein the derivative-free optimization operations comprise particle swarm optimization.Client Ref. No. P67055WO1 17. The method of claim 12, wherein the derivative-free optimization methods comprise simulated annealing.

18. The method of claim 1, further comprising: decoding, from the network, swept transmissions from the set B of Tx beams; and sweeping, by the UE, the set B of Rx beams.

19. The method of claim 18, further comprising encoding, for transmission to the network, measurements on the set B of Tx beams and the set B of Rx beams.

20. The method of claim 19, further comprising: decoding, from the network, information about a fine Tx beam determined by the network based on the measurements; and determining, by the UE, a fine Rx beam from the measurements.

21. The method of claim 20, further comprising: determining, by the UE, a Reference Signal Received Power (RSRP) with the fine Tx beam and the fine Rx beam and reporting the RSRP to the network.

22. The method of claim 1, further comprising initializing a coarse wide beam codebook V for transmission and a coarse wide beam codebook W for reception, where V is a positive integer.

23. The method of claim 22, further comprising: sweeping, by the network, wide beams in the coarse wide beam codebook V; using, by the UE, sensing beams in the coarse wide beam codebook W to make a power measurement for each beam pair in (V,W); and denoting the power measurements as Z, where W is a positive integer.

24. The method of claim 23, further comprising: using the power measurements Z as input to a Rx reception weight generator function that generates a fineClient Ref. No. P67055WO1 beam reception weight to be used at the UE; and sending the power measurements Z to the network to serve as input to a transmission weight generator function that generates a fine beam transmission weight to be used at the network, where Z is a positive integer.

25. An apparatus, having one or more processors, coupled to a memory, configured to cause a user equipment (UE) to perform any of the methods of claims 1 to 24.

26. An apparatus of a user equipment (UE) comprising: one or more processors, coupled to a memory, configured to: dynamically determine and enhance a set B of transmission (Tx) beams based on data collected from a network across user equipment (UE) distributions from a particular site; dynamically determine and enhance a set B of reception (Rx) beams for a UE based on data collected from one or more of the network or the UE; determine a Tx and Rx beam pair from the set B of Tx beams and the set B of Rx beams representing a true optimal Tx / Rx beam pair using one or more artificial intelligence (AI) models, wherein the one or more AI model comprise one or more two-sided AI models; and perform channel-based beam forming based on the Tx and Rx beam pair, wherein the channel-based beam forming adapts weight coefficients using reinforcement learning.

27. An apparatus of a base station comprising: one or more processors, coupled to a memory, configured to: dynamically determine and enhance a set B of transmission (Tx) beams based on data collected across one or more user equipment (UE) distributions from a particular site; sweep the set B of Tx beams across one or more UEs; decode, from the one or more UEs, measurement data on the set B of Tx beams and a set B of reception (Rx) beams, wherein no set A beam measurement set is used;Client Ref. No. P67055WO1 determine, using a generator function, a fine Tx beam for each of the one or more UEs based on the measurement data; perform channel-based beam forming using the fine Tx beams; adjust weight coefficients for beam forming based on maximization of an optimization function, wherein the adjusting is performed using reinforcement learning; update the set B of Tx beams and the generator function based on adjusted weight coefficients; and perform a two-sided artificial intelligence (AI) model in conjunction with the one or more UEs for beam management.

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

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