Advanced wireless communication AI / ML training techniques

Abstract

Event triggering and / or periodic methods for AI / ML applications of a Radio Access Network (RAN) are disclosed. These methods facilitate federated learning, which may include distributing a global model located in a radio access network or server BS to UEs after collecting local model feedback.

Description

Technical Field

[0001] Various embodiments relate generally to the field of wireless communications. Background Art

[0002] Artificial intelligence (AI) or machine learning (ML) is used in many different applications and fields as it contributes to greater performance improvements than existing technologies. In wireless or mobile communication networks, when two or more devices communicate wirelessly, AI / ML can also be used to achieve better performance in various use cases or applications. However, there are challenges in applying AI / ML, and some of these challenges include high signaling traffic load and increased device power consumption due to AI / ML operations in wireless devices.

[0003] In the Radio Access Network (RAN) with wireless device connections, it is necessary to consider the interaction between the mobile device (UE) and the base station (BS) and other network devices (such as mobile edge computing devices (MEC) and non-terrestrial network devices (NTN)), so that AI / ML operations in the RAN can overcome the key challenges of high signaling traffic load and increased device power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 A block diagram of an example wireless communication network environment for a network device (e.g., UE, AN, gNB, or eNB) according to various aspects or embodiments is shown.

[0005] Figure 2 is a diagram illustrating example AI / ML federated learning in accordance with one or more embodiments.

[0006] Figure 3 is an example parameter threshold (L) for an event triggering method according to one or more embodiments. th ) curve graph.

[0007] Figure 4 is a flow chart illustrating a method of distributing a threshold profile index to a candidate set(s) of UEs in accordance with one or more embodiments.

[0008] Figure 5 is a flow chart illustrating an event-triggered convergence method according to one or more embodiments.

[0009] Figure 6 is a diagram illustrating signaling between a BS and a UE for an event triggering method according to one or more embodiments.

[0010] Figure 7 is a flow chart illustrating a periodic method according to one or more embodiments.

[0011] Figure 8 is a flow chart illustrating a hybrid method using a triggered technique and a periodic technique according to one or more embodiments.

[0012] Fig. 9 and Fig.10 Examples of event and periodic training according to one or more aspects / embodiments are presented.

[0013] Fig.11 is a flow chart illustrating a hybrid method using a triggered technique and a periodic technique according to one or more embodiments. DETAILED DESCRIPTION

[0014] The present disclosure will now be described with reference to the accompanying drawings, wherein throughout the text, the same reference numerals are used to refer to the same elements, and wherein the structures and devices shown are not necessarily drawn to scale. The same reference numerals may be used to identify the same or similar elements in different figures. In the following description, for the purpose of explanation rather than limitation, specific details such as specific structures, architectures, interfaces, technologies, etc. are described to provide a thorough understanding of various aspects of various embodiments. However, it will be apparent to those skilled in the art who benefit from the present disclosure that various aspects of various embodiments can be practiced in other examples that deviate from these specific details. In some instances, descriptions of well-known devices, circuits, and methods are omitted to avoid blurring the description of various embodiments with unnecessary details. The embodiments herein may relate to RAN1, RAN2, 5G, etc.

[0015] As used herein, the terms "component," "system," "interface," and the like are intended to refer to computer-related entities, hardware, software (e.g., in execution), and / or firmware. For example, a component can be a processor, a process running on a processor, a controller, an object, an executable program, a program, a storage device, and / or a computer having a processing device. By way of illustration, an application running on a server and the server can also be a component. One or more components can reside within a process, and components can be located on one computer and / or distributed between two or more computers. A group of elements or a group of other components can be described herein, where the term "group" can be interpreted as "one or more."

[0016] Further, for example, the components may be executed from various computer-readable storage media having various data structures stored thereon, such as using modules. The components may communicate via local and / or remote processes, such as according to signals having one or more data packets (e.g., data from one component interacts with another component in a local system, a distributed system, and / or other systems across a network (e.g., the Internet, a local area network, a wide area network, or the like) via the signal).

[0017] As another example, a component may be a device having a specific functionality provided by a mechanical part operated by an electrical or electronic circuit system, wherein the electrical or electronic circuit system may be operated by a software application or by a firmware application executed by one or more processors. The one or more processors may be internal or external to the device and may execute at least a portion of the software or firmware application. As yet another example, a component may be a device providing a specific functionality by an electronic component without a mechanical part; the electronic component may include one or more processors therein to execute the software and / or firmware that at least partially imparts the functionality of the electronic component.

[0018] The use of the word "exemplary" is intended to present concepts in a concrete manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any natural inclusive arrangement. That is, if X employs A; X employs B; or X employs both A and B, then in any of the aforementioned instances, "X employs A or B" is satisfied. In addition, unless otherwise specified or clear from the context to refer to a singular form, the articles "a / an" as used in this application and the appended claims should generally be interpreted to mean "one or more". In addition, if the terms "including / includes", "having / has", "with" or variations thereof are used in the detailed description and claims, these terms are intended to be inclusive in a manner similar to the term "comprising".

[0019] Mobile communications have evolved from early voice systems to highly complex integrated communication systems or platforms. It is expected that the next generation of wireless / mobile communication systems (such as 5G and New Radio (NR)) are unified networks / systems whose goal is to meet different and even conflicting performance dimensions and services. This diverse multi-dimensional requirement is driven by different services and applications. Typically, NR will evolve based on 3GPP LTE-Advanced, with the addition of potential new radio access. Further, it is expected that NR will evolve with the addition of potential new radio access technologies (RATs) to enrich mobile communications with improved, simple and seamless wireless connection solutions. NR can enable mobile communications that provide fast and rich content and services.

[0020] Some approaches for mobile communications utilize models with data and algorithmic solutions as input to generate outputs for use cases.

[0021] The AI / ML approach uses an AI / ML model with data and training sets / data as input to generate output for the use case.

[0022] It should be understood that AI / ML and / or AI / ML models can be used for use cases, data set collection, data set validation, interactive work and data information flow, architecture interfaces, processing capabilities of terminal devices, etc.

[0023] Technologies based on artificial intelligence / machine learning (AI / ML) can be used with 3GPP. AI / ML can be used in 5G evolution and 6G phases.

[0024] By considering BS-UE / UE-UE / BS-BS collaboration scenarios to support AI / ML operations, AI / ML can be used for RAN applications such as PHY, MAC, etc. AI / ML can facilitate collaborative-level interactive operations and data information flows to achieve AI / ML support for AI / ML-supported communication modes.

[0025] It should be appreciated that by employing AI / ML, AI / ML can enhance the performance of many different layers / levels of wireless networks.

[0026] Typically, AI / ML federated learning involves collecting local models (w) to update feedback before distributing a global model (g) from a BS or server to multiple UEs.

[0027] One or more embodiments are disclosed to facilitate managing UE participation in AI / ML model training. These include event-triggered methods, periodic methods, combinations of event-triggered methods and periodic methods, and the like.

[0028] Figure 1 The architecture of a system 100 of a network according to some embodiments is shown. The system 100 is shown to include user equipment (UE) 101, 102, 103, and 104. The UEs 101 to 104 are shown as smart phones (e.g., handheld touch screen mobile computing devices that can connect to one or more cellular networks), but may also include any mobile or non-mobile computing devices, such as personal digital assistants (PDAs), pagers, laptop computers, desktop computers, wireless handheld devices, automotive equipment (e.g., vehicles), or any computing device that includes a wireless communication interface.

[0029] In some embodiments, any of UEs 101 to 104 may include an Internet of Things (IoT) UE, which may include a network access layer designed for low-power IoT applications (which utilize short-term UE connections). The IoT UE may utilize technologies such as machine-to-machine (M2M) or machine-type communication (MTC) to exchange data with an MTC server or device via a public land mobile network (PLMN), proximity-based services (ProSe) or device-to-device (D2D) communication, a sensor network, or an IoT network. The M2M or MTC data exchange may be a machine-initiated data exchange. The IoT network describes IoT UEs interconnected with short-term connections, which may include uniquely identifiable embedded computing devices (within the Internet infrastructure). The IoT UE may execute background applications (e.g., keep-alive messages, status updates, etc.) to facilitate connectivity to the IoT network.

[0030] The UEs 101 to 104 may be configured to connect (e.g., be communicatively coupled) to radio access networks (RANs) 111 and 112, which may be, for example, evolved universal mobile telecommunications system (UMTS) terrestrial radio access networks (E-UTRANs), NextGen RANs (NG RANs), or some other type of RANs. The UEs 101 to 104 are wirelessly connected to the BSs, and the air interface technology may be based on a cellular communication protocol, such as a global system for mobile communications (GSM) protocol, a code division multiple access (CDMA) network protocol, a push-to-talk (PTT) protocol, a cellular PTT (POC) protocol, a universal mobile telecommunications system (UMTS) protocol, a 3GPP long term evolution (LTE) protocol, a fifth generation (5G) protocol, a new radio (NR) protocol, etc.

[0031] In this embodiment, UE 101 to 104 can further directly exchange communication data via a sidelink interface, which includes one or more logical channels, including but not limited to a physical sidelink control channel (PSCCH), a physical sidelink shared channel (PSSCH), a physical sidelink discovery channel (PSDCH), and a physical sidelink broadcast channel (PSBCH).

[0032] An access node (AN) may be referred to as a base station (BS), NodeB, evolved NodeB (eNB), next generation NodeB (gNB), RAN node, etc., and may include a ground station (e.g., a terrestrial access point) or a satellite station providing coverage within a geographic area (e.g., a cell). A network device as referred to herein may include any of these APs, ANs, UEs, or any other network components.

[0033] In this embodiment, CN 121 provides the functionality to communicate with the UE, store its subscriptions and credentials, allow access to external networks and services, provide security, and manage network access and mobility.

[0034] The AN may include circuitry (eg, baseband circuitry), memory, a network interface (eg, an RF interface), one or more processors, and the like.

[0035] Figure 2 is a diagram illustrating example AI / ML federated learning in accordance with one or more embodiments.

[0036] For AI / ML federated learning, after the initial shared global model configuration, the global model ("g") update in the BS is distributed to each UE after collecting local model ("w") update feedback from each UE. However, the location of the global model can be not only the BS, but also an edge computing device or a remote server, etc.

[0037] This needs to be done iteratively, and each UE can be controlled to participate in model training more efficiently.

[0038] For current BS-UE / UE-UE / BS-BS communications, support for AI / ML operations is generally not considered.

[0039] For example, distributed AI / ML training between a BS and multiple UEs has potential challenges, such as large signaling traffic during the training process and increased device power consumption.

[0040] The event-triggered approach is applied to schedule a subset of UEs and their local AI / ML model training under a certain communication mode for global AI / ML model training.

[0041] In order to determine the UE subset, a threshold (L th ) to filter out UEs that have not participated in AI / ML model training. Specifically,

[0042] If ≥ L th (predefined threshold), the UE device joins the global model training

[0043] if <L th (predefined threshold), the UE device does not participate in the global model training

[0044] Pre-configured thresholds can be set based on the configured threshold profile index (L th ).

[0045] Figure 3 is an example parameter threshold (L) for an event triggering method according to one or more embodiments. th ) curve graph.

[0046] The x-axis depicts the UE index and the y-axis depicts the prioritized parameter index.

[0047] Parameter thresholds are shown as dashed lines.

[0048] In this example, UE devices greater than or equal to the threshold join the global model training, while UE devices below the threshold do not join.

[0049] UE subset selection using multiple parameter thresholds with parameter prioritization. The selection criteria are based on predefined parameter thresholds for prioritized parameters. Multiple thresholds may be used for UE subset selection triggering, where different weights may be assigned to each parameter prioritization for selection decision.

[0050] Figure 4 is a flow chart illustrating a method of distributing threshold profile indexes to (multiple) UE candidate sets according to one or more embodiments. For illustrative purposes, the method is shown in a certain order, but it should be understood that it can be operated in other suitable orders.

[0051] The method may be performed by an AN (such as a BS).

[0052] The method begins at 401 where a threshold profile for AI / ML model training is extracted.

[0053] At 402, an applicable threshold profile for triggering selection of a subset of UEs is determined.

[0054] At 403, the determined threshold profile is distributed to the candidate set of UEs.

[0055] The parameter profiles of the threshold index are generated based on the appropriate given policies in the network to apply relevant thresholds for different situations.

[0056] The parameter profile of the threshold index is used to trigger a subset of UEs to perform AI / ML local training.

[0057] The parameter configuration file contains parameter sets for the following:

[0058] Communication domain: Link quality indication of wireless channels

[0059] Training domain: an indication of the AI / ML training configuration (e.g., minimum number of iterations, minimum level of performance improvement, accuracy, sensitivity, data size, convergence error level)

[0060] Device domain: indication of computing power, energy consumption, memory capacity

[0061] Data domain: an indication of the data / feature characteristics of AI / ML training

[0062] The relevant threshold index is selected based on the AI / ML model training strategy and application use case.

[0063] Figure 5 is a flow chart illustrating an event-triggered convergence method according to one or more embodiments.

[0064] At 501 , the BS initiates AI / ML global model training with parameter configuration.

[0065] At 502, the BS identifies a target UE group to perform AI / ML local model training based on a threshold profile index.

[0066] At 503, the BS transmits the global AI / ML model to the target UE group.

[0067] At 504, the local AI / ML model is updated using the local data.

[0068] At 505, a feedback local AI / ML model update is generated.

[0069] At 506, the BS updates the global AI / ML model.

[0070] At 507, the BS determines whether the global AI / ML model meets the target training convergence. If so, the target training convergence has been obtained. If not, resetting the UE group is considered at 508, and the method returns to 502.

[0071] Figure 6 is a diagram illustrating signaling between a BS and a UE for an event triggering method according to one or more embodiments.

[0072] At 601, the BS and UE signal RRC setup.

[0073] At 602, the UE provides UE capability information to the BS.

[0074] At 603, the BS and UE signal RRC reconfiguration.

[0075] At 604, the UE provides a UE measurement report.

[0076] At 605, the BS determines a target UE group for training and global model initiation.

[0077] At 606, the BS provides the AI / ML global model update to the UE.

[0078] At 607, the UE determines whether the local model training update satisfies a predefined threshold profile index.

[0079] At 608, the UE provides the determination as AI / ML local model feedback to the BS.

[0080] At 609, the BS adapts the predefined threshold profile index for the target UE group and (re-)adapts.

[0081] At 610, the BS provides the AI / ML global model update to the UE.

[0082] Figure 7 is a flow chart illustrating a periodic method according to one or more embodiments.

[0083] A timer for UE (e.g., periodicGlobalModel-Timer) is introduced to enable each UE to join the global AI / ML model training as a periodic method.

[0084] As an example, the UE may initiate a periodicGlobalModel-Timer and an initial UE subset selection after receiving the AI / ML model configuration information.

[0085] When periodicGlobalModel-Timer expires, the UE joins the candidate UE subset to operate local AI / ML model training. The UE restarts periodicGlobalModel-Timer after joining each global model training.

[0086] The gNB configures the periodicGlobalModel-Timer via, for example, an RRC reconfiguration message or system information.

[0087] The maximum value of the periodicGlobalModel-Timer can be set to "infinity", which means that the periodic method can be disabled.

[0088] refer to Figure 7 , at 701, reset the timer for each UE's global AI / ML model training participation.

[0089] At 702, a timer check is performed.

[0090] After the timer at 702 expires, at 704, receive a global AI / ML model update. Otherwise, at 703, wait for the timer to expire.

[0091] At 705, local AI / ML model training is updated.

[0092] At 706, feedback of the local AI / ML model is generated / updated.

[0093] At 707, the global AI / ML model is updated.

[0094] Figure 8 is a flow chart illustrating a hybrid method using a triggered technique and a periodic technique according to one or more embodiments.

[0095] Based on the event-triggered method and the periodic method, a combination of the two can also be used to enable a subset of UEs to be used for global AI / ML model training.

[0096] For this combined approach (Type 1) implementation, if ≥ L th (pre-configured threshold) and the timer expires, the UE device joins the global model training. <L th (pre-configured threshold) and the timer expires, the UE device does not join the global model training.

[0097] Pre-configured thresholds can be set based on the configured threshold profile index (L th ).

[0098] At 802, a timer for each UE's global AI / ML model training participation is reset.

[0099] At 804, a timer check is performed.

[0100] If the timer expires, a threshold check is performed at 806. Otherwise, no AI / ML model training is performed at 808.

[0101] If the threshold check is negative, then no AI / ML model training is performed at 808 either.

[0102] If the threshold check is yes, then at 810, the set of candidate UEs participating in the global AI / ML model training is joined.

[0103] At 812, local AI / ML model training is updated.

[0104] At 814, local AI / ML model updates are provided as feedback.

[0105] At 816, the global AI / ML model is updated.

[0106] Fig. 9 and Fig.10 Examples of event and periodic training according to one or more aspects / embodiments are presented.

[0107] exist Fig. 9 In , there is an event trigger and the UE periodically skips global AI / ML model training. Here, the event is triggered before the timer expires (Type 2).

[0108] exist Fig.10 In the example, if no triggering event occurs before the timer expires (type 3), the UE periodically joins the global AI / ML model training.

[0109] Fig.11 is a flow chart illustrating a hybrid method using a triggered technique and a periodic technique according to one or more embodiments.

[0110] At 1101, the timer for each UE's global AI / ML model training participation is reset.

[0111] At 1102, a check of a timer is performed.

[0112] At 1103, an event may be triggered.

[0113] If the timer expires and / or the event is not triggered, then at 1104, join the set of candidate UEs participating in the global AI / ML model training.

[0114] At 1105, local AI / ML model training is updated.

[0115] At 1106, local AI / ML model updates are provided as feedback.

[0116] At 1107, the global AI / ML model is updated.

[0117] If an event is triggered at 1103 , participation in global AI / ML model training is skipped at 1108 .

[0118] It should be understood that the disclosure of multiple steps or functions disclosed in the specification or claims cannot be interpreted as being in a specific order. Therefore, the disclosure of multiple steps or functions does not limit these to a specific order unless such steps or functions are not interchangeable for technical reasons. In addition, in some embodiments, a single step may include or may be divided into multiple sub-steps. Unless explicitly excluded, such sub-steps may be included in the disclosure of the single step and be part of it.

[0119] A system of one or more computers may be configured to perform specific operations or actions by installing software, firmware, hardware, or a combination thereof on the system that, in operation, causes the system to perform actions or causes the system to perform actions. One or more computer programs may be configured to perform specific operations or actions by including instructions that, when executed by a data processing device, cause the device, system, etc. to perform these actions.

[0120] As used herein, the term "circuitry" may refer to, components of, or include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group), and / or memory (shared, dedicated, or group) that executes one or more software or firmware programs, a combinational logic circuit, and / or other suitable hardware components that provide the described functionality. In some embodiments, the circuitry may be implemented in one or more software or firmware modules, or functions associated with the circuitry may be implemented by one or more software or firmware modules. In some embodiments, the circuitry may include logic that is at least partially operable in hardware.

[0121] As used in this specification, the term "processor" may refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single processor with software multithreaded execution capability; a multi-core processor; a multi-core processor with software multithreaded execution capability; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application-specific integrated circuit, a digital signal processor, a field programmable gate array, a programmable logic controller, a complex programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions and / or processes described herein. The processor may utilize nanoscale architectures, such as, but not limited to, transistors, switches, and gates based on molecules and quantum dots, in order to optimize space usage or enhance the performance of mobile devices. The processor may also be implemented as a combination of computing processing units.

[0122] In this specification, terms such as "storage", "data storage", "data storage device", "database", and any other information storage components substantially related to the operation and functionality of the components and / or processes refer to "memory components", or entities embodied in "memory" or components that include memory. It should be noted that the memory components described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory.

[0123] By way of illustration and not limitation, for example, non-volatile memory may be included in memory, non-volatile memory (see below), disk storage (see below), and memory storage (see below). Further, non-volatile memory may be included in read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable programmable read-only memory, or flash memory. Volatile memory may include random access memory, which acts as an external cache memory.

[0124] It should be understood that the various aspects described herein can be implemented by hardware, software, firmware or any combination thereof. When implemented with software, the function can be transmitted or stored on a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, and communication media include any media that facilitates the transfer of computer programs from one place to another. Storage media or computer-readable storage devices can be any available media that can be accessed by general or special computers. By way of example and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage devices, disk storage devices or other magnetic storage devices, or other tangible and / or non-transient media, which can be used to carry or store desired information or executable instructions. In addition, any connection is properly referred to as a computer-readable medium. For example, if a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave are used to transmit software from a website, server or other remote source, then coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital disc (DVD), floppy disk and Blu-ray disc, wherein disks usually reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0125] The various illustrative logics, logic blocks, modules, and circuits described in conjunction with the aspects disclosed herein may be implemented or performed using a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, a combination of one or more microprocessors and a DSP core, or any other such configuration. In addition, at least one processor may include one or more modules operable to perform one or more steps and / or actions described herein.

[0126] For software implementation, the technology described herein can be implemented with a module (e.g., program, function, etc.) that performs the functions described herein. The software code can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor, in which case the memory unit can be communicatively coupled to the processor by various means known in the art. In addition, at least one processor may include one or more modules that are operable to perform the functions described herein.

[0127] The technology described herein can be used for various wireless communication systems, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA and other systems. The terms "system" and "network" are usually used interchangeably. A CDMA system can implement radio technologies such as Universal Terrestrial Radio Access (UTRA), CDMA1800, etc. UTRA includes other variants of Wideband-CDMA (W-CDMA) and CDMA. Further, CDMA1800 covers IS-1800, IS-95 and IS-856 standards. A TDMA system can implement radio technologies such as Global System for Mobile Communications (GSM). An OFDMA system can implement radio technologies such as Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.18, Flash-OFDM, etc. UTRA and E-UTRA are part of Universal Mobile Telecommunications System (UMTS). 3GPP Long Term Evolution (LTE) is a version of UMTS that uses E-UTRA, which employs OFDMA on the downlink and SC-FDMA on the uplink. UTRA, E-UTRA, UMTS, LTE, and GSM are described in documents from an organization named "3rd Generation Partnership Project (3GPP)". Additionally, CDMA1800 and UMB are described in documents from an organization named "3rd Generation Partnership Project 2" (3GPP2). Further, such wireless communication systems may additionally include peer-to-peer (e.g., mobile-to-mobile) ad hoc network systems, which typically use unpaired unlicensed spectrum, 802.xx wireless LAN, BLUETOOTH, and any other short-range or long-range wireless communication technology.

[0128] Single carrier frequency division multiple access (SC-FDMA) utilizing single carrier modulation and frequency domain equalization is a technique that may be used with the disclosed aspects. SC-FDMA has similar performance and substantially similar overall complexity to OFDMA systems. SC-FDMA signals have lower peak-to-average power ratio (PAPR) due to their inherent single carrier structure. SC-FDMA may be used in uplink communications, where lower PAPR may benefit mobile terminals in terms of transmit power efficiency.

[0129] In addition, the various aspects or features described herein can be implemented as methods, devices or products using standard programming and / or engineering techniques. The term "product" used herein is intended to include a computer program that can be accessed from any computer-readable device, carrier or medium. For example, computer-readable media can include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., EPROMs, cards, sticks, key drives, etc.). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but are not limited to, wireless channels and various other media capable of storing, containing and / or carrying (multiple) instructions and / or data. In addition, a computer program product can include a computer-readable medium having one or more instructions or codes that can be operated to cause a computer to perform the functions described herein.

[0130] Communication media embodies computer readable instructions, data structures, program modules, or other structured or unstructured data as a data signal, such as a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery or transmission media. The term "modulated data signal" or multiple signals refers to a signal that has one or more characteristics of the signal set or changed in a manner that encodes information in one or more signals. By way of example, and not limitation, communication media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared and other wireless media.

[0131] In addition, the actions of the methods or algorithms described in conjunction with the aspects disclosed herein may be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An example storage medium may be coupled to a processor so that the processor can read information from a storage medium and write the information to the storage medium. In an alternative, the storage medium may be integrated into the processor. Further, in some aspects, the processor and the storage medium may reside in an ASIC. In addition, the ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components. In addition, in some aspects, the steps and / or actions of the method or algorithm may reside in a machine-readable medium and / or a computer-readable medium that may be incorporated into a computer program product as one or any combination or set of codes and / or instructions.

[0132] The above description of the illustrated embodiments of the subject disclosure, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. Although specific embodiments and examples are described herein for illustrative purposes, those skilled in the relevant art may recognize that various modifications are possible within the scope of such embodiments and examples.

[0133] In this regard, while the disclosed subject matter has been described in conjunction with various embodiments and corresponding figures, it should be understood that other similar embodiments may be used or modifications and additions may be made to the described embodiments, where applicable, to perform the same, similar, alternative, or alternative functions of the disclosed subject matter without departing from the disclosed subject matter. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the claims appended hereto.

[0134] In particular, with respect to the various functions performed by the above-described components (assemblies, devices, circuits, systems, etc.), unless otherwise specified, the terms used to describe such components (including references to "means") are intended to correspond to any component or structure (e.g., functionally equivalent) that performs the specified function of the described component, even if not structurally equivalent to the disclosed structure that performs the functions in the exemplary embodiments shown in the present disclosure herein. In addition, while a particular feature may have been disclosed for only one of several embodiments, such feature may be combined with one or more other features of other embodiments, which may be desirable and advantageous for any given or specific application.

Claims

1. A system (100) having a base station (BS) (111, 112) and / or a UE, comprising a circuit system having the following items: a radio frequency (RF) interface; and One or more processors configured to: Perform federated learning for AI / ML.

2. The system of claim 1, wherein the one or more processors are configured to use an event-triggered approach.

3. The system of claim 1, the one or more processors being configured to use a periodic approach.

4. The system of claim 1, the one or more processors being configured to use a hybrid approach combining event triggering and periodicity.

5. A system (100) having a base station (BS) (111, 112) and / or a UE, comprising a circuit system having the following items: a radio frequency (RF) interface; and One or more processors configured to: Set timers for global AI / ML model training; Based on the event trigger or the timer, adding a set of candidate UEs for participating in the global AI / ML model training; Update local AI / ML model training; Feedback local AI / ML model updates; and The global AI / ML model is updated based on this feedback.

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