Method and apparatus for configuring topology for artificial intelligence or machine learning

Abstract

Aspects of the present disclosure provide methods and apparatus for configuring an artificial intelligence or machine learning (AI / ML) topology in a wireless communication network to address problems such as data isomerism in a conventional federated learning-based training process. The topology may be centrally configured by network devices, or may be self-organized by nodes in the topology with advertisement and discovery messages. The nodes in the topology may be configured to collect and aggregate a plurality of AI / ML models to obtain type 1 nodes of a first type of AI / ML model, or configured to obtain type 2 nodes of a second type of AI / ML model using its own training data without aggregation operations, the configured topology includes connections between at least one type 1 node and zero or more type 2 nodes, and / or connections between at least two type 1 nodes.

Description

Technical Field

[0001] The present disclosure relates generally to wireless communications and, in particular embodiments, to methods and apparatus for configuring topologies for artificial intelligence / machine learning (AI / ML). Background Art

[0002] In some implementations, the AI ​​architecture in a wireless communication network may include multiple nodes, where the multiple nodes may be organized in one of two modes: centralized and distributed, both of which may be deployed in an access network, core network, or edge computing system or a third-party network. Centralized training and computing architectures are subject to potentially large communication overhead and strict user data privacy protection. Distributed training and computing architectures may include several frameworks, such as distributed machine learning and federated learning.

[0003] However, communications in wireless communication systems, including communications associated with AI training of multiple nodes, are often conducted over non-ideal channels. For example, non-ideal conditions such as electromagnetic interference, signal degradation, phase delay, fading, and other non-ideal situations may attenuate and / or distort communication signals, or may otherwise interfere with or degrade the communication capabilities of the system.

[0004] Traditional AI training processes typically rely on hybrid automatic repeat request (HARQ) feedback and retransmission processes to try to ensure that data transmitted between devices participating in AI training is successfully received. However, the communication overhead and latency associated with such retransmissions can be problematic.

[0005] In addition, the processing power and / or training data availability for the AI ​​training process may vary significantly between different nodes / devices, which means that the ability of different nodes to effectively participate in the AI ​​training process may vary significantly. In practice, this difference usually means that the training delay of an AI training process involving multiple nodes / devices (e.g., an AI training process based on distributed learning or federated learning) is determined by the node / device with the largest delay, which is caused by communication delay and / or computational delay.

[0006] Federated learning, also known as collaborative learning, is a machine learning technique that trains algorithms across multiple distributed edge devices or servers. Each distributed edge device or server holds local data samples, but may not exchange them with other devices or servers. The difference between federated learning and traditional centralized machine learning is that local data samples are not shared in federated learning, while in traditional centralized machine learning, all local data sets are uploaded to one server.

[0007] In a wireless federated learning-based (FL-based) AI training process, the network node / device / node initializes a global AI model, samples a set of user devices, and broadcasts the global AI model parameters to the user devices. Each user device then initializes its local AI model using the global AI model parameters and updates (trains) its local AI model using its own data. Each user device can then report the parameters of its updated local AI model to the network device, which then aggregates the updated parameters reported by the user devices and updates the global AI model. The above process is one iteration of the traditional FL-based AI model training process. The network device and participating user devices typically perform multiple iterations until the AI ​​model has sufficiently converged to meet one or more training objectives / criteria and the AI ​​model is finalized.

[0008] However, different user devices participating in the FL-based AI training process may observe different training data sets, which may not represent the distribution of all training data observed by other user devices participating in the FL-based AI training process, that is, the training data may not be independent and identically distributed (non-iid) on the devices participating in the traditional FL-based AI training process. In the traditional FL-based AI training process, non-iid training data between devices has been shown to reduce the convergence speed and model accuracy. Therefore, in the traditional FL-based AI training process, data heterogeneity is a typical problem.

[0009] For these and other reasons, new protocols and signaling mechanisms are needed so that new AI-enabled applications and processes can be implemented while minimizing the signaling and communication overhead and latency associated with existing AI training procedures. Summary of the invention

[0010] In the traditional federated learning (FL-based) AI training process, there are some limitations. For example, as mentioned above, data heterogeneity is a typical problem in the traditional FL-based AI training process. In addition, the server node needs to collect a large amount of training data sets from multiple associated client nodes (e.g., gradients updated by client nodes). In addition, the traditional FL-based AI training process requires the server node and the associated client nodes to have the same AI / ML model structure. However, since different local nodes may support different AI / ML model structures, data heterogeneity may be a problem in the traditional FL-based AI training process.

[0011] Various aspects of the present disclosure provide solutions to overcome the above limitations, such as specific methods and devices for configuring topologies of artificial intelligence or machine learning (AI / ML).

[0012] According to a first broad aspect of the present disclosure, a method for configuring a topology of artificial intelligence or machine learning (AI / ML) in a wireless communication network is provided herein. The method according to the first broad aspect of the present disclosure may include receiving information including a report related to the AI / ML capabilities of the node from a node. The method according to the first broad aspect of the present disclosure may also include configuring the node based on the received information, wherein configuring the node includes configuring the node type of the node, and the configured node type is one of a plurality of node types. For example, the plurality of node types may include: type 1, indicating a node for collecting a plurality of AI / ML models and aggregating the collected AI / ML models to obtain a first type of AI / ML model; or type 2, indicating a node for obtaining a second type of AI / ML model using a set of training data without the need for aggregation operations. The method according to the first broad aspect of the present disclosure may also include configuring one or more other nodes associated with the configured node based on the configured node type. The configured topology supports the transmission of AI / ML models over the air interface of the wireless communication network, and includes at least a connection between at least one type 1 node and zero or more type 2 nodes and / or a connection between at least two type 1 nodes.

[0013] In some embodiments of the method according to the first broad aspect of the present disclosure, the configured node type indicates that the node is a type 1 node, and the one or more other nodes include at least one of the following: one or more type 2 nodes; a second type 1 node, which provides the first type AI / ML model of the second type 1 node to the node; or a third type 1 node, which receives the first type AI / ML model of the node.

[0014] In some embodiments of the method according to the first broad aspect of the present disclosure, the one or more other nodes include the one or more type 2 nodes, and configuring the nodes includes configuring the nodes to collect corresponding second type AI / ML models from the one or more type 2 nodes.

[0015] In some embodiments of the method according to the first broad aspect of the present disclosure, the configured node type indicates that the node is a type 2 node, and the one or more other nodes include one or more type 1 nodes connected to the node.

[0016] In some embodiments of the method according to the first broad aspect of the present disclosure, the information further comprises a request for the node to be configured as a type 1 node.

[0017] In some embodiments of the method according to the first broad aspect of the present disclosure, the node is connected to the one or more other nodes via a sidelink using device-to-device (D2D) communication, via a network device, or via an interface between network devices.

[0018] In some embodiments of the method according to the first broad aspect of the present disclosure, the node is a user equipment (UE), a relay station, a base station (BS), a transmission and reception point (TRP), an edge device, a network system or an integrated access backhauled (IAB) node.

[0019] According to the second broad aspect of the present disclosure, a method for configuring the topology of artificial intelligence or machine learning (AI / ML) in a wireless communication network is provided herein. The method according to the second broad aspect of the present disclosure may include establishing an aggregate connection with a second node based on an aggregate response message. The configured topology according to the second broad aspect of the present disclosure supports the transmission of AI / ML models through the air interface of the wireless communication network, and includes a connection between at least one type 1 node and zero or more type 2 nodes and / or a connection between at least two type 1 nodes, wherein the type 1 node is a node for collecting multiple AI / ML models and aggregating the collected AI / ML models to obtain a first type AI / ML model, and the type 2 node is a node for obtaining a second type AI / ML model using a set of training data without the need for an aggregate operation.

[0020] Optionally, before establishing the aggregation connection with the second node, the method further includes: the first node sending an aggregation message to the second node. After sending the aggregation message, the first node may receive a response message from the second node.

[0021] Additionally, the establishment step is optional.

[0022] In some embodiments of the method according to the second broad aspect of the present disclosure, the aggregate message is a discovery message comprising aggregate information to be used by the second node to discover the first node.

[0023] In some embodiments of the method according to the second broad aspect of the present disclosure, the aggregate information to be used by the second node comprises aggregate capabilities of the first node.

[0024] In some embodiments of the method according to the second broad aspect of the present disclosure, the discovery message includes at least one of a model collection indicator or a reference AI / ML model.

[0025] In some embodiments of the method according to the second broad aspect of the present disclosure, the model collection indicator includes one of a distillation indicator, an expansion indicator, or a distillation and expansion indicator.

[0026] In some embodiments of the method according to the second broad aspect of the present disclosure, the aggregation message is an aggregation request message, and the aggregation request message includes an aggregation request or information related to the second type of AI / ML model of the first node.

[0027] In some embodiments of the method according to the second broad aspect of the present disclosure, the first node is a type 2 node, and the aggregation request message includes the information related to the second type AI / ML model of the first node.

[0028] In some embodiments of the method according to the second broad aspect of the present disclosure, the information related to the second type of AI / ML model of the first node includes at least one of the following: information related to the neural network of the second type of AI / ML model of the first node; information related to the size of the second type of AI / ML model of the first node; or information related to the complexity of the second type of AI / ML model of the first node.

[0029] In some embodiments of the method according to the second broad aspect of the present disclosure, the first node is a type 1 node, and the aggregation request message comprises the aggregation request.

[0030] In some embodiments of the method according to the second broad aspect of the present disclosure, the second node is one of multiple other nodes in the wireless communication network, and the first node can only connect to one of the multiple other nodes, wherein the processor-executable instructions also include processor-executable instructions that, when executed, cause the processor to perform the following operations: the first node sends a corresponding confirmation to at least the one of the multiple other nodes, and the confirmation notifies whether the connection to the first node is successfully established.

[0031] In some embodiments of the method according to the second broad aspect of the present disclosure, the first node is a type 2 node and the second node is a type 1 node.

[0032] In some embodiments of the method according to the second broad aspect of the present disclosure, the first node is a first type 1 node and the second node is a second type 1 node, and the second node provides the first type AI / ML model of the second node to the first node or receives the first type AI / ML model of the first node from the first node.

[0033] In some embodiments of the method according to the second broad aspect of the disclosure, the first node is a type 1 node that is only associated with one or more other type 1 nodes.

[0034] In some embodiments of the method according to the second broad aspect of the present disclosure, sending the aggregate message includes broadcasting, multicasting or unicasting the aggregate message at predetermined intervals.

[0035] According to the third broad aspect of the present disclosure, a method for configuring the topology of artificial intelligence or machine learning (AI / ML) in a wireless communication network is provided herein. The method according to the third broad aspect of the present disclosure may include a first node receiving an aggregate message from a second node. After receiving the aggregate message, the first node may determine whether to connect to the second node and send a response indicating the determination to the second node. The method according to the third broad aspect of the present disclosure may also include selectively establishing an aggregate connection with the second node based on the determination. The configured topology according to the third broad aspect of the present disclosure supports the transmission of AI / ML models through the air interface of the wireless communication network, and includes a connection between at least one type 1 node and zero or more type 2 nodes and / or a connection between at least two type 1 nodes, wherein the type 1 node is a node for collecting multiple AI / ML models and aggregating the collected AI / ML models to obtain a first type AI / ML model, and the type 2 node is a node for obtaining a second type AI / ML model using a set of training data without the need for aggregation operations.

[0036] In some embodiments of the method according to the third broad aspect of the present disclosure, the aggregate message is a discovery message including aggregate information to be used by the first node to discover the second node.

[0037] In some embodiments of the method according to the third broad aspect of the disclosure, the aggregate information to be used by the first node comprises aggregate capabilities of the second node.

[0038] In some embodiments of the method according to the third broad aspect of the present disclosure, the discovery message includes at least one of a model collection indicator or a reference AI / ML model.

[0039] In some embodiments of the method according to the third broad aspect of the present disclosure, the model collection indicator includes one of a distillation indicator, an expansion indicator, or a distillation and expansion indicator.

[0040] In some embodiments of the method according to the third broad aspect of the present disclosure, the aggregation message is an aggregation request message, and the aggregation request message includes an aggregation request or information related to the second type of AI / ML model of the second node.

[0041] In some embodiments of the method according to the third broad aspect of the present disclosure, the second node is a type 2 node, and the aggregation request message includes the information related to the second type AI / ML model of the second node.

[0042] In some embodiments of the method according to the third broad aspect of the present disclosure, the information related to the second type of AI / ML model of the second node includes at least one of the following: information related to the neural network of the second type of AI / ML model of the second node; information related to the size of the second type of AI / ML model of the second node; or information related to the complexity of the second type of AI / ML model of the second node.

[0043] In some embodiments of the method according to the third broad aspect of the present disclosure, the first node determines whether to connect with the second node based on the aggregation capability of the first node and the information related to the second type of AI / ML model of the second node.

[0044] In some embodiments of the method according to the third broad aspect of the present disclosure, the second node is a type 1 node, and the aggregation request message includes the aggregation request.

[0045] In some embodiments of the method according to the third broad aspect of the present disclosure, the second node is only capable of connecting to one node, wherein the processor-executable instructions also include processor-executable instructions that, when executed, cause the processor to perform the following operations: receive a confirmation from the second node, the confirmation notifying whether the connection between the second node and the first node is successfully established.

[0046] In some embodiments of the method according to the third broad aspect of the present disclosure, the second node is a type 2 node and the first node is a type 1 node.

[0047] In some embodiments of the method according to the third broad aspect of the present disclosure, the second node is a first type 1 node and the first node is a second type 1 node, and the first node provides the first type AI / ML model of the first node to the second node or receives the first type AI / ML model of the second node from the second node.

[0048] In some embodiments of the method according to the third broad aspect of the present disclosure, the second node is a type 1 node that is only associated with one or more other type 1 nodes.

[0049] Corresponding apparatus and devices for performing the method are disclosed.

[0050] For example, according to another aspect of the present disclosure, a device is provided, which includes a processor and a memory storing processor-executable instructions, and the processor-executable instructions, when executed, cause the processor to perform a method according to the first broad aspect, the second broad aspect or the third broad aspect of the present disclosure mentioned above.

[0051] According to other aspects of the present disclosure, a device is provided, which includes one or more units for implementing any method aspects disclosed in the present disclosure. The term "unit" is used in a broad sense and can be referred to by any name, including, for example, a module, a component, an element, a device, etc. These units can be implemented using hardware, software, firmware, or any combination thereof.

[0052] By virtue of some aspects of the present disclosure, heterogeneous AI / ML capabilities may be enabled in various network devices and user devices, and heterogeneous AI / ML models may be supported to be delivered over the air interface of a wireless communication network.

[0053] By virtue of some aspects of the present disclosure, the topology in a wireless communication network may be centrally configured by a network device (eg, a base station (BS), a transmission reception point (TRP)) or a network system.

[0054] By virtue of some aspects of the present disclosure, the topology in a wireless communication network can be autonomously configured by network devices (e.g., base stations (BS), transmission reception points (TRPs)) and / or user equipment as needed using the discovery process described in the present disclosure, thereby supporting flexible topology configuration.

[0055] By virtue of some aspects of the present disclosure, the topology configured by the various methods described in the present disclosure in a wireless communication network supports parallel AI / ML model aggregation, reduces AI / ML model routing latency, and increases the robustness of AI / ML model routing. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Reference will now be made, by way of example only, to the accompanying drawings which show exemplary embodiments of the present application, in which:

[0057] Figure 1 is a simplified schematic diagram of a communication system according to an example;

[0058] Figure 2 Another example of a communication system is shown;

[0059] Figure 3 Examples of electronic devices (EDs), terrestrial transmit and receive points (T-TRPs), and non-terrestrial transmit and receive points (NT-TRPs) are shown;

[0060] Figure 4 Exemplary units or modules in the device are shown;

[0061] Figure 5shows four EDs communicating with a network device in a communication system according to an embodiment of the present disclosure;

[0062] Fig. 6A An example of a neural network with multiple layers of neurons according to an embodiment of the present disclosure is shown;

[0063] Figure 6B shows examples of neurons that may be used as building blocks of a neural network according to embodiments of the present disclosure;

[0064] Figure 7 shows the star topology used in the traditional federated learning (FL) process;

[0065] Figure 8 The difference between the traditional FL-based AI / ML model training process and the AI / ML model learning scheme of the present disclosure is shown;

[0066] Fig.9A and 9B Two different types of nodes for learning AI / ML models according to embodiments of the present disclosure are shown;

[0067] Fig.10 shows an exemplary self-organizing topology according to an embodiment of the present disclosure;

[0068] Fig.11 shows an exemplary configured topology supporting AI / ML model delivery according to an embodiment of the present disclosure;

[0069] Fig.12 An exemplary process of establishing an aggregation connection between an aggregation node and a base node when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown;

[0070] Fig.13 An exemplary process of establishing an aggregation connection between two aggregation nodes when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown;

[0071] Fig.14 Another exemplary process of establishing an aggregate connection between an aggregation node and a base node when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown;

[0072] Fig.15 Another exemplary process of establishing an aggregation connection between two aggregation nodes when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown;

[0073] Fig.16An exemplary configured topology supporting flexible communication between aggregation nodes according to an embodiment of the present disclosure is shown;

[0074] Fig.17 An exemplary configured topology supporting flexible communication between an aggregation node and base nodes according to an embodiment of the present disclosure is shown.

[0075] Similar reference numerals may be used in different drawings to represent similar components. DETAILED DESCRIPTION

[0076] In this disclosure, "data collection" refers to the process by which a network node, a management entity, or a user equipment (UE) collects data for the purpose of artificial intelligence (AI) / machine learning (ML) model training, data analysis, and inference.

[0077] In this disclosure, an “AI / ML model” refers to a data-driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0078] In the present disclosure, “AI / ML model training” refers to the process of training an AI / ML model by learning input / output relationships in a data-driven manner and obtaining a trained AI / ML model for reasoning.

[0079] In this disclosure, “AI / ML inference” refers to the process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0080] In this disclosure, “online training” refers to situations where the machine learning program is not running and receiving new information in real time.

[0081] In this disclosure, “offline training” refers to the situation where the machine learning program processes incoming data in real time.

[0082] In this disclosure, "on-UE training" refers to online / offline training at the UE.

[0083] In this disclosure, "on-network training" refers to online / offline training at the network.

[0084] In this disclosure, a “UE-side (AI / ML) model” refers to an AI / ML model whose reasoning is entirely performed at the UE.

[0085] In this disclosure, a “network-side (AI / ML) model” refers to an AI / ML model whose reasoning is performed entirely at the network.

[0086] In this disclosure, "model delivery" refers to the transmission of an AI / ML model over the air interface, which may be parameters of a model structure known to the receiving end, or a new model with parameters. The transmission may include a complete model or a partial model.

[0087] In this disclosure, "model download" refers to the transfer of a model from the network to the UE.

[0088] In this disclosure, "model upload" refers to the transfer of a model from a UE to a network.

[0089] In this disclosure, “model deployment” refers to the delivery of a fully developed and tested model runtime image to the target UE / gNodeB (gNB) where inference will be performed.

[0090] In this disclosure, "federated learning / federated training" refers to a machine learning technique that trains AI / ML models across multiple distributed edge nodes (e.g., UE, gNB), each of which uses local data samples for local model training. This technique requires multiple model exchanges, but does not require the exchange of local data samples.

[0091] In this disclosure, “model monitoring” refers to the process of monitoring the inference performance of an AI / ML model.

[0092] In this disclosure, “model update” refers to retraining or fine-tuning the AI / ML model through online / offline training to improve the model reasoning performance.

[0093] For illustrative purposes, specific exemplary embodiments will be explained in more detail below with reference to the accompanying drawings.

[0094] The embodiments described herein represent information sufficient to practice the claimed subject matter and illustrate methods of practicing the subject matter. After reading the following description in light of the accompanying drawings, those skilled in the art will understand the concepts of the claimed subject matter and recognize the applications of these concepts that are not specifically described herein. It should be understood that these concepts and applications fall within the scope of this disclosure and the appended claims.

[0095] In addition, it will be appreciated that any module, component, or device for executing instructions disclosed herein may include or otherwise access one or more non-transitory computer / processor readable storage media to store information, such as computer / processor readable instructions, data structures, program modules, and / or other data. A non-exhaustive list of examples of non-transitory computer / processor readable storage media includes cassettes, magnetic tapes, disk storage or other magnetic storage devices, compact disc read-only memory (CD-ROM), digital video discs or digital versatile discs (i.e., DVDs), Blu-ray discs, and other optical disks. TMSuch non-transitory computer / processor storage media may be part of a device or may be accessed or connected to such a device. Computer / processor readable / executable instructions for implementing the applications or modules described herein may be stored or otherwise saved by such non-transitory computer / processor readable storage media.

[0096] Exemplary Communication Systems and Devices

[0097] refer to Figure 1 , a simplified schematic diagram of a communication system is provided as a non-limiting illustrative example. The communication system 100 includes a radio access network 120. The radio access network 120 may be a next generation (e.g., sixth generation (6G) or higher) radio access network or a traditional (e.g., 5G, 4G, 3G or 2G) radio access network. In the radio access network 120, one or more communication electric devices (ED) 110a to 110j (generally referred to as 110) may be interconnected with each other or connected to one or more network nodes (170a, 170b, generally referred to as 170). The core network 130 may be part of the communication system and may be dependent on or independent of the radio access technology used in the communication system 100. In addition, the communication system 100 includes a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160.

[0098] Figure 2An exemplary communication system 100 is shown. Typically, the communication system 100 enables multiple wireless or wired elements to transmit data and other content. The purpose of the communication system 100 may be to provide content such as voice, data, video and / or text through broadcast, multicast and unicast, etc. The communication system 100 may operate by sharing resources such as carrier spectrum bandwidth between its constituent elements. The communication system 100 may include a terrestrial communication system and / or a non-terrestrial communication system. The communication system 100 may provide a wide range of communication services and applications (such as earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery and mobility, etc.). The communication system 100 may provide a high degree of availability and robustness through the joint operation of a terrestrial communication system and a non-terrestrial communication system. For example, integrating a non-terrestrial communication system (or its components) into a terrestrial communication system may produce a heterogeneous network including multiple layers. Compared with traditional communication networks, heterogeneous networks may achieve better overall performance through efficient multi-link joint operation, more flexible function sharing, and faster physical layer link switching between terrestrial networks and non-terrestrial networks.

[0099] The ground communication system and the non-ground communication system can be considered as subsystems of the communication system. In the example shown, the communication system 100 includes electronic devices (EDs) 110a to 110d (generally referred to as ED 110), radio access networks (RANs) 120a to 120b, non-ground communication networks 120c, core networks 130, public switched telephone networks (PSTNs) 140, the Internet 150, and other networks 160. The RANs 120a to 120b include corresponding base stations (BSs) 170a to 170b, which can be generally referred to as ground transmission reception points (T-TRPs) 170a to 170b. The non-ground communication network 120c includes an access node 120c, which can be generally referred to as a non-ground transmission reception point (NT-TRP) 172.

[0100] Any ED 110 may alternatively or additionally be used to connect, access, or communicate with any other T-TRP 170a to 170b, NT-TRP 172, Internet 150, core network 130, PSTN 140, other network 160, or any combination thereof. In some examples, ED 110a may perform uplink and / or downlink transmissions with T-TRP 170a via interface 190a. In some examples, ED 110a, 110b, and 110d may also communicate directly with each other via one or more sidelink air interfaces 190b. In some examples, ED 110d may perform uplink and / or downlink transmissions with NT-TRP 172 via interface 190c.

[0101] The air interfaces 190a and 190b may use similar communication technologies, such as any suitable wireless access technology. For example, the communication system 100 may implement one or more channel access methods in the air interfaces 190a and 190b, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or single-carrier FDMA (SC-FDMA). The air interfaces 190a and 190b may utilize other high-dimensional signal spaces, which may include a combination of orthogonal dimensions and / or non-orthogonal dimensions.

[0102] Air interface 190c may enable communication between ED 110d and one or more NT-TRPs 172 via a wireless link or a simple link. For some examples, the link is a dedicated connection for unicast transmission, a connection for broadcast transmission, or a connection between a group of EDs and one or more NT-TRPs for multicast transmission.

[0103] The RANs 120a and 120b communicate with the core network 130 to provide various services, such as voice, data, and other services, to the EDs 110a, 110b, and 110c. The RANs 120a and 120b and / or the core network 130 may communicate directly or indirectly with one or more other RANs (not shown), which may or may not be directly served by the core network 130 and may or may not employ the same radio access technology as the RANs 120a, 120b, or both. The core network 130 may also serve as a gateway access between (i) the RANs 120a and 120b, or the EDs 110a, 110b, and 110c, or both, and (ii) other networks, such as the PSTN 140, the Internet 150, and other networks 160. In addition, some or all of the EDs 110a, 110b, and 110c may include functionality for communicating with different wireless networks over different wireless links using different wireless technologies and / or protocols. Instead of (or in addition to) wireless communication, EDs 110a, 110b, and 110c may also communicate with a service provider or switch (not shown) and with the Internet 150 through a wired communication channel. PSTN 140 may include a circuit-switched telephone network for providing plain old telephone service (POTS). Internet 150 may include a computer network and a subnet (intranet) or both, and include protocols such as Internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), etc. EDs 110a, 110b, and 110c may be multi-mode devices capable of operating according to a variety of wireless access technologies and include a plurality of transceivers required to support these technologies.

[0104] Figure 3Another example of ED 110 and base stations 170a, 170b and / or 170c is shown. ED 110 is used to connect people, objects, machines, etc. ED 110 can be widely used in various scenarios, such as cellular communication, device-to-device (D2D), vehicle to everything (vehicle to everything, V2X), peer-to-peer (P2P), machine-to-machine (machine-to-machine, M2M), machine-type communication (machine-type communication, MTC), Internet of things (internet of things, IOT), virtual reality (virtual reality, VR), augmented reality (augmented reality, AR), industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable devices, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.

[0105] Each ED 110 represents any suitable end-user device for wireless operation, and may include (or may be referred to as): user equipment / device (UE), wireless transmit / receive unit (WTRU), mobile station, fixed or mobile user unit, cellular phone, station (STA), machine type communication (MTC) equipment, personal digital assistant (PDA), smartphone, laptop, computer, tablet, wireless sensor, consumer electronic device, smartbook, vehicle, car, truck, bus, train or IoT device, industrial equipment or devices in the aforementioned devices (e.g., communication module, modem or chip), etc. The next generation ED 110 may be referred to using other terms. Base stations 170a and 170b are T-TRPs and will be referred to as T-TRP 170 hereinafter. Also in Figure 3 , the NT-TRP will be referred to hereinafter as NT-TRP 172. Each ED 110 connected to T-TRP 170 and / or NT-TRP 172 may be dynamically or semi-statically started (i.e., established, activated, or enabled), shut down (i.e., released, deactivated, or disabled), and / or configured in response to one or more of connection availability and connection necessity.

[0106] ED 110 includes a transmitter 201 and a receiver 203 coupled to one or more antennas 204. Only one antenna 204 is shown. Alternatively, one, some or all of the antennas may be panels. Transmitter 201 and receiver 203 may be integrated together, for example as a transceiver. The transceiver is used to modulate data or other content for transmission via at least one antenna 204 or a network interface controller (NIC). The transceiver is also used to demodulate data or other content received by at least one antenna 204. Each transceiver includes: any suitable structure for generating signals for wireless or wired transmission and / or for processing signals received by wireless or wired means. Each antenna 204 includes: any suitable structure for sending and / or receiving wireless signals or wired signals.

[0107] ED 110 includes at least one memory 208. Memory 208 stores instructions and data used, generated, or collected by ED 110. For example, memory 208 may store software instructions or modules executed by one or more processing units 210, which are used to implement some or all of the functions and / or embodiments described herein. Each memory 208 includes any suitable volatile and / or non-volatile storage and retrieval device. Any suitable type of memory may be used, such as random access memory (RAM), read-only memory (ROM), hard disk, optical disk, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, processor cache, etc.

[0108] ED 110 may also include one or more input / output devices (not shown) or interfaces (e.g., to Figure 1 The input / output devices support interaction with users or other devices in the network. Each input / output device includes any structure suitable for providing information to or receiving information from a user, such as a speaker, microphone, keypad, keyboard, display or touch screen, including network interface communications.

[0109] ED 110 also includes a processor 210 for performing operations, including operations related to preparing transmissions for uplink transmission to NT-TRP 172 and / or T-TRP 170, processing downlink transmissions received from NT-TRP 172 and / or T-TRP 170, and processing sidelink transmissions with another ED 110. Processing operations related to preparing transmissions for uplink transmissions may include operations such as encoding, modulation, transmit beamforming, and generating symbols for transmission. Processing operations related to processing downlink transmissions may include operations such as receive beamforming, demodulation, and decoding received symbols. According to an embodiment, the downlink transmission may be received by receiver 203 using receive beamforming, and processor 210 may extract signaling from the downlink transmission (e.g., by detecting and / or decoding the signaling). An example of signaling may be a reference signal sent by NT-TRP 172 and / or T-TRP 170. In some embodiments, the processor 276 implements transmit beamforming and / or receive beamforming based on an indication of a beam direction, such as beam angle information (BAI), received from the T-TRP 170. In some embodiments, the processor 210 may perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as operations related to detecting a synchronization sequence, decoding, and obtaining system information. In some embodiments, the processor 210 may perform channel estimation, for example, using a reference signal received from the NT-TRP 172 and / or the T-TRP 170.

[0110] Although not shown, the processor 210 may form a part of the transmitter 201 and / or the receiver 203. Although not shown, the memory 208 may form a part of the processor 210.

[0111] The processor 210 and the processing components of the transmitter 201 and the receiver 203 may each be implemented by the same or different one or more processors, which are used to execute instructions stored in a memory (e.g., the memory 208). Alternatively, the processor 210 and some or all of the processing components of the transmitter 201 and the receiver 203 may be implemented using a programmed field-programmable gate array (FPGA), a graphical processing unit (GPU), or an application-specific integrated circuit (ASIC) or other dedicated circuit.

[0112] T-TRP 170 may use other names in some implementations, such as base station, base transceiver station (BTS), wireless base station, network node, network device, network side device, transmission / reception node, node B, evolved NodeB (eNodeB or eNB), home eNodeB, next generation NodeB (gNB), transmission point (TP), site controller, access point (AP), wireless router, relay station, remote radio head, ground node, ground network device, ground base station, base band unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), centralized unit (CU), distributed unit (DU), positioning node, etc. T-TRP 170 may be a macro BS, a micro BS, a relay node, a host node, etc. or a combination thereof. T-TRP 170 may refer to the above-mentioned device, or refer to a device in the above-mentioned device (eg, a communication module, a modem, or a chip).

[0113] In some embodiments, various parts of T-TRP 170 may be distributed. For example, some modules of T-TRP 170 may be located away from the device that houses the antenna of T-TRP 170, and may be coupled to the device that houses the antenna through a communication link (not shown) sometimes referred to as a fronthaul, such as a common public radio interface (CPRI). Therefore, in some embodiments, the term T-TRP 170 may also refer to modules on the network side that perform processing operations such as determining the location of ED 110, resource allocation (scheduling), message generation, and encoding / decoding, and are not necessarily part of the device that houses the antenna of T-TRP 170. These modules may also be coupled to other T-TRPs. In some embodiments, T-TRP 170 may actually be multiple T-TRPs that operate together to serve ED 110 through coordinated multi-point transmission or the like.

[0114] The T-TRP 170 includes at least one transmitter 252 and at least one receiver 254 coupled to one or more antennas 256. Only one antenna 256 is shown. Alternatively, one, some, or all of the antennas may be panels. The transmitter 252 and the receiver 254 may be integrated as a transceiver. The T-TRP 170 also includes a processor 260 for performing operations related to preparing transmissions for downlink transmission to the ED 110, processing uplink transmissions received from the ED 110, preparing transmissions for backhaul transmission to the NT-TRP 172, and processing transmissions received from the NT-TRP 172 via the backhaul. Processing operations related to preparing transmissions for downlink transmission or backhaul transmission may include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or on the backhaul may include operations such as receive beamforming, demodulation, and decoding received symbols. The processor 260 may also perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as generating the content of a synchronization signal block (SSB), generating system information, etc. In some embodiments, the processor 260 also generates an indication of a beam direction, such as a BAI, which may be scheduled by the scheduler 253 for transmission. The processor 260 performs other network-side processing operations described herein, such as determining the location of the ED 110, determining the location of the deployed NT-TRP 172, etc. In some embodiments, the processor 260 may generate signaling, such as to configure one or more parameters of the ED 110 and / or one or more parameters of the NT-TRP 172. Any signaling generated by the processor 260 is sent by the transmitter 252. Note that "signaling" as used herein may also be referred to as control signaling. Dynamic signaling can be sent in a control channel such as a physical downlink control channel (PDCCH), while static or semi-static higher-layer signaling can be included in a data packet sent in a data channel such as a physical downlink shared channel (PDSCH).

[0115] The scheduler 253 may be coupled to the processor 260. The scheduler 253 may be included within the T-TRP 170 or run separately from the T-TRP 170, and the scheduler 253 may schedule uplink transmissions, downlink transmissions, and / or backhaul transmissions, including issuing scheduling grants and / or configuring scheduling-free (“configuration grants”) resources. The T-TRP 170 also includes a memory 258 for storing information and data. The memory 258 stores instructions and data used, generated, or collected by the T-TRP 170. For example, the memory 258 may store software instructions or modules executed by the processor 260, which are used to implement some or all of the functions and / or embodiments described herein.

[0116] Although not shown, the processor 260 may form a part of the transmitter 252 and / or the receiver 254. In addition, although not shown, the processor 260 may implement the scheduler 253. Although not shown, the memory 258 may form a part of the processor 260.

[0117] The processor 260, the scheduler 253, and the processing components of the transmitter 252 and the receiver 254 may each be implemented by the same or different one or more processors that execute instructions stored in a memory, such as the memory 258. Alternatively, some or all of the processor 260, the scheduler 253, and the processing components of the transmitter 252 and the receiver 254 may be implemented using a dedicated circuit such as an FPGA, a GPU, or an ASIC.

[0118] Although the NT-TRP 172 is shown as a drone by way of example only, the NT-TRP 172 may be implemented in any suitable non-ground form. In addition, in some implementations, the NT-TRP 172 may use other names, such as a non-ground node, a non-ground network device, or a non-ground base station. The NT-TRP 172 includes a transmitter 272 and a receiver 274 coupled to one or more antennas 280. Only one antenna 280 is shown. Alternatively, one, some, or all of the antennas may be panels. The transmitter 272 and the receiver 274 may be integrated into a transceiver. The NT-TRP 172 also includes a processor 276 for performing operations related to preparing transmissions for downlink transmission to the ED 110, processing uplink transmissions received from the ED 110, preparing transmissions for backhaul transmission to the T-TRP 170, and processing transmissions received from the T-TRP 170 via backhaul. Processing operations associated with preparing a transmission for downlink transmission or backhaul transmission may include operations such as encoding, modulation, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations associated with processing received transmissions in the uplink or on the backhaul may include operations such as receive beamforming, demodulation, and decoding received symbols. In some embodiments, the processor 276 implements transmit beamforming and / or receive beamforming based on beam direction information (e.g., BAI) received from the T-TRP 170. In some embodiments, the processor 276 may generate signaling, for example, to configure one or more parameters of the ED 110. In some embodiments, the NT-TRP 172 implements physical layer processing but does not implement higher layer functions, such as functions of the medium access control (MAC) layer or the radio link control (RLC) layer. Since this is just an example, more generally, the NT-TRP 172 may implement higher layer functions in addition to physical layer processing.

[0119] The NT-TRP 172 also includes a memory 278 for storing information and data. Although not shown, the processor 276 may form part of the transmitter 272 and / or the receiver 274. Although not shown, the memory 278 may form part of the processor 276.

[0120] Processor 276 and the processing components of transmitter 272 and receiver 274 may each be implemented by the same or different one or more processors that execute instructions stored in a memory, such as memory 278. Alternatively, processor 276 and some or all of the processing components of transmitter 272 and receiver 274 may be implemented using a programmed FPGA, GPU, or ASIC, etc., dedicated circuitry. In some embodiments, NT-TRP 172 may actually be multiple NT-TRPs that operate together to serve ED 110, such as through coordinated multi-point transmission.

[0121] Note that as used herein, "TRP" may refer to T-TRP or NT-TRP.

[0122] The T-TRP 170, NT-TRP 172, and / or ED 110 may include other components, but these components have been omitted for clarity.

[0123] according to Figure 4 , one or more steps of the embodiment method provided herein may be performed by corresponding units or modules. Figure 4 Units or modules in a device, such as ED 110, T-TRP 170, or NT-TRP 172 are shown. For example, a signal can be sent by a sending unit or a sending module. For example, a signal can be sent by a sending unit or a sending module. A signal can be received by a receiving unit or a receiving module. The signal can be processed by a processing unit or a processing module. Other steps can be performed by an artificial intelligence (AI) module or a machine learning (ML) module. The corresponding units or modules can be implemented using hardware, one or more components or devices that execute software, or a combination thereof. For example, one or more units or modules can be programmed integrated circuits such as FPGAs, GPUs, or ASICs. It should be understood that if these modules are implemented, for example, using software executed by a processor, these modules can be retrieved by the processor in whole or in part as needed, retrieved individually or collectively for processing, retrieved in one or more instances, and these modules themselves can include instructions for further deployment and instantiation.

[0124] Additional details regarding ED 110, T-TRP 170, and NT-TRP 172 are known to those skilled in the art. Therefore, these details are omitted here.

[0125] Control signaling is discussed in some embodiments herein. Control signaling may also sometimes be referred to as signaling, or control information, or configuration information, or configuration. In some cases, control signaling may be indicated dynamically, such as dynamically indicated in a physical layer in a control channel. An example of dynamically indicated control signaling is physical layer control signaling, such as information sent in downlink control information (DCI). Control signaling may sometimes be indicated semi-statically, such as semi-statically indicated in RRC signaling or in a MAC control element (CE). A dynamic indication may be an indication in a low layer, such as an indication in a physical layer / layer 1 signaling (e.g., in a DCI), rather than an indication in a high layer (e.g., rather than an indication in an RRC signaling or a MAC CE). A semi-static indication may be an indication in a semi-static signaling. Semi-static signaling used herein may refer to non-dynamic signaling, such as high layer signaling, RRC signaling, and / or MAC CE. Dynamic signaling used herein may refer to dynamic signaling, such as physical layer control signaling sent in a physical layer, such as a DCI.

[0126] An air interface typically includes multiple components and associated parameters that together specify how to send and / or receive transmissions over a wireless communication link between two or more communication devices. For example, an air interface may include one or more components that define one or more waveforms, one or more frame structures, one or more multiple access schemes, one or more protocols, one or more coding schemes, and / or one or more modulation schemes for transmitting information (e.g., data) over a wireless communication link. A wireless communication link may support a link between a radio access network and a user device (e.g., a "Uu" link), and / or a wireless communication link may support a link between devices, such as a link between two user devices (e.g., a "sidelink"), and / or a wireless communication link may support a link between a non-terrestrial (NT) communication network and a user equipment (UE). The following are some examples of the above components:

[0127] The waveform component can specify the shape and form of the signal being sent. Waveform options can include orthogonal multiple access waveforms and non-orthogonal multiple access waveforms. Non-limiting examples of such waveform options include orthogonal frequency division multiplexing (OFDM), filtered OFDM (filtered OFDM, f-OFDM), time-domain windowed OFDM, filter bank multicarrier (FBMC), universal filtered multicarrier (UFMC), generalized frequency division multiplexing (GFDM), wavelet packet modulation (WPM), faster than Nyquist (FTN) waveforms, and low peak to average power ratio waveforms (PAPR WF).

[0128] ● The frame structure component may specify the configuration of a frame or a group of frames. The frame structure component may indicate one or more of the time, frequency, pilot signature, code, or other parameters of a frame or a group of frames. More details of the frame structure will be discussed below.

[0129] ●The multiple access scheme component can specify multiple access technology options, including technologies that define how communication devices share a common physical channel, such as: time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), single carrier frequency division multiple access (SC-FDMA), low density signature multicarrier code division multiple access (LDS-MC-CDMA), non-orthogonal multiple access (NOMA), pattern division multiple access (PDMA), lattice partition multiple access (LPMA), resource spread multiple access (RSMA), and sparse code multiple access (SCMA). In addition, multiple access technology options can include: scheduled access versus unscheduled access, also known as unlicensed access; non-orthogonal multiple access versus orthogonal multiple access, for example, through dedicated channel resources (e.g., not shared between multiple communication devices); contention-based shared channel resources versus non-contention-based shared channel resources; and cognitive radio-based access.

[0130] • A Hybrid Automatic Repeat Request (HARQ) protocol component may specify how transmission and / or retransmissions are performed. Non-limiting examples of transmission and / or retransmission mechanism options include those that specify a scheduled data pipe size, a signaling mechanism for transmission and / or retransmission, and a retransmission mechanism.

[0131] The Coding and Modulation components may specify how the information being sent may be encoded / decoded and modulated / demodulated for transmission / reception purposes. Coding may refer to methods of error detection and forward error correction. Non-limiting examples of coding options include turbo trellis codes, turbo product codes, fountain codes, low-density parity supervision codes, and polar codes. Modulation may simply refer to a constellation (including, for example, a modulation technique and order), or more specifically to various types of advanced modulation methods, such as hierarchical modulation and low PAPR modulation.

[0132] In some embodiments, the air interface can be a "one-size-fits-all concept". For example, once the air interface is defined, the components within the air interface cannot be changed or adapted. In some implementations, only limited parameters or modes of the air interface can be configured, such as the cyclic prefix (CP) length or the multiple input multiple output (MIMO) mode. In some embodiments, the air interface design can provide a unified or flexible framework to support frequency bands below 6 GHz and frequency bands above 6 GHz (e.g., millimeter wave) for authorized and unauthorized access. For example, the flexibility of the configurable air interface provided by extensible system parameters and symbol duration can support transmission parameter optimization for different spectrum bands and different services / devices. For another example, a unified air interface can be self-contained in the frequency domain, and the frequency domain self-contained design can support more flexible radio access network (RAN) slicing by sharing channel resources between different services in frequency and time.

[0133] Frame structure

[0134] The frame structure is a feature of the physical layer of wireless communication, which defines the time domain signal transmission structure to achieve the timing reference and timing alignment of the basic time domain transmission unit, etc. Wireless communication between communication devices can be carried out on the time and frequency resources controlled by the frame structure. The frame structure is sometimes also called the radio frame structure.

[0135] Depending on the frame structure and / or the frame ratio in the frame structure, it is possible to perform frequency division duplex (FDD) communication and / or time division duplex (TDD) communication and / or full duplex (FD) communication. FDD communication means that transmissions in different directions (such as uplink and downlink) are performed on different frequency bands. TDD communication means that transmissions in different directions (such as uplink and downlink) are performed in different durations. FD communication means that transmission and reception are performed on the same time-frequency resources, that is, the device can send and receive on the same frequency resources at the same time.

[0136] An example of a frame structure is the frame structure in long-term evolution (LTE), which has the following specifications: the duration of each frame is 10 ms; each frame has 10 subframes, each of which has a duration of 1 ms; each subframe includes two time slots, each of which has a duration of 0.5 ms; each time slot is used to transmit 7 OFDM symbols (assuming normal CP); each OFDM symbol has a symbol duration and a specific bandwidth (or partial bandwidth or bandwidth partition) related to the number of subcarriers and the subcarrier spacing; the frame structure is based on OFDM waveform parameters, such as subcarrier spacing and CP length (where CP has a fixed length or finite length option); in TDD, the switching gap between uplink and downlink needs to be an integer multiple of the OFDM symbol duration.

[0137] Another example of a frame structure is the frame structure in new radio (NR), which has the following specifications: multiple subcarrier spacings are supported, each subcarrier spacing corresponds to a corresponding system parameter; the frame structure depends on the system parameters, but in any case, the frame length is set to 10ms, each frame consists of 10 subframes, each subframe is 1ms; the time slot is defined as 14 OFDM symbols; the time slot length depends on the system parameters. For example, the NR frame structure of normal CP 15kHz subcarrier spacing ("system parameter 1") and the NR frame structure of normal CP 30kHz subcarrier spacing ("system parameter 2") are different. For a subcarrier spacing of 15kHz, the time slot length is 1ms; for a subcarrier spacing of 30kHz, the time slot length is 0.5ms. The NR frame structure can have greater flexibility than the LTE frame structure.

[0138] Another example of a frame structure is an exemplary flexible frame structure, such as for 6G networks or higher. In a flexible frame structure, a symbol block can be defined as the shortest duration that can be scheduled in the flexible frame structure. A symbol block can be a transmission unit with an optional redundant portion (e.g., a CP portion) and an information (e.g., data) portion. An OFDM symbol is an example of a symbol block. Alternatively, a symbol block can be referred to as a symbol. Embodiments of a flexible frame structure include different parameters that may be configurable, such as a frame length, a subframe length, a symbol block length, etc. In some embodiments of the flexible frame structure, a non-exhaustive list of possible configurable parameters includes:

[0139] (1) Frame: The frame length does not need to be limited to 10ms, and the frame length can be configurable and change over time. In some embodiments, each frame includes one or more downlink synchronization channels and / or one or more downlink broadcast channels, and each synchronization channel and / or broadcast channel can be sent in different directions through different beamforming. The frame length can be more than one possible value and is configured based on the application scenario. For example, an autonomous driving vehicle may require relatively fast initial access, in which case the frame length corresponding to the autonomous driving vehicle application can be set to 5ms. For another example, a smart meter on a house may not require fast initial access, in which case the frame length corresponding to the smart meter application can be set to 20ms.

[0140] (2) Subframe duration: A subframe may or may not be defined in a flexible frame structure, depending on the implementation. For example, a frame may be defined to include time slots but not subframes. In a frame that is defined with subframes, for example, for time domain alignment, the duration of the subframe may be configurable. For example, the length of a subframe may be configured to be 0.1ms or 0.2ms or 0.5ms or 1ms or 2ms or 5ms, etc. In some embodiments, if a subframe is not required in a particular scenario, the subframe length may be defined to be the same as the frame length, or it may not be defined.

[0141] (3) Time slot allocation: Time slots may or may not be defined in the flexible frame structure, depending on the implementation. In a frame with defined time slots, the definition of the time slots (for example, in terms of duration and / or number of symbol blocks) may be configurable. In one embodiment, the time slot allocation is common to all UEs or a group of UEs. In this case, the time slot allocation information may be sent to the UE in a broadcast channel or one or more common control channels. In other embodiments, the time slot allocation may be UE-specific, in which case the time slot allocation information may be sent in a UE-specific control channel. In some embodiments, the time slot allocation signaling may be sent together with the frame allocation signaling and / or the subframe allocation signaling. In other embodiments, the time slot allocation may be sent independently of the frame allocation signaling and / or the subframe allocation signaling. In general, the time slot allocation may be system-wide, base station-wide, UE group-wide, or UE-specific.

[0142] (4) Subcarrier spacing (SCS): SCS is a parameter of scalable system parameters, which can support a range of SCS between 15KHz and 480KHz. SCS can vary with spectrum frequency and / or maximum UE speed to minimize the impact of Doppler frequency offset and phase noise. In some examples, there can be separate transmit frames and receive frames, and the symbol SCS in the receive frame structure can be independent of the SCS configuration of the symbol in the transmit frame structure. The SCS in the receive frame can be different from the SCS in the transmit frame. In some examples, the SCS of each transmit frame can be half the SCS of each receive frame. If the SCS between the receive frame and the transmit frame is different, the difference does not have to be scaled by a factor of 2, for example, if an inverse discrete Fourier transform (IDFT) is used instead of a fast Fourier transform (FFT) to achieve a more flexible symbol duration. Other examples of frame structures can be used with different SCSs.

[0143] (5) Flexible transmission duration of basic transmission unit: The basic transmission unit can be a symbol block (also referred to as a symbol), which generally includes a redundant part (referred to as a CP) and an information (e.g., data) part, although in some embodiments, the CP can be omitted from the symbol block. The CP length can be flexible and configurable. The CP length can be fixed within a frame or flexible within a frame, and the CP length can change with changes in frames, or with changes in frame groups, or with changes in subframes, or with changes in time slots, or dynamically with changes in scheduling. The information (e.g., data) part can be flexible and configurable. Another possible parameter associated with the symbol block that can be defined is the ratio of the CP duration to the information (e.g., data) duration. In some embodiments, the symbol block length can be adjusted based on: channel conditions (e.g., multipath delay, Doppler); and / or delay requirements; and / or available duration. For another example, the symbol block length can be adjusted to fit the available duration in the frame.

[0144] (6) Flexible switching gap: A frame may include a downlink portion for downlink transmissions sent by a base station and an uplink portion for uplink transmissions sent by a UE. There may be a gap between each uplink portion and the downlink portion, which is called a switching gap. The switching gap length (duration) may be configurable. The switching gap duration may be fixed within a frame or may be flexible within a frame, and the switching gap duration may change with changes in frames, changes in frame groups, changes in subframes, changes in time slots, or dynamically changes with changes in scheduling.

[0145] Cell / Carrier / Bandwidth part (BWP) / Occupied bandwidth

[0146] Equipment such as base stations can provide cell coverage. Wireless communication with a device can be performed on one or more carrier frequencies. A carrier frequency is called a carrier. Alternatively, a carrier can be called a component carrier (CC). A carrier can be characterized by its bandwidth and a reference frequency (e.g., the center frequency, the lowest frequency, or the highest frequency of the carrier). A carrier can be on a licensed spectrum or an unlicensed spectrum. Additionally or alternatively, wireless communication with a device can be performed on one or more bandwidth parts (BWPs). For example, a carrier can have one or more BWPs. More generally, wireless communication with a device can be performed on a spectrum. A spectrum can include one or more carriers and / or one or more BWPs.

[0147] The cell may include one or more downlink resources and optionally one or more uplink resources, or the cell may include one or more uplink resources and optionally one or more downlink resources, or the cell may include both one or more downlink resources and one or more uplink resources. For example, the cell may include only one downlink carrier / BWP, or only one uplink carrier / BWP, or multiple downlink carriers / BWP, or multiple uplink carriers / BWP, or one downlink carrier / BWP and one uplink carrier / BWP, or one downlink carrier / BWP and multiple uplink carriers / BWP, or multiple downlink carriers / BWP and one uplink carrier / BWP, or multiple downlink carriers / BWP and multiple uplink carriers / BWP. In some embodiments, alternatively or additionally, the cell may include one or more sidelink resources, and the one or more sidelink resources include sidelink transmission and reception resources.

[0148] A BWP is a set of contiguous or non-contiguous frequency subcarriers on a carrier, or a set of contiguous or non-contiguous frequency subcarriers on multiple carriers, or a set of non-contiguous or contiguous frequency subcarriers, and may have one or more carriers.

[0149] In some embodiments, a carrier may have one or more BWPs, for example, a carrier may have a bandwidth of 20 MHz and consist of one BWP, or a carrier may have a bandwidth of 80 MHz and consist of two adjacent continuous BWPs, and so on. In other embodiments, a BWP may have one or more carriers, for example, a BWP may have a bandwidth of 40 MHz and consist of two adjacent continuous carriers, each of which has a bandwidth of 20 MHz. In some embodiments, a BWP may include non-continuous spectrum resources consisting of non-continuous multi-carriers, wherein the first carrier in the non-continuous multi-carrier may be in the mmW band, the second carrier may be in the low frequency band (e.g., the 2 GHz band), the third carrier (if present) may be in the THz band, and the fourth carrier (if present) may be in the visible light band. The resources in a carrier belonging to a BWP may be continuous or non-continuous. In some embodiments, a BWP has non-continuous spectrum resources on a carrier.

[0150] Wireless communication can be performed over an occupied bandwidth. The occupied bandwidth can be defined as the width of the frequency band such that below a lower frequency limit and above an upper frequency limit, the average power transmitted is each equal to a specified percentage β / 2 of the total average transmitted power, for example, β / 2 is 0.5%.

[0151] The carrier, BWP, or occupied bandwidth may be dynamically signaled by a network device (e.g., a base station) (e.g., in physical layer control signaling (e.g., downlink control information (DCI)) or semi-statically signaled (e.g., in radio resource control (RRC) signaling, or in the medium access control (MAC) layer), or predefined based on an application scenario; or determined by the UE as a function of other parameters known to the UE, or may be fixed by, for example, a standard.

[0152] Artificial Intelligence (AI) and / or Machine Learning (ML)

[0153] The number of new devices in future wireless networks is expected to grow exponentially, and the functionality of devices is expected to become increasingly diverse. In addition, many new applications and use cases are expected to emerge, with more diverse quality of service requirements compared to 5G applications / use cases. These will generate new key performance indications (KPIs) for future wireless networks (e.g., 6G networks), which may be extremely challenging. AI technologies, such as ML technologies (e.g., deep learning), have been introduced into telecommunication applications with the goal of improving system performance and efficiency.

[0154] In addition, antenna and bandwidth capabilities continue to advance, making it possible to carry out more and / or better quality communications over wireless links. In addition, advances in computer architecture and computing power continue, for example, the introduction of general-purpose graphics processing units (GP-GPUs). Future generations of communication devices may have more computing and / or communication capabilities than previous generations, which may support the use of AI to implement air interface components. Future generations of networks may also obtain more accurate and / or new information (compared to previous generations of networks), which can become the input basis of AI models, such as the physical speed at which the device moves, the link budget of the device, the channel conditions of the device, one or more device capabilities and / or the type of services that need to be supported, sensing information and / or positioning information, etc. In order to obtain sensing information, the TRP may send a signal to a target object (e.g., a potential UE), and the TRP or another network device calculates the angle (for beamforming of the device) based on the reflection of the signal, the distance between the device and the TRP, and / or Doppler frequency deviation information. Positioning information is sometimes referred to as positioning, and it can be obtained in a variety of ways, such as positioning reports from the UE (e.g., reports of the UE's GPS coordinates), using a positioning reference signal (PRS), using the above-mentioned sensing, tracking and / or predicting the location of the device, etc.

[0155] AI technology (including ML technology) can be applied to communications, including AI-based communications in the physical layer and / or AI-based communications in the MAC layer. For the physical layer, AI communication can be aimed at optimizing component design and / or improving algorithm performance. For example, AI can be used to implement the following aspects: channel coding, channel modeling, channel estimation, channel decoding, modulation, demodulation, MIMO, waveform, multiple access, physical layer unit parameter optimization and update, beamforming, tracking, sensing and / or positioning, etc. In the MAC layer, AI communication can use AI capabilities to learn, predict and / or make decisions to use potentially better strategies and / or optimal solutions to solve complex optimization problems, such as optimizing functions in the MAC layer. For example, AI can be applied to implement: intelligent TRP management, intelligent beam management, intelligent channel resource allocation, intelligent power control, intelligent spectrum utilization, intelligent MCS, intelligent HARQ strategy and / or intelligent transmit / receive mode adaptation, etc.

[0156] In some embodiments, the AI ​​architecture may involve multiple nodes, where the multiple nodes may be organized in one of two modes: centralized and distributed, both of which may be deployed in an access network, core network, or edge computing system or a third-party network. Centralized training and computing architectures are subject to potentially large communication overhead and strict user data privacy limitations. Distributed training and computing architectures may include several frameworks, such as distributed machine learning and federated learning. In some embodiments, the AI ​​architecture may include an intelligent controller that may execute as a single agent or multiple agents based on joint optimization or individual optimization. New protocols and signaling mechanisms are required so that the corresponding interface links can be personalized with custom parameters to meet specific needs, while minimizing signaling overhead and maximizing the overall system spectrum efficiency through personalized AI technology.

[0157] In some embodiments herein, new protocols and signaling mechanisms are provided for operating within and switching between different operating modes for AI training, including switching between training and normal operating modes, and for measurement and feedback to accommodate different possible measurements and information that may require feedback, depending on the implementation.

[0158] AI Training

[0159] Reference again Figure 1 and 2 , the embodiments of the present disclosure may be used to implement AI training involving two or more communication devices in the communication system 100. For example, Figure 5 Four EDs are shown for one embodiment in communication with a network device 452 in a communication system 100. The four EDs are shown as respective different UEs and will be referred to hereinafter as UEs 402, 404, 406, and 408. However, an ED does not necessarily have to be a UE.

[0160] Network device 452 is part of a network (e.g., wireless access network 120). Network device 452 can be deployed in an access network, a core network, an edge computing system, or a third-party network, depending on the implementation. Network device 452 can be a T-TRP or a server (or a part thereof). In one example, network device 452 can be a T-TRP 170 or a NT-TRP 172 (or implemented therein). In another example, network device 452 can be a T-TRP controller and / or a NT-TRP controller that can manage T-TRP 170 or NT-TRP 172. In some embodiments, the components of network device 452 can be distributed. UE 402, 404, 406, and 408 can communicate directly with network device 452, for example, if network device 452 is part of a T-TRP serving UE 402, 404, 406, and 408. Alternatively, UEs 402, 404, 406, and 408 may communicate with network device 452 through one or more intermediate components such as T-TRP and / or NT-TRP. For example, network device 452 may send and / or receive information (e.g., control signaling, data, training sequence, etc.) to / from one or more of UEs 402, 404, 406, and 408 through a backhaul link and a wireless channel inserted between network device 452 and UEs 402, 404, 406, and 408.

[0161] As described above, each UE 402, 404, 406 and 408 includes a corresponding processor 210, a memory 208, a transmitter 201, a receiver 203 and one or more antennas 204 (or alternatively, a panel). For simplicity, only the processor 210, the memory 208, the transmitter 201, the receiver 203 and the antenna 204 of the UE 402 are shown, but the other UEs 404, 406 and 408 also include the same corresponding components.

[0162] For each UE 402, 404, 406 and 408, the communication link between the UE and the corresponding TRP in the network is an air interface. The air interface typically includes many components and associated parameters that together specify how to send and / or receive transmissions over a wireless medium.

[0163] Figure 5The processor 210 of the UE in the embodiment implements one or more air interface components on the UE side. The air interface component configures and / or implements transmission and / or reception on the air interface. Examples of air interface components are described herein. The air interface component may be located in the physical layer, for example, a channel encoder (or decoder) that implements the air interface coding component of the UE, and / or a modulator (or demodulator) that implements the air interface modulation component of the UE, and / or a waveform generator that implements the air interface waveform component of the UE, and so on. The air interface component may be in a higher layer (or a part thereof) such as the MAC layer, for example, a module that implements channel prediction / tracking, and / or a module that implements a retransmission protocol (for example, an air interface HARQ protocol component that implements the UE), and so on. The processor 210 also directly executes (or controls the UE to execute) the UE-side operations described herein.

[0164] The network device 452 includes a processor 454, a memory 456, and an input / output device 458. The processor 454 implements or instructs other network devices (e.g., T-TRP) to implement one or more air interface components on the network side. Compared with another UE, the air interface component implementation mode of one UE on the network side may be different. The processor 454 directly executes (or controls the network component to execute) the network side operations described herein.

[0165] Processor 454 may be implemented by the same or different one or more processors for executing instructions stored in memory (e.g., memory 456). Alternatively, some or all of processor 454 may be implemented using dedicated circuits (e.g., programmed FPGA, GPU, or ASIC). Memory 456 may be implemented by volatile and / or non-volatile memory. Any suitable type of memory may be used, such as RAM, ROM, hard disk, optical disk, on-processor cache, etc.

[0166] The input / output device 458 supports interaction with other devices by receiving (input) and sending (output) information. In some embodiments, the input / output device 458 can be implemented by a transmitter and / or a receiver (or transceiver) and / or one or more interfaces (e.g., a wired interface to an internal network or to the Internet, etc.). In some implementations, the input / output device 458 can be implemented by a network interface, which may be implemented as a network interface card (NIC) and / or a computer port (e.g., a physical socket to which a plug or cable is connected) and / or a network socket, etc., depending on the implementation.

[0167] The network device 452 and the UE 402 have the capability to implement one or more AI-enabled processes. Figure 5In the embodiment of the present invention, the network device 452 and the UE 402 include ML modules 410 and 460, respectively. The ML module 410 is implemented by the processor 210 of the UE 402, and the ML module 460 is implemented by the processor 454 of the network device 452. Figure 5 , ML module 410 is shown as being within processor 210, and ML module 460 is shown as being within processor 454. For example, ML modules 410 and 460 execute one or more AI / ML algorithms to perform one or more AI-enabled processes (e.g., AI-enabled link adaptation) to optimize the communication link between the network and UE 402.

[0168] ML modules 410 and 460 can be implemented using an AI model. The term AI model may refer to a computer algorithm for receiving defined input data and outputting defined reasoning data, wherein the parameters of the algorithm (e.g., weights) can be updated and optimized by training (e.g., using a training data set, or using data collected in real life). The AI ​​model can be implemented using one or more neural networks (e.g., including deep neural networks (DNNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and combinations thereof) and using various neural network architectures (e.g., autoencoders, generative adversarial networks, etc.). Various techniques can be used to train the AI ​​model in order to update and optimize its parameters. For example, backpropagation is a common technique for training DNNs, in which a loss function is calculated between the reasoning data generated by the DNN and a certain target output (e.g., ground truth data). The gradient of the loss function is calculated relative to the parameters of the DNN, and the parameters are updated using the calculated gradient (e.g., using a gradient descent algorithm) to minimize the loss function.

[0169] In some embodiments, the AI ​​model covers a neural network for machine learning. A neural network is composed of multiple computing units (which may also be referred to as neurons) arranged in one or more layers. The process of receiving input at an input layer and generating output at an output layer can be referred to as forward propagation. In forward propagation, each layer receives an input (which may have any suitable data format, such as a vector, matrix, or multidimensional array) and performs calculations to generate an output (which may have a different dimension than the input). The calculations performed by the layer typically involve applying (e.g., multiplying) a set of weights (also called coefficients) to the input. Except for the first layer (i.e., input layer) of the neural network, the input of each layer is the output of the previous layer. The neural network may include one or more layers between the first layer (i.e., input layer) and the last layer (i.e., output layer), which may be referred to as inner layers or hidden layers. For example, Fig. 6AAn example of a neural network 600 including an input layer, an output layer, and two hidden layers is depicted. In this example, it can be seen that the output of each of the three neurons in the input layer of the neural network 600 is included in the input vector of each of the three neurons in the first hidden layer. Similarly, the output of each of the three neurons in the first hidden layer is included in the input vector of each of the three neurons in the second hidden layer, and the output of each of the three neurons in the second hidden layer is included in the input vector of each of the two neurons in the output layer. As described above, the basic computational unit in a neural network is a neuron, such as Fig. 6A As shown in 650. Figure 6B An example of a neuron 650 that can be used as a building block of the neural network 600 is shown. Figure 6B As shown in , in this example, neuron 650 takes vector x as input and performs a dot product w of the vector associated with the weights. The final output z of the neuron is the result of the activation function f() on the dot product. Various neural networks can be designed to have various architectures (e.g., various numbers of layers, each layer performing various functions).

[0170] Neural networks are trained to optimize the parameters (e.g., weights) of the neural network. This optimization is performed in an automated manner and can be referred to as machine learning. The training of a neural network includes forward propagation of input data samples to generate output values ​​(also called predicted output values ​​or inferred output values), and comparing the generated output values ​​with known or desired target values ​​(e.g., ground truth values). A loss function is defined to quantitatively represent the difference between the generated output value and the target value, and the goal of training the neural network is to minimize the loss function. Backpropagation is an algorithm for training a neural network. Backpropagation is used to adjust (also called update) the values ​​of parameters (e.g., weights) in a neural network so that the calculated loss function becomes smaller. Backpropagation involves calculating the gradient of the loss function relative to the parameters to be optimized, and updating the parameters using a gradient algorithm (e.g., gradient descent) to reduce the loss function. Backpropagation is performed iteratively so that the loss function converges or is minimized over multiple iterations. After the training conditions are met (e.g., the loss function has converged, or a predefined number of training iterations have been performed), the neural network is considered trained. The trained neural network can be deployed (or executed) to generate inferred output data from the input data. In some embodiments, training of the neural network may continue even after the neural network has been deployed, such that parameters of the neural network may be repeatedly updated with the latest training data.

[0171] Reference again Figure 5In some embodiments, the UE 402 and the network device 452 may exchange information for training purposes. The information exchanged between the UE 402 and the network device 452 is implementation specific and may not have a human-understandable meaning (e.g., it may be intermediate data generated during execution of an ML algorithm). Additionally or alternatively, the information exchanged may not be predefined by the standard, e.g., bits may be exchanged, but these bits may not be associated with a predefined meaning. In some embodiments, the network device 452 may provide or indicate to the UE 402 one or more parameters to be used in the ML module 410 implemented at the UE 402. As an example, the network device 452 may send or indicate updated neural network weights to be implemented in a neural network executed by the ML module 410 on the UE side in order to attempt to optimize one or more aspects of modulation and / or coding for communications between the UE 402 and the T-TRP or NT-TRP.

[0172] In some embodiments, UE 402 may implement AI itself, such as performing learning, while in other embodiments, UE 402 may not be able to perform learning itself, but may be able to operate in conjunction with the AI ​​implementation on the network side, for example, by receiving the configuration of an AI model (e.g., a neural network or other ML algorithm) implemented by ML module 410 from the network, and / or by providing requested measurements or observations to assist other devices (e.g., network devices or other UEs with AI capabilities) in training AI models (e.g., neural networks or other ML algorithms). For example, in some embodiments, UE 402 may not be able to implement learning or training itself, but UE 402 may receive trained configuration information of an ML model determined by network device 452 and execute the model.

[0173] although Figure 5 The examples in assume AI / ML capabilities on the network side, but it is possible that the network itself does not perform training / learning, but the UE may be able to perform learning / training by itself using dedicated training signals sent from the network. In other embodiments, end-to-end (E2E) learning can be implemented by the UE and the network device 452.

[0174] Using AI, for example, by implementing an AI model as described above, various processes such as link adaptation may be AI-enabled. The following describes some examples of possible AI / ML training processes and over-the-air information exchange processes between devices for facilitating AI-enabled processes during a training phase according to embodiments of the present disclosure.

[0175] Reference again Figure 5For wireless federated learning (FL), the network device 452 may initialize a global AI / ML model implemented by the ML module 460 for a group of UEs (e.g., Figure 5 The four UEs 402, 404, 406, and 408 shown in the figure are sampled and the global AI / ML model parameters are broadcast to the UEs. Each of the UEs 402, 404, 406, and 408 can then initialize its local AI / ML model with the global AI / ML model parameters and update (train) its local AI / ML model with its own data. Each of the UEs 402, 404, 406, and 408 can then report the parameters of its updated local AI / ML model to the network device 452. The network device 452 can then aggregate the updated parameters reported from the UEs 402, 404, 406, and 408 and update the global AI / ML model. The above process is one iteration of the FL-based AI / ML model training process. The network device 452 and the UEs 402, 404, 406, and 408 perform multiple iterations until the AI / ML model has sufficiently converged to meet one or more training objectives / criteria and the AI / ML model is finally completed.

[0176] Figure 7 An exemplary star topology used in a conventional FL process is shown. Figure 7 In the star topology shown in , there is a master node / device 700 (e.g., server, TRP, BS) and several client nodes / devices 701 to 708. The master node 700 initializes the master AI / ML model, samples a group of user client nodes 701 to 708, and distributes the master model (mastermodel, MM) to the client nodes 701 to 708. The client nodes 701 to 708 then initialize their local AI / ML models M1 to M8 using the master AI / ML model, and update (train) their local AI / ML models M1 to M8 using their own data. The client nodes 701 to 708 then report their updated local AI / ML models to the master node 700. The master node 700 aggregates the updated AI / ML models M1 to M8 reported by the client nodes 701 to 708, and updates the master AI / ML model.

[0177] This traditional FL-based AI / ML model training process may have some limitations. In the star topology used in the FL-based AI / ML model training process, the master node 700 needs to collect a large amount of training data sets (e.g., gradients updated by the client nodes) from each of the client nodes 701 to 708. In the traditional FL-based AI training process, the structure of the AI / ML model (e.g., the master AI / ML model and the local AI / ML model) needs to be consistent. However, since different client nodes may support different AI / ML model structures, data heterogeneity may be a problem in the traditional FL-based AI training process.

[0178] Various aspects of the present disclosure provide solutions to overcome the above limitations, such as specific methods and apparatus for configuring the topology of artificial intelligence or machine learning (AI / ML) in a wireless communication network. The configured topology can support the delivery of heterogeneous AI / ML models over the air interface of the wireless communication network. The methods and apparatus described in the present disclosure can be implemented and deployed with consideration of the computing power of each network node and the potential inward / outward expansion of AI / ML model learning and reasoning.

[0179] The AI / ML model learning scheme described in this disclosure is different from the traditional FL-based AI / ML model training scheme. Figure 8 The differences between the traditional FL-based AI / ML model training process and the AI / ML model learning scheme of the present disclosure are shown.

[0180] like Figure 8 As shown in , the master model 810 does not move in the FL-based AI / ML model training scheme. Instead, a large amount of training data sets 815 (e.g., gradients of client node updates) can be passed from the client node 811a to the master node / device 811, which updates the master AI / ML model 810 based on the collected data. In other words, the training data set 815 follows the AI / ML model 810 in the FL-based AI / ML model training scheme. In contrast, in the AI / ML model learning scheme of the present disclosure, the AI / ML model 820 follows the training data set maintained at the corresponding network edge or node / device 821. In other words, the AI / ML model 820 (e.g., a deep neural model) is routed around the network edge or node / device 821, and the deep learning algorithm is executed locally at each node / device 821.

[0181] In the FL-based AI / ML model training, there is only one master node / device 811 that collects AI / ML models and aggregates the collected AI / ML models. Only the master node / device 811 can be called an aggregation node. On the other hand, in the AI / ML model learning scheme of the present disclosure, any node in the topology can collect AI / ML models and aggregate the collected AI / ML models, so there can be several aggregation nodes (e.g., node 821). In fact, unlike the FL-based AI / ML model training in which nodes 811 and 811a are arranged in a star topology, multiple nodes / devices 821 are arranged in a self-organizing topology, such as Figure 8 As shown in .

[0182] A further difference between FL-based AI / ML model training and the AI / ML model learning scheme of the present disclosure relates to the type of data / information exchanged between nodes. In FL-based AI / ML model training, a large amount of training data sets 815 (e.g., gradients updated by client nodes) can be exchanged between client nodes 811a and master nodes / devices 811. In the AI / ML model learning scheme of the present disclosure, AI / ML models 820 are exchanged between nodes 821.

[0183] In some embodiments, any node in the network may be a node that collects one or more AI / ML models and aggregates the collected AI / ML models to generate a new or updated AI / ML model (e.g., Figure 8 Node 821 in ). This node may be a type 1 node. A type 1 node is a node for collecting multiple AI / ML models and aggregating the collected AI / ML models to obtain a new or updated AI / ML model (e.g., a first type AI / ML model). Type 1 nodes may also be referred to as aggregate AI / ML nodes or aggregate nodes below or elsewhere in this disclosure. Each aggregate node (or type 1 node) may be communicatively and / or operatively connected to or associated with another aggregate node (or type 1 node). Each aggregate node (or type 1 node) may be communicatively and / or operatively connected to or associated with one or more other nodes. One or more other nodes may be type 2 nodes. A type 2 node is a node for training an AI / ML model (e.g., a second type AI / ML model) using a set of training data (e.g., local data) without the need for an aggregation operation. Type 2 nodes may be referred to as basic AI / ML nodes or basic nodes below or elsewhere in this disclosure. In some embodiments, some aggregate nodes (or type 1 nodes) may not be associated with a basic node (or type 2 node) (i.e., only associated with other aggregate nodes (or type 1 nodes)).

[0184] In some embodiments, the AI / ML model can be passed between nodes (e.g., BS, TRP, UE) according to the self-organizing topology. In this regard, the training gradients are not sent or shared between nodes at the network level (e.g., aggregation nodes and base nodes). This is because the number of training gradients is huge and it is difficult to control the accuracy during the calculation process.

[0185] In some embodiments, there may be at least two types of AI / ML models. The first type of AI / ML model is an AI / ML model generated by an aggregation node, for example, based on one or more AI / ML models collected by the aggregation node. The first type of AI / ML model may be referred to as a general AI / ML model hereinafter or elsewhere in this disclosure. The second type of AI / ML model is an AI / ML model trained by a basic AI / ML node without the need for aggregation operations. The second type of AI / ML model may be referred to as a local AI / ML model hereinafter or elsewhere in this disclosure. It should be noted that the general AI / ML model and the local AI / ML model may have different names. Those skilled in the art will readily understand that even if the AI / ML model is called differently, if the AI / ML model has substantially similar characteristics to those of a general AI / ML model or a local AI / ML model, or is generated / trained / updated in a substantially similar manner to the general AI / ML model or the local AI / ML model described herein, the AI / ML model should also be regarded as a general AI / ML model or a local AI / ML model.

[0186] Various aspects of the present disclosure provide methods for configuring or discovering topologies of artificial intelligence or machine learning (AI / ML). In some embodiments, topology configuration or topology discovery in a wireless communication network has two modes. In the first mode, a network device (e.g., a BS or TRP) configures a topology that supports the transmission of heterogeneous AI / ML models through the air interface of a wireless communication network. This configuration may be referred to as a centralized configuration. In the second mode, a network device (e.g., a BS or TRP) and / or a user device may autonomously configure the topology in the wireless communication network. In some embodiments, an aggregation node announces its aggregation capabilities to other nodes. To this end, an aggregation node may send a discovery message including aggregation information that other nodes will use to discover the aggregation node. In some embodiments, a node sends an aggregation request message (e.g., an aggregation request) to other nodes. The node that sends the aggregation request message may be an aggregation node or a base node. The aggregation request message may include an aggregation request or requested aggregation information that other nodes may be interested in discovering.

[0187] In some embodiments, the topology can be configured so that AI / ML model aggregations can be processed in parallel.

[0188] In a self-organizing topology, there are multiple nodes that perform communication and computing functions. These nodes can receive at least one computing model (AI / ML model) and send at least one computing model (AI / ML model). Although the received model and the sent model are not the same entity, they may have the same or different data, information and / or neural network structure. The received AI / ML model and the sent AI / ML model may include multiple parameters, such as one or more parameters related to a graph model, a parameter model, a model table, a model algorithm, and a database.

[0189] In terms of learning AI / ML models, the nodes in the network can be divided into two node types. Fig.9A and 9B Two different types of nodes for learning AI / ML models according to embodiments of the present disclosure are shown. Fig.9A A node of a type 2 node or a basic AI / ML node or a basic node is shown. Fig. 9B Another type of node is shown as a Type 1 node or an aggregated AI / ML node or an aggregated node. A Type 2 / basic node may also be referred to as a local AI / ML node or a local node.

[0190] The basic AI / ML node can receive one or more general AI / ML models from other nodes (e.g., aggregation nodes) and train its own (customized) AI / ML model. The customized AI / ML model may be referred to as a local AI / ML model. The basic node uses its own AI / ML model training algorithm to train its own local AI / ML model with the assistance of the (current) general AI / ML model (e.g., information related to distillation and / or expansion) received from one or more aggregation nodes. In some embodiments, the neural network (NN) structure of the local AI / ML model may be the same as the NN structure of at least some of the received general AI / ML models. In some embodiments, the NN structure of the local AI / ML model may be different from the NN structure of all received general AI / ML models. When the training of the local AI / ML model is completed, the basic node sends the local AI / ML model to one or more other nodes (e.g., aggregation nodes).

[0191] The aggregation node may receive one or more AI / ML models from other nodes in the network. The received AI / ML models include one or more local AI / ML models from the associated base node and / or one or more common AI / ML models from other aggregation nodes. Fig. 9BThe aggregate AI / ML node shown in receives a local AI / ML model, but in some embodiments, the aggregate node may not receive any local AI / ML model from the base node (i.e., only receives a general AI / ML model from other aggregate nodes). In some embodiments, some or all of the received AI / ML models have the same NN structure. In some embodiments, all received AI / ML models have different NN structures. After collecting AI / ML models from other nodes, the aggregate node aggregates the collected AI / ML models (general AI / ML models, local AI / ML models) to generate a new or updated general AI / ML model. The aggregate node then sends the new / updated AI / ML model to one or more other nodes.

[0192] In some embodiments, the general AI / ML model is a predefined or preconfigured AI / ML model.

[0193] As described above, the general AI / ML model and the local AI / ML model involved in the AI / ML model learning process have different NN structures. For example, when the aggregation node receives a general AI / ML model and three local AI / ML models, the received AL / ML models may have completely different NN structures. The general AI / ML model may be a deep neural network (DNN) model with 6 layers, the first local AI / ML model may be a DNN model with 4 layers, the second local AI / ML model may be a DNN model with 8 layers, and the third local AI / ML model may be a convolutional neural network (CNN). The NN structure can be regarded as the model structure of the AI / ML model.

[0194] The self-organizing topology of the present disclosure includes a connection between an aggregation node and one or more base nodes and / or a connection between multiple aggregation nodes.

[0195] Any network device / apparatus can operate as an aggregation node in the network. An aggregation node may be communicatively and / or operatively connected to one or more base nodes. Such connection may indicate that the aggregation node collects local AI / ML models from the associated base nodes. However, it should be noted that in some embodiments, some aggregation nodes may not be communicatively and / or operatively connected to any base node. Such aggregation nodes may not collect local AI / ML models, but only receive general AI / ML models from other aggregation nodes.

[0196] It should be noted that any aggregation node and / or any base node may be, for example, a UE, a relay station, a base station (BS), a transmission reception point (TRP), an edge device, an edge computing system, or a network system.

[0197] Fig.101 shows an exemplary self-organizing topology 1000 according to an embodiment of the present disclosure. Topology 1000 includes connections between aggregation nodes, and connections between aggregation nodes and base nodes. With respect to connections between aggregation nodes, each of aggregation nodes 901 to 908 is communicatively and operatively connected to its neighboring aggregation nodes. For example, aggregation node 901 is communicatively and operatively connected to aggregation nodes 902 and 908, as shown in FIG. Fig.10 In this example, the connection indicates that the aggregation node 901 can receive the general AI / ML model M from the aggregation node 908. j And send its general AI / ML model M1 to the aggregation node 902.

[0198] Regarding the connection between the aggregation node and the base node, in this example, each of the aggregation nodes 901 to 908 is communicatively and operatively connected to the base node. Specifically, the aggregation node 901 is communicatively and operatively connected to the base nodes 901a and 901b. These connections indicate that the aggregation node 901 can receive the general AI / ML model M from the node 908. j901a and 901b, and collect local AI / ML models from base nodes 901a and / or 901b. Similarly, aggregation node 902 is communicatively and operatively connected to base nodes 902a and 902b. These connections indicate that aggregation node 902 can send the general AI / ML model M1 received from node 901 to base nodes 902a and 902b, and collect local AI / ML models from base nodes 902a and / or 902b. In addition, aggregation node 903 is communicatively and operatively connected to base nodes 903a and 903b. These connections indicate that aggregation node 903 can send the general AI / ML model M2 received from node 902 to base nodes 903a and 903b, and collect local AI / ML models from base nodes 903a and / or 903b. In addition, aggregation node 904 is communicatively and operatively connected to base nodes 904a and 904b. These connections indicate that the aggregation node 904 can send the general AI / ML model M3 received from the node 903 to the base nodes 904a and 904b, and collect local AI / ML models from the base nodes 904a and / or 904b. In addition, the aggregation node 905 is communicatively and operatively connected to the base nodes 905a and 905b. These connections indicate that the aggregation node 905 can send the general AI / ML model M4 received from the node 904 to the base nodes 905a and 905b, and collect local AI / ML models from the base nodes 905a and / or 905b. In addition, the aggregation node 906 is communicatively and operatively connected to the base nodes 906a and 906b. These connections indicate that the aggregation node 906 can send the general AI / ML model M5 received from the node 905 to the base nodes 906a and 906b, and collect local AI / ML models from the base nodes 906a and / or 906b. In addition, the aggregation node 907 is communicatively and operatively connected to the base nodes 907a and 907b. These connections indicate that the aggregation node 907 can send the general AI / ML model M6 received from the node 906 to the base nodes 907a and 907b, and collect local AI / ML models from the base nodes 907a and / or 907b. In addition, the aggregation node 908 is communicatively and operatively connected to the base nodes 908a and 908b. These connections indicate that the aggregation node 908 can send the general AI / ML model Mi received from the node 907 to the base nodes 908a and 908b, and collect local AI / ML models from the base nodes 908a and / or 908b.

[0199] Although Fig.10 Each base node in the embodiment is communicatively and operatively connected to only one aggregation node, but in some embodiments, a base node may be communicatively and operatively connected to more than one aggregation node. Fig.10Each aggregation node in the system is communicatively and operatively connected to some base nodes, but in some embodiments, some aggregation nodes may not be communicatively and / or operatively connected to any base node. Such aggregation nodes may not collect local AI / ML models, but only receive general AI / ML models from other aggregation nodes.

[0200] Assuming that there is an aggregation node i in the network, the aggregation node i can receive information related to the corresponding AI / ML model from one or more aggregation nodes that are communicatively and operatively connected to the aggregation node i. Each aggregation node that sends information related to its AI / ML model can be referred to as the previous aggregation node connected to the aggregation node i. The aggregation node i can send information related to its AI / ML model to one or more other aggregation nodes that are communicatively and operatively connected to the aggregation node i. Each aggregation node that receives information related to the AI / ML model of the aggregation node i can be referred to as the next aggregation node connected to the aggregation node i. Fig.10 In the network shown, for aggregation node 902 , the previous aggregation node is aggregation node 901 , and the next aggregation node is aggregation node 903 .

[0201] Although Fig.10 The topology 1000 in FIG. 1 shows that each aggregation node has one previous aggregation node and one next aggregation node, but it should be noted that an aggregation node may have one or more previous aggregation nodes and one or more next aggregation nodes, such as Fig.16 as shown in . Fig.16 It is shown that each aggregation node may have one or more previous aggregation nodes and one or more next aggregation nodes.

[0202] As described above, aspects of the present disclosure provide methods for configuring the topology of AI / ML. Some methods for configuring the topology of AI / ML in a wireless communication network are shown below and elsewhere in the present disclosure.

[0203] According to some aspects of the present disclosure, a wireless communication network or a network device (e.g., a BS, a TRP) configures the topology of AI / ML in the wireless communication network. In order to configure the topology, the network or network device receives information including a report related to the AI / ML capabilities of the node from the node. Then, the network or network device may configure the node based on the received information. The node configuration may include configuring the node type of the node. For example, the configured node type may be one of a plurality of node types including at least a type 1 node type (e.g., an aggregation node) and a type 2 node type (e.g., a basic node). As described above, a type 1 node is a node for collecting multiple AI / ML models and aggregating the collected AI / ML models to obtain a new or updated AI / ML model (e.g., a first type AI / ML model). In addition, a type 2 node is a node for training an AI / ML model (e.g., a second type AI / ML model) using a set of training data (e.g., local data) without the need for an aggregation operation. After the network or network device configures the node, the network or network device also configures one or more other nodes associated with the configured node. The network or network device may configure one or more other nodes differently based on the node type of the configured node.

[0204] In this way, the network or network device can configure the topology of AI / ML in the wireless communication network. The configured topology can support the transmission of AI / ML models through the air interface of the wireless communication network. AI / ML model transmission can be heterogeneous AI / ML model transmission, wherein the AI / ML model transmission between different nodes in the topology may involve AI / ML models with different neural network structures. AI / ML model transmission may include the transmission of a complete AI / ML model or a partial AI / ML model. The configured topology may include at least one of a connection between at least one aggregation node and zero or more basic nodes or a connection between at least two aggregation nodes.

[0205] Fig.11 An exemplary configured topology for supporting AI / ML model delivery according to an embodiment of the present disclosure is shown. Fig.11 , node 1101 i–1 ,1101 i ,1101 i+1 Each of nodes 1101a to 1101f may be a user equipment (UE), a relay station, a base station (BS), a transmission reception point (TRP), an edge device, a network system, or an integrated access backhaul (IAB) node. In order to configure a topology that supports AI / ML model delivery, the network 1100 or the network device 1150 receives data from the node 1101. i–1 ,1101i,1101i+1 Each of the nodes 1101a to 1101f receives information including a report related to its AI / ML capabilities. For example, the network 1100 or the network device 1150 receives information including a report related to its AI / ML capabilities from the node 1101 i Receiving includes with node 1101 i Node 1101 i–1 ,1101 i and 1101 i+1 An aggregation node request (i.e., requesting to be configured as an aggregation node) may optionally be sent. An aggregation node request for each node may be included in the information, including a report related to the AI / ML capabilities of the node.

[0206] The network 1100 or the network device 1150 provides each node (i.e., the node 1101 i–1 ,1101 i ,1101 i+1 and 1101a to 1101f). Specifically, network 1100 or network device 1150 configures the node type of each node. The node type of each node can be configured based on information received from the node including a report related to the AI / ML capabilities of the node. In some embodiments, the node type of each node can be configured as type 1 (e.g., an aggregate node type) or type 2 (e.g., a basic node type). For example, in Fig.11 In the case shown, node 1101 i–1 ,1101 i ,1101 i+1 The node type of nodes 1101a to 1101f can be configured as type 1, and the node type of nodes 1101a to 1101f can be configured as type 2.

[0207] When the node (ie, node 1101) is i–1 ,1101 i ,1101 i+1 When configuring one or more nodes associated with the configured node (e.g., one or more nodes 1101a to 1101f), the network 1100 or the network device 1150 may also configure one or more other nodes associated with the configured node. For example, one or more other nodes may be configured differently based on the node type of the configured node.

[0208] For example, when aggregation node 1101 i When configured by the network 1100 or the network device 1150, the basic node 1101a, the basic node 1101b, the aggregation node 1101 i–1 or aggregation node 1101 i+1At least one of the nodes may also be configured by the network 1100 or the network device 1150. The basic nodes 1101a and 1101b may be used to communicate with the node 1101 i The base nodes 1101a and 1101b can also be used to provide the node 1101 with a communication link. i Send its local AI / ML model. Aggregation node 1101 i–1 Can be configured as node 1101 i The previous aggregation node, making node 1101 i–1 General AI / ML Model CM i–1 Send to node 1101 i Aggregation Node 1101 i+1 Can be configured as node 1101 i The next aggregation node makes node 1101 i+1 From node 1101 i Receive general AI / ML model CM i .

[0209] In another example, when the base nodes 1101a and 1101b are configured by the network 1100 or the network device 1150, the aggregation node 1101 i It can also be configured by the network 1100 or the network device 1150. For example, the aggregation node 1101 i It can be used to communicate and operatively connect with base nodes 1101a and 1101b. Fig.11 Base nodes 1101a and 1101b are shown communicatively and operatively connected to an aggregation node (i.e., node 1101 i ), but in some embodiments, some basic nodes (e.g., Fig.17 The node 1701 in can be communicatively and operatively connected to more than one aggregation node, such as Fig.17 In some embodiments, when the basic nodes 1101a and 1101b are configured, the aggregation node 1101 i Can be used to collect local AI / ML models from base nodes 1101a and 1101b respectively.

[0210] It should be noted that the node 1101 included in the network 1100 i–1 ,1101 i ,1101 i+1 , 1101a, 1101b and other nodes may be communicatively and operatively connected in the topology via side links using device-to-device (D2D) communication, via network devices (e.g., BS, TRP, etc.), or via interfaces between network devices. For example, in some embodiments, aggregation node 1101 iThe aggregation node 1101 and its associated base nodes 1101a and 1101b may be communicatively and operatively connected via a side link using device-to-device (D2D) communications. i and 1101 i+1 The aggregation node 1101 may be communicatively and operatively connected via the network device 1150. i and 1101 i+1 can be achieved through network device 1150 and another network device ( Fig.11 In this case, the aggregation node 1101 is communicatively and operatively connected to the aggregation node 1101. i and 1101 i+1 The network device 1150 may be communicatively and operatively connected via an interface between the network device and another network device.

[0211] According to some aspects of the present disclosure, a network node such as a user device may autonomously configure the topology of AI / ML in a wireless communication network. In order to configure the topology of AI / ML, a node (a first node) sends an aggregate message to another node (a second node). The second node determines whether to be communicatively and operatively connected to the first node in order to send an AI / ML model to the first node or to receive an AI / ML model from the first node. Then, the second node sends a response indicating the determination to the first node. After the first node receives the response (e.g., an aggregate response message), the first node and the second node selectively establish an aggregate connection therebetween based on the determination of the second node. For example, if the second node determines to be connected to the first node, the second node may send a positive aggregate response message to the first node. After the first node receives the positive aggregate response message, the first node and the second node may establish a connection therebetween. If the second node determines not to be connected to the first node, the second node may send a negative aggregate response message to the first node. In this scenario, if the first node receives a negative aggregate response message, a connection between the first and second nodes will not be established.

[0212] In this way, a network node (e.g., a user equipment) can autonomously configure the topology of AI / ML in a wireless communication network. The configured topology can support the delivery of AI / ML models over the air interface of the wireless communication network. The AI / ML model delivery can be a heterogeneous AI / ML model delivery. The AI / ML model delivery can include the transmission of a complete AI / ML model or a partial AI / ML model. The configured topology may include at least one of a connection between at least one aggregation node and zero or more basic nodes or a connection between at least two aggregation nodes.

[0213] In order to establish an aggregate connection with another node, the aggregate node may send information indicating its aggregation capability to the other node. In this case, the aggregate node may be referred to as a notification node, and the other node may be referred to as a monitoring node. A notification node is a node that sends aggregate information, which may be used by other nodes (e.g., monitoring nodes) to discover the notification node. A monitoring node is a node that monitors the aggregate information sent by the notification node. The monitoring node may be a base node or another aggregation node (i.e., not a notification node). For example, when the monitoring node is another aggregation node, the monitoring node may be the previous aggregation node of the notification node. In some embodiments, if the previous aggregation node is not found through the discovery process, the aggregation node (i.e., the notification node) may send a request to a network device (e.g., BS, TRP) to obtain the latest general AI / ML model, rather than receiving the latest general AI / ML model from the previous aggregation node.

[0214] Fig.12 The exemplary process of establishing an aggregation connection between an aggregation node and a base node when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown. Fig.12 In the aggregation node 1101 i is a notifying node, and basic nodes 1101a and 1101b are monitoring nodes.

[0215] In step 1210, the aggregation node 1101 i Announce aggregation information. Specifically, aggregation node 1101 i The discovery message is sent to the base nodes 1101a and 1101b. In some embodiments, the aggregation node 1101 i The discovery message may be broadcast, multicast, or unicast at predetermined intervals, for example, via a broadcast / multicast / unicast RRC, MAC-CE, or DCI, or interface between TRP / BS. The discovery message includes aggregate information to be used by base nodes 1101a and 1101b to discover the announcing node. For example, the discovery message may include a model collection indicator, a reference AI / ML model, or both. The model collection indicator may include a distillation indicator, an expansion indicator, or a distillation and expansion indicator. The distillation and expansion operations associated with the AI / ML model will be discussed in further detail below.

[0216] At step 1220, if the base nodes 1101a and 1101b are connected to the aggregation node 1101 i If the received discovery message is of interest, the base nodes 1101a and 1101b may read and process the received discovery message. Then, the base nodes 1101a and 1101b may determine whether to communicate with the aggregation node 1101. i Communicatively and operatively connected.

[0217] At step 1230, the base nodes 1101a and 1101b may send the following information to the aggregation node 1101: i Each response indicates whether the base node 1101a or 1101b has determined whether it has a connection with the aggregation node 1101. i In some embodiments, the response is a positive aggregation response message or a negative aggregation response message. For example, the base node 1101a may determine that the aggregation node 1101 i connection, and the basic node 1101b can determine not to connect with the aggregation node 1101 i In this case, the base node 1101a sends a signal to the aggregation node 1101. i The node sends a positive aggregation acknowledgement message (eg, ACK), and the base node 1101b sends a positive aggregation acknowledgement message (eg, ACK) to the aggregation node 1101. i A negative aggregate acknowledgement message (eg, NACK) is sent.

[0218] At step 1240, at aggregation node 1101 i After receiving the response, aggregation node 1101 i Based on the received response, selectively establish an aggregation connection with one or both of the base node 1101a and the base node 1101b. In the illustrated example, assume that at step 1230, both the base nodes 1101a and 1101b respond with a positive aggregation response message, so at step 1240, the aggregation node 1101 i An aggregation connection is established with each of the base nodes 1101a and 1101b. i An aggregate connection may not be established with any associated base node that responds with a negative aggregate acknowledgement message at step 1230 .

[0219] Using the above discovery process to establish aggregation node 1101 i After the aggregation connection between the basic nodes 1101a and 1101b, the aggregation node 1101 i Local AI / ML models may be collected from base nodes 1101a and 1101b and aggregation operations may be performed to generate new or updated general AI / ML models.

[0220] For example, refer again to Fig.11 , when establishing aggregation node 1101 i After the aggregation connection between the basic nodes 1101a and 1101b, the node 1101 i Can receive data from node 1101 i–1 AI / ML Model CM i–1 For example, node 1101 i–1The AI / ML model CM may be transmitted via broadcast, multicast, or unicast signaling (e.g., via broadcast / multicast / unicast RRC, MAC-CE, or DCI, or an interface between TRP / BS) i–1 . In receiving AI / ML model CM i–1 After that, node 1101 i AI / ML model CM i–1 is sent to one or more of its associated nodes 1101a and 1101b. For example, node 1101 i The AI / ML model CM may be transmitted via broadcast, multicast or unicast signaling (e.g., via broadcast / multicast / unicast RRC, MAC-CE or DCI or TRP / BS interface). i–1 .

[0221] Upon receiving node 1101 i AI / ML model CM sent i–1 Thereafter, the associated nodes 1101a and 1101b may train their respective AI / ML models. Each of these AI / ML models may be a local AI / ML model of one of the associated nodes 1101a and 1101b. Each of the associated nodes 1101a and 1101b may use its own training data set, its own AI / ML algorithm (e.g., an AI / ML model training algorithm), or a local AI / ML model from the node 1101a. i Received AI / ML Model CM i–1 In some embodiments, the AI / ML model can be trained based on at least one of the nodes 1101. i Received AI / ML Model CM i–1 This training is performed through transfer learning, knowledge distillation, and / or knowledge augmentation. AI / ML Model CM i–1 The AI / ML models associated with the associated nodes 1101a and 1101b may have the same input and / or output types. However, the AI / ML model CM i–1 The AI / ML models associated with the associated nodes 1101a and 1101b may have different neural network (NN) structures. In other words, the AI / ML models associated with the associated nodes 1101a and 1101b do not need to have the same AI / ML model CM as the AI / ML model CM. i–1 The NN structure of the NN structure.

[0222] In some embodiments, node 1101 i An indicator may be sent to collect AI / ML models from associated nodes 1101a and 1101b. For example, the indicator may be sent by node 1101 iTransmitted by broadcast, multicast or unicast signaling, for example, by broadcast / multicast / unicast RRC, MAC-CE or DCI or TRP / BS interface. This indicator may be referred to as a model collection indicator. Node 1101 i The model collection indicator may be sent to the associated nodes 1101a and 1101b to request a report related to the corresponding associated AI / ML model of the associated nodes 1101a and 1101b. In some embodiments, the associated nodes 1101a and 1101b generate a report related to the associated AI / ML model of the associated nodes 1101a and 1101b based on the model collection indicator. In such embodiments, the model collection indicator may be a dynamic indicator or an event-triggered indicator. For example, if the model collection indicator is a dynamic indicator, the node 1101 i Can be used by node 1101 i Receive reports related to the corresponding associated AI / ML models of the associated nodes 1101a and 1101b from the associated nodes 1101a and 1101b at preconfigured corresponding times. On the other hand, if the model collection indicator is an event trigger indicator, the associated nodes 1101a and 1101b may send their corresponding reports when a specific event is triggered. The event may be triggered when the training of the corresponding associated AI / ML models of the associated nodes 1101a and 1101b is completed or the performance of the corresponding associated AI / ML models of the associated nodes exceeds a certain performance metric. The specific performance metric may be predetermined based on at least one of accuracy or precision. If the performance of one of the associated AI / ML models does not exceed a predetermined performance metric (e.g., the accuracy is lower than a predetermined accuracy metric), the report related to the AI / ML model may not be generated and / or sent to the node 1101. i (e.g. no local AL / ML model reports sent).

[0223] In some embodiments, each of the associated nodes 1101a and 1101b optionally sends an acknowledgment indicator for the transfer of its associated AI / ML model. For example, the acknowledgment indicator can be transmitted via PUCCH or PUSCH, or a sidelink channel, or an interface between TRP / BS. A positive acknowledgment indicator (e.g., ACK) indicates that a report related to the associated AI / ML model will be sent. A negative acknowledgment indicator (e.g., NACK) indicates that a report related to the associated AI / ML model will not be sent (e.g., the associated AI / ML model will not be reported). In some embodiments, the acknowledgment indicator may be sent before sending a report related to the associated AI / ML model. In some other embodiments, the acknowledgment indicator is included in the report related to the associated AI / ML model.

[0224] The associated nodes 1101a and 1101b can then send a i Send a report related to the associated AI / ML model of the associated nodes 1101a and 1101b. In some embodiments, the report related to the associated AI / ML model of the associated nodes 1101a and 1101b may include at least one of information related to the corresponding associated AI / ML model of the associated nodes 1101a and 1101b, information related to the training data of the corresponding associated AI / ML model of the associated nodes 1101a and 1101b, or information related to the performance of the corresponding associated AI / ML model of the associated nodes 1101a and 1101b. The information related to the corresponding associated AI / ML model may include the NN structure (NN algorithm, width, depth, etc.) of the associated nodes 1101a and 1101b and / or one or more AI / ML parameters (weights, biases, activation functions, etc.). The information related to the training data of the corresponding associated AI / ML model may include a certain amount of training data and / or information related to the distribution of the training data. Information related to the performance of the corresponding associated AI / ML model may include accuracy, precision, recall, loss information (e.g., average cross entropy loss), validation / test data sets configured by node 1100i (or other aggregation nodes or BS). It should be noted that in some embodiments, information related to the training data of the corresponding associated AI / ML model and / or information related to the performance of the corresponding associated AI / ML model may assist the aggregation operation of the aggregation node. For example, training data information and / or performance information may be used to determine AI / ML model aggregation weights.

[0225] At node 1101 i After receiving the reports related to the associated AI / ML models of the associated nodes 1101a and 1101b, node 1101 i Generate AI / ML model CM based on received reports i AI / ML Model CM i It can be an updated general AI / ML model. AI / ML model CM i–1 and CM i can have the same NN structure. In some embodiments, node 1101 i Generate AI / ML model CM using aggregation algorithms (e.g., model distillation, model expansion) iIn some embodiments, node 1200i may generate its own AI / ML model. The AI / ML model may be a local AI / ML model. The AI / ML model may be generated without reporting related to the corresponding associated AI / ML models of associated nodes 1101a and 1101b.

[0226] Then, node 1101 i AI / ML model CM i Send to node 1101 i+1 AI / ML Model CM i May include one or more AI / ML model parameters (e.g., weights, biases), but does not include any i In other words, the AI / ML model CM i The information related to the NN structure may not be obtained from node 1101 i Send to node 1101 i+1 , because AI / ML model CM i NN structures can be pre-configured and / or AI / ML models CM i The NN structure can be found at node 1101 i+1 In some embodiments, the AI / ML model CM sent to the node 1200i i It can be a complete AI / ML model or a partial AI / ML model.

[0227] As described above, the associated AI / ML models of the associated nodes 1101a and 1101b are not limited to having the same AI / ML model CM as i–1 and AI / ML model CM i In other words, according to the embodiments of the present disclosure, heterogeneous AI / ML model aggregation can be achieved by configuring the topology of AI / ML in the wireless communication network. For example, in some embodiments, node 1101 i From the previous node 1101 i–1 Received AI / ML Model CM i–1 and by AI / ML model based on node 1101 i The AI / ML model CM generated from the reports received from its associated nodes 1101a and 1101b i A NN having 4 layers may be included, but the local AI / ML model generated by the associated node 1101a and / or 1101b may have a different NN structure. For example, the local AI / ML model generated by the associated node 1101a may include a NN having 5 layers, and the local AI / ML model generated by the associated node 1101b may include a NN having 3 layers.

[0228] Node 1101 i After receiving the report related to the local AI / ML models of the nodes 1101a and 1101b associated therewith, an aggregation operation (e.g., model distillation, model expansion) may be performed to generate an AI / ML model CM based on the local AI / ML models generated by the associated nodes 1101a and 1101b. i Since each of the local AI / ML models generated by the associated nodes 1101a and 1101b is important for generating the AI / ML model CM i The local AI / ML models generated by the associated nodes 1101a and 1101b may have different importance (meaning), so when aggregating the local AI / ML models, each of the local AI / ML models generated by the associated nodes 1101a and 1101b may be weighted with a corresponding weight (e.g., Wa and Wb). Wa and Wb may indicate the importance of the local AI / ML models generated by the associated nodes 1101a and 1101b. The generated AI / ML model CM i It can be an updated general AI / ML model. CM i The NN structure can be combined with AI / ML model CM i–1 In this case, for example, the AI / ML model CM i and AI / ML model CM i–1 Both may include a NN with 4 layers.

[0229] However, as mentioned above, AI / ML model CM i The local AI / ML models LM1, LM2, and LM3 may have different neural network (NN) structures. The local AI / ML models generated by the associated nodes 1101a and 1101b do not need to have an NN structure equivalent to the NN structure of the general AI / ML model in order to generate the AI / ML model CM i In other words, heterogeneous AI / ML model aggregation is achieved on a self-organizing topology.

[0230] In some embodiments, the aggregation node receives one or more heterogeneous AI / ML models from one or more associated base nodes that are communicatively and operatively connected to the aggregation node. The aggregation node may distill and / or expand the received heterogeneous AI / ML models. The aggregation may then aggregate the distilled and / or expanded AI / ML models to generate a new or updated general AI / ML model. In some embodiments, the aggregation node may obtain an average value of the distilled AI / ML models and / or an average value of the expanded AI / ML models. After obtaining the general AI / ML model, the aggregation node may send the new / updated general AI / ML model to one or more other nodes that may be the next aggregation node.

[0231] As described above, if the NN structure of the associated base node of the aggregation node is different from the NN structure of the general AI / ML model of the aggregation node, the aggregation node may distill and / or expand the AI / ML model. Otherwise, if the NN structure of the associated base node of the aggregation node is the same as the NN structure of the general AI / ML model of the aggregation node, the distillation and / or expansion operation may not be performed. Distillation and / or expansion may be part of the aggregation operation of the aggregation node. Distillation may include generating a smaller AI / ML model from the AI / ML model received by the node, and expansion may include generating a larger AI / ML model from the AI / ML model received by the node. Therefore, the distillation of the aggregation node may include generating a general AI / ML by the aggregation node that is smaller than the AI / ML model received from other nodes (e.g., a local AI / ML model received from the associated base node). Similarly, the expansion of the aggregation node may include generating a general AI / ML model by the aggregation node that is larger than the AI / ML model received from other nodes (e.g., a local AI / ML model received from the associated base node).

[0232] To illustrate distillation and / or expansion, when a first AI / ML model is larger than a second AI / ML model, the first AI / ML model may have more floating-point operations than the second model, more total parameters than the second model, more trainable parameters than the second model, a required buffer size greater than the second model, a width greater than the second model, a depth greater than the second model, or any combination thereof. Similarly, to illustrate distillation and / or expansion, when a first AI / ML model is smaller than a second AI / ML model, the first AI / ML model may have fewer floating-point operations than the second model, fewer total parameters than the second model, fewer trainable parameters than the second model, a required buffer size less than the second model, a width less than the second model, a depth less than the second model, or any combination thereof.

[0233] Fig.13The exemplary process of establishing an aggregation connection between two aggregation nodes when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown. Fig.13 In the aggregation node 1101 i Is a notification node, and aggregation node 1101 i–1 and 1101 k It is a monitoring node. Aggregation node 1101 i–1 It can be aggregation node 1101 i The previous aggregation node.

[0234] In step 1310, the aggregation node 1101 i Announce aggregation information. Specifically, aggregation node 1101 i To aggregation node 1101 i–1 and 1101 k In some embodiments, the aggregation node 1101 i The discovery message may be broadcast, multicast or unicast at predetermined intervals. The discovery message includes the aggregation node 1101 i–1 and 1101 k It will be used to discover the aggregation information of the advertising node. For example, the aggregation node 1101 i–1 and 1101 k The aggregation information to be used may include aggregation node 1101 i aggregation capacity.

[0235] At step 1320, if the aggregation node 1101 i–1 and 1101 k From aggregation node 1101 i The received discovery message is of interest, then the aggregation node 1101 i–1 and 1101 k The received discovery message can be read and processed. Then, the aggregation node 1101 i–1 and 1101 k It can be determined whether it is connected to the aggregation node 1101 i Communicatively and operatively connected.

[0236] At step 1330, the aggregation node 1101 i–1 and 1101 k The responses can be sent to the aggregation node 1101 respectively. i Each response indicates that the aggregation node 1101 i–1 or 1101 k Has the aggregation node 1101 been determined? i In some embodiments, the response is a positive aggregation response message or a negative aggregation response message.i–1 It can be determined that the aggregation node 1101 i Connect and aggregate node 1101 k It can be determined that it is not related to the aggregation node 1101 i In this case, the aggregation node 1101 i–1 To aggregation node 1101 i The node sends a positive aggregation response message (eg, ACK), and the aggregation node 1101 k To aggregation node 1101 i A negative aggregate acknowledgement message (eg, NACK) is sent.

[0237] In some embodiments, an aggregator node may be communicatively and operatively connected to only one other aggregator node. For example, an aggregator node in a network may have a one-to-one correspondence with another aggregator node. In such embodiments, the announcing aggregator node sends a contention acknowledgement to the monitoring aggregator node notifying whether a connection to the announcing node was successfully established. Fig.13 If the aggregation node 1101 i Can only communicate with aggregation node 1101 i–1 and 1101 k If there is a connection in i At step 1335, optionally, to the aggregation node 1101 i–1 and 1101 k Send a contention confirmation to inform each of them whether it has successfully connected to the aggregation node 1101 i .

[0238] At step 1340, it is assumed that the aggregation node 1101 i Only able to communicate with aggregation node 1101 i–1 Connection, then the aggregation node 1101 i Based on the received response, contention confirmation, or both, the aggregation node 1101 i–1 Establish an aggregation connection. Aggregation node 1101 i With aggregation node 1101 k There is no aggregate connection established between them.

[0239] After the aggregation node 1101 is established i With aggregation node 1101 i–1 After the aggregation connection between them, the aggregation node 1101 i From the aggregation node 1101 i–1 Receive general AI / ML models.

[0240] In order to establish an aggregate connection with another node, the node may send an aggregate request message (e.g., an aggregate request) to the other node. In this case, the node that sends the aggregate request message may be referred to as a discoverer node, and the other node that receives the aggregate request message may be referred to as a discovered node. If the discoverer node is a base node, the discoverer node may send an aggregate request message to the associated aggregate node. If the discoverer node is an aggregate node, the discoverer node may send an aggregate request message to another aggregate node to request the other aggregate node as the next hop (e.g., requesting another aggregate as the next aggregate node). In some embodiments, if the next aggregate node is not found through the discovery process, the aggregate node (discoverer node) may send the aggregated general AI / ML model to a network device (e.g., BS, TRP).

[0241] Fig.14 Another exemplary process of establishing an aggregate connection between an aggregation node and a base node when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown. Fig.14 In the example, the basic node 1101a is the discoverer node, and the aggregation node 1101 i and 1101 i+1 It is the discoverer node.

[0242] At step 1410, the base node 1101a sends an aggregation request message to the aggregation node 1101. i and 1101 i+1 . In some embodiments, the base node 1101a may broadcast, multicast, or unicast an aggregation request message at predetermined intervals. The aggregation request message may include the requested aggregation information that the base node 1101a is interested in discovering. Assuming that the base node 1101a is a discoverer node, the aggregation request message may include information related to the local AI / ML model of the base node 1101a. The information related to the local AI / ML model of the base node 1101a may include information related to the neural network of the local AI / ML model of the base node 1101a, information related to the size of the local AI / ML model of the base node 1101a, or information related to the complexity of the local AI / ML model of the base node 1101a. In some embodiments, the information related to the size and / or complexity of the local AI / ML model may be the relative size and / or complexity of the local AI / ML model compared to a reference AI / ML model.

[0243] At step 1420, if the aggregation node 1101 i and 1101 i+1 If the aggregation node 1101 is interested in the aggregation request message received from the base node 1101a, the aggregation node 1101 i and 1101i+1 The aggregation node 1101 can read and process the received aggregation request message. i and 1101 i+1 It may be determined whether the base node 1101a is communicatively and operatively connected. For example, if the discoverer aggregation node 1101 i With distillation capability, and the discoverer base node 1101a reports an AI / ML model that is larger than the reference AI / ML model, then the aggregation node 1101 i Determine that it is communicatively and operatively connected to the base node 1101a. If the found aggregation node 1101 i+1 With expansion capability, and the discoverer base node 1101a reports an AI / ML model that is smaller than the reference AI / ML model, then the aggregation node 1101 i+1 Determine to be communicatively and operatively connected to base node 1101a.

[0244] At step 1430, the aggregation node 1101 i and 1101 i+1 Each response indicates that the aggregation node 1101 i or 1101 i+1 Whether it has been determined that the connection with the base node 1101a has been established. In some embodiments, the response is a positive aggregation response message or a negative aggregation response message. i It can be determined that the base node 1101a is connected, and the aggregation node 1101 i+1 It can be determined that there is no connection with the basic node 1101a because the discoverer aggregation node 1101 i Has distillation capability and is discovered by aggregation node 1101 i+1 The discoverer base node 1101a reports an AI / ML model that is larger than the reference AI / ML model. In this case, the aggregator node 1101 i The aggregation node 1101a sends a positive aggregation response message (eg, ACK) to the base node 1101a, and the aggregation node 1101 i+1 A negative aggregation acknowledgement message (eg, NACK) is sent to the base node 1101a.

[0245] At step 1435, the base node 1101a may optionally send a request to the aggregation node 1101 i and 1101 i+1 A contention confirmation is sent to confirm whether each of them is successfully connected to the base node 1101a.

[0246] At step 1440, the base node 1101a communicates with the aggregation node 1101 based on the received response, contention confirmation, or both.i Establish an aggregation connection. The basic node 1101a and the aggregation node 1101 i+1 There is no aggregate connection established between them.

[0247] The basic node 1101a and the aggregation node 1101 i After the aggregation connection between them is established through the discovery process, the aggregation node 1101 i Local AI / ML models may be collected from the base node 1101a and aggregation operations may be performed to generate a new or updated general AI / ML model.

[0248] Fig.15 Another exemplary process of establishing an aggregation connection between two aggregation nodes when configuring the topology of AI / ML in a wireless communication network according to an embodiment of the present disclosure is shown. Fig.15 In the aggregation node 1101 i Is the discoverer node, and the aggregation node 1101 i+1 and 1101 k It is the discoverer node. Aggregation node 1101 i+1 It can be aggregation node 1101 i The next aggregation node.

[0249] At step 1510, the aggregation node 1101 i To aggregation node 1101 k and 1101 i+1 Send an aggregation request message. In some embodiments, the aggregation node 1101 i The aggregation request message may be broadcast, multicast or unicast at predetermined intervals, for example, via broadcast / multicast / unicast RRC, MAC-CE or DCI or TRP / BS interface. The aggregation request message may include: i Aggregation request.

[0250] At step 1520, if the aggregation node 1101 k and 1101 i+1 From aggregation node 1101 i The aggregation node 1101 is interested in the received aggregation request message. k and 1101 i+1 The aggregation node 1101 can read and process the received aggregation request message. k and 1101 i+1 It can be determined whether to communicate and operatively communicate with the aggregation node 1101 i connect.

[0251] At step 1530, the aggregation node 1101 k and 1101i+1 can be sent to the aggregation node 1101 i Each response indicates that the aggregation node 1101 k or 1101 i+1 Has the aggregation node 1101 been determined? i In some embodiments, the response is a positive aggregation response message or a negative aggregation response message. i+1 It can be determined that the aggregation node 1101 i Connect and aggregate node 1101 k It can be determined that it is not related to the aggregation node 1101 i In this case, the aggregation node 1101 i+1 To aggregation node 1101 i The node will confirm the aggregation response message (such as ACK), and the aggregation node 1101 k A negative aggregation acknowledgement message (eg, NACK) is sent to the aggregation node 1101i.

[0252] In some embodiments, the discoverer aggregator node may be communicatively and operatively connected to only one other aggregator node. For example, a discoverer aggregator node in a network may have a one-to-one correspondence with another aggregator node. In such embodiments, the discoverer aggregator node sends a contention confirmation to the discoverer aggregator node notifying whether a connection to the discoverer node was successfully established. Fig.15 If the finder aggregates node 1101 i Can only aggregate nodes 1101 with the discoverer k and 1101 i+1 If a connection is found in the aggregation node 1101 i At step 1535, optionally, the discoverer aggregation node 1101 k and 1101 i+1 Send a contention confirmation to confirm whether each of them is successfully connected to the aggregation node 1101 i .

[0253] At step 1540, it is assumed that the aggregation node 1101 i Only able to communicate with aggregation node 1101 i+1 Connection, then the aggregation node 1101 i Based on the received response, contention confirmation, or both, the aggregation node 1101 i+1 Establish an aggregation connection. Aggregation node 1101 i With aggregation node 1101 k There is no aggregate connection established between them.

[0254] At aggregation node 1101 iWith aggregation node 1101 i+1 After the aggregation connection between them is established through the discovery process, the discovered aggregation node 1101 i+1 Can aggregate node 1101 from the discoverer i Receive general AI / ML models.

[0255] According to some aspects of the present disclosure, the topology of AI / ML in a wireless communication network can be configured so that AI / ML model aggregation can be processed in parallel between multiple aggregation nodes. In other words, the topology configured based on the method described in the present disclosure can be flexible.

[0256] In some embodiments, the topology of AI / ML in the wireless communication network can be configured so that each aggregation node is connected to one or more aggregation nodes, such as Fig.16 as shown in . Fig.16 An exemplary configured topology 1600 supporting flexible communication between aggregation nodes according to an embodiment of the present disclosure is shown.

[0257] In the configured topology 1600, the aggregation node can send a common AI / ML model to one or more AI / ML aggregation nodes. For example, the aggregation node 901 sends the common AI / ML model M1 to the aggregation nodes 902, 903, 904, and 905. On the other hand, the aggregation node 907 only sends the common AI / ML model Mi to the aggregation node 901. It should be noted that Fig.16 The aggregation node in can send its common AI / ML model via broadcast, multicast or unicast signaling, for example, through broadcast / multicast / unicast RRC, MAC-CE or DCI or the interface between TRP / BS.

[0258] In the configured topology 1600, the aggregation node can receive a common AI / ML model from multiple AI / ML aggregation nodes. For example, the aggregation node 908 receives AI / ML models M2, M3, M4, and M5 from the aggregation nodes 902, 903, 904, and 905, respectively.

[0259] Regarding AI / ML model aggregation, the following uses the aggregation node 908 to illustrate the behavior of aggregation. As described above, the aggregation node 908 receives AI / ML models M2, M3, M4, and M5 from the aggregation nodes 902, 903, 904, and 905, respectively. Then, the aggregation node 908 aggregates the received AI / ML models M2, M3, M4, and M5. In some embodiments, the aggregation node 908 may calculate the average of the AI / ML models M2, M3, M4, and M5 to obtain an aggregated AI / ML model. After obtaining the aggregated AI / ML model, the aggregation node 908 sends the aggregated AI / ML model to its associated base nodes, which in this example include three base nodes 908a, 908b, and 908c, which are communicatively and operatively connected to the aggregation node 908. The base nodes 908a, 908b, and 908c use the aggregated general AI / ML model sent from the aggregation node 908 to train their local AI / ML models. When the training is completed, the aggregation node 908 collects the local AI / ML models from the base nodes 908a, 908b, and 908c. The aggregation node 908 then aggregates the collected local AI / ML models to generate an updated general AI / ML model M. j The aggregation node 908 may send the updated general AI / ML model to its next aggregation node 901 .

[0260] In some embodiments, the topology of AI / ML in a wireless communication network may be configured such that each base node is connected to zero or more aggregation nodes, such as Fig.17 as shown in . Fig.17 An exemplary configured topology 1700 supporting flexible communication between aggregation nodes and base nodes according to an embodiment of the present disclosure is shown.

[0261] In the configured topology 1700, each of the aggregation nodes 901 to 905, 907, and 908 is communicatively and operatively connected to one or more base nodes. For example, the aggregation node 901 is communicatively and operatively connected to the base nodes 901a and 1701. However, the aggregation node 906 is not communicatively and operatively connected to any base node. Since the aggregation node 906 has no associated base node, the aggregation node 906 can generate an updated general AI / ML model in a different manner. For example, the aggregation node 906 receives a general AI / ML model from another aggregation node (e.g., the aggregation node 905), and the aggregation node 906 generates its own local AI / ML model. Then, the aggregation node 906 calculates the average of the received general AI / ML model and its own local AI / ML model to form an updated general AI / ML model. When the aggregation node 906 obtains the updated general AI / ML model, the aggregation node 906 sends the updated AI / ML model to its next aggregation node (e.g., the aggregation node 907).

[0262] In the configured topology 1700 , each base node is communicatively and operatively connected to one or more aggregation nodes. For example, base node 901a is communicatively and operatively connected to one aggregation node 901 .

[0263] On the other hand, the base node 1701 is communicatively and operatively connected to two aggregation nodes 901 and 908. The connection of the base node 1701 can be implemented using a network device (e.g., a BS). For example, the network device broadcasts multiple local AI / ML models (each of which has an AI / ML model identifier). The network device indicates the set of AI / ML model identifiers that each aggregation node will aggregate. When the network device indicates the AI / ML model identifier, the same AI / ML model identifier can be assigned to multiple aggregation nodes. Therefore, the identifier of the local AI / ML model associated with the base node 1701 can be assigned to both aggregation nodes 901 and 908.

[0264] By virtue of some aspects of the present disclosure, heterogeneous AI / ML capabilities may be enabled in various network devices and user devices, and heterogeneous AI / ML models may be supported to be delivered over the air interface of a wireless communication network.

[0265] By virtue of some aspects of the present disclosure, the topology in a wireless communication network may be centrally configured by a network device (eg, a base station (BS), a transmission reception point (TRP)) or a network system.

[0266] By virtue of some aspects of the present disclosure, the topology in a wireless communication network can be autonomously configured by network devices (e.g., base stations (BS), transmission reception points (TRPs)) and / or user equipment as needed using the discovery process described in the present disclosure, thereby supporting flexible topology configuration.

[0267] By virtue of some aspects of the present disclosure, the topology configured by the various methods described in the present disclosure in a wireless communication network supports parallel AI / ML model aggregation, reduces AI / ML model routing latency, and increases the robustness of AI / ML model routing.

[0268] Also disclosed are examples of devices (e.g., ED or UE and TRP or network device) for performing the various methods described herein.

[0269] For example, the first device may include a memory for storing processor executable instructions, and a processor for executing the processor executable instructions. When the processor executes the processor executable instructions, the processor may execute, for example, Figures 11 to 15 One or more method steps in the described device. For example, the processor may cause the device to communicate over the air interface in the operating mode by implementing operations consistent with the operating mode, such as performing necessary measurements and generating content from those measurements (as configured for the operating mode), preparing uplink transmissions and processing downlink transmissions (e.g., encoding, decoding, etc.), and configuring and / or instructing transmission / reception on one or more RF chains and one or more antennas.

[0270] Note that the expression "at least one of A or B" as used herein is interchangeable with the expression "A and / or B". It refers to a list where either A or B or both A and B can be selected. Similarly, "at least one of A, B, or C" as used herein is interchangeable with "A and / or B and / or C" or "A, B, and / or C". The expression refers to a list where either A or B or C can be selected, or both A and B, or both A and C, or both B and C, or all of A, B, and C. The same principles apply to longer lists with the same format.

[0271] Although the present invention has been described with reference to the specific features and embodiments of the present invention, various modifications and combinations can be made to the present invention without departing from the present invention. The specification and the drawings are therefore only regarded as an explanation of some embodiments of the present invention as defined by the appended claims, and any and all modifications, variants, combinations or equivalents within the scope of the present invention are considered to be covered. Although the present invention and its advantages have been described in detail, various changes, substitutions and modifications can be made herein without departing from the present invention as defined by the appended claims. In addition, the scope of the present application is not intended to be limited to the specific embodiments of the processes, machines, manufactured products, material compositions, components, methods and steps described in the specification. According to the disclosure of the present invention, it will be easy for a person of ordinary skill in the art to understand that existing or to be developed according to the present invention can be used to perform functions substantially the same as the corresponding embodiments described herein, or processes, machines, manufactured products, material compositions, components, methods or steps that can achieve substantially the same results as these embodiments. Therefore, the appended claims are intended to include these processes, machines, manufactured products, material compositions, components, methods or steps within their scope.

[0272] In addition, any module, component, or device that executes instructions as illustrated herein may include or otherwise access one or more non-transitory computer / processor readable storage media to store information, such as computer / processor readable instructions, data structures, program modules, and / or other data. A non-exhaustive list of examples of non-transitory computer / processor readable storage media includes cassettes, magnetic tape, disk storage or other magnetic storage devices, compact disk-read only memory (CD-ROM), digital video disk or digital versatile disk (DVD), Blu-ray TM Such as optical disks, or other optical storage, volatile and non-volatile, removable and non-removable media implemented in any method or technology, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other storage technology. Any such non-transitory computer / processor storage medium can be part of a device or can be accessed or connected by such a device. Any application or module described in this article can be implemented using computer / processor readable / executable instructions, which can be stored or otherwise maintained by such non-transitory computer / processor readable storage media.

[0273] Abbreviation Definition

[0274] LTE Long Term Evolution

[0275] NR New Radio

[0276] BWP Bandwidth Part

[0277] BS Base Station

[0278] CA Carrier Aggregation

[0279] CC Component Carrier

[0280] CG Cell Group

[0281] CSI Channel State Information

[0282] CSI-RS Channel State Information Reference Signal

[0283] DC Dual Connection

[0284] DCI Downlink Control Information

[0285] DL Downlink

[0286] DL-SCH Downlink Shared Channel

[0287] EN-DC E-UTRA NR dual connectivity, MCG uses E-UTRA, SCG uses NR

[0288] gNB Next Generation (or 5G) Base Station

[0289] HARQ-ACK Hybrid Automatic Repeat Request Acknowledgement

[0290] MCG Master Cell Group

[0291] MCS Modulation Coding Scheme

[0292] MAC-CE Medium Access Control Element

[0293] PBCH Physical Broadcast Channel

[0294] PCell Primary Cell

[0295] PDCCH Physical Downlink Control Channel

[0296] PDSCH Physical Downlink Shared Channel

[0297] PRACH Physical Random Access Channel

[0298] PRG Physical Resource Group

[0299] PSCell Main SCG cell

[0300] PSS Primary Synchronization Signal

[0301] PUCCH Physical Uplink Control Channel

[0302] PUSCH Physical Uplink Shared Channel

[0303] RACH Random Access Channel

[0304] RAPID Random Access Preamble Identifier

[0305] RB Resource Block

[0306] RE Resource Element

[0307] RRM Radio Resource Management

[0308] RMSI Remaining System Information

[0309] RS reference signal

[0310] RSRP Reference Signal Received Power

[0311] RRC Radio Resource Control

[0312] SCG Secondary Cell Group

[0313] SFN System Frame Number

[0314] SL Sidelink

[0315] SCell Secondary Cell

[0316] SPS Semi-Persistent Scheduling

[0317] SR Scheduling Request

[0318] SRI SRS Resource Indicator

[0319] SRS Sounding Reference Signal

[0320] SSS Secondary synchronization signal

[0321] SSB Synchronous Signal Block

[0322] SUL Supplementary Uplink

[0323] TA Time Advance

[0324] TAG Timing Advance Group

[0325] TUE Target UE

[0326] UCI Uplink Control Information

[0327] UE User Equipment

[0328] UL Uplink

[0329] UL-SCH Uplink Shared Channel

Claims

1. A method for configuring an artificial intelligence or machine learning (AI / ML) topology in a wireless communication network, characterized in that: The method comprises: receiving information including a report related to AI / ML capabilities of the node from a node; The node is configured based on the received information, wherein the configuring the node includes configuring a node type of the node, and the configured node type is one of multiple node types, and the multiple node types include: Type 1, indicating a node for collecting a plurality of AI / ML models and aggregating the collected AI / ML models to obtain a first type of AI / ML model, or Type 2, indicating a node for obtaining a second type of AI / ML model using a set of training data without aggregation operations; configuring one or more other nodes associated with the configured node based on the configured node type; The configured topology supports the delivery of the AI / ML model through the air interface of the wireless communication network, and the configured topology includes at least one of the following: a connection between at least one type 1 node and zero or more type 2 nodes, or A connection between at least two type 1 nodes.

2. The method according to claim 1, characterized in that The configured node type indicates that the node is a type 1 node, and the one or more other nodes include at least one of the following: One or more type 2 nodes; a second type 1 node, providing the node with a first type AI / ML model of the second type 1 node; or A third type 1 node receives the first type AI / ML model of the node.

3. The method according to claim 2, characterized in that The one or more other nodes include the one or more type 2 nodes, and configuring the nodes includes: The node is configured to collect corresponding second type AI / ML models from the one or more type 2 nodes.

4. The method according to claim 1, characterized in that: The configured node type indicates that the node is a type 2 node, and the one or more other nodes include one or more type 1 nodes connected to the node.

5. The method according to any one of claims 1 to 4, characterized in that The information also includes a request for the node to be configured as a type 1 node.

6. The method according to any one of claims 1 to 5, characterized in that The node is connected to the one or more other nodes via a sidelink using device-to-device (D2D) communication, via a network device, or via an interface between network devices.

7. The method according to any one of claims 1 to 6, characterized in that The node is a user equipment (UE), a relay station, a base station (BS), a transmission reception point (TRP), an edge device, a network system or an integrated access backhaul (IAB) node.

8. A method for configuring an artificial intelligence or machine learning (AI / ML) topology in a wireless communication network, characterized in that: The method comprises: The first node establishes an aggregate connection with the second node based on the aggregate response message; The configured topology supports the delivery of the AI / ML model through the air interface of the wireless communication network, and the configured topology includes at least one of the following: a connection between at least one type 1 node and zero or more type 2 nodes, or A connection between at least two type 1 nodes; Among them, the type 1 node is a node used to collect multiple AI / ML models and aggregate the collected AI / ML models to obtain a first type of AI / ML model, and the type 2 node is a node used to obtain a second type of AI / ML model using a set of training data without the need for aggregation operations.

9. The method according to claim 8, characterized in that The aggregate message is a discovery message including aggregate information to be used by the second node to discover the first node.

10. The method according to claim 9, characterized in that The aggregation information to be used by the second node includes the aggregation capabilities of the first node.

11. The method according to claim 9, characterized in that The discovery message includes at least one of a model collection indicator or a reference AI / ML model.

12. The method according to claim 11, characterized in that The model collection indicator includes one of a distillation indicator, an expansion indicator, or a distillation and expansion indicator.

13. The method according to claim 8, characterized in that The aggregation message is an aggregation request message, and the aggregation request message includes an aggregation request or information related to the second type of AI / ML model of the first node.

14. The method according to claim 13, characterized in that The first node is a type 2 node, and the aggregation request message includes the information related to the second type AI / ML model of the first node.

15. The method according to claim 14, characterized in that The information related to the second type of AI / ML model of the first node includes at least one of the following: information related to a neural network of the second type of AI / ML model of the first node; information related to the size of the second type of AI / ML model of the first node; or Information related to the complexity of the second type of AI / ML model of the first node.

16. The method according to claim 13, characterized in that The first node is a type 1 node, and the aggregation request message includes the aggregation request.

17. The method according to claim 8, characterized in that The second node is one of a plurality of other nodes in the wireless communication network, and the first node can only connect to one of the plurality of other nodes, and the method further includes: The first node sends a corresponding confirmation to at least the one of the plurality of other nodes, the confirmation notifying whether the connection to the first node is successfully established.

18. The method according to claim 17, characterized in that The first node is a type 2 node, and the second node is a type 1 node.

19. The method according to claim 17, characterized in that The first node is a first type 1 node, and the second node is a second type 1 node, and the second node provides a first type AI / ML model of the second node to the first node or receives the first type AI / ML model of the first node from the first node.

20. The method according to claim 8, characterized in that The first node is a type 1 node that is only associated with one or more other type 1 nodes.

21. The method according to any one of claims 8 to 20, characterized in that Sending the aggregate message includes broadcasting, multicasting, or unicasting the aggregate message at predetermined intervals.

22. A network device for configuring an artificial intelligence or machine learning (AI / ML) topology in a wireless communication network, characterized in that: The network equipment includes: processor; A memory storing processor-executable instructions, wherein the processor-executable instructions, when executed, cause the processor to: receiving information including a report related to AI / ML capabilities of the node from a node; The node is configured based on the received information, wherein the configuring the node includes configuring a node type of the node, and the configured node type is one of multiple node types, and the multiple node types include: Type 1, indicating a node for collecting a plurality of AI / ML models and aggregating the collected AI / ML models to obtain a first type of AI / ML model, or Type 2, indicating a node for obtaining a second type of AI / ML model using a set of training data without aggregation operations; configuring one or more other nodes associated with the configured node based on the configured node type; The configured topology supports the delivery of the AI / ML model through the air interface of the wireless communication network, and the configured topology includes at least one of the following: a connection between at least one type 1 node and zero or more type 2 nodes, or A connection between at least two type 1 nodes.

23. The network device according to claim 22, characterized in that: The configured node type indicates that the node is a type 1 node, and the one or more other nodes include at least one of the following: One or more type 2 nodes; a second type 1 node, which provides the node with a first type AI / ML model of the second type 1 node; or A third type 1 node, the third type 1 node receiving the first type AI / ML model of the node.

24. The network device according to claim 23, characterized in that: The one or more other nodes include the one or more type 2 nodes, and configuring the nodes includes: The node is configured to collect corresponding second type AI / ML models from the one or more type 2 nodes.

25. The network device according to claim 22, characterized in that: The configured node type indicates that the node is a type 2 node, and the one or more other nodes include one or more type 1 nodes connected to the node.

26. The network device according to any one of claims 22 to 25, characterized in that: The information also includes a request for the node to be configured as a type 1 node.

27. The network device according to any one of claims 22 to 26, characterized in that: The node is connected to the one or more other nodes via a sidelink using device-to-device (D2D) communication, via a network device, or via an interface between network devices.

28. The network device according to any one of claims 22 to 29, characterized in that: The node is a user equipment (UE), a relay station, a base station (BS), a transmission reception point (TRP), an edge device, a network system or an integrated access backhaul (IAB) node.

29. A device, characterized in that: The method comprises one or more units for executing the method according to any one of claims 1 to 7.

30. An apparatus for configuring a node for artificial intelligence or machine learning (AI / ML) topology in a wireless communication network, characterized in that: The device comprises: processor; A memory storing processor-executable instructions, wherein the processor-executable instructions, when executed, cause the processor to: Establishing an aggregate connection with the second node based on the aggregate response message; The configured topology supports the delivery of the AI / ML model through the air interface of the wireless communication network, and the configured topology includes at least one of the following: a connection between at least one type 1 node and zero or more type 2 nodes, or A connection between at least two type 1 nodes; Among them, the type 1 node is a node used to collect multiple AI / ML models and aggregate the collected AI / ML models to obtain a first type of AI / ML model, and the type 2 node is a node used to obtain a second type of AI / ML model using a set of training data without the need for aggregation operations.

31. The device according to claim 30, characterized in that The aggregate message is a discovery message including aggregate information to be used by the second node to discover the first node.

32. The device according to claim 31, characterized in that The aggregation information to be used by the second node includes the aggregation capabilities of the first node.

33. The device according to claim 31, characterized in that The discovery message includes at least one of a model collection indicator or a reference AI / ML model.

34. The device according to claim 33, characterized in that The model collection indicator includes one of a distillation indicator, an expansion indicator, or a distillation and expansion indicator.

35. The device according to claim 30, characterized in that The aggregation message is an aggregation request message, and the aggregation request message includes an aggregation request or information related to the second type of AI / ML model of the first node.

36. The device according to claim 35, characterized in that The first node is a type 2 node, and the aggregation request message includes the information related to the second type AI / ML model of the first node.

37. The device according to claim 36, characterized in that The information related to the second type of AI / ML model of the first node includes at least one of the following: information related to a neural network of the second type of AI / ML model of the first node; information related to the size of the second type of AI / ML model of the first node; or Information related to the complexity of the second type of AI / ML model of the first node.

38. The device according to claim 35, characterized in that The first node is a type 1 node, and the aggregation request message includes the aggregation request.

39. The device according to claim 30, characterized in that The second node is one of a plurality of other nodes in the wireless communication network, and the first node can only connect to one of the plurality of other nodes, wherein the processor-executable instructions further include processor-executable instructions that, when executed, cause the processor to perform the following operations: The first node sends a corresponding confirmation to at least the one of the plurality of other nodes, the confirmation notifying whether the connection to the first node is successfully established.

40. The device according to claim 39, characterized in that The first node is a type 2 node, and the second node is a type 1 node.

41. A device, characterized in that The method comprises one or more units configured to execute the method according to any one of claims 8 to 21.

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