A communication method and related apparatus

Abstract

A communication method and related apparatus, in which a first communication apparatus performs a first processing on first data, and a second data obtained by the first processing can be used to generate a first signal to be transmitted. The first processing is performed based on a matrix trained by artificial intelligence (AI). In other words, the generation of the first signal to be transmitted is based on the processing result (i.e., the second data) of the first processing, and the first processing is performed based on the matrix trained by AI. In this way, in the process of generating the first signal to be transmitted, the first communication apparatus can perform data processing based on the matrix trained by AI, and AI can be introduced into the signal generation process to improve the processing efficiency by using AI. Optionally, the first processing includes one or more linear processes, or the first processing includes one or more nonlinear processes.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method and related apparatus. Background Technology

[0002] In communication systems, communication nodes typically possess both signal transmission and reception capabilities and computational capabilities. Taking network devices with computational capabilities as an example, the computational capabilities of network devices primarily provide computing power support for signal transmission and reception capabilities, enabling communication between network devices and other communication nodes.

[0003] In addition to processing communication signals within the communication network, communication nodes may also need to handle artificial intelligence (AI) related processing. Therefore, how to integrate AI-related processing with the communication network is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This application provides a communication method and related apparatus for introducing AI into the signal generation process and using AI to improve signal processing efficiency.

[0005] The first aspect of this application provides a communication method applied to a first communication device.

[0006] For example, the first communication device may be a terminal, or it may be a communication module and / or computing module within the terminal, or it may be a circuit or chip within the terminal responsible for communication functions (such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip), or a circuit or chip within the terminal responsible for communication and / or computing functions (such as a graphics processing unit (GPU), AI processor, neural processing unit (NPU), or application-specific integrated circuit (ASIC)). Alternatively, the method may be executed by a logical node, logical module, or software capable of implementing all or part of the terminal's functions.

[0007] For example, the first communication device may be a network-side device, or it may be a module (e.g., a circuit, chip, or chip system) within a network-side device. Alternatively, the first communication device may be a circuit or chip (e.g., a GPU, AI processor, NPU, or ASIC) within a network-side device responsible for communication and / or computing functions. Alternatively, the method may be executed by a logical node, logical module, or software capable of implementing all or part of the functions of the network-side device. The network-side device may include access network equipment or other devices defined in the future network definition.

[0008] In the following description, the method of the first aspect is taken as an example, performed by a first communication device. In this method, the first communication device acquires first data; the first communication device performs a first processing on the first data to obtain second data, the first processing being a processing based on a matrix obtained through AI training; wherein the second data is used to generate a first signal to be transmitted.

[0009] Based on the above scheme, the first communication device performs a first process on the first data, and the resulting second data can be used to generate a first signal to be transmitted. This first process is based on a matrix trained by AI. In other words, the generation of the first signal to be transmitted is based on the processing result of the first process (i.e., the second data), and this first process is based on a matrix trained by AI. In this way, during the generation of the first signal to be transmitted, the first communication device can perform data processing based on a matrix trained by AI, enabling the introduction of AI into the signal generation process, improving processing efficiency, reducing processing latency, and enhancing communication efficiency.

[0010] In this application, the matrix corresponding to the first processing can be a matrix obtained based on AI training. The AI ​​training may include: AI-based training, fine-tuning, fine-tuning, updating, iteration, optimization, or other implementations defined in the future network.

[0011] In this application, the matrix can be replaced with other implementations, including but not limited to: tensors, vectors, dictionaries, computation graphs, or other implementations defined in the future network.

[0012] In this application, AI can be replaced by other implementations, including but not limited to: neural networks, artificial general intelligence (AGI), agents, machine learning, or other implementations defined by future networks. Optionally, the first processing is based on the matrix obtained through AI training, and this first processing can be referred to as AI processing, neural network processing, AGI processing, or other implementations defined by future networks.

[0013] For example, the first process described above includes one or more linear processes. Alternatively, the first process described above includes one or more nonlinear processes. In this way, the first process can be implemented in multiple ways to improve the flexibility of the solution implementation.

[0014] Optionally, the nonlinear processing involved in this application may include linear processing and processing corresponding to nonlinear functions.

[0015] In one possible implementation of the first aspect, the first data is data obtained by encoding and modulation of the third data. For example, the third data may include data to be transmitted.

[0016] Based on the above scheme, the first data serves as the input to the first processing. This first data can be data obtained by encoding and modulation of the third data. For example, in this case, the first processing may include one or more linear processing steps. In other words, the third data can be data before encoding (such as a source symbol stream, bit sequence, bit stream, etc.), and the first data can be data after modulation (such as modulation symbols, complex symbol streams, etc.). This first data can undergo one or more linear processing steps to obtain the second data, which may include the matrix obtained based on AI training. In this way, during the generation of the first signal to be transmitted, the first processing can perform linear processing on the modulated data, making the above scheme adaptable to AI processing scenarios after modulation.

[0017] Optionally, the coding process involved in this application may include channel coding and / or source coding.

[0018] Optionally, the modulation processing involved in this application may include AI-based modulation processing, or modulation processing based on traditional software and / or hardware modules.

[0019] In one possible implementation of the first aspect, the first data includes at least two modulation symbols, which correspond to different resources. For example, the resources involved in this application may include one or more of the following: time-domain resources, frequency-domain resources, spatial-domain resources, code-domain resources, or other implementations defined by a future network.

[0020] Based on the above scheme, when the first data is data obtained by encoding and modulation of the third data, the first data can be modulated data, and the first data includes at least two modulation symbols corresponding to different resources. In this way, the first processing can jointly process at least two modulation symbols on different resources, which can improve processing efficiency and reduce processing latency.

[0021] Optionally, the first processing includes one or more linear processing steps, and the first data and the second data have the same dimension. In other words, when the first processing includes one or more linear processing steps, the first data and the second data have the same dimension, meaning the first processing may not change the dimensions of the input and output.

[0022] In one possible implementation of the first aspect, the first data is data obtained by encoding the third data. For example, the third data may include data to be transmitted.

[0023] Based on the above scheme, the first data serves as the input to the first processing. This first data can be data obtained by encoding the third data. For example, in this case, the first processing may include one or more nonlinear processing steps. In other words, the third data can be data before encoding (such as a source symbol stream, bit sequence, bit stream, etc.), and the first data can be data after encoding (such as a bit sequence, bit stream, etc.). The first data can undergo one or more nonlinear processing steps to obtain the second data, which may include the matrix obtained based on AI training as described above. In this way, during the generation of the first signal to be transmitted, the first processing can perform nonlinear processing on the encoded data, making the above scheme adaptable to AI processing scenarios after encoding.

[0024] In one possible implementation of the first aspect, the first processing further includes interleaving and deinterleaving. The first communication device performs the first processing on the first data to obtain the second data, which includes the first communication device sequentially performing the interleaving processing, the one or more linear processing steps, and the deinterleaving processing on the first data to obtain the second data.

[0025] Based on the above scheme, the first communication device can sequentially perform interleaving, one or more linear processing, and deinterleaving on the first data to obtain the second data. In this way, the modulation symbols contained in the first data can be randomized, which can randomize errors / interference (e.g., erroneous data in the first data can be distributed among multiple codewords contained in the second data), enhance error correction capability, and improve the reliability of data transmission.

[0026] In one possible implementation of the first aspect, the first processing further includes transpose processing and inverse transpose processing. The first communication device performs the first processing on the first data to obtain the second data, which includes: the first communication device sequentially performing the transpose processing, the one or more linear processing steps, and the inverse transpose processing on the first data to obtain the second data.

[0027] Based on the above scheme, the first communication device can sequentially perform transpose processing, one or more linear processing steps, and inverse transpose processing on the first data to obtain the second data. In this way, transpose and inverse transpose can be achieved by reusing the linear operation matrix one or more times, limiting the dimension of the linear operation matrix, thereby reducing complexity and processing latency.

[0028] In one possible implementation of the first aspect, the dimension of the first data is used to determine the AI ​​processing parameters corresponding to the first processing. For example, the AI ​​processing parameters may include one or more parameters involved in linear processing, nonlinear processing, interleaving processing, deinterleaving processing, transpose processing, and inverse transpose processing. For instance, the AI ​​processing parameters may include the matrix obtained based on AI training, or the dimension of that matrix, etc.

[0029] Based on the above scheme, the first data is used as the input of the first processing. The dimension of the first data can be used to determine the AI ​​processing parameters corresponding to the first processing. The first data can be processed by AI processing parameters that are adapted to the dimension of the first processing to improve processing performance.

[0030] In one possible implementation of the first aspect, the second data includes a reference signal used to determine channel information of the transmission channel of the first signal. For example, the reference signal included in the second data may be a reference signal superimposed on the second data after the first processing.

[0031] Based on the above scheme, the second data output from the first processing can include a reference signal. This allows the receiver of the first signal to obtain the reference signal from the recovered second data after the first signal has been transmitted. Furthermore, the receiver can determine the channel information of the transmission channel using the obtained reference signal and perform data parsing or demodulation based on this information to achieve better data parsing or demodulation performance. In addition, because the second data output from the first processing includes the aforementioned reference signal, the sender of the first signal can avoid the process of inserting a reference signal, reducing processing latency and saving the resource overhead of the reference signal, thereby improving data transmission performance.

[0032] Optionally, when the second data includes a reference signal, the second data includes the reference signal and the data to be transmitted. The reference signal and the data to be transmitted may occupy some or all of the same resources, which can save the resource overhead occupied by the reference signal and improve resource utilization.

[0033] In one possible implementation of the first aspect, the second data does not include a reference signal.

[0034] Based on the above scheme, the second data output from the first processing can be free of the reference signal. This allows the receiver of the first signal to recover the second data after transmission, saving the resource overhead of the reference signal and improving data transmission performance. Furthermore, the second data not containing the reference signal can also be understood as the first signal not containing the reference signal, meaning the sender of the first signal can avoid the process of inserting a reference signal, thus reducing processing latency.

[0035] Optionally, the first signal is generated from the second data through one or more of the following processes: reference signal insertion, resource mapping, precoding, and beamforming. Reference signal insertion can be understood as the second data containing the data to be transmitted, and the inserted reference signal occupying different or orthogonal resources than the data to be transmitted.

[0036] In one possible implementation of the first aspect, the second data includes layer indication information, wherein the layer indication information is used to recover the first data.

[0037] Based on the above scheme, the second data is the output of the first processing. The second data may contain layer indication information, so that after the first signal is transmitted, the receiver of the first signal can distinguish data of different layers based on the layer indication information. This enables the receiver to use the corresponding layer indication information to distinguish the information of each received stream when detecting or parsing each stream, thereby improving the data detection or parsing performance.

[0038] In one possible implementation of the first aspect, the method further includes: the first communication device transmitting the first signal.

[0039] Based on the above scheme, the first communication device can also generate a first signal based on the second data and transmit the first signal, making the above scheme applicable to scenarios where the first communication device acts as a signal sender.

[0040] In one possible implementation of the first aspect, the method further includes: the first communication device sending or receiving indication information, the indication information being used to indicate AI processing parameters corresponding to the first processing; wherein the AI ​​processing parameters corresponding to the first processing are used to determine the processing method and / or AI model for parsing the first signal.

[0041] Based on the above scheme, the first communication device can also send or receive indication information, so that the sender and receiver of the indication information can determine the AI ​​processing parameters corresponding to the first processing in the first communication device. Subsequently, the signal receiver can use the AI ​​processing parameters corresponding to the first processing to determine the processing method and / or AI model for parsing the first signal, so as to perform signal detection or parsing through the processing method and / or AI model adapted to the first processing, thereby improving the data detection or parsing performance.

[0042] Optionally, in this application, the AI ​​model can be replaced with other implementations, such as a neural network model, an AI neural network model, a machine learning model, or an AI processing model, etc.

[0043] A second aspect of this application provides a communication method applied to a second communication device.

[0044] For example, the second communication device can be a terminal, or it can be a communication module and / or computing module in the terminal, or it can be a circuit or chip in the terminal responsible for communication functions (such as a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), or a circuit or chip in the terminal responsible for communication and / or computing functions (such as a GPU, NPU, AI processor, or ASIC). Alternatively, the method can be executed by a logical node, logical module, or software that can implement all or part of the terminal functions through the second communication device.

[0045] For example, the second communication device may be a network-side device, or it may be a module (e.g., a circuit, chip, or chip system) within a network-side device, or it may be a circuit or chip (e.g., a GPU, NPU, AI processor, or ASIC) within a network-side device responsible for communication and / or computing functions. Alternatively, the method can be executed by a logical node, logical module, or software capable of implementing all or part of the functions of the network-side device. The network-side device may include access network equipment or other devices defined in the future network.

[0046] In the following description, the method of the second aspect is taken as an example, performed by a second communication device. In this method, the second communication device sends or receives indication information for indicating AI processing parameters corresponding to the first processing; wherein the AI ​​processing parameters are used to determine the processing method and / or AI model for parsing the first signal; the second communication device receives the first signal, which is obtained based on the first processing; the second communication device parses the first signal based on the processing method and / or the AI ​​model.

[0047] Based on the above scheme, the second communication device can send or receive indication information, enabling both the sender and receiver of the indication information to determine the AI ​​processing parameters corresponding to the first processing in the first communication device. Here, the first processing is the process performed by the signal sender to generate the first signal to be sent. Therefore, the second communication device, as the signal receiver, can determine the processing method and / or AI model for parsing the first signal based on the AI ​​processing parameters corresponding to the first processing, so as to improve data detection or parsing performance by using a processing method and / or AI model adapted to the first processing.

[0048] A third aspect of this application provides a communication device, which includes a processing unit; the processing unit is configured to acquire first data; the processing unit is further configured to perform a first processing on the first data to obtain second data, the first processing being a processing based on a matrix obtained through AI training; wherein the second data is used to generate a first signal to be transmitted.

[0049] In the third aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects. For details, please refer to the first aspect, which will not be repeated here.

[0050] A fourth aspect of this application provides a communication device, which is a second communication device. The device includes a transceiver unit and a processing unit. The transceiver unit is used to send or receive indication information, which indicates AI processing parameters corresponding to a first processing step. The AI ​​processing parameters are used to determine a processing method and / or AI model for parsing the first signal. The transceiver unit is also used to receive a first signal obtained based on the first processing step. The processing unit is used to parse the first signal based on the processing method and / or the AI ​​model.

[0051] In the fourth aspect of this application, the constituent modules of the communication device can also be used to perform the steps executed in various possible implementations of the second aspect and achieve the corresponding technical effects. For details, please refer to the second aspect, which will not be repeated here.

[0052] A fifth aspect of this application provides a communication device including at least one processor coupled to a memory; the memory is used to store a program or instructions; the at least one processor is used to execute the program or instructions to cause the device to implement the method described in any possible implementation of any of the first to second aspects. Optionally, the communication device may include the memory.

[0053] The sixth aspect of this application provides a communication device including at least one logic circuit and an input / output interface; the logic circuit is used to perform the method as described in any one of the possible implementations of the first to second aspects described above.

[0054] The seventh aspect of this application provides a communication system, which includes the first communication device and the second communication device described above.

[0055] An eighth aspect of this application provides a computer-readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, perform the method as described in any possible implementation of any of the first to second aspects described above.

[0056] The ninth aspect of this application provides a computer program product (or computer program) that, when executed by a processor, performs the method described in any possible implementation of any of the first to second aspects described above.

[0057] The tenth aspect of this application provides a chip or chip system including at least one processor for supporting a communication device in implementing the methods described in any possible implementation of any of the first to second aspects. For example, the chip may be a baseband chip, a modem chip, a SoC chip (such as an SoC chip containing a modem core), a SIP chip, or a communication module, etc.

[0058] In one possible design, the chip or chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices. Optionally, the chip system may also include interface circuitry that provides program instructions and / or data to the at least one processor.

[0059] The technical effects of any of the design methods in aspects three through ten can be found in the technical effects of the different design methods in aspects one through two above, and will not be repeated here. Attached Figure Description

[0060] Figures 1 to 3 A schematic diagram of the communication system provided in this application;

[0061] Figure 4 This is a schematic diagram of the signal processing procedure involved in this application;

[0062] Figure 5 A schematic diagram of the communication method provided in this application;

[0063] Figures 6a to 6j A schematic diagram of the first process provided in this application;

[0064] Figures 7 to 11 A schematic diagram of the communication device provided in this application. Detailed Implementation

[0065] First, some terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

[0066] (1) The terms "system" and "network" in the embodiments of this application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of this application are used to distinguish multiple objects and are not used to limit the order, sequence, priority or importance of multiple objects.

[0067] (2) In the embodiments of this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include sending directly through the air interface or sending indirectly through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which may include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0068] In other words, sending and receiving can occur between devices, such as between network devices and terminals, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, wiring, or interfaces.

[0069] It is understandable that information may undergo necessary processing, such as encoding and modulation, between the source and destination, but the destination can understand the valid information from the source. Similar statements in this application can be interpreted in a similar way and will not be elaborated further.

[0070] (3) In the embodiments of this application, "instruction" may include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction. The information indicated by a certain piece of information (as described below, the instruction information) is called the information to be instructed. In the specific implementation process, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is an association between the other information and the information to be instructed; or it can only indicate a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon. For example, the instruction can be implemented by using a pre-agreed (e.g., protocol predefined) arrangement of various information, thereby reducing the instruction overhead to a certain extent. This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to indicate the information to be instructed, and for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

[0071] (4) Configuration and pre-configuration: Configuration and pre-configuration may be used in this application. Configuration refers to the network device / server sending configuration information or parameter values ​​to the communication device (such as the terminal device or network-side device mentioned later) via messages or signaling, so that the communication device can determine the communication parameters or resources for transmission based on these values ​​or information. Pre-configuration can refer to the network device / server negotiating parameter information or parameter values ​​with the communication device in advance, or it can refer to the parameter information or parameter values ​​specified by the standard protocol for the communication device, or it can refer to the parameter information or parameter values ​​pre-stored in the communication device. This application does not limit this.

[0072] Furthermore, these values ​​and parameters can be changed or updated.

[0073] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, and the various methods / designs / implementations within each embodiment, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the various methods / designs / implementations within each embodiment are consistent and can be mutually referenced. The technical features in different embodiments and the various methods / designs / implementations within each embodiment can be combined to form new embodiments, methods, or implementations based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0074] This application can be applied to long-term evolution (LTE) systems, new radio (NR) systems, or future communication systems. These communication systems include at least one network device and / or at least one terminal.

[0075] Please see Figure 1 This is a schematic diagram of the architecture of the communication system 1000 used in an embodiment of this application. Figure 1 As shown, the communication system 1000 includes a radio access network (RAN) 100 and a core network (CN) 200. RAN 100 includes at least one RAN node (e.g., ...). Figure 1 110a and 110b (collectively referred to as 110) and at least one terminal (such as Figure 1 RAN 100, denoted as RAN 120a-120j, is collectively referred to as RAN 120. RAN 100 may also include other RAN nodes, such as wireless relay equipment and / or wireless backhaul equipment. Figure 1 (Not shown in the image). Terminal 120 is connected to RAN node 110 wirelessly. RAN node 110 is connected to core network 200 wirelessly or via wired connection. The core network equipment in core network 200 and RAN node 110 in RAN 100 can be different physical devices, or they can be the same physical device integrating core network logical functions and radio access network logical functions.

[0076] RAN 100 can be a cellular system related to the 3rd Generation Partnership Project (3GPP), such as 4G, 5G mobile communication systems, or future-oriented evolution systems. RAN 100 can also be an open RAN (O-RAN or ORAN), a cloud RAN (CRAN), a virtualized RAN (vRAN), an artificial intelligence radio access network (AI RAN), or a wireless fidelity (WiFi) system. RAN 100 can also be a communication system that integrates two or more of the above systems.

[0077] RAN node 110, sometimes also referred to as access network equipment, RAN entity, or access node, constitutes part of the communication system and assists terminals in achieving wireless access. Multiple RAN nodes 110 in the communication system 1000 can be of the same type or different types. In some scenarios, the roles of RAN node 110 and terminal 120 are relative, for example... Figure 1 Network element 120i can be a helicopter or a drone, and it can be configured as a mobile base station. For terminals 120j that access RAN 100 through network element 120i, network element 120i is a base station; however, for base station 110a, network element 120i is a terminal. RAN node 110 and terminal 120 are sometimes referred to as communication devices, for example... Figure 1 Network elements 110a and 110b can be understood as communication devices with base station functions, while network elements 120a-120j can be understood as communication devices with terminal functions.

[0078] In one possible scenario, a RAN node can be a base station, an evolved NodeB (eNodeB), an access point (AP), a transmission reception point (TRP), a next-generation NodeB (gNB), a base station in a future mobile communication system, or an access node in a WiFi system, etc. Figure 1 110a), micro base stations or indoor stations (such as Figure 1 The RAN node can be a relay node or donor node (as described in section 110b), or a wireless controller in a CRAN scenario. Optionally, the RAN node can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). All or part of the functions of the RAN node in this application can also be implemented through software functions running on hardware, or through virtualization functions instantiated on a platform (e.g., a cloud platform). The RAN node can also be equipped with communication modules, circuits, or chips that perform corresponding communication functions. The RAN node can also be configured with program instructions for performing corresponding communication functions and corresponding program instructions. The RAN node in this application can also be a logical node, logical module, or software that can implement all or part of the access node functions, or a circuit or chip (such as a GPU, AI processor, NPU, or ASIC) responsible for communication and / or computing functions in the access node.

[0079] In another possible scenario, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be central units (CUs), distributed units (DUs), CU-control plane (CPs), CU-user plane (UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs). Furthermore, RAN nodes can also be computing units, providing computing power for tasks (such as model inference and / or model training), and can also be used to implement one or more of the following: task partitioning, scheduling, and orchestration. The functionality of a computing unit can be implemented by a separate module independent of other units (e.g., CU, DU, RU), or by one or more other units (e.g., one or more of CU, DU, RU).

[0080] In different systems, CU (or CU-CP and CU-UP), DU, computing unit, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, computing unit, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, computing unit, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0081] A terminal can be a device or module that accesses the aforementioned communication system and has corresponding communication functions. A terminal can also be called a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, etc. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, transportation vehicles with wireless communication capabilities, communication modules, etc. The embodiments of this application do not limit the device form of the terminal. The terminal typically contains communication modules, circuits, or chips that perform corresponding communication functions. Furthermore, it may also contain modules, circuits, or chips (such as GPUs, AI processors, NPUs, or ASICs) that perform corresponding communication and / or computing functions. The terminal can also be configured with program instructions for performing these communication and / or computing functions.

[0082] To support AI technology in wireless networks, AI nodes may also be introduced into the network.

[0083] AI nodes can be deployed in one or more of the following locations within the communication system: access network nodes (RAN nodes), terminal devices, or core network devices. Alternatively, AI nodes can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. AI nodes can communicate with other devices in the communication system, which can be one or more of the following: network devices, terminal devices, or core network elements.

[0084] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, these nodes can be divided based on function, such as different AI nodes being responsible for different functions.

[0085] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to achieve different functions. Alternatively, they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the aforementioned AI nodes.

[0086] Optionally, the AI ​​node can be an AI network element or an AI module.

[0087] Please see Figure 2 This is a schematic diagram of a possible application framework in a communication system. For example... Figure 2 As shown, network elements in a communication system are connected via interfaces (e.g., NG, Xn) or air interfaces. These network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in operations administration and maintenance (OAM), are equipped with one or more AI modules (for clarity, ...). Figure 2 (Only one is shown in the image). An access network node can be a single RAN node or can include multiple RAN nodes, such as a CU and a DU. The CU and / or DU can also be equipped with one or more AI modules. The CU can also be split into CU-CP and CU-UP, and one or more AI modules can be set in the CU-CP and / or CU-UP.

[0088] AI modules are used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. The models of AI modules can achieve different functions depending on the parameter configurations. The models of AI modules can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or biases in the activation function), input parameters (e.g., the type and / or dimension of the input parameters), or output parameters (e.g., the type and / or dimension of the output parameters). The biases in the activation function can also be referred to as the biases of the neural network.

[0089] In one example, the neural network mentioned above can be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), or a generative adversarial network (GAN).

[0090] Deep Neural Networks (DNNs) are artificial neural network architectures with multiple layers of nonlinear transformation units stacked in a hierarchical structure to form deep computational models. Compared to shallow neural networks, deep neural networks have more hidden layers, allowing the network model to capture more complex data structures and higher-level abstract features.

[0091] A CNN is a deep neural network with a convolutional structure. A CNN contains a feature extractor consisting of convolutional layers and subsampling layers. This feature extractor can be viewed as a filter, and the convolution process can be seen as performing convolution between a trainable filter and an input image or a convolutional feature map.

[0092] RNN is a type of recursive neural network that takes sequence data as input, recursively moves along the direction of sequence evolution, and connects all nodes (recurrent units) in a chain-like manner.

[0093] GAN is a deep learning model. It consists of a generator and a discriminator, and is trained through adversarial learning. Its purpose is to estimate the potential distribution of data samples and generate new data samples.

[0094] For example, an AI module may have one or more models. A model can infer an output that includes one or more parameters. The learning, training, or inference processes of different models may be deployed on different nodes or devices, or they may be deployed on the same node or device.

[0095] Optionally, the AI ​​processing involved in this application may include LoRA. LoRA is a low-rank fine-tuning method whose core idea is to freeze the weights of the pre-trained model and then inject the trainable low-rank factorization matrix into one or more layers of the Transformer architecture, thereby significantly reducing the number of trainable parameters for downstream tasks. Furthermore, by fine-tuning the same pre-trained model using different LoRA parameters, the resulting fine-tuned model can be used for different downstream tasks.

[0096] Please see Figure 3 This is a schematic diagram of a possible application framework in a communication system. For example... Figure 3 As shown, the communication system includes a RAN intelligent controller (RIC). For example, the RIC could be... Figure 2The AI ​​module shown is used to implement AI-related functions. RICs include near-real-time RICs (near-RT RICs) and non-real-time RICs (non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0097] Near real-time (NRT) RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. NRT RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, compute nodes, and / or RUs) and / or terminals. This information can be used as training data or inference data. NRT RICs can deliver inference results to RAN nodes and / or terminals. Inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, a NRT RIC delivers an inference result to a DU, which then forwards it to an RU.

[0098] Non-real-time RICs are also used for model training and inference. For example, they are used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, compute nodes, and / or RUs) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and / or terminals. Inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers inference results to a DU, which then forwards them to an RU.

[0099] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be set in RAN nodes (e.g., CU, DU, compute nodes), while non-real-time RICs can be set in OAM, cloud servers, core network devices, or other network devices.

[0100] As described above, in a communication network, communication nodes not only need to process communication signals but may also need to handle AI-related processing. However, how to integrate AI-related processing with the communication network is a technical problem that urgently needs to be solved.

[0101] like Figure 4As shown in the example, the processing of communication signals may involve multiple processes. Traditional communication signal processing generally includes the processing by the transmitter and the processing by the receiver, as illustrated in the diagram. These two processes are related and include the following steps.

[0102] (1) Bit-level processing, including source coding and channel coding of the transmitter, and source decoding and channel decoding of the receiver.

[0103] (2) Bit-to-symbol processing includes: modulation by the transmitter and demodulation by the receiver.

[0104] (3) Symbol-level processing, including layer mapping and precoding and waveform processing of the transmitter; and channel equalization and waveform processing of the receiver.

[0105] (4) Analog processing, including digital-to-analog (DA) conversion and radio frequency processing of the transmitter; and analog-to-digital (AD) conversion and radio frequency processing of the receiver.

[0106] Through the above processing steps and the wireless channel, wireless signal transmission between the transmitter and receiver can be achieved. Optionally, since the wireless channel may be affected by multipath effects, fading, and other factors, the use of a reference signal (such as a demodulation reference signal (DMRS)) can reduce these effects. For example, the transmitter can insert a reference signal (such as a DMRS insert) after modulation and before layer mapping and precoding. The resources occupied by this reference signal can be orthogonal to the resources occupied by the data. Correspondingly, the receiver can perform channel estimation using the received reference signal to obtain channel information, and perform data parsing or demodulation based on this channel information. However, how to introduce AI into the above wireless signal processing is currently a hot research topic.

[0107] To address the aforementioned problems, this application provides a communication method and related apparatus, which will be described in detail below with reference to the accompanying drawings.

[0108] It should be understood that the following description uses different communication devices as examples to illustrate the method, but this application does not limit the subject of the interaction.

[0109] For example, the first communication device may be a terminal, or a communication and / or computing module within the terminal, or a circuit or chip within the terminal responsible for communication and / or computing functions (such as a modem chip (also known as a baseband chip), or a SoC chip / SIP chip containing a modem core, or a GPU / NPU / AI processor / ASIC). Alternatively, the method involving the first communication device can be executed by a logical node, logical module, or software capable of implementing all or part of the terminal's functions. Or, the first communication device may be a network-side device, or a circuit or chip within the network-side device responsible for communication and / or computing functions (such as a GPU, NPU, AI processor, or ASIC). Alternatively, the method involving the first communication device can be executed by a logical node, logical module, or software capable of implementing all or part of the network-side device's functions.

[0110] For example, the second communication device can be a terminal, or a communication and / or computing module within the terminal, or a circuit or chip within the terminal responsible for communication and / or computing functions (such as a modem chip (also known as a baseband chip), or a SoC chip / SIP chip containing a modem core, or a GPU / NPU / AI processor / ASIC). Alternatively, the method involving the second communication device can be implemented through a logical node, logical module, or software capable of implementing all or part of the terminal's functions. Or, the second communication device can be a network-side device, or a circuit or chip within the network-side device responsible for communication and / or computing functions (such as a GPU, NPU, AI processor, or ASIC), or the method involving the second communication device can be implemented through a logical node, logical module, or software capable of implementing all or part of the network-side device's functions.

[0111] Optionally, the aforementioned network-side equipment may include access network equipment or other equipment defined in the future network.

[0112] Please see Figure 5 This is a schematic diagram of an implementation of the communication method provided in this application, which includes the following steps.

[0113] S501. The first communication device acquires the first data.

[0114] S502. The first communication device performs a first process on the first data to obtain second data. The first process is based on a matrix obtained through AI training, and the second data is used to generate a first signal to be transmitted.

[0115] In this application, the matrix corresponding to the first processing can be a matrix obtained based on AI training. The AI ​​training may include: AI-based training, fine-tuning, fine-tuning, updating, iteration, optimization, or other implementations defined in the future network.

[0116] In this application, the matrix can be replaced with other implementations, including but not limited to: tensors, vectors, dictionaries, computation graphs, or other implementations defined in the future network.

[0117] In this application, AI can be replaced by other implementations, including but not limited to: neural networks, artificial general intelligence (AGI), agents, machine learning, or other implementations defined by future networks. Optionally, the first processing is based on the matrix obtained through AI training, and this first processing can be referred to as AI processing, neural network processing, AGI processing, or other implementations defined by future networks.

[0118] For example, the first processing described above includes one or more linear processing steps. Alternatively, the first processing described above includes one or more nonlinear processing steps. In this way, the first processing can be implemented in multiple ways to improve the flexibility of the solution implementation. Optionally, the nonlinear processing involved in this application can include linear processing and processing corresponding to nonlinear functions.

[0119] based on Figure 5 In the illustrated scheme, the first communication device performs a first processing on the first data in step S502, and the resulting second data can be used to generate a first signal to be transmitted. This first processing is based on a matrix trained by AI. In other words, the generation of the first signal to be transmitted is based on the processing result (i.e., the second data) including the first processing, and this first processing is based on a matrix trained by AI. In this way, during the generation of the first signal to be transmitted, the first communication device can perform data processing based on a matrix trained by AI, enabling the introduction of AI into the signal generation process, improving processing efficiency, reducing processing latency, and enhancing communication efficiency.

[0120] It should be noted that the first communication device can be implemented in a variety of ways.

[0121] As an example, the first communication device may be at least a circuit or chip used to implement the first processing described above. For example, the first communication device may be a communication chip, an AI chip, or the like capable of performing the first processing based on a matrix obtained through AI training. Accordingly, after obtaining the second data in step S502, the first communication device may send the second data to other circuits or chips (e.g., other circuits or chips used for baseband signal processing and / or radio frequency signal processing) so that the other circuits or chips can generate a first signal to be transmitted based on the second data. Subsequently, the other circuits or chips can transmit the first signal via a wireless channel.

[0122] As another example, the first communication device may be a circuit or chip for implementing the aforementioned first processing and other processing (e.g., the other processing includes baseband signal processing and / or radio frequency signal processing). For example, the first communication device may be a communication chip, AI chip, etc., capable of performing the first processing based on a matrix obtained through AI training. Accordingly, after obtaining the second data in step S502, the first communication device may further perform the other processing on the second data.

[0123] As another example, the first communication device can be a communication device (such as a terminal device or network device) used to implement the first processing described above. Accordingly, after obtaining the second data in step S502, the first communication device can further process the second data to generate a first signal to be transmitted, which can then be transmitted via a wireless channel. For example, in this case, Figure 5 The method shown also includes:

[0124] S503. The first communication device sends a first signal, and correspondingly, the second communication device receives the first signal. In other words, the first communication device can also generate the first signal based on the second data and send the first signal, making the above scheme applicable to scenarios where the first communication device acts as the signal sender.

[0125] From the previous text Figure 4 As illustrated in the example, traditional signal processing may involve multiple stages. In the process described above, the first signal to be transmitted is generated through a first process, which may correspond to various flexible implementations of traditional signal processing. Some implementation examples will be provided below for description.

[0126] In Implementation Example 1, the input to the first process includes the data after encoding and modulation processing. In Implementation Example 1, the aforementioned first data is the data obtained by encoding and modulation processing the third data. For example, the third data may include data to be transmitted (such as a bit stream to be transmitted).

[0127] like Figure 6a In the example shown, the first process can be located after the encoding and modulation processes, meaning that the input to the first process can include the output of the modulation process.

[0128] It should be noted that, in Figure 6a And the following text Figures 6b to 6g , Figure 6i and Figure 6jIn the example, the processing after the first processing includes layer mapping and precoding processing. In practical applications, the signal transmitting side may also include other processing during the generation of the first signal, such as waveform processing, digital-to-analog conversion processing, radio frequency processing, filtering processing (not shown in the figure), or other implementations defined by the future network. This is not limited here.

[0129] In Example 1, the first data serves as the input to the first process. This first data can be data obtained by encoding and modulation of the third data. For example, in this case, the first process may include one or more linear processes. In other words, the third data can be data before encoding (such as a source symbol stream, bit sequence, bit stream, etc.), and the first data can be data after modulation (such as modulation symbols, complex symbol stream, etc.). This first data can undergo one or more linear processes to obtain the second data, which may include the matrix obtained based on AI training as described above. In this way, during the generation of the first signal to be transmitted, the first process can perform linear processing on the modulated data, making the above scheme adaptable to AI processing scenarios after modulation.

[0130] Optionally, the coding process involved in this application may include channel coding and / or source coding.

[0131] Optionally, the modulation processing involved in this application may include AI-based modulation processing, or modulation processing based on traditional software and / or hardware modules.

[0132] In one possible implementation of Example 1, the first data includes at least two modulation symbols corresponding to different resources. For example, the resources involved in this application may include one or more of the following: time-domain resources, frequency-domain resources, spatial-domain resources, code-domain resources, or other implementations defined by the future network. Thus, when the first data is data obtained by encoding and modulation of the third data, the first data can be modulated data, and the first data includes at least two modulation symbols corresponding to different resources. In this way, the first processing can jointly process at least two modulation symbols on different resources, improving processing efficiency and reducing processing latency.

[0133] Optionally, the first processing includes one or more linear processing steps, and the first data and the second data have the same dimension. In other words, when the first processing includes one or more linear processing steps, the first data and the second data have the same dimension, meaning the first processing may not change the dimensions of the input and output.

[0134] As an example, in Implementation Example 1, the first communication device may be a circuit or chip capable of performing encoding processing, modulation processing, and the first processing. Accordingly, in step S501, the process of the first communication device acquiring the first data includes: the first communication device performing encoding processing and modulation processing on the third data to obtain the first data.

[0135] As another example, in Implementation Example 1, the first communication device may be a circuit or chip capable of first processing. Accordingly, in step S501, the process of the first communication device acquiring the first data includes: the first communication device receiving the first data sent by other circuits or chips, wherein the other circuits or chips are used to encode and modulate the third data to obtain the first data.

[0136] In Implementation Example 2, the input to the first process includes the encoded data. In Implementation Example 2, the aforementioned first data is the data obtained by encoding the third data. For example, the third data may include the data to be transmitted.

[0137] like Figure 6b In the example shown, the first processing can be performed after the encoding processing, meaning that the input to the first processing can include the output of the encoding processing.

[0138] In Example 2, the first data serves as the input to the first processing. This first data can be data obtained by encoding the third data. For example, in this case, the first processing may include one or more nonlinear processing steps. In other words, the third data can be data before encoding (such as a source symbol stream, bit sequence, bit stream, etc.), and the first data can be data after encoding (such as a bit sequence, bit stream, etc.). The first data can undergo one or more nonlinear processing steps to obtain the second data, which may include the matrix obtained based on AI training as described above. In this way, during the generation of the first signal to be transmitted, the first processing can perform nonlinear processing on the encoded data, making the above scheme adaptable to AI processing scenarios after encoding.

[0139] As an example, in implementing Example 2, the first communication device may be a circuit or chip capable of performing encoding processing and the first processing. Accordingly, in step S501, the process of the first communication device acquiring the first data includes: the first communication device encoding the third data to obtain the first data.

[0140] As another example, in Implementation Example 2, the first communication device may be a circuit or chip capable of first processing. Accordingly, in step S501, the process of the first communication device acquiring the first data includes: the first communication device receiving first data sent by other circuits or chips, wherein the other circuits or chips are used to encode third data to obtain the first data.

[0141] From the previous text Figure 4 As illustrated in the example, traditional signal processing may also involve the insertion of a reference signal. In the process described above, the first signal to be transmitted is generated through a first process, which may include related processing of the reference signal. Some possible implementations will be described below.

[0142] In Method A, the second data includes a reference signal used to determine channel information of the transmission channel of the first signal. For example, the reference signal included in the second data may be a reference signal superimposed on the second data after the first processing.

[0143] In Method A, the second data output from the first processing may include a reference signal, enabling the receiver of the first signal to obtain the reference signal from the recovered second data after the first signal has been transmitted. Subsequently, the receiver can determine the channel information of the transmission channel using the obtained reference signal and perform data parsing or demodulation based on this channel information to achieve improved data parsing or demodulation performance. Furthermore, because the second data output from the first processing includes the aforementioned reference signal, the sender of the first signal can avoid the process of inserting a reference signal, reducing processing latency and saving the resource overhead of the reference signal, thereby improving data transmission performance.

[0144] Optionally, in mode A, when the second data includes a reference signal, the second data includes the reference signal and the data to be transmitted. The reference signal and the data to be transmitted may occupy some or all of the same resources. That is, the resources occupied by the reference signal and the data to be transmitted may not be orthogonal (or completely orthogonal), which can save the resource overhead occupied by the reference signal and improve resource utilization.

[0145] In Method B, the second data does not include a reference signal.

[0146] In method B, the second data output from the first processing may not contain a reference signal. This allows the receiver of the first signal to recover the second data after transmission, saving the resource overhead of the reference signal and improving data transmission performance. Furthermore, the absence of a reference signal in the second data can also be understood as the absence of a reference signal in the first signal, meaning the sender of the first signal can avoid the process of inserting a reference signal, thus reducing processing latency.

[0147] Optionally, the first signal is generated from the second data through one or more of the following processes: reference signal insertion, resource mapping, precoding, and beamforming. Reference signal insertion can be understood as the second data containing the data to be transmitted, and the inserted reference signal occupying different or orthogonal resources than the data to be transmitted.

[0148] As can be seen from methods A and B above, the reference signal insertion process may not be executed during the implementation of the first process. This will be described below with some examples.

[0149] like Figure 6c As shown in the example, taking the aforementioned implementation example one as an example, after modulation processing, there is no need to perform the process of inserting a reference signal. Instead, the processing result of the modulation processing is used as the input of the first processing to obtain the second data in the above-mentioned method A or method B through the first processing.

[0150] like Figure 6d As shown in the example, taking the aforementioned implementation example two as an example, after the encoding process, there is no need to perform the reference signal insertion process. Instead, the processing result of the encoding process is used as the input of the first process to obtain the second data in the above method A or method B through the first process.

[0151] In one possible implementation, methods A and B can be further improved. For example, in methods A and B, a reference signal insertion process can be performed on the second data output from the first processing, and the result of the reference signal insertion process can be used as input for layer mapping and precoding processing. It is understood that the resources corresponding to the reference signal involved in the reference signal insertion process are orthogonal to the resources corresponding to the second data. Therefore, the receiver of the first signal can perform channel estimation using the reference signal involved in the reference signal insertion process to obtain channel information, and perform data parsing or demodulation based on this channel information. Compared to methods A or B, this improves the performance of data parsing or demodulation.

[0152] like Figure 6eAs shown in the example, taking the aforementioned implementation example one as an example, after modulation processing, there is no need to perform the reference signal insertion process. Instead, the processing result of the modulation processing can be used as the input of the first processing to obtain the second data in the above-mentioned method A or method B through the first processing. Furthermore, after obtaining the second data through the first processing, processing can be performed through reference signal insertion to obtain the input for layer mapping and precoding processing.

[0153] like Figure 6f As shown in the example, taking the aforementioned implementation example two as an example, after the encoding process, it is not necessary to perform the reference signal insertion process. Instead, the processing result of the encoding process can be used as the input of the first process to obtain the second data in method A or method B. Furthermore, after obtaining the second data through the first process, processing can be performed through reference signal insertion to obtain the input for layer mapping and precoding processing.

[0154] Optionally, the inserted reference signal can be a reference signal that occupies sparse resources, that is, such a reference signal occupies fewer resources than a traditional reference signal (e.g., ...). Figure 4 The resources occupied by the reference signal (as shown) are, for example, less than those occupied by the reference signal in the time domain, frequency domain, or spatial domain than those occupied by the second data. It is understood that the resources corresponding to the sparsely resourced reference signal are orthogonal to the resources corresponding to the second data. Therefore, compared to a traditional reference signal (such as...) Figure 4 The implementation of the reference signal insertion shown can reduce the resources occupied by the reference signal by transmitting the reference signal that occupies sparse resources, thereby improving resource utilization.

[0155] Furthermore, when method A is implemented in conjunction with the insertion of a reference signal that occupies sparse resources, the receiver of the first signal can obtain both the reference signal contained in the second data and the reference signal through the aforementioned reference signal insertion. Based on these two reference signals, channel estimation can be performed to obtain more accurate channel information, thereby further improving the performance of data parsing or demodulation.

[0156] In one possible implementation, such as Figure 4 The implementation process shown may involve layer mapping in traditional signal processing. Correspondingly, in the above process, the second data output from the first processing may include layer indication information, which is used to recover the first data. Thus, the second data, as the output of the first processing, can include layer indication information, enabling the receiver of the first signal to distinguish data from different layers based on this layer indication information after transmission. This allows the receiver to use the corresponding layer indication information to distinguish the information of each received stream when detecting or parsing it, thereby improving data detection or parsing performance.

[0157] In one possible implementation, as described above, the first process may include one or more linear processes, or it may include one or more nonlinear processes. The following will use the linear processing process as an example to illustrate the possible implementation of the first process.

[0158] Case 1: The first processing includes not only one or more linear processing steps, but also interleaving and deinterleaving processing.

[0159] like Figure 6g As shown in the example, the first processing may include interleaving, linear processing, and deinterleaving. In case one, the process by which the first communication device performs the first processing on the first data in step S502 above to obtain the second data includes: the first communication device sequentially performing the interleaving processing, the one or more linear processing steps, and the deinterleaving processing on the first data to obtain the second data.

[0160] Therefore, the first communication device can sequentially perform interleaving, one or more linear processing, and deinterleaving on the first data to obtain the second data. In this way, the modulation symbols contained in the first data can be randomized, which can randomize errors / interference (e.g., erroneous data in the first data can be distributed among multiple codewords contained in the second data), enhance error correction capabilities, and improve the reliability of data transmission.

[0161] Alternatively, in case one, Figure 6g The data dimensions involved in the processing can be related, satisfying one or more of the following:

[0162] The input (e.g., bit stream) of the modulation processing has an n*m dimension, and the output (e.g., complex symbol stream) has an n dimension, where n represents the number of modulation symbols and m represents the modulation order.

[0163] The input dimension of the interleaving process (such as a complex symbol stream) is n, which is the same as the output dimension of the modulation process. The output dimension is n. The interleaving process can be performed by interleaving rows and columns, random interleaving, or other pre-configured interleaving methods according to the input order of the input data.

[0164] The input dimension of linear processing is n, the same as the output dimension of interleaving processing, which is also n. Linear processing can be one or more combinations of matrix operations such as multiplication and addition. For example, taking matrix multiplication as an example, the input of linear processing (denoted as matrix A) has a dimension of 1*n, the parameter or matrix of linear processing (denoted as matrix B) has a dimension of n*n, and the output of linear processing (denoted as matrix C) has a dimension of 1*n. The operation of linear processing can be represented as A. 1×n ×B n×n =C 1×nFor example, taking matrix addition as an example, the input of linear processing (denoted as matrix A) has a dimension of 1*n, the parameter matrix of linear processing (denoted as matrix B) has a dimension of 1*n, and the output of linear processing (denoted as matrix C) has a dimension of 1*n. The linear processing operation can be represented as A 1×n +B 1×n =C 1×n .

[0165] Optionally, the linear input is represented as X, and the linear output is represented as Y, satisfying: Y = W(X + A) + B. Here, W, A, and B are pre-configured or pre-trained parameters (such as matrices or vectors); alternatively, W, A, and B can be obtained from a combination of one or more pre-configured or pre-trained parameters, such as combining a subset of parameters from multiple matrices, scaling the matrix using scalars, repeating or diagonalizing a vector into a matrix, or expanding a small-dimensional matrix into a large-dimensional matrix (such as the Kronecker product), etc.

[0166] The input dimension of the deinterleaving process is n, the same as the output dimension of the linear process, and the output dimension is also n. In other words, the output of the first process (such as a complex symbol stream or a pilotless symbol stream in method B above) has a dimension of n, the same as the output dimension of the deinterleaving process. The input of the deinterleaving process is represented as X, and the output as Y, satisfying: Y = V(W(X+A)+B). The parameter W corresponding to the linear process can change the dimension of X, while the parameter V corresponding to the deinterleaving process changes the dimension inversely.

[0167] Optionally, after the deinterleaving process, the first process may further include a power normalization process, such that the result of the normalization process is used as part or all of the input to the layer mapping and precoding processes.

[0168] Alternatively, in this application, interleaving can be replaced with permutation or other implementations of the future network definition.

[0169] The following examples illustrate the interleaving process. In these examples, the input representation for the interleaving process (e.g., the sequence before interleaving) is: a symbol sequence of s0, s1, s2, ... s E-1 The length is E (E is greater than 1), and the output representation of the interleaving process (such as the interleaved sequence) is: f0, f1, f2, ... f E-1 Interleaving (also known as an interleaver) can be represented as: the interleaving sequence is p0, p1, p2, ... p E-1 .

[0170] As an example, the interleaver contains interleaving sequences used to interleave the indices of the symbol sequences input to the interleaving process. For example, the interleaving process satisfies:

[0171] for i = 0 to E-1 / / This represents a for loop operation, where the variable i takes values ​​from 0 to E-1, and increments by 1 in each iteration.

[0172] f i =s pi / / Represents f i The value of s pi The values ​​are the same

[0173] end for / / indicates exiting the loop

[0174] For example, when E is 5, with the input symbol sequence s0, s1, s2, ... sE-1 being 1, 2, 3, 4, 5, and the interleaver's interleaving sequence p0, p1, p2, ... p E-1 Taking 2, 3, 1, 0, 4 as an example, the following conditions are met:

[0175] i = 0, f i =s pi This can be represented as: f0 = s p0 =s2=3;

[0176] i = 1, f i =s pi This can be represented as: f1 = s p1 =s3=4;

[0177] i = 2, f i =s pi This can be expressed as: f2 = s p2 =s1=2;

[0178] i = 3, f i =s pi This can be represented as: f3 = s p3 =s0=1;

[0179] i = 4, f i =s pi This can be represented as: f4 = s p4 =s4=5;

[0180] Therefore, the outputs of the interleaving process are f0, f1, f2, ... f E-1 The numbers are: 3, 4, 2, 1, 5.

[0181] As another example, the interleaver contains interleaving sequences used to interleave the indices of the symbol sequence output from the interleaving process. For example, the interleaving process satisfies:

[0182] for i = 0 to E-1 / / This represents a for loop operation, where the variable i takes values ​​from 0 to E-1, and increments by 1 in each iteration.

[0183] f pi=s i / / Represents f pi The value of s i The values ​​are the same

[0184] end for / / indicates exiting the loop

[0185] For example, when E is 5, the input symbol sequence s0, s1, s2, ... s E-1 The numbers are 1, 2, 3, 4, 5, and the interleaving sequence of the interleaver is p0, p1, p2, ... p. E-1 Taking 2, 3, 1, 0, 4 as an example, the following conditions are met:

[0186] i = 0, f pi =s i Represented as: f p0 =s0, that is, f2 = s0 = 1;

[0187] i = 1, f pi =s i Represented as: f p1 =s1, that is, f3 = s1 = 2;

[0188] i = 2, f pi =s i Represented as: f p2 =s2, that is, f1 = s2 = 3;

[0189] i = 3, f pi =s i Represented as: f p3 =s3, that is, f0 = s3 = 4;

[0190] i = 4, f pi =s i This can be expressed as: fx4 = s4, that is, f4 = s4 = 5;

[0191] Therefore, the outputs of the interleaving process are f0, f1, f2, ... f E-1 The numbers are: 4, 3, 1, 2, 5.

[0192] Optionally, the sequence of the interleaver can be predefined (such as standard pre-configuration or other device configuration or indication), and the sequence generation rules of the interleaver can also be predefined (e.g., the sequence generation rules of the interleaver can be generated based on the length E).

[0193] As another example, the interleaving process can include triangular interleaving. For example, in the process of triangular interleaving, a matrix of T*(T+1) is constructed, where T is a positive integer, and upper / lower triangles are taken (e.g., upper left, lower left, upper right, lower right, four types of triangles).

[0194] like Figure 6hTaking a value of 4 for T as an example, the input symbols for interleaving are 1, 2, 3, 4, 5, 6, 7, 8. The input symbols are filled in row by row and then extracted column by column to obtain the output symbols (or, the input symbols are filled in column by column and then extracted row by row). Figure 6h In the example shown, taking the upper left triangle as an example of triangular interlacing, and filling the input symbols into the sequence row by row, we get:

[0195] First row: 1, 2, 3, 4, 5;

[0196] Second row: 6, 7, 8;

[0197] Then extract by column to get:

[0198] 1, 6, 2, 7, 3, 8, 4, 5.

[0199] Furthermore, the above triangular interleaving process can be referred to in the description of 3GPP standard 38.212, section 5.4.1.3.

[0200] As another example, the interleaving process can include row-column interleaving. In the following process, the interleaving sequence corresponding to the interleaving process can be determined by R and C, where R is the number of rows and C is the number of columns (or R is the number of columns and C is the number of rows; here, R is the number of rows and C is the number of columns as an example), and R*C>=E. The parameter R in the interleaving sequence is used to process the index of the symbol sequence output by the interleaving process, and the parameter C in the interleaving sequence is used to process the index of the symbol sequence output by the interleaving process. This row-column interleaving process satisfies:

[0201]

[0202]

[0203] For example, when E is 8, the input symbol sequence s0, s1, s2, ... s E-1 The sequence is 1, 2, 3, 4, 5, 6, 7, 8, and the parameters R and C involved in the interleaving sequence are 2 and 4 respectively, satisfying the following:

[0204] i = 0, j = 0, f j*R+i =s i*C+j Represented as: f 0*2+0 =s 0*4+0 That is, f0 = s0 = 1;

[0205] i = 0, j = 1, f j*R+i =s i*C+j Represented as: f 1*2+0 =s 0*4+1 That is, f2 = s1 = 2;

[0206] i = 0, j = 2, fj*R+i =s i*C+j Represented as: f 2*2+0 =s 0*4+2 That is, f4 = s2 = 3;

[0207] i = 0, j = 3, f j*R+i =s i*C+j Represented as: f 3*2+0 =s 0*4+3 That is, f6 = s3 = 4;

[0208] i = 1, j = 0, f j*R+i =s i*C+j Represented as: f 0*2+1 =s 1*4+0 That is, f1 = s4 = 5;

[0209] i = 1, j = 1, f j*R+i =s i*C+j Represented as: f 1*2+1 =s 1*4+1 That is, f3 = s5 = 6;

[0210] i = 1, j = 2, f j*R+i =S i*C+j Represented as: f 2*2+1 =s 1*4+2 That is, f5 = s4 = 7;

[0211] i = 1, j = 3, f j*R+i =s i*C+j Represented as: f 3*2+1 =s 1*4+3 That is, f7 = s5 = 8;

[0212] Therefore, the outputs of the interleaving process are f0, f1, f2, ... f E-1 The numbers are: 1, 5, 2, 6, 3, 7, 4, 8.

[0213] Case 2: In addition to one or more linear processing steps, the first processing step also includes transpose processing and inverse transpose processing.

[0214] like Figure 6i As shown in the example, the first processing may include transpose processing, linear processing, and inverse transpose processing. In case two, the process by which the first communication device performs the first processing on the first data in step S502 above to obtain the second data includes: the first communication device sequentially performing the transpose processing, the one or more linear processing steps, and the inverse transpose processing on the first data to obtain the second data.

[0215] Therefore, the first communication device can sequentially perform transpose processing, one or more linear processing steps, and inverse transpose processing on the first data to obtain the second data. In this way, transpose and inverse transpose can be achieved by reusing the linear operation matrix one or more times, limiting the dimension of the linear operation matrix, thereby reducing complexity and processing latency.

[0216] Alternatively, in case two, Figure 6i The data dimensions involved in the processing can be related, satisfying one or more of the following:

[0217] The input (e.g., bit stream) of the modulation processing has a dimension of n*m, and the output (e.g., complex symbol stream) has a dimension of n, where n represents the number of modulation symbols and m represents the modulation order.

[0218] The input (e.g., a complex symbol stream) of the transpose processing has a dimension of n, the same as the output dimension of the modulation processing, and the output dimension is n2*n1. Here, n2 represents the number of rows in the matrix processing and n1 represents the number of columns in the matrix processing, or n2 represents the number of columns in the matrix processing and n1 represents the number of rows in the matrix processing.

[0219] The input dimension of linear processing is n²*n¹, the same as the output dimension of transpose processing, which is n²*n¹. Linear processing can be one or more combinations of matrix operations such as multiplication and addition. For example, taking matrix multiplication as an example, the input of linear processing (denoted as matrix A) has a dimension of n²*n¹, the parameter or matrix of linear processing (denoted as matrix B) has a dimension of n¹*n¹, and the output of linear processing (denoted as matrix C) has a dimension of n²*n¹. The operation of linear processing can be represented as follows:

[0220] Optionally, the linear input is represented as X, and the linear output is represented as Y, satisfying: Y = W(X + A) + B. Here, W, A, and B are pre-configured or pre-trained parameters (such as matrices or vectors); alternatively, W, A, and B can be obtained from a combination of one or more pre-configured or pre-trained parameters, such as combining a subset of parameters from multiple matrices, scaling the matrix using scalars, repeating or diagonalizing a vector into a matrix, or expanding a small-dimensional matrix into a large-dimensional matrix (such as the Kronecker product), etc.

[0221] The input dimension of the inverted reset process is n²*n¹, which is the same as the output dimension of the linear process, and the output dimension is n. In other words, the output of the first process (such as a complex symbol stream or a pilotless symbol stream in method B above) has a dimension of n, which is the same as the output dimension of the inverted reset process.

[0222] Optionally, both transpose and inverse transpose can achieve dimensional transformation of a matrix; therefore, inverse transpose can also be called transpose.

[0223] In the above process, the purpose of transpose and inverse transpose is to reuse the linear operation matrix multiple times, limit the dimension of the linear operation matrix, and make the processed data adapt to pre-configured parameters, thereby reducing complexity. For example, in the above processing, the n symbols in the figure can be transposed to an n2*n1 dimension, that is, perform n2 n1-dimensional linear operations, limiting the granularity of the joint processing to n1. Then, inverse transpose is performed to transform back into n symbols.

[0224] The transpose process will be described below with some examples. In the examples below, the input representation of the transpose process (such as the pre-interleaving sequence) is: the symbol sequence is s0, s1, s2, ... s E-1 The length is E (E is greater than 1), and the output representation of the transpose (such as the transposed sequence) is: f0, f1, f2, ... f E-1 .

[0225] As an example, let's take the transpose operation as an example of processing a matrix of dimension n2*n1 to obtain a matrix of dimension n.

[0226]

[0227] By using the above method, the i+n2+jth symbol in the output sequence (i.e., f) can be made... i+n2+j ) is equal to the sign of the i-th column and j-th row of the input sequence (i.e., s) i,j ).

[0228] As an example, let's take the transpose operation as an example of processing a matrix of dimension n to obtain a matrix of dimension n2*n1.

[0229]

[0230] By using the above method, the sign (i.e., f) in the i-th column and j-th row of the output sequence can be determined. i,j ) equals the i*n2+jth symbol of the input sequence (i.e. ).

[0231] As an example, let's take the transpose operation as an example to process a matrix of dimension n1*n2 to obtain a matrix of dimension n2*n1.

[0232]

[0233] By using the above method, the sign (i.e., f) in the i-th column and j-th row of the output sequence can be determined. i,j ) is equal to the sign of the j-th column and i-th row of the input sequence (i.e., s) j,i ).

[0234] As an example, let's take the transpose operation as an example to process a matrix of dimension n1*n2 to obtain a matrix of dimension n2*n1.

[0235]

[0236] By using the above method, the sign of the i-th column and j-th row in the output sequence (i.e. ) is equal to the sign of the j-th column and i-th row of the input sequence (i.e. ).

[0237] In one possible implementation, in scenario two, the first processing may include at least two linear processes. These at least two linear processes can be linear processes across different resource dimensions; for example, these different resource dimensions may include one or more of the following: time-domain resource dimensions, frequency-domain resource dimensions, and spatial-domain resource dimensions. In this way, the first processing can be performed across different resource dimensions, reducing complexity while improving the interpretability of the solution and avoiding the "black box" problem in traditional AI scenarios. The following will combine... Figure 6j The example shown describes this implementation.

[0238] like Figure 6j The example shown illustrates a first process comprising three linear processes. The first process can include transpose processing_1, linear processing_1, transpose processing_2, linear processing_2, transpose processing_3, and linear processing_3, as shown in the diagram. These three linear processes correspond to three resource dimensions. In subsequent examples, linear processing_1 corresponds to the time-domain resource dimension, linear processing_2 corresponds to the frequency-domain resource dimension, and linear processing_3 corresponds to the spatial-domain resource dimension. Accordingly, in case two, the process by which the first communication device performs the first processing on the first data in step S502 to obtain the second data includes: the first communication device sequentially performing transpose processing_1, linear processing_1, transpose processing_2, linear processing_2, transpose processing_3, and linear processing_3 on the first data to obtain the second data.

[0239] Alternatively, in case two, Figure 6j The data dimensions involved in the processing can be related, satisfying one or more of the following:

[0240] The input (e.g., bit stream) of the modulation processing has a dimension of n*m, and the output (e.g., complex symbol stream) has a dimension of n, where n represents the number of modulation symbols and m represents the modulation order.

[0241] The input (e.g., a complex symbol stream) of transpose processing_1 has a dimension of n, the same as the output dimension of modulation processing, which has an output dimension of n. l *n f *n t nl Represents spatial resource dimensions (such as number of streams, number of ports, etc.), n f n represents the frequency domain resource dimension (such as the number of subcarriers, the number of resource blocks (RBs), etc.) t This indicates the time-domain resource dimension (such as the number of time-domain symbols, the number of time slots, etc.).

[0242] The input dimension of linear processing_1 is n l *n f *n t The output dimension is the same as that of transpose_1, which is n. l *n f *n t Linear processing can be one or more combinations of matrix multiplication, addition, and other operations. For example, taking matrix multiplication as an example, the input (denoted as matrix A) of linear processing_1 has a dimension of n. l *n f *n t The parameter or matrix used for linear processing (denoted as matrix B) has an n-dimensional dimension. t *n t The output of linear processing (denoted as matrix C) has a dimension of n. l *n f *n t The linear processing operation can be represented as In other words, linear processing_1 can process time-domain data using parameters or matrices used for processing time-domain data.

[0243] The input dimension of transpose processing_2 is n. l *n f *n t The output dimension is the same as that of linear processing _1, which is n. l *n t *n f .

[0244] The input dimension of linear processing_2 is n. l *n t *n f The output dimension is the same as that of transpose processing_2, with an output dimension of n. l *n t *n f Linear processing can be one or more combinations of matrix multiplication, addition, and other operations. For example, taking matrix multiplication as an example, the input (denoted as matrix A) of linear processing_2 has a dimension of n. l *n t *n fThe parameter or matrix used for linear processing (this parameter or matrix is ​​used for processing frequency domain dimension data, denoted as matrix B) has a dimension of n. f *n f The output of linear processing (denoted as matrix C) has a dimension of n. l *n t *n f The linear processing operation can be represented as In other words, linear processing_2 can process frequency domain data using parameters or matrices used for processing frequency domain data.

[0245] The input dimension of transpose processing_3 is n. l *n t *n f The output dimension is the same as that of linear processing_2, which is n. f *n t *n l .

[0246] The input dimension of linear processing_3 is n. f *n t *n l The output dimension is the same as that of transpose processing_3, with an output dimension of n. f *n t *n l Linear processing can be one or more combinations of matrix multiplication, addition, and other operations. For example, taking matrix multiplication as an example, the input (denoted as matrix A) of linear processing_3 has a dimension of n. f *n t *n l The parameter or matrix used for linear processing (denoted as matrix B) has a dimension of n. l * n l, the output of linear processing (denoted as matrix C) has dimension n. f *n t *n l The linear processing operation can be represented as In other words, linear processing_3 can process spatial dimension data through parameters or matrices used for processing spatial dimension data.

[0247] As can be seen from the above process, the first processing can transpose a one-dimensional vector into a high-dimensional tensor by transposing it using transpose processing_1, and then perform linear operations (such as AI operations) on each dimension to reduce complexity and improve interpretability.

[0248] In one possible implementation, the dimension of the first data is used to determine the AI ​​processing parameters corresponding to the first processing. For example, the AI ​​processing parameters may include one or more parameters involved in linear processing, nonlinear processing, interleaving processing, deinterleaving processing, transpose processing, and inverse transpose processing. For instance, the AI ​​processing parameters may include the matrix obtained based on AI training, or the dimension of that matrix. In other words, the first data serves as the input to the first processing, and the dimension of the first data can be used to determine the AI ​​processing parameters corresponding to the first processing. This allows the first data to be processed using AI processing parameters that are compatible with the dimension of the first processing, thereby improving processing performance.

[0249] Optionally, the AI ​​processing parameters corresponding to the first processing can be some or all of the parameters in the parameter set. The dimension of the first data can be used to select some or all of the parameters in the parameter set as the AI ​​processing parameters corresponding to the first processing. This selection method can be implemented in a variety of ways, which will be described below with some implementation examples.

[0250] It should be noted that in the following example, the dimension of the first data is used to determine the AI ​​processing parameters corresponding to the linear processing included in the first processing. For example, the linear processing may include the parameters mentioned above. Figure 6g Linear processing in any of the accompanying figures in / 6i / 6j. As described above, the first data serves as the input to the first processing, and the second data serves as the output of the first processing. These two data sets can have the same dimension; it is assumed below that both data sets have a dimension of n (n is a positive number), and the parameter set mentioned above contains parameters of dimension n. m1 *n m2 n m1 and n m2 All are greater than or equal to n. In other words, in linear processing, taking matrix multiplication as an example, the input dimension of linear processing is n, and the parameter or matrix dimension of linear processing is n*n (n*n is derived from n). m1 *n m2 (Selected), the output dimension of linear processing is n, and the operation of linear processing can be represented as (n)@(n,n)=(n), where @ represents matrix multiplication.

[0251] Method 1: Sequential selection. For example, the first communication device can select data based on the input dimension (n) of the first data in a dimension of n. m1 *n m2 From the parameter set, the first n*n parameters are selected sequentially as parameters or matrices for linear processing.

[0252] For example, in method one, n m1 The value is 100, n m2Taking a value of 200 and n as an example, Method 1 can be understood as follows: The first communication device selects the first 10*10 parameters from 100*200 parameters and determines the first 10*10 parameters as parameters or matrices for linear processing.

[0253] Method 2: Reverse selection. For example, the first communication device can select data in a dimension of n based on the input dimension (n) of the first data. m1 *n m2 In the parameter set, the last n*n parameters are selected in reverse order as parameters or matrices for linear processing.

[0254] For example, in method two, n m1 The value is 100, n m2 Taking a value of 200 and n as an example, Method 2 can be understood as follows: The first communication device selects the last 10*10 parameters from 100*200 parameters, and determines the last 10*10 parameters as parameters or matrices for linear processing.

[0255] Method 3: Dimension-based selection. For example, the first communication device can select data based on the input dimension (n) of the first data in a dimension of n. m1 *n m2 In the parameter set, in n m1 Select the parameters for the first n rows, the last n rows, the first n columns, or the last n columns in the corresponding dimension, in n m2 Select the first n rows, the last n rows, the first n columns, or the last n columns of parameters in the corresponding dimension, and then concatenate the selected parameters together to obtain the parameters or matrix for linear processing.

[0256] For example, in method three, n m1 The value is 100, n m2 Taking a value of 200 and n as an example, Method 3 can be understood as: the first communication device in n m1 Selecting 10 rows or 10 columns in the given dimension yields 10*200 parameters. These 10*200 parameters are then based on n. m2 Select 10 rows or 10 columns in the dimension to obtain 10*10 parameters, and use the determined 10*10 parameters as parameters or matrices for linear processing.

[0257] Optionally, the first communication device can also communicate with a pre-configured dimension n. m1 *n m2 The parameter set is selected through interleaving. For example, the first communication device can select parameters of dimension n. m1 *n m2 The parameter set is interleaved (refer to the interleaving process in Case 1 and its example above) to obtain the interleaved parameter set as the parameter set in Method 1, Method 2 or Method 3 above.

[0258] In one possible implementation, Figure 5 The method shown also includes:

[0259] S500. The first communication device sends an indication message, and correspondingly, the second communication device receives the indication message. Alternatively, the second communication device sends an indication message, and correspondingly, the first communication device receives the indication message. The indication message is used to indicate the AI ​​processing parameters corresponding to the first processing, and these AI processing parameters are used to determine the processing method and / or AI model for parsing the first signal. For example, the AI ​​processing parameters may include one or more parameters involved in linear processing, nonlinear processing, interleaving processing, deinterleaving processing, transpose processing, and inverse transpose processing, including but not limited to n, n1, n2, n... mentioned above. l n f n t n m1 n m2 One or more of them.

[0260] Therefore, the first communication device can also send or receive indication information, enabling the sender and receiver of the indication information to determine the AI ​​processing parameters corresponding to the first processing in the first communication device. Subsequently, the signal receiver can use the AI ​​processing parameters corresponding to the first processing to determine the processing method and / or AI model for parsing the first signal, so as to perform signal detection or parsing through the processing method and / or AI model adapted to the first processing, thereby improving the data detection or parsing performance.

[0261] For example, one or more parameters in the AI ​​processing parameters can serve as model IDs, mapping to one or more receiver models; that is, the AI ​​processing parameters can implicitly indicate the model switching process. Alternatively, the AI ​​processing parameters can serve as model inputs to the AI ​​receiver.

[0262] For example, the sender of the aforementioned indication information can be a network device, and the receiver can be a terminal device. Accordingly, the indication information can be implemented through radio resource control (RRC) messages, medium access control (MAC) or media access control (MAC) control element (CE), downlink control information (DCI), or other implementations defined by the future network.

[0263] Optionally, the AI ​​processing parameters corresponding to the first processing can also be pre-configured via a protocol / standard, which can reduce indication overhead. Furthermore, the data sender can execute the first processing using the pre-configured AI processing parameters, thereby avoiding or reducing processing latency caused by the transmission of the aforementioned indication information and improving processing performance.

[0264] Optionally, in this application, the AI ​​model can be replaced with other implementations, such as a neural network model, an AI neural network model, a machine learning model, or an AI processing model, etc.

[0265] Please see Figure 7 This application provides a communication device 700, which can realize the functions of the second or first communication device in the above method embodiments, and thus also achieve the beneficial effects of the above method embodiments. In this application embodiment, the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip.

[0266] It should be noted that the transceiver unit 702 may include a transmitting unit and a receiving unit, which are used to perform transmitting and receiving respectively.

[0267] In one possible implementation, when the device 700 is used to perform the aforementioned Figure 5 When the method executed by the first communication device in the related embodiments is performed, the processing unit 701 is used to acquire first data; the processing unit 701 is also used to perform a first processing on the first data to obtain second data, the first processing being the processing based on a matrix obtained through AI training; wherein, the second data is used to generate a first signal to be transmitted. Optionally, the device 700 further includes a transceiver unit 702, the transceiver unit 702 being used to transmit the first signal.

[0268] In one possible implementation, when the device 700 is used to perform the aforementioned Figure 5 When the method executed by the second communication device in the related embodiments is implemented, the device 700 includes a transceiver unit 702; the transceiver unit 702 is used to send or receive indication information, the indication information being used to indicate AI processing parameters corresponding to the first processing; wherein, the AI ​​processing parameters are used to determine the processing method and / or AI model for parsing the first signal; the transceiver unit 702 is also used to receive the first signal, the first signal being obtained based on the first processing; the processing unit 701 is used to parse the first signal based on the processing method and / or the AI ​​model.

[0269] It should be noted that the information execution process of the unit of the above-mentioned communication device 700 can be specifically described in the method embodiment shown above in this application, and will not be repeated here.

[0270] Please see Figure 8 This is another schematic diagram of the communication device 800 provided in this application. The communication device 800 includes a logic circuit 801 and an input / output interface 802. The communication device 800 can be a chip or an integrated circuit.

[0271] in, Figure 7 The transceiver unit 702 shown can be a communication interface, which can be... Figure 8 The input / output interface 802 may include an input interface and an output interface. Alternatively, the communication interface may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0272] Optionally, the logic circuit 801 is used to acquire first data; the logic circuit 801 is also used to perform a first processing on the first data to obtain second data, the first processing being the processing based on a matrix obtained through AI training; wherein, the second data is used to generate a first signal to be transmitted. Optionally, the device 800 also includes an input / output interface 802, the input / output interface 802 being used to send the first signal.

[0273] Optionally, the input / output interface 802 is used to send or receive indication information, which indicates AI processing parameters corresponding to the first processing; wherein the AI ​​processing parameters are used to determine the processing method and / or AI model for parsing the first signal; the input / output interface 802 is also used to receive the first signal, which is obtained based on the first processing; the logic circuit 801 is used to parse the first signal based on the processing method and / or the AI ​​model.

[0274] The logic circuit 801 and the input / output interface 802 can also perform other steps performed by the first or second communication device in any embodiment and achieve corresponding beneficial effects, which will not be elaborated here.

[0275] In one possible implementation, Figure 7 The processing unit 701 shown can be Figure 8 The logic circuit 801 in the middle.

[0276] Optionally, the logic circuit 801 can be a processing device, the functions of which can be partially or entirely implemented in software.

[0277] Optionally, the processing apparatus may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and / or steps in any of the method embodiments.

[0278] Optionally, the processing device may consist of only a processor. A memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuitry / wires to read and execute the computer programs stored in the memory. The memory and processor may be integrated together or physically independent of each other.

[0279] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system on-chips (SoCs), central processors (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0280] Please see Figure 9 The communication device 900 mentioned in the above embodiments provided for the purposes of this application can specifically be the communication device serving as a terminal device in the above embodiments. Figure 9 The example shown illustrates how a terminal device can be implemented through a terminal device (or a component within a terminal device).

[0281] The present invention provides a possible logical structure diagram of the communication device 900, which may include, but is not limited to, at least one processor 901 and a communication port 902.

[0282] in, Figure 7 The transceiver unit 702 shown can be a communication interface, which can be... Figure 9 The communication port 902 in the diagram may include an input interface and an output interface. Alternatively, the communication port 902 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0283] Further optionally, the device may also include at least one of a memory 903 and a bus 904. In the embodiments of this application, the at least one processor 901 is used to control the operation of the communication device 900.

[0284] Furthermore, the processor 901 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0285] It should be noted that, Figure 9 The communication device 900 shown can be used to implement the steps implemented by the terminal device in the aforementioned method embodiments, and to achieve the corresponding technical effects of the terminal device. Figure 9 The specific implementation of the communication device shown can be referred to the description in the foregoing method embodiments, and will not be repeated here.

[0286] Please see Figure 10 The above-described embodiment of the communication device 10 is provided as an example of the present application. Specifically, the communication device 10 can be a network device as described in the above-described embodiment. Figure 10 The example shown illustrates a network device implemented through a network device (or a component within a network device). The structure of this communication device can be referenced. Figure 10 The structure shown.

[0287] The communication device 10 includes at least one processor 1011 and at least one network interface 1014. Optionally, the communication device further includes at least one memory 1012, at least one transceiver 1013, and one or more antennas 1015. The processor 1011, memory 1012, transceiver 1013, and network interface 1014 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited thereto. The antenna 1015 is connected to the transceiver 1013. The network interface 1014 enables the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 may include a network interface between the communication device and core network equipment, such as an S1 interface; the network interface may also include a network interface between the communication device and other communication devices (e.g., other network devices or core network equipment), such as an X2 or Xn interface.

[0288] in, Figure 7 The transceiver unit 702 shown can be a communication interface, which can be... Figure 10 The network interface 1014 may include an input interface and an output interface. Alternatively, the network interface 1014 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0289] The processor 1011 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from the software programs, for example, to support the communication device in performing the actions described in the embodiments. The communication device may include a baseband processor and a central processing unit (CPU). The baseband processor is primarily used to process communication protocols and communication data, while the CPU is primarily used to control the entire terminal device, execute software programs, and process data from the software programs. Figure 10 The processor 1011 can integrate the functions of a baseband processor and a central processing unit. Those skilled in the art will understand that the baseband processor and the central processing unit can also be independent processors interconnected via technologies such as buses. Those skilled in the art will understand that a terminal device can include multiple baseband processors to adapt to different network standards, and a terminal device can include multiple central processing units to enhance its processing capabilities. The various components of the terminal device can be connected via various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processing unit can also be described as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data can be built into the processor or stored in memory as a software program, with the processor executing the software program to implement the baseband processing function.

[0290] The memory is primarily used to store software programs and data. The memory 1012 can exist independently or be connected to the processor 1011. Optionally, the memory 1012 can be integrated with the processor 1011, for example, integrated within a single chip. The memory 1012 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 1011. The various types of computer program code being executed can also be considered as drivers for the processor 1011.

[0291] Figure 10 Only one memory and one processor are shown. In actual terminal devices, there may be multiple processors and multiple memories. Memory can also be called storage medium or storage device, etc. Memory can be a storage element on the same chip as the processor, i.e., an on-chip storage element, or it can be a separate storage element; this application does not limit this.

[0292] Transceiver 1013 can be used to support the reception or transmission of radio frequency (RF) signals between a communication device and a terminal. Transceiver 1013 can be connected to antenna 1015. Transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive RF signals. The receiver Rx of transceiver 1013 is used to receive the RF signals from the antennas, convert the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provide the digital baseband signals or IF signals to processor 1011 so that processor 1011 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding. In addition, the transmitter Tx in transceiver 1013 is also used to receive modulated digital baseband signals or IF signals from processor 1011, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal. The order of these downmixing and IF conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal. The order of these upmixing and IF conversion processes is also adjustable. The digital baseband signal and the digital IF signal can be collectively referred to as digital signals.

[0293] The transceiver 1013 can also be called a transceiver unit, transceiver, transceiver device, etc. Optionally, the device in the transceiver unit that performs the receiving function can be regarded as the receiving unit, and the device in the transceiver unit that performs the transmitting function can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit can also be called a receiver, input port, receiving circuit, etc., and the transmitting unit can be called a transmitter, transmitter, or transmitting circuit, etc.

[0294] It should be noted that, Figure 10 The communication device 10 shown can be used to implement the steps implemented by the network device in the aforementioned method embodiments, and to achieve the corresponding technical effects of the network device. Figure 10 The specific implementation of the communication device 10 shown can be referred to the description in the foregoing method embodiments, and will not be repeated here.

[0295] Please see Figure 11 The above-described embodiments of the communication device provided in this application are schematic diagrams of the structure of the communication device.

[0296] It is understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to execute the technical solutions provided in this application. The communication device 110 may be the terminal device or network device described above, or a component (e.g., a chip) within these devices, used to implement the methods described in the following method embodiments. The communication device 110 includes one or more processors 111. The processor 111 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (e.g., a RAN node, terminal, or chip), execute software programs, and process data from the software programs.

[0297] Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instructions), which can be executed on the processor 111 to cause the communication device 110 to perform the methods described in the embodiments below. In yet another possible design, the communication device 110 includes circuitry (…). Figure 11 (Not shown).

[0298] Optionally, the communication device 110 may include one or more memories 112 storing a program 114 (sometimes referred to as code or instructions), which can be run on the processor 111 to cause the communication device 110 to perform the methods described in the above method embodiments.

[0299] Optionally, the processor 111 and / or memory 112 may include AI modules 117 and 118, which are used to implement AI-related functions. The AI ​​modules can be implemented through software, hardware, or a combination of both. For example, the AI ​​module may include a radio intelligence control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0300] Optionally, the processor 111 and / or memory 112 may also store data. The processor and memory may be configured separately or integrated together.

[0301] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111, sometimes referred to as a processing unit, controls the communication device (e.g., a RAN node or terminal). The transceiver 115, sometimes referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver, is used to realize the transmission and reception functions of the communication device through the antenna 116.

[0302] in, Figure 7 The processing unit 701 shown may be a processor 111. Figure 7 The transceiver unit 702 shown can be a communication interface, which can be... Figure 11 The transceiver 115 may include an input interface and an output interface. Alternatively, the transceiver 115 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0303] This application also provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor performs the method described in the possible implementations of the first or second communication device in the foregoing embodiments.

[0304] This application also provides a computer program product (or computer program) containing programs or instructions. When the computer program product is executed by the processor, the processor executes the method of the first communication device or the second communication device that may be implemented as described above.

[0305] This application also provides a chip system including at least one processor for supporting a communication device in implementing the functions involved in the possible implementations of the communication device described above. Optionally, the chip system further includes an interface circuit that provides program instructions and / or data to the at least one processor. In one possible design, the chip system may further include a memory for storing the program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices, wherein the communication device may specifically be the first communication device or the second communication device in the aforementioned method embodiments.

[0306] This application also provides a communication system, the network system architecture of which includes a first communication device and a second communication device in any of the above embodiments.

[0307] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0308] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0309] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A communication method, characterized in that, include: Get the first data; The first data is processed to obtain the second data. The first processing includes one or more linear or nonlinear processing operations. The first processing is performed on a matrix obtained by training with artificial intelligence (AI). The second data is used to generate the first signal to be transmitted.

2. The method according to claim 1, characterized in that, The first processing further includes interleaving and deinterleaving, and the first processing of the first data to obtain the second data includes: The first data is subjected to the interleaving process, the one or more linear processes, and the deinterleaving process in sequence to obtain the second data.

3. The method according to claim 1, characterized in that, The first processing further includes transpose processing and inverse transpose processing, and the first processing of the first data to obtain the second data includes: The first data is subjected to the transpose process, the linear process (one or more), and the inverse transpose process in sequence to obtain the second data.

4. The method according to any one of claims 1 to 3, characterized in that, The first data is the data obtained by encoding and modulation of the third data.

5. The method according to claim 4, characterized in that, The first data contains at least two modulation symbols, which correspond to different resources.

6. The method according to claim 4 or 5, characterized in that, The first processing includes one or more linear processing steps, and the first data and the second data have the same dimension.

7. The method according to any one of claims 4 to 6, characterized in that, The modulation processing is AI-based.

8. The method according to any one of claims 1 to 3, characterized in that, The first data is the data obtained by encoding the third data.

9. The method according to any one of claims 1 to 8, characterized in that, The dimension of the first data is used to determine the AI ​​processing parameters corresponding to the first processing.

10. The method according to any one of claims 1 to 9, characterized in that, The second data includes a reference signal, which is used to determine channel information of the transmission channel of the first signal; or, The second data does not include a reference signal.

11. The method according to any one of claims 1 to 10, characterized in that, The first signal is generated from the second data through one or more of the following processes: reference signal insertion, resource mapping, precoding, and beamforming.

12. The method according to any one of claims 1 to 11, characterized in that, The second data includes layer indication information, wherein the layer indication information is used to recover the first data.

13. The method according to any one of claims 1 to 12, characterized in that, The method further includes: Send the first signal.

14. The method according to any one of claims 1 to 13, characterized in that, The method further includes: Sending or receiving indication information, the indication information being used to indicate the AI ​​processing parameters corresponding to the first processing; wherein, the AI ​​processing parameters corresponding to the first processing are used to determine the processing method and / or AI model for parsing the first signal.

15. A communication method, characterized in that, include: Sending or receiving indication information, the indication information being used to indicate the artificial intelligence (AI) processing parameters corresponding to the first processing; wherein, the AI ​​processing parameters are used to determine the processing method and / or AI model for parsing the first signal; Receive a first signal, which is obtained based on the first processing; The first signal is analyzed based on the processing method and / or the AI ​​model.

16. A communication device, characterized in that, Includes a module for performing the method as described in any one of claims 1 to 15.

17. A communication device, characterized in that, It includes at least one processor, said at least one processor being used to perform the method as described in any one of claims 1 to 15.

18. A readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the method as described in any one of claims 1 to 15.

19. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a computer, implement the method as described in any one of claims 1 to 15.

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