Communication method, apparatus, and system

Abstract

Embodiments of the present application provide a communication method, an apparatus, and a system. The method comprises: a network device receives first transmission data; the network device inputs the first transmission data into a preset model for processing, and obtains first noise; and the network device further obtains first decoded data on the basis of the first transmission data and the first noise. In this example, obtaining noise contained in received data on the basis of a preset model can effectively reduce the impact of channel noise on transmission, thereby improving transmission efficiency.

Description

Communication method and device, system

[0001] The present application claims priority to the Chinese patent application No. 202411377792.4, filed on September 27, 2024, with the State Intellectual Property Office of China, and the Chinese patent application No. 202411377792.4 has the invention name of “Communication method and device, system”, the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of artificial intelligence, in particular to a communication method and device, system. BACKGROUND

[0003] Generative AI is a technology that uses machine learning algorithms to generate new data samples that are similar to the training data in some way. Generative AI usually relies on deep learning techniques, especially generative adversarial networks, variational autoencoders, diffusion models, and autoregressive models. These models learn the distribution of the training data through training, and then generate new data points that are statistically similar to the training data but novel in detail. This technology has a wide range of applications in many fields, including image generation, music composition, text generation, etc.

[0004] In modern communication technology, the effective transmission and processing of information is crucial. With the surge in data volume and the diversification of communication needs, traditional communication methods face challenges in efficiency and adaptability. Generative AI provides an innovative solution by learning existing communication patterns and data to generate new communication strategies and information content, thereby improving the efficiency and quality of communication.

[0005] The wireless transmission process is a natural superimposed noise environment, and how to use AI models to remove channel noise is a problem that needs to be solved. SUMMARY

[0006] The present application discloses a communication method and device, system, which can effectively reduce the influence of channel noise on transmission.

[0007] In a first aspect, an embodiment of the present application provides a communication method. The method can be applied to a network side device, such as an access network device at the network side, a module (such as a circuit, a chip or a chip system, etc.) in the access network device, or a logic node, a logic module or software capable of realizing all or part of the functions of the access network device. Taking the case where the method is applied to the access network device, in the method, the access network device (or referred to as a network device) receives first transmission data. The access network device inputs the first transmission data into a preset model for processing to obtain first noise. Then, the access network device obtains first decoding data based on the first transmission data and the first noise.

[0008] In an embodiment of the present application, the network device inputs the first transmission data from the terminal device into the preset model for processing to obtain the first noise. Then, the network device obtains the first decoding data based on the first transmission data and the first noise. In this example, the noise contained in the received data based on the preset model can effectively reduce the influence of channel noise on transmission and improve transmission efficiency.

[0009] In a possible implementation, the preset model is obtained by training in the following manner: the access network device receives second transmission data. The access network device obtains second noise based on the second transmission data and training data, the second transmission data being obtained based on the training data. The access network device inputs the second transmission data and the second noise into an initial model for processing to obtain third noise. The access network device updates the initial model based on the second noise, the third noise and a preset loss function to obtain the preset model.

[0010] In this example, the denoising model can be obtained based on the training method described above.

[0011] In this example, the second transmission data obtained based on the training data can be understood as data obtained by adding noise to the encoded training data.

[0012] In a possible implementation, the access network device further receives the training data.

[0013] In another possible implementation, the access network device further receives first indication information indicating the training data, wherein the training data is preset, which can effectively reduce the amount of data indicating the training data and is beneficial to improving the transmission efficiency of the air interface.

[0014] In a possible implementation, the access network device further updates the preset model based on the first transmission data, the first noise and the first decoding data to obtain an updated preset model.

[0015] In this example, the performance of the model can be improved by online updating of the model.

[0016] In a possible implementation, the access network device inputs the first transmission data, the first noise, and the first decoding data into the preset model for processing to obtain fourth noise. The access network device updates the preset model based on the fourth noise, the first noise, and a preset loss function to obtain an updated preset model.

[0017] In a possible implementation, the preset model is an artificial intelligence (AI) model, and the access network device further sends second indication information indicating that the access network device supports the use of the denoising function of the AI model. The terminal device can more flexibly select whether to use the denoising function of the AI model.

[0018] In a second aspect, an embodiment of the present application provides a communication method. The method can be applied to a terminal-side device, for example, a terminal or a communication module / processing module in the terminal, or a circuit or chip responsible for a communication function in the terminal (such as a modem chip, also known as a baseband chip, or a system on chip (SoC) chip or a system in package (SIP) chip containing a modem core), or a circuit or chip responsible for processing functions in the terminal (such as a graphics processing unit (GPU)). Taking the case where the method is applied to a terminal device as an example, in the method, the terminal device sends first transmission data, wherein the first decoding data is obtained based on the first transmission data and first noise, and the first noise is obtained by inputting the first transmission data into a preset model for processing.

[0019] In a possible implementation, the terminal device further sends second transmission data. The preset model is obtained by the network device based on the following manner: based on the second transmission data and training data, second noise is obtained, the second transmission data being obtained based on the training data. The second transmission data and the second noise are input into an initial model for processing to obtain third noise. The initial model is updated based on the second noise, the third noise, and a preset loss function to obtain the preset model.

[0020] In a possible implementation, the terminal device further sends training data.

[0021] In another possible implementation, the terminal device further sends first indication information indicating the training data, wherein the training data is preset.

[0022] In a possible implementation, the preset model is an AI model, and the terminal device further receives second indication information indicating that the network device supports the use of the denoising function of the AI model.

[0023] In a third aspect, the present application provides a communication apparatus, which has the function of the first aspect, e.g., the communication apparatus comprises a module or unit or means corresponding to the operations of the first aspect, which can be implemented by software, or by hardware, or by a combination of software and hardware.

[0024] In one implementation, the communication apparatus comprises a communication module configured to receive the first transmission data.

[0025] The processing module is configured to input the first transmission data into the preset model for processing to obtain the first noise.

[0026] The processing module is further configured to obtain the first decoding data based on the first transmission data and the first noise.

[0027] In one possible implementation, the preset model is obtained by training based on the following manner: the communication module is configured to receive second transmission data. The processing module is configured to obtain second noise based on the second transmission data and training data, the second transmission data being obtained based on the training data. The processing module is further configured to input the second transmission data and the second noise into an initial model for processing to obtain third noise. The processing module is further configured to update the initial model based on the second noise, the third noise and a preset loss function to obtain the preset model.

[0028] In one possible implementation, the communication module is further configured to receive the training data.

[0029] In another possible implementation, the communication module is further configured to receive first indication information, the first indication information indicating the training data, wherein the training data is preset.

[0030] In one possible implementation, the processing module is further configured to update the preset model based on the first transmission data, the first noise and the first decoding data to obtain an updated preset model.

[0031] In one possible implementation, the processing module is further configured to input the first transmission data, the first noise and the first decoding data into the preset model for processing to obtain fourth noise.

[0032] The processing module is further configured to update the preset model based on the fourth noise, the first noise and the preset loss function to obtain an updated preset model.

[0033] In a possible implementation, the preset model is an artificial intelligence (AI) model, and the communication module is further configured to send second indication information, the second indication information indicating that the access network device supports the denoising function of the AI model.

[0034] In a fourth aspect, the present application also provides a communication apparatus, which has the functions of the second aspect, for example, the communication apparatus includes modules or units or means corresponding to the operations of the second aspect, which can be implemented by software, or by hardware, or by a combination of software and hardware.

[0035] In one implementation, the communication apparatus includes a communication module configured to send first transmission data, wherein the first decoded data is obtained based on the first transmission data and first noise, and the first noise is obtained by inputting the first transmission data into a preset model.

[0036] In a possible implementation, the communication module is further configured to send second transmission data. The preset model is obtained by a network device based on the following manner: obtaining second noise based on the second transmission data and training data, the second transmission data being obtained based on the training data; inputting the second transmission data and the second noise into an initial model to obtain third noise; and updating the initial model based on the second noise, the third noise, and a preset loss function to obtain the preset model.

[0037] In a possible implementation, the communication module is further configured to send the training data.

[0038] In another possible implementation, the communication module is further configured to send first indication information, the first indication information indicating the training data, wherein the training data is preset.

[0039] In a possible implementation, the preset model is an AI model, and the communication module is further configured to receive second indication information, the second indication information indicating that the network device supports the denoising function of the AI model.

[0040] In a fifth aspect, the present application provides a communication apparatus, including a processor, the processor being configured to cause the apparatus to perform the method provided in any one of the possible implementations of the first aspect to the second aspect by executing computer programs or computer executable instructions stored in a memory, and / or by a logic circuit.

[0041] In a possible implementation, the apparatus further includes a memory. Optionally, the memory and the processor can be integrated together.

[0042] In a possible implementation, the apparatus further includes an interface circuit.

[0043] In a possible implementation, the apparatus is a chip or a chip system.

[0044] In a sixth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method provided in any possible implementation of the first aspect to the second aspect.

[0045] In a seventh aspect, the present application provides a computer program product, which, when executed on a computer, causes the computer to perform the method provided in any possible implementation of the first aspect to the second aspect.

[0046] In an eighth aspect, the present application provides a chip, which comprises at least one processor and an interface, and the processor is configured to execute a computer instruction or program, and when the computer instruction or program is executed, the chip is configured to perform the method provided in any possible implementation of the first aspect to the second aspect.

[0047] It can be understood that the apparatus provided in the third aspect, the apparatus provided in the fourth aspect, the apparatus provided in the fifth aspect, the computer readable storage medium provided in the sixth aspect, the computer program product provided in the seventh aspect, or the chip provided in the eighth aspect are all used to execute the method provided in any of the first aspect or the second aspect. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0048] The drawings used in the embodiments of the present application are described below.

[0049] FIG. 1 is a schematic diagram of a communication system provided by an embodiment of the present application;

[0050] FIG. 2 is a flow diagram of a communication method provided by an embodiment of the present application;

[0051] FIG. 3 is a schematic diagram of a training method provided by an embodiment of the present application;

[0052] FIG. 4 is a schematic diagram of a communication method provided by an embodiment of the present application;

[0053] FIG. 5 is a schematic diagram of an inference and updating method provided by an embodiment of the present application;

[0054] FIG. 6a is a schematic diagram of a structure of a communication apparatus provided by an embodiment of the present application;

[0055] FIG. 6b is a schematic diagram of a structure of another communication apparatus provided by an embodiment of the present application;

[0056] FIG. 7 is a structural schematic diagram of another communication apparatus provided by an embodiment of the present application. DETAILED DESCRIPTION

[0057] The embodiments of the present application are described below in conjunction with the accompanying drawings. The terms used in the implementation part of the embodiments of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0058] FIG. 1 shows a possible, non-limiting, schematic diagram of a system. As shown in FIG. 1, the communication system 1000 includes a radio access network (RAN) 100. Optionally, it also includes a core network (CN) 200. Optionally, it also includes the Internet 300. The RAN 100 includes at least one RAN node (e.g., 110a and 110b in FIG. 1, collectively referred to as 110) and at least one terminal (e.g., 120a-120j in FIG. 1, collectively referred to as 120). The RAN 100 can also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in FIG. 1), etc. The terminal 120 is connected to the RAN node 110 in a wireless manner. The RAN node 110 is connected to the core network 200 in a wireless or wired manner. The core network device in the core network 200 and the RAN node 110 in the RAN 100 can be different physical devices respectively, or can be the same physical device integrated with the logical functions of the core network and the logical functions of the radio access network.

[0059] The RAN 100 can be a 3rd generation partnership project (3GPP) related cellular system, such as a 4G, 5G mobile communication system, or an evolved system after 5G (e.g., a 6th generation (6G) mobile communication system). The RAN 100 can also be an open radio access network (O-RAN or ORAN), a cloud radio access network (CRAN), or a wireless-fidelity (Wi-Fi) system based on IEEE 802.11 standards. The RAN 100 can also be a communication system in which two or more of the above systems are fused.

[0060] The RAN node 110, which can also be referred to as an access network device, a RAN entity, or an access node, etc., forms part of the communication system, and is configured to facilitate wireless access to the communication system by terminals. The RAN nodes 110 in the communication system 1000 can be of the same type or different types. In some scenarios, the roles of a RAN node 110 and a terminal 120 are relative, e.g., a drone or a helicopter 120i in Figure 1 can be configured to move like a mobile base station, and for a terminal 120j accessing the RAN 100 via the drone 120i, the drone 120i is a base station; but for a base station 110a, the drone 120i is a terminal. Both the RAN nodes 110 and the terminals 120 are sometimes referred to as communication devices, e.g., the network elements 110a and 110b in Figure 1 can be understood as communication devices with base station functionalities, and the network elements 120a-120j can be understood as communication devices with terminal functionalities.

[0061] In a possible scenario, the 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 next generation base station in a 6G mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. The RAN node can be a macro base station (e.g., 110a in Figure 1), a micro base station or an indoor station (e.g., 110b in Figure 1), a relay node or a donor node, or a wireless controller in a CRAN scenario. Optionally, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, an access network device in a vehicle to everything (V2X) technology can be a road side unit (RSU).

[0062] In another possible scenario, a terminal is assisted by multiple RAN nodes to implement wireless access, and different RAN nodes respectively implement part of functions of a base station. For example, a RAN node can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be separately arranged, or can also be included in the same network element, for example, in a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, for example, included in a remote radio unit (RRU), an active antenna processing unit (AAU), or a remote radio head (RRH).

[0063] In different systems, the CU (or CU-CP and CU-UP), DU or RU can also have different names, but those skilled in the art can understand their meanings. For example, in an ORAN system, the CU can also be referred to as an O-CU (open CU), the DU can also be referred to as an O-DU, the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. For the convenience of description, the CU, CU-CP, CU-UP, DU and RU are taken as examples for description in this application. Any one of the CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0064] The terminal can also be referred to as a terminal device, user equipment (UE), mobile station, mobile terminal, etc. The terminal can be widely applied to various scenarios, such as device-to-device (D2D), vehicle to everything (V2X) communication, machine-type communication (MTC), internet of things (IoT), virtual reality (VR) device, augmented reality (AR) device, industrial control, automatic driving, remote medical treatment, smart power grid, smart furniture, smart office, smart wear, smart transportation, smart city, etc. The terminal can be a mobile phone, tablet computer, computer with wireless transceiver function, wearable device, vehicle, unmanned aerial vehicle, helicopter, airplane, ship, robot, mechanical arm, smart home device, light terminal device (light UE), reduced capability user equipment (REDCAP UE), smart point of sale (POS) machine, customer-premises equipment (CPE), etc. The terminal can also be a vehicle device, such as a whole vehicle device, vehicle-mounted module, vehicle-mounted chip, on board unit (OBU), or telematics box (T-BOX), etc. Embodiments of the present application do not limit the device form of the terminal.

[0065] The communication between the access network device and the terminal device complies with a certain protocol layer structure. The protocol layer can include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer can include at least one of a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, or a physical (PHY) layer, etc. The user plane protocol layer can include at least one of a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, or a physical layer, etc.

[0066] For network elements in the ORAN system, the corresponding relationship of the protocol layer functions that can be implemented by the network elements is shown in Table 1:

[0067] Table 1

[0068] Base stations and terminals can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can be deployed on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the base stations and terminals.

[0069] The roles of base stations and terminals can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile base station. For terminals 120j that access the wireless access network 100 through 120i, terminal 120i is a base station; however, for base station 110a, 120i is a terminal, meaning that 110a and 120i communicate via a wireless air interface protocol. Of course, 110a and 120i can also communicate via a base station-to-base station interface protocol. In this case, relative to 110a, 120i is also a base station. Therefore, both base stations and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 1 can be called communication devices with base station functions, and 120a-120j in Figure 1 can be called communication devices with terminal functions.

[0070] In this embodiment, the base station is also referred to as an access network device. The apparatus used to implement the functions of the access network device can be the access network device itself; it can also be any apparatus capable of supporting the access network device in implementing these functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed in the access network device or used in conjunction with the access network device. In this embodiment, the example of an access network device being used to implement the functions of the access network device is used only and does not constitute a limitation on the solutions of this embodiment.

[0071] It is understood that this application can be applied between access network equipment and terminals.

[0072] It should be understood that the number and type of each device in the communication system shown in Figure 1 are for illustrative purposes only, and this application is not limited thereto. In actual applications, the communication system may include more terminals, more access network devices, and other network elements, such as core network devices and / or network elements used to implement artificial intelligence functions.

[0073] It can be understood that all or part of the functions implemented by one or more of the terminal, the access network device, the core network device, or the network element for implementing the artificial intelligence function can be virtualized, that is, implemented by one or more of a special processor or a general processor and a corresponding software module. Among them, the terminal and the access network device involve the interface of air interface transmission, and the transceiving function of the interface can be implemented by hardware. The core network device, such as the operation administration and maintenance (OAM) network element, can be virtualized. Optionally, one or more functions of the virtualized terminal, access network device, core network device, or network element for implementing the artificial intelligence function can be implemented by a cloud device, such as a cloud device in an over the top (OTT) system.

[0074] In the present application, "sending information to (for example, a terminal)" or related illustrations in the drawings can be understood as that the destination of the information is the terminal. It can include directly or indirectly sending information to the terminal. "Receiving information from (for example, a terminal)" or "receiving information from (for example, a terminal)", or related illustrations in the drawings can be understood as that the source of the information is the terminal, and can include directly or indirectly receiving information from the terminal. The information can be processed as necessary between the source and the destination of the information, such as format change, etc., but the destination can understand the valid information from the source. Similar expressions in the present application can be similarly understood, and will not be repeated here.

[0075] The architecture of the embodiments of the present application is described above, and the method of the embodiments of the present application is described in detail below.

[0076] Referring to FIG. 2, it is a flowchart of a communication method provided by an embodiment of the present application. Optionally, the method can be applied to the communication system described above, such as the communication system shown in FIG. 1. The communication method shown in FIG. 2 can include steps 201-203. Steps 201-203 are as follows:

[0077] 201. The terminal device sends first transmission data to the network device. Correspondingly, the network device receives the first transmission data.

[0078] For example, the terminal device encodes the pre-transmission data to obtain encoded pre-transmission data. Then, the terminal device sends the first transmission data to the network device.

[0079] The first transmission data can be understood as being obtained by superimposing noise on the above-mentioned encoded pre-transmission data.

[0080] In a possible implementation, the network device sends second indication information to the terminal device, where the second indication information indicates that the network device supports the denoising function using the AI model. Further, the terminal device sends the first transmission data to the network device.

[0081] Optionally, the network device sends second indication information to the terminal device, where the second indication information indicates that the network device supports the denoising function using the AI model. Further, the terminal device sends third indication information to the network device, where the third indication information indicates the use of the denoising function using the AI model.

[0082] In another possible implementation, the terminal device sends a request to the network device, where the request is used to request the use of the denoising function of the network device. The network device sends fourth indication information to the terminal device, where the fourth indication information indicates that the network device supports the denoising function using the AI model. In this way, the terminal device can more flexibly select whether to use the denoising function using the AI model. For example, the terminal device sends fifth indication information to the network device, where the fifth indication information indicates the use of the denoising function using the AI model.

[0083] 202. The network device inputs the first transmission data into a preset model for processing to obtain first noise.

[0084] For example, the preset model can be a diffusion model. The diffusion model can be used to remove channel noise.

[0085] In a possible implementation, the preset model is obtained by training based on the following manner:

[0086] S1. The terminal device sends second transmission data to the network device. Correspondingly, the network device receives the second transmission data.

[0087] S2. The network device obtains second noise based on the second transmission data and training data.

[0088] In a possible implementation, the terminal device sends the training data to the network device. Correspondingly, the network device receives the training data.

[0089] In another possible implementation, the terminal device sends the network device to receive first indication information, where the first indication information indicates the training data. The training data is preset in this way. This can effectively reduce the amount of data indicating the training data, which is conducive to improving the transmission efficiency of the air interface.

[0090] That is, the training data can be directly sent by the terminal device, or can be determined by the network device based on the indication information.

[0091] The second transmission data is obtained based on the training data. For example, the second transmission data is data obtained by encoding the training data and superimposing noise.

[0092] The second noise can be understood as the superimposed noise (or referred to as real noise).

[0093] For example, the network device can calculate the second noise based on yr=H*s+n, where yr is the transmission data (e.g., the second transmission data), H is a channel matrix, s is a preset sequence (e.g., agreed), and n is noise. The network device can obtain H through channel estimation. Then, the network device can obtain n.

[0094] S3, the network device inputs the second transmission data and the second noise into the initial model for processing to obtain third noise.

[0095] The third noise can be understood as predicted noise.

[0096] S4, the network device updates the initial model based on the second noise, the third noise, and a preset loss function to obtain a preset model.

[0097] For example, the preset loss function can be min(MSE(n-n0)). Where n0 is the third noise, and n is the second noise.

[0098] Optionally, step S4 can include: updating the initial model based on the second noise, the third noise, and the preset loss function to obtain a first updated initial model. Then, repeating steps S1-S4, calculating a loss value based on the preset loss function, and determining whether to stop training based on a comparison of the calculated loss value and a threshold value (or based on whether the number of repeated executions reaches a preset number, etc.). If the training is stopped, a trained model, i.e., the preset model, is obtained.

[0099] Further, by inputting the first transmission data into the preset model for processing, the first noise (i.e., predicted noise) can be obtained.

[0100] 203, the network device obtains first decoding data based on the first transmission data and the first noise.

[0101] Optionally, the network device can perform denoising processing on the first transmission data based on the first noise, and then decode the data obtained by the denoising processing to obtain the first decoding data.

[0102] In this example, the preset model can effectively reduce the impact of channel noise on transmission and improve transmission efficiency based on the noise contained in the received data.

[0103] In a possible implementation, the network device further updates the preset model based on the first transmission data, the first noise, and the first decoding data, to obtain an updated preset model.

[0104] For example, the network device inputs the first transmission data, the first noise, and the first decoding data into the preset model for processing, to obtain a fourth noise.

[0105] The network device updates the preset model based on the fourth noise, the first noise, and a preset loss function, to obtain an updated preset model.

[0106] That is, the network device also updates the preset model in real time, to improve the performance of the preset model.

[0107] In the embodiment, the network device inputs the first transmission data from the terminal device into the preset model for processing, to obtain the first noise. Then, the network device obtains the first decoding data based on the first transmission data and the first noise. In this example, the preset model can effectively reduce the influence of channel noise on transmission, and improve the transmission efficiency, based on the noise contained in the received data.

[0108] The training method of the preset model provided in the embodiment will be described below in combination with FIG. 3. As shown in FIG. 3, the training method can include steps 301-303, and the details are as follows.

[0109] 301. The network device exposes the denoising function.

[0110] For example, the network device determines whether the denoising function is supported by the terminal device based on self-implementation or based on the type of service, and whether the denoising function is supported by the network device. If the network device supports the denoising function using the AI model, the terminal device can report whether the denoising function using the AI model is needed.

[0111] Alternatively, the terminal device actively sends a signaling to request the denoising function of the network device. The network device informs the terminal device whether the denoising function using the AI model is supported by the network device through the signaling.

[0112] 302. The network device and the terminal device align the training data and the model.

[0113] For example, the network device and the terminal device agree in advance that the trained model is a convolutional neural network (U-Net) specially designed for biomedical image segmentation; the content of the training data is a certain agreed random sequence, or one or more of a set of random sequences, or one or more agreed training data sets, or an agreed calculation formula, and the terminal device is notified to use one or more of the multiple training data sets for training, or to calculate the parameters of the random sequence formula.

[0114] Of course, it can also be other models, and the present scheme does not limit the category of the model.

[0115] 303、The network device and the terminal device start training the initial model.

[0116] After the network device receives the data yr from the terminal device, the network device obtains the real noise n = yr-H*s based on yr=H*s+n. Wherein, s is a sequence agreed in advance, so the network device knows the sequence information; in addition, the network device can obtain the channel matrix H through channel estimation.

[0117] In combination with FIG. 4, the network device inputs the noise n and the received data yr and the channel matrix H into the U-Net for training to obtain the predicted noise n0. The loss function of the U-Net training is min(MSE(n-n0)), and finally the denoising model is obtained.

[0118] It can be understood that the above steps 301-303 are only an example, wherein only step 303 can be included, and the present scheme does not limit this.

[0119] In this example, the denoising model can be obtained based on the above training method.

[0120] The inference and updating method of the preset model provided by the embodiment of the present application will be introduced in detail below in combination with FIG. 5. As shown in FIG. 5, the inference and updating method can include steps 501-502, which are specifically as follows:

[0121] 501、The terminal device sends data to the network device.

[0122] The terminal device sends uplink transmission data. After the network device receives the transmission data yr, the network device inputs yr into the U-Net for denoising to obtain the predicted noise n0, and then uses the channel matrix H obtained through channel estimation to decode s through yr-n0=H*s.

[0123] 502、The denoising model is trained online.

[0124] The network device can obtain the channel matrix H through channel estimation, so as to obtain the real noise n = yr-H*s. The noise n and the received transmission data yr and the channel matrix H are input into the U-Net for training, so as to obtain the predicted noise n0. For example, the loss function of the U-Net training is min(MSE(n-n0)). The denoising model is updated online based on the loss function.

[0125] In this example, the denoising model can effectively reduce the influence of channel noise on transmission and improve transmission efficiency. On the other hand, by updating the model online, the performance of the model can be improved.

[0126] It should be noted that in each embodiment of the present application, the terms and / or descriptions of each embodiment are consistent and can be mutually referred to if there is no special description and logical conflict. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0127] The above describes the method of the embodiments of the present application in detail. The apparatus of the embodiments of the present application is provided below. It can be understood that the division of a plurality of units or modules in each apparatus embodiment of the present application is only a logical division according to functions, and does not limit the specific structure of the apparatus. In a specific implementation, some of the function modules can be subdivided into more detailed function modules, and some of the function modules can be combined into one function module, but regardless of whether the function modules are subdivided or combined, the general flow performed by the apparatus is the same. For example, some apparatuses include a receiving unit and a sending unit. In some designs, the sending unit and the receiving unit can also be integrated into a communication unit, which can realize the functions realized by the receiving unit and the sending unit. Generally, each unit corresponds to a respective program code (or program instruction), and the respective program code of each unit, when running on a processor, causes the unit to be controlled by the processing unit to execute the corresponding flow to realize the corresponding function.

[0128] The embodiments of the present application also provide an apparatus for implementing any one of the above methods, for example, a communication apparatus including a module (or means) for implementing each step performed by the network device or the terminal device in any one of the above methods.

[0129] For example, referring to FIG. 6a, which is a structural schematic diagram of a communication apparatus provided by an embodiment of the present application. The communication apparatus is used to implement the communication method described above, such as the communication methods shown in FIG. 2, FIG. 3 and FIG. 5.

[0130] As shown in FIG. 6a, the apparatus can include a communication module 601 and a processing module 602, specifically as follows:

[0131] The communication module 601 is configured to receive first transmission data.

[0132] The processing module 602 is configured to input the first transmission data into a preset model for processing to obtain first noise.

[0133] The processing module 602 is further configured to obtain first decoding data based on the first transmission data and the first noise.

[0134] The above modules can refer to the description of the foregoing embodiments, and will not be described here again.

[0135] For another example, referring to FIG. 6b, which is a structural schematic diagram of another communication device provided by the embodiments of the present application. The communication device is configured to implement the foregoing communication method, such as the communication methods shown in FIG. 2, FIG. 3 and FIG. 5.

[0136] As shown in FIG. 6b, the device can include a communication module 603, which is specifically configured as follows.

[0137] The communication module 603 is configured to send first transmission data, wherein the first decoding data is obtained based on the first transmission data and first noise, and the first noise is obtained by inputting the first transmission data into a preset model for processing.

[0138] The above modules can refer to the description of the foregoing embodiments, and will not be described here again.

[0139] It should be understood that the division of each module in each of the above devices is only a logical functional division, and all or part of the modules can be integrated into one physical entity or physically separated in actual implementation. In addition, the modules in the communication device can be implemented in the form of processor calling software; for example, the communication device includes a processor, the processor is connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any one of the above methods or to realize the functions of the modules of the device, wherein the processor is, for example, a general processor such as a central processing unit (CPU) or a microprocessor, and the memory is an internal memory of the device or an external memory of the device. Alternatively, the modules in the device can be implemented in the form of hardware circuit, and the functions of part or all of the units can be realized by the design of the hardware circuit, which can be understood as one or more processors; for example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the units are realized by the design of the logical relationship of the elements in the circuit; for example, in another implementation, the hardware circuit is a programmable logic device (PLD), and a field programmable gate array (FPGA) is taken as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of part or all of the units. All the modules of the above device can be implemented in the form of processor calling software, or all the modules can be implemented in the form of hardware circuit, or part of the modules can be implemented in the form of processor calling software, and the remaining part can be implemented in the form of hardware circuit.

[0140] Referring to FIG. 7, a hardware structure of another communication device provided by the embodiment of the application is shown. As shown in FIG. 7, the communication device 700 includes one or more processors 701 (one processor is shown in the figure).

[0141] The processor 701 is a circuit with a processing capability of signals. In one implementation, the processor 701 can be a circuit with an instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), and the like. In another implementation, the processor 701 can implement certain functions through a logic relationship of a hardware circuit, which is fixed or reconfigurable. For example, the processor 701 is an ASIC or a programmable logic device (PLD) such as an FPGA. In the reconfigurable hardware circuit, the processor loads a configuration document to implement the hardware circuit configuration. It can be understood that the processor loads an instruction to implement the functions of the above modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), and the like. The processor 701 is configured to execute a related program to implement the functions required by the units in the communication device according to the embodiments of the present application, or execute the communication method according to the method embodiments of the present application.

[0142] Optionally, the communication device 700 can further include a memory (for example, the memory 703, the memory 704, and the memory 705) (indicated by a dashed line in the figure). The memory is configured to store instructions executed by the processor 701, or store input data required by the processor 701 for running the instructions, or store data generated after the processor 701 runs the instructions.

[0143] Optionally, the memory can be located in the one or more processors (for example, the memory 703), or located outside the one or more processors (for example, the memory 704 and the memory 705), or can include a memory part located in the one or more processors and a memory part located outside the one or more processors.

[0144] In the embodiments of the present application, the memory (for example, the memory 703, the memory 704, and the memory 705) can include, but is not limited to, a cache, a read-only memory (ROM), a random access memory (RAM), a synchronous dynamic random access memory (SDRAM), a hard disk drive (HDD), or a solid-state drive (SSD), an erasable programmable ROM (EPROM), or a compact disc read-only memory (CD-ROM), and the like. The memory can be any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but is not limited thereto. The memory in the embodiments of the present application can also be a circuit or any other device capable of realizing a storage function, used for storing computer programs or instructions and / or data.

[0145] Optionally, the communication device 700 can further include a communication interface 702 (indicated by a dashed line in the figure). The processor 701 and the communication interface 702 are coupled to each other. The communication interface 702 can be a transceiver or an interface circuit, a bus, a module, or any other type of communication interface.

[0146] The memory can store programs, and when the programs stored in the memory are executed by the processor 701, the processor 701 and the communication interface 702 are used to perform various steps of the communication method according to the embodiments of the present application.

[0147] It can be seen that each module in the above device can be one or more processors (or processing circuits) configured to implement the above method, for example, a CPU, a GPU, an NPU, a TPU, a DPU, a microprocessor, a DSP, an ASIC, an FPGA, or a combination of at least two of these processor forms or part of the processing circuits in these processors.

[0148] In addition, each module in the above device can be integrated together or can be independently implemented. In one implementation, the modules are integrated together to form a system-on-a-chip (SOC). The SOC can include at least one processor for implementing any of the above methods or the functions of the modules of the device, and the at least one processor can be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, and the like.

[0149] It should be noted that although the apparatus 700 shown in FIG. 7 only shows the memory, the processor, the communication interface, in the specific implementation process, those skilled in the art should understand that the apparatus 700 also includes other devices necessary for normal operation. At the same time, according to the specific needs, those skilled in the art should understand that the apparatus 700 can also include hardware devices that realize other additional functions. In addition, those skilled in the art should understand that the apparatus 700 can also only include devices necessary for the implementation of the embodiments of the present application, and does not have to include all the devices shown in FIG. 7.

[0150] It can be understood that the communication apparatus can be a chip system. The chip system can be composed of a chip, or can include a chip and other discrete devices. The communication apparatus includes one or more processors for implementing or supporting the communication apparatus to implement the functions in the methods provided in the present application. The processor can also be referred to as a processing unit or a processing module, and can implement certain control functions. The processor can be a general-purpose processor or a special-purpose processor, etc. For example, it includes: a central processing unit, an application processor, a modem processor, a graphics processor, an image signal processor, a digital signal processor, a video coding and decoding processor, a controller, a memory, and / or a neural network processor, etc. The central processing unit can be used to control the communication apparatus, execute software programs and / or process data. Different processors can be independent devices, or can be integrated into one or more processors, for example, integrated into one or more application specific integrated circuits. It can be understood that the processor in the embodiments of the present application can be a CPU, and can also be other general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor, or any conventional processor.

[0151] Optionally, the communication apparatus includes one or more memories for storing instructions that can be run on the processor. The memory and the processor are coupled, and the coupling in the present application is indirect coupling or communication connection between the apparatus, units or modules, which can be electrical, mechanical or other forms, for information interaction between the apparatus, units or modules.

[0152] Optionally, the memory can also store data. The processor and the memory can be separately arranged, or can be integrated together. The memory can be a non-volatile memory, such as an HDD or an SSD, etc., and can also be a volatile memory, such as a RAM. The processor in the embodiments of the present application can also be a flash memory, a ROM, a PROM, an EPROM, an electrically EPROM (EEPROM), a register, a hard disk, a mobile hard disk, a CD-ROM, or any other form of storage medium well known in the art.

[0153] Optionally, the communication device can include instructions (which can also be referred to as code or programs at times) that can be run on the processor.

[0154] Optionally, the communication device can further include a transceiver and an antenna. The transceiver can be referred to as a transceiving unit, a transceiving module, a transceiver, a transceiving circuit, a transceiver, an input / output interface, etc., and is used to realize the transceiving function of the communication device through the antenna.

[0155] The embodiments of the present application also provide a computer readable storage medium, which stores instructions, and when the instructions are run on a computer or a processor, the computer or the processor executes one or more steps in any of the above methods.

[0156] The embodiments of the present application also provide a computer program product containing instructions. When the computer program product is run on a computer or a processor, the computer or the processor executes one or more steps in any of the above methods.

[0157] It should be understood that, in the present application, "indication" can include direct indication, indirect indication, display indication, and implicit indication. When describing that certain indication information is used to indicate A, it can be understood that the indication information carries A, directly indicates A, or indirectly indicates A. In the present application, the information indicated by the indication information is referred to as to-be-indicated information. In the specific implementation process, there are many ways to indicate the to-be-indicated information, for example but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or an index of the to-be-indicated information, or the to-be-indicated information can be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. It can also be indicated only a part of the to-be-indicated information, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of a specific information can also be achieved by means of the arrangement order of each information agreed in advance (for example, specified by a protocol), thereby reducing the indication overhead to a certain extent. The to-be-indicated information can be sent together as a whole, or can be sent separately into multiple sub-information, and the sending period and / or sending occasion of the sub-information can be the same or different. The specific sending method is not limited in the present application. The sending period and / or sending occasion of the sub-information can be predefined, for example, predefined according to a protocol, or configured by a transmitting end device through sending configuration information to a receiving end device.

[0158] It should be understood that, in the description of the present application, unless otherwise specified, " / " represents that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; where A, B can be singular or plural. And, in the description of the present application, unless otherwise specified, "multiple" means two or more than two. "At least one of the following" or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same function and role are distinguished by using "first", "second", etc. The skilled in the art can understand that "first", "second", etc. do not limit the quantity and execution order, and "first", "second", etc. also do not necessarily mean different. At the same time, in the embodiments of the present application, the words "exemplary" or "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplary" or "for example" are intended to present the relevant concept in a specific manner, for understanding.

[0159] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0160] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0161] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted by the computer readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available medium can be a read-only memory (ROM), or a random access memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape, a magnetic disk, or an optical medium, such as a digital versatile disc (DVD), or a semiconductor medium, such as a solid state disk (SSD), etc.

[0162] The above merely illustrates the specific implementation of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited to this. Any change or replacement within the technical scope disclosed by the embodiments of the present application should be covered in the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application should be subject to the protection scope of the claims.

Claims

1. A communication method characterized by comprising: The method comprises: receiving first transmission data; inputting the first transmission data into a preset model for processing to obtain first noise; obtaining first decoding data based on the first transmission data and the first noise.

2. The method of claim 1, wherein, The preset model is obtained based on the following manner: receiving second transmission data; obtaining second noise based on the second transmission data and training data, the second transmission data being obtained based on the training data; inputting the second transmission data and the second noise into an initial model for processing to obtain third noise; updating the initial model based on the second noise, the third noise and a preset loss function to obtain the preset model.

3. The method of claim 2, wherein, The method further comprises: receiving the training data; or receiving first indication information, the first indication information indicating the training data, wherein the training data is preset.

4. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: updating the preset model based on the first transmission data, the first noise and the first decoding data to obtain an updated preset model.

5. The method of claim 4, wherein, The updating of the preset model based on the first transmission data, the first noise and the first decoding data to obtain an updated preset model comprises: inputting the first transmission data, the first noise and the first decoding data into the preset model for processing to obtain fourth noise; updating the preset model based on the fourth noise, the first noise and a preset loss function to obtain an updated preset model.

6. The method according to any one of claims 1 to 5, characterized in that, The preset model is an artificial intelligence (AI) model, and the method further comprises: sending second indication information, the second indication information indicating that a network device supports the use of the denoising function of the AI model.

7. A communication method characterized by comprising: The method comprises: sending first transmission data, wherein first decoding data is obtained based on the first transmission data and first noise, the first noise being obtained by inputting the first transmission data into a preset model for processing.

8. The method of claim 7, wherein, The method further comprises: sending second transmission data; The preset model is obtained by a network device based on the following manner: obtaining second noise based on the second transmission data and training data, the second transmission data being obtained based on the training data; inputting the second transmission data and the second noise into an initial model for processing to obtain third noise; updating the initial model based on the second noise, the third noise and a preset loss function to obtain the preset model.

9. The method of claim 8, wherein, The method further comprises: sending the training data; or sending first indication information, the first indication information indicating the training data, wherein the training data is preset.

10. The method according to any one of claims 7 to 9, characterized in that, The preset model is an AI model, and the method further comprises: receiving second indication information, the second indication information indicating that a network device supports the use of the denoising function of the AI model.

11. A communications device, characterized by The method comprises modules or units for implementing the method of any one of claims 1-10.

12. A communications device, characterized by The apparatus comprises a processor configured to cause the apparatus to perform the method of any of claims 1-6 by executing computer programs or computer executable instructions stored in a memory, and / or by logic circuitry.

13. A communications device, characterized by The apparatus comprises a processor configured to cause the apparatus to perform the method of any of claims 7-10 by executing computer programs or computer executable instructions stored in a memory, and / or by logic circuitry.

14. A communication system, characterized by The system comprises the apparatus of claim 12, and the apparatus of claim 13.

15. A computer-readable storage medium, characterized in that, A computer program is stored, which, when executed by a processor, causes the method of any of claims 1-6 to be implemented; or causes the method of any of claims 7-10 to be implemented.

16. A computer program product comprising instructions which, when executed on a processor, cause the method of any of claims 1-6 to be implemented; or cause the method of any of claims 7-10 to be implemented.

17. A chip, characterized by The chip comprises at least one processor and an interface, the processor configured to execute computer instructions or programs which, when executed, cause the chip to perform the method of any of claims 1-6, or cause the chip to perform the method of any of claims 7-10.

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