Receiving mode identification method and apparatus, and network device and terminal device

Abstract

A receiving mode identification method, comprising: a first device sending a request instruction to a second device, wherein the request instruction is used for acquiring the smallest power distribution coefficient among a plurality of existing power distribution coefficients of the second device, and / or the difference between the greatest power distribution coefficient and the smallest power distribution coefficient among the plurality of existing power distribution coefficients of the second device and; the first device receiving a response instruction sent by the second device; and on the basis of the smallest power distribution coefficient of the second device and / or the difference of the second device, the first device determining a receiving mode supported by the second device. In the embodiments of the present application, a first device may request a second device to report the historical minimum power distribution coefficient of the second device when a pilot signal and a data signal were superimposed, or the difference between historical power distribution coefficients of the second device when a pilot signal and a data signal were superimposed, and on the basis of the reported minimum power distribution coefficient or the reported difference between the power distribution coefficients, the first device can quickly determine a receiving mode supported by the second device, such that a superimposed signal that can be received is sent to the second device.

Description

A method, apparatus, network device, and terminal device for identifying a receiving mode.

[0001] This application claims priority to Chinese Patent Application No. 202410574600.2, filed on May 8, 2024, entitled "A Method, Apparatus, Network Device and Terminal Device for Identifying Receiving Mode", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of communication technology, and in particular to a method, apparatus, network device, and terminal device for identifying reception modes. Background Technology

[0003] To address the vision of a future of intelligent and inclusive access, wireless network architecture can be intelligently evolved. This includes deeply integrating artificial intelligence (AI) with wireless networks, enabling them to provide not only traditional communication connectivity services but also computing and AI services, and equipping terminal devices with certain AI capabilities. However, when network devices provide services to terminal devices within a served community, they cannot determine whether those devices possess AI capabilities. Therefore, determining whether a terminal device has AI capabilities is a pressing issue that needs to be addressed. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of this application provide a method for identifying reception modes. This method can quickly determine the reception mode of a terminal device or network device based on a power allocation coefficient. Furthermore, this application also provides a reception mode identification device, a network device, and a terminal device corresponding to this reception mode identification method.

[0005] Therefore, the following technical solutions are adopted in the embodiments of this application:

[0006] In a first aspect, embodiments of this application provide a method for identifying a receiving mode, including a first device and a second device. The first device is one of a network device and a terminal device, and the second device is the other of the network device and the terminal device. The second device is deployed with an AI model. The method is executed by the first device and includes: sending a request instruction to the second device; the request instruction is used to obtain the minimum power allocation coefficient among multiple existing power allocation coefficients of the second device, and / or the difference between the maximum power allocation coefficient and the minimum power allocation coefficient among multiple existing power allocation coefficients of the second device; receiving a response instruction sent by the second device; the response instruction includes the minimum power allocation coefficient of the second device, and... / or the difference of the second device; based on the minimum power allocation coefficient of the second device and / or the difference of the second device, determine the reception mode supported by the second device; the reception modes supported by the second device include a first reception mode, a second reception mode, and a third reception mode; the first reception mode refers to the mode in which the second device does not receive superimposed signals; the second reception mode refers to the mode in which the second device receives superimposed signals that are not desuperimposed based on the AI ​​model; the third reception mode refers to the mode in which the second device receives superimposed signals that are desuperimposed based on the AI ​​model and superimposed signals that are not desuperimposed based on the AI ​​model; the superimposed information refers to the signal obtained by superimposing the pilot signal on the data signal according to the power allocation coefficient.

[0007] In this embodiment, the first device can request the second device to report the minimum power allocation coefficient or the difference between the power allocation coefficients when it historically superimposed the pilot signal and the data signal. Based on the reported minimum power allocation coefficient or the difference between the power allocation coefficients, the receiving mode supported by the second device can be quickly determined so that a superimposed signal that can be received by the second device can be sent.

[0008] In one implementation, determining the reception mode supported by the second device based on the minimum power allocation coefficient of the second device and / or the difference between the two devices specifically includes: detecting whether the minimum power allocation coefficient of the second device is 1 or null; if the minimum power allocation coefficient of the second device is 1 or null, determining that the second device supports the first reception mode; if the minimum power allocation coefficient of the second device is not 1 or null, detecting whether the minimum power allocation coefficient of the second device is greater than a first preset threshold; if the minimum power allocation coefficient of the second device is greater than the first preset threshold, determining that the second device supports the second reception mode; and if the minimum power allocation coefficient of the second device is equal to or less than the first preset threshold, determining that the second device supports the third reception mode.

[0009] In this embodiment, after receiving the minimum power allocation coefficient sent by the second device, the first device can accurately determine which receiving mode the second device supports based on whether the minimum power allocation coefficient is 1 or empty, and whether it is greater than a first set threshold, thereby improving the speed and accuracy of the determination.

[0010] In one implementation, the first set threshold is the minimum power allocation coefficient obtained by de-superimposing superimposed signals based on the AI ​​model trained on the channel of the cell served by the network device.

[0011] In this embodiment, the network device can train the AI ​​model based on the channel of the cell it serves, so that the AI ​​model can obtain a threshold to distinguish between the second reception mode and the third reception mode, thereby improving the accuracy of the first device in distinguishing using the first set threshold.

[0012] In one implementation, determining the reception mode supported by the second device based on the minimum power allocation coefficient of the second device and / or the difference between the second devices specifically includes: detecting whether the difference between the second devices exists; if the difference between the second devices does not exist, determining that the second device supports the first reception mode; if the difference between the second devices exists, detecting whether the difference between the second devices is less than a second preset threshold; if the difference between the second devices is less than the second preset threshold, determining that the second device supports the second reception mode; and if the difference between the second devices is greater than or equal to the second preset threshold, determining that the second device supports the third reception mode.

[0013] In this embodiment, after receiving the difference in power allocation coefficients sent by the second device, the first device can accurately determine which receiving mode the second device supports based on whether the difference in power allocation coefficients exists and whether it is less than a second set threshold, thereby improving the speed and accuracy of the determination.

[0014] In one implementation, the second set threshold is the difference in minimum power allocation coefficients obtained by de-superimposing superimposed signals from the AI ​​model trained on the channel of the cell served by the network device.

[0015] In this embodiment, the network device can train the AI ​​model based on the channel of the cell it serves, so that the AI ​​model can obtain a threshold to distinguish between the second reception mode and the third reception mode, thereby improving the accuracy of the first device in distinguishing using the second set threshold.

[0016] In one embodiment, the method further includes: sending service function information to the second device if the second device supports the third receiving mode.

[0017] Secondly, this application provides a receiving mode identification device, comprising: a transceiver unit, configured to send a request instruction to a second device; the request instruction is configured to obtain the minimum power allocation coefficient among a plurality of existing power allocation coefficients of the second device, and / or the difference between the maximum power allocation coefficient and the minimum power allocation coefficient among a plurality of existing power allocation coefficients of the second device; the transceiver unit is further configured to receive a response instruction sent by the second device; the response instruction includes the minimum power allocation coefficient of the second device, and / or the difference between the two devices; a processing unit, configured to determine the receiving modes supported by the second device based on the minimum power allocation coefficient of the second device, and / or the difference between the two devices; the receiving modes supported by the second device include a first receiving mode, a second receiving mode, and a third receiving mode; the first receiving mode refers to a mode in which the second device does not receive superimposed signals; the second receiving mode refers to a mode in which the second device receives superimposed signals that are not desuperimposed based on the AI ​​model; the third receiving mode refers to a mode in which the second device receives superimposed signals that are desuperimposed based on the AI ​​model and superimposed signals that are not desuperimposed based on the AI ​​model; the superimposed information refers to a signal obtained by superimposing pilot signals onto data signals according to power allocation coefficients.

[0018] In one embodiment, the processing unit is specifically configured to detect whether the minimum power allocation coefficient of the second device is 1 or null; if the minimum power allocation coefficient of the second device is 1 or null, determine that the second device supports the first receiving mode; if the minimum power allocation coefficient of the second device is not 1 or null, detect whether the minimum power allocation coefficient of the second device is greater than a first preset threshold; if the minimum power allocation coefficient of the second device is greater than the first preset threshold, determine that the second device supports the second receiving mode; and if the minimum power allocation coefficient of the second device is equal to or less than the first preset threshold, determine that the second device supports the third receiving mode.

[0019] In one implementation, the first set threshold is the minimum power allocation coefficient obtained by de-superimposing superimposed signals based on the AI ​​model trained on the channel of the cell served by the network device.

[0020] In one embodiment, the processing unit is specifically configured to detect whether a difference exists in the second device; if no difference exists in the second device, determine that the second device supports the first receiving mode; if a difference exists in the second device, detect whether the difference is less than a second preset threshold; if the difference is less than the second preset threshold, determine that the second device supports the second receiving mode; and if the difference is greater than or equal to the second preset threshold, determine that the second device supports the third receiving mode.

[0021] In one implementation, the second set threshold is the difference in minimum power allocation coefficients obtained by de-superimposing superimposed signals from the AI ​​model trained on the channel of the cell served by the network device.

[0022] In one embodiment, the transceiver unit is further configured to send service function information to the second device when the second device supports the third receiving mode.

[0023] Thirdly, embodiments of this application provide a network device, including: at least one memory; and at least one processor, the processor being configured to execute instructions stored in the memory to cause the network device to perform the various possible implementations of the first aspect.

[0024] Fourthly, embodiments of this application provide a terminal device, including: at least one memory; and at least one processor, the processor being configured to execute instructions stored in the memory to cause the terminal device to perform the various possible implementations of the first aspect.

[0025] Fifthly, embodiments of this application provide a computer-readable storage medium including computer program instructions, which, when executed by a network device or a terminal device, perform the various possible implementations of the first aspect.

[0026] In a sixth aspect, this application provides a computer program product containing instructions, characterized in that the computer program product stores instructions that, when executed by a network device or a terminal device, cause the network device or terminal device to implement various possible implementations of the first aspect. Attached Figure Description

[0027] The accompanying drawings used in the description of the embodiments or prior art are briefly introduced below.

[0028] Figure 1 is a schematic diagram of a DMRS time-frequency resource sample;

[0029] Figure 2 is a schematic diagram of a single RB pattern in which pilot signals are superimposed on data for transmission;

[0030] Figure 3 is a schematic diagram of an application scenario of a wireless communication system provided in an embodiment of this application;

[0031] Figure 4 is a schematic diagram of an application scenario of a wireless communication system provided in the embodiments of this application;

[0032] Figure 5 is a schematic diagram of the application framework of a wireless communication system provided in an embodiment of this application;

[0033] Figure 6 is a schematic diagram of the application framework of a wireless communication system provided in an embodiment of this application;

[0034] Figure 7 is a flowchart of a method for identifying the receiving mode between a network device and a terminal device provided in an embodiment of this application;

[0035] Figure 8 is a schematic diagram of the structure of a receiving mode identification device provided in an embodiment of this application;

[0036] Figure 9 is a schematic diagram of the structure of an identification device for another receiving mode provided in an embodiment of this application;

[0037] Figure 10 is a schematic diagram of the structure of a base station provided in an embodiment of this application. Detailed Implementation

[0038] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0039] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0040] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0041] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0042] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0043] Before introducing the technical solution protected by this application, several technical terms involved in the technical solution protected by this application will be explained in advance, namely:

[0044] Machine learning (ML) is an important technological approach to achieving AI. ML can be divided into supervised learning, unsupervised learning, and reinforcement learning.

[0045] Supervised learning, based on collected sample values ​​and labels, uses machine learning (ML) algorithms to learn the mapping relationship between sample values ​​and labels, and expresses this learned mapping relationship using an ML model. The training process of the ML model is the process of learning this mapping relationship. For example, in signal detection, the noisy received signal is the sample, and the corresponding ground truth constellation point is the label. ML expectation refers to learning the mapping relationship between samples and labels through training; that is, enabling the ML model to learn a signal detector. During training, the ML algorithm optimizes the model parameters by calculating the error between the model's predicted values ​​and the ground truth labels. After learning the mapping relationship, the ML algorithm can use the learned mapping to predict the label of each new sample. The mapping relationship learned by supervised learning in ML algorithms can include linear mappings and nonlinear mappings. Based on the type of label, ML algorithms can categorize learning tasks into classification tasks and regression tasks.

[0046] Unsupervised learning relies solely on collected sample values, using algorithms to discover inherent patterns within the samples. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning. During training, unsupervised learning can optimize model parameters by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; commonly used algorithms include autoencoders and generative adversarial networks.

[0047] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with its environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithms used in reinforcement learning need to interact with the environment, obtain reward signals from the environment's feedback, and then adjust decision actions to obtain larger reward signal values. For example, in downlink power control, a reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal decision action. Because reinforcement learning cannot obtain the "correct action" label in advance, it cannot optimize the network by calculating the error between actions and the "correct action." Training in reinforcement learning is achieved through iterative interaction with the environment.

[0048] Deep neural networks (DNNs) are a specific implementation of machine learning. Based on general approximation theorems, DNNs can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Communication systems in related technologies rely on extensive expert knowledge to design communication modules, while DNN-based deep learning communication systems can automatically discover hidden pattern structures from large datasets, establishing mapping relationships between data and achieving performance superior to related modeling methods. DNNs can be categorized based on their network construction methods into feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recursive neural networks (RNNs).

[0049] FNN is a basic neural network model in which information is transmitted in only one direction, from the input layer through layers of hidden layers to the output layer, without feedback or loops. In an FNN, each neuron receives the output of the previous layer as input, processes it through an activation function, and then passes it to the next layer. FNNs are commonly used in various machine learning and deep learning tasks, such as classification and regression.

[0050] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (discrete sampling along the time axis) and image data (two-dimensional discrete sampling) can both be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (e.g., people and objects in an image represent different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0051] Recurrent Neural Networks (RNNs) are a type of distributed neural network (DNN) that utilizes feedback time-series information. The input to an RNN includes the current input value and its own output value from the previous time step. RNNs are well-suited for acquiring temporally correlated sequence features, and are particularly applicable to applications such as speech recognition and channel coding / decoding.

[0052] Channel estimation is a crucial technique in wireless communication, used to estimate the channel characteristics a signal traverses during transmission. In wireless communication, signals are affected by factors such as multipath fading and multipath delay during transmission, leading to signal distortion and attenuation. The purpose of channel estimation is to infer the channel characteristics experienced by the signal during transmission from the received signal, thereby compensating for the signal at the receiving end to recover the original information.

[0053] Reference signals, as a crucial component of system design, are primarily responsible for channel state measurement, data demodulation, beam training, and time-frequency parameter tracking. The design of reference signals mainly involves three aspects: the design of random sequence generation, the design of time-frequency resource mapping, and the power of the corresponding transmission sequence, forming a complete reference signal pattern design. Reference signal pattern design / configuration can at least include the configuration of the reference signal's position, power, and sequence. Reference signals refer to different signals in uplink and downlink transmissions, as shown in Table 1. One reference signal is the Channel State Information-Reference Signal (CSI-RS), used in downlink transmission for downlink channel detection to obtain downlink channel state information for beam management, mobility management, and rate matching. Another reference signal is the Demodulation Reference Signal (DMRS), used in uplink or downlink transmission for channel estimation to demodulate the corresponding physical channel. The reference signal is the channel sounding reference signal (SRS), which is used during uplink transmission to measure the uplink channel, perform time and frequency synchronization, beam management, etc.

[0054] Table 1 shows the specific signals that the reference signals refer to in the uplink and downlink.

[0055] A primary function of reference signals is channel estimation. Since reference signals carry almost no useful information, their overhead must be considered to balance channel estimation performance with available time-frequency resources for data transmission. Reference signals are sparse in the time, frequency, and spatial domains. Therefore, after estimating the channel at the time-frequency resource unit containing the reference signal using a channel estimation algorithm, it is also necessary to estimate the wireless channel on the time-frequency spatial resources where the reference signal has not been transmitted. Common channel estimation algorithms include least squares (LS), linear minimum mean square error (LMMESE), and compressed sensing (CS) algorithms.

[0056] In 5G NR (Fifth Generation New Radio), reference signals play a crucial role in channel estimation. Reference signals are specific signals transmitted from the base station to the user equipment (UE) to help the UE estimate channel characteristics, thereby enabling efficient data reception and decoding. Since the frequency and location of the reference signal are known in the frequency domain, the UE can determine the frequency domain characteristics of the current channel by comparing the received signal with the reference signal at the known frequency domain location. The UE can also estimate the time domain characteristics of the channel, such as multipath propagation delay and multipath fading, by analyzing the time domain characteristics of the reference signal. In multi-input multi-output (MIMO) systems, reference signals can be used to estimate the channel between multiple antennas. By collecting reference signals from different antennas and performing appropriate channel estimation, the UE can achieve efficient communication in the MIMO system and improve system throughput and performance.

[0057] In communication systems, many reference signals serve different purposes. Among them, the DMRS (Digital Modulation Reference Signal) is used for data demodulation. That is, the communication system can use the DMRS to estimate the channel response of the time-frequency resources occupied by the data. There is a trade-off between the accuracy of channel estimation and the density / overhead of the DMRS. If the channel exhibits significant frequency selectivity (i.e., the channel varies greatly in the frequency domain), the density of the DMRS in the frequency domain should be increased. Similarly, if the channel varies rapidly in the time domain, more resources need to be allocated in the time domain to deploy the reference signal. After determining the DMRS density in the time and frequency domains, the location of the DMRS in the time-domain resource block (RB) can be further considered. For example, under stationary channel conditions, to reduce interpolation errors and implementation complexity, the DMRS signal can be evenly distributed in both the frequency and time domains. Since the DMRS itself does not transmit any data signals useful to the user, it needs to be allocated with an appropriate density to maximize throughput.

[0058] The generation method of the DMRS random sequence depends on the specific waveform used. Currently, 5G mobile communication supports two waveforms: Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) and Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing (DFT-s-OFDM). The mapping of the DMRS sequence to physical time-frequency resource units is clearly defined in the standard protocol. Specifically, the location of the DMRS within a single time-frequency resource block is mainly determined by the following parameters:

[0059] Mapping types: There are Type A and Type B. The two mapping types have different restrictions on the starting symbol position and the number of PDSCH symbols.

[0060] DMRS configuration type: determines the location of frequency domain resources in DMRS.

[0061] DMRS - Additional position: Determines whether there is an additional DMRS in the time domain.

[0062] Maximum Length (maxLength): Determines whether it is a single-symbol DMRS or a double-symbol DMRS.

[0063] For example, Figure 1 is a schematic diagram of a DMRS time-frequency resource pattern. As shown in Figure 1, in this DMRS time-frequency resource pattern, the black time-frequency resource block represents the location carrying DMRS. In this case, the pattern corresponds to DMRS mapping type A, DMRS configuration type 1, maxLength = 1, DMRS-additional position = 0, and DMRS typeA position = 2. The protocol generates a finite number of DMRS patterns by giving the possible values ​​(ranges) of each parameter.

[0064] In related technologies, during channel estimation and modulation / demodulation processes, communication systems can superimpose pilot signals onto data signals for transmission. This allows each resource element (RE) to simultaneously transmit both pilot and data signals, improving the performance and reliability of the communication system. During this superposition process, the superposition ratio of pilot and data signals can be controlled by a power allocation coefficient to meet the system's performance and communication requirements. The power allocation coefficient is a parameter used to adjust the power ratio between the pilot and data signals; it refers to allocating a predetermined proportion of energy to the pilot signal from time-frequency resources with power normalized to 1.

[0065] For example, on a certain RE, the pilot signal and the data signal are superimposed with a power distribution factor of 0.5 to obtain the superimposed signal X:

[0066] Here, "pilot" represents the pilot signal on a specific RE, and "data symbol" represents the data signal on the same RE. Both pilot and data symbol can be generated based on a certain constellation mapping method. Typically, the pilot can be QPSK, and the data symbol can be QPSK, 16 / 64 / 256 / 1024QAM, etc.

[0067] For example, Figure 2 is a schematic diagram of a single RB pattern in which a pilot signal is superimposed on a data signal for transmission. As shown in Figure 2, each RE in this RB can superimpose the pilot signal and the data signal with different power allocation coefficients, which can free up data resources (which can occupy the original pilot position) to improve throughput gain and increase the degree of freedom of the pilot signal (length, placement position / method), so that different streams can be directly distinguished by orthogonal sequences.

[0068] In communication technologies designed for AI models, when a base station trains an AI model deployed on user equipment (UE), it first sends a capability reporting request to the UE. This request can ask the UE to report its AI and non-AI capabilities, including but not limited to the amount of memory available for storing AI / ML models and computing power information (the computational capacity to run AI / ML models). Based on the capability reporting request, the UE sends a reporting response instruction to the base station, which may include, but is not limited to, whether the UE supports running AI / ML models and the types of AI / ML models supported (such as CNN, RNN, fully connected, random forest models, etc.). Only after determining that the UE supports AI / ML models will the base station allow the UE to use the AI ​​network for pattern or sequence compression meshing and send the recovered network to the UE.

[0069] In related technologies, when a base station requests a UE to report its capabilities, the ability of the UE to desuperimpose pilot signals is determined by combining at least one or more different capabilities, such as supported pilot signal placement symbol bits, the UE's AI capabilities, and related receiver capabilities. Because some of the UE's capabilities are considered UE privacy, and the correspondence between specific capabilities and superimposed signal transmission capabilities is difficult to define, the base station finds it challenging to determine the UE's reception mode. That is, whether the UE has the capability to desuperimpose AI-based superimposed signals, whether it has the capability to desuperimpose non-AI-based superimposed signals, and whether it supports the transmission of superimposed signals.

[0070] To address the shortcomings of related technologies, this application provides a method for identifying reception modes. The base station can request the UE to report a single parameter, which represents the range of power allocation coefficients for the superimposed pilot and data signals historically supported by the UE. The base station can quickly determine the reception modes supported by the UE based on the range of these power allocation coefficients.

[0071] Based on whether the UE supports de-addressing of superimposed signals locally and whether it supports de-addressing of superimposed signals based on an AI model, three receiving modes can be defined: the first receiving mode, the second receiving mode, and the third receiving mode. The first receiving mode can be defined as a mode where the UE does not support de-addressing of superimposed signals and therefore cannot receive superimposed signals. The second receiving mode can be defined as a mode where the UE supports de-addressing of superimposed signals but only supports de-addressing of superimposed signals without an AI model, thus receiving superimposed signals that are not de-addressed based on an AI model. The third receiving mode can be defined as a mode where the UE supports de-addressing of superimposed signals and supports both de-addressing of superimposed signals based on and without an AI model, thus receiving superimposed signals de-addressed based on and without an AI model.

[0072] The technical solutions protected by this application can be applied to various communication systems, such as 5G systems, new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area network (WLAN) systems, satellite communication systems, mobile communication systems after 5G networks (e.g., 6G mobile communication systems), or integrated systems of multiple systems. The technical solutions protected by this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0073] In a communication system, one network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. The term "network element" can also be replaced by an entity, network entity, device, communication equipment, communication module, node, communication node, etc. This application uses a network element as an example for description. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device. It is understood that the terminal device in this application can be replaced by a first network element, and the network device can be replaced by a second network element, both performing the corresponding reception mode identification method described in this application.

[0074] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, thus requiring increasingly diverse demands. For example, networks need to support ultra-high speeds, ultra-low latency, and / or massive connectivity. This characteristic makes network planning, network configuration, and / or resource scheduling increasingly complex. Furthermore, as network functions become more powerful, such as supporting higher spectrum, supporting higher-order multiple-input multiple-output (MIMO) technologies, supporting beamforming, and / or supporting beam management, network energy efficiency has become a hot research topic. These new demands, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence (AI) technology can be introduced into wireless communication networks to achieve network intelligence. To support AI technology in wireless networks, AI nodes may also be introduced.

[0075] Figure 3 is a schematic diagram of an application scenario of a wireless communication system provided in an embodiment of this application. As shown in Figure 3, the wireless communication system 300 includes at least one network device, such as network device 310 shown in Figure 3. The wireless communication system 300 includes at least one terminal device, such as terminal device 320-1 and terminal device 320-2 shown in Figure 3. Network device 310 and terminal devices (such as terminal devices 320-1 and 320-2) can communicate via a wireless link. The communication devices in the wireless communication system 300, for example, network device 310 and terminal device 320-1, can communicate via multi-antenna technology.

[0076] Figure 4 is a schematic diagram of an application scenario of a wireless communication system provided in an embodiment of this application. As shown in Figure 4, the wireless communication system 400 includes at least one network device, such as network device 410 shown in Figure 4. The wireless communication system 400 includes at least one terminal device, such as terminal device 420-1 and terminal device 420-2 shown in Figure 4. The wireless communication system 400 includes at least one AI network element, such as AI network element 430 shown in Figure 4. Compared with the wireless communication system 300 shown in Figure 3, the AI ​​network element 430 in the wireless communication system 400 can communicate with the network device 410 and the terminal devices (such as terminal devices 420-1 and terminal devices 420-2) via a wireless link. The AI ​​network element 430 is used to perform AI-related operations, such as building training datasets or training AI models.

[0077] In one possible implementation, network device 410 can send data related to the training of the AI ​​model to AI network element 430, which then constructs a training dataset and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. AI network element 430 can send the results of operations related to the AI ​​model to network device 410, which then forwards them to the terminal device. For example, the results of operations related to the AI ​​model may include at least one of the following: a trained AI model, model evaluation results, or test results. Exemplarily, a portion of the trained AI model may be deployed on network device 410, and another portion on the terminal device. Alternatively, the trained AI model may be deployed on network device 410. Or, the trained AI model may be deployed on the terminal device.

[0078] It should be understood that Figure 4 is only used as an example of AI network element 430 being connected to both network device 410 and terminal device simultaneously. In other scenarios, AI network element 430 can also be connected to terminal device. Alternatively, AI network element 430 can be directly connected to network device 410. Alternatively, AI network element 430 can also be connected to network device 410 through a third-party network element. This application embodiment does not limit the connection relationship between AI network element 430 and other network elements.

[0079] AI element 140 can also be set as a module in network devices and / or terminal devices, for example, in network device 410 or terminal device shown in Figure 4.

[0080] It should be noted that Figures 3 and 4 are simplified schematic diagrams for ease of understanding. For example, the wireless communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 3 and 4. In practical applications, the wireless communication system may include multiple network devices or multiple terminal devices. The embodiments of this application do not limit the number of network devices and terminal devices included in the wireless communication system.

[0081] In the embodiments of this application, the terminal device may also be referred to as UE, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user equipment.

[0082] Terminal devices can be devices that provide voice / data, such as handheld devices with wireless connectivity, in-vehicle devices, etc. Currently, examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving vehicles, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, wearable devices, terminal devices in 5G networks, and future evolution of public land mobile communication networks. Terminal devices in a network (PLMN), etc., are not limited to this in the embodiments of this application.

[0083] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0084] The apparatus for implementing the functions of a terminal device can be the terminal device itself, or it can be any apparatus that supports the terminal device in implementing those functions, such as a chip system. This apparatus can be installed in or used in conjunction with the terminal device. In this embodiment, the chip system can consist of chips or include chips and other discrete components. This embodiment uses the terminal device as an example to illustrate the apparatus for implementing the functions of the terminal device, and does not limit the scope of the embodiments described herein.

[0085] In this embodiment, the network device can be a device used to communicate with the terminal device. This network device can also be called an access network device or a wireless access network device, such as a base station. In this embodiment, the network device can refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network. A base station can broadly encompass, or be replaced by, various names including: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, auxiliary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. A base station can be a macro base station, micro base station, relay node, donor node, or similar entities, or combinations thereof. A base station can also refer to a communication module, modem, or chip installed within the aforementioned equipment or apparatus. A base station can also be a mobile switching center, equipment performing base station functions in D2D, V2X, and M2M communications, network-side equipment in 6G networks, and equipment performing base station functions in future communication systems. A base station can support networks using the same or different access technologies. Optionally, a 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). The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

[0086] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0087] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, or DU, or devices including both CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0088] In some deployments, 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 CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio frequency equipment or radio frequency units, such as RRUs, AAUs, or RRHs.

[0089] RAN nodes can support one or more types of fronthaul interfaces, each corresponding to a DU and RU with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is another type of interface, relative to CPRI, some downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix addition (CP), are moved from the DU to the RU; and for uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / cyclic prefix removal (CP), are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0090] Taking eCPRI Cat A as an example, for downlink transmission, layer mapping is used as the dividing line. DU is configured to implement one or more functions preceding layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions following layer mapping (e.g., RE mapping, digital beamforming (BF), or one or more inverse fast Fourier transform (IFFT) / cyclic prefix (CP)) are moved to RU. For uplink transmission, de-RE mapping is used as the dividing line. DU is configured to implement one or more functions preceding de-mapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and de-RE mapping), while other functions following de-mapping (e.g., digital BF or fast Fourier transform (FFT) / CP removal) are moved to RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0091] In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0092] In different systems, CU (or CU-CP and CU-UP), DU, 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. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0093] The apparatus for implementing the functions of a network device can be a network device itself; it can also be an apparatus capable of supporting the network device in implementing those functions, such as a chip system, hardware circuitry, software module, or a combination of hardware circuitry and software module. This apparatus can be installed in or used in conjunction with the network device. In this application embodiment, the apparatus for implementing the functions of a network device is only described as a network device as an example, and does not constitute a limitation on the solutions of the embodiments of this application.

[0094] In this application embodiment, network devices and / or terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application embodiment does not limit the scenario in which the network devices and terminal devices are located. Furthermore, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware, or software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities that include dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of terminal devices and network devices.

[0095] Optionally, the AI ​​node can be deployed in one or more of the following locations within the communication system: access network devices, terminal devices, or core network devices, etc. Alternatively, the AI ​​node 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. The AI ​​node 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, etc.

[0096] 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.

[0097] 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.

[0098] AI nodes can be AI network elements or AI modules.

[0099] Figure 5 is a schematic diagram of the application framework of a wireless communication system provided in an embodiment of this application. As shown in Figure 5, network elements in this wireless communication system are connected through 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 OAM, are equipped with one or more AI modules (only one is shown in Figure 5 for clarity). The access network node can be a single RAN node or can include multiple RAN nodes, for example, including CU and DU. The CU and / or DU can also be equipped with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are set in CU-CP and / or CU-UP.

[0100] The AI ​​module is used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI ​​module can implement different functions. The AI ​​module model 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 bias in the activation function), input parameters (e.g., type and / or dimension of input parameters), or output parameters (e.g., type and / or dimension of output parameters). The bias in the activation function can also be referred to as the neural network bias.

[0101] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0102] Figure 6 is a schematic diagram of the application framework of a wireless communication system provided in an embodiment of this application. As shown in Figure 6, the wireless communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​module shown in Figure 5, used to implement AI-related functions. The RIC includes near-real-time RIC (near-RT RIC) and non-real-time RIC (non-RT RIC). The non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to latency, with a latency in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, with a latency in the order of tens of milliseconds.

[0103] The network device can be a network device equipped with one or more AI modules. The network device can be one or more devices in the core network, access network (RAN) node, or OAM as shown in Figure 5. For example, the AI ​​module can be the RIC shown in Figure 6, such as a near real-time RIC or a non-real-time RIC. For example, the near real-time RIC is set in the RAN node (e.g., in CU, DU), while the non-real-time RIC is set in the OAM, cloud server, core network device, or other network device. The RIC can obtain subsets from multiple terminal devices from the RAN node (e.g., CU, CU-CP, CU-UP, DU, and / or RU), reassemble them into a training dataset #2, and train based on the training dataset #2. Exemplarily, the near real-time RIC and the non-real-time RIC can also be set up separately as a network element; the network device can be a near real-time RIC or a non-real-time RIC.

[0104] The near real-time RIC is used for model training and inference. For example, it can be used to train an AI model and then use that AI model for inference. The near real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data. Optionally, the near real-time RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the near real-time RIC delivers the inference result to the DU, and the DU sends it to the RU.

[0105] The non-real-time RIC is also used for model training and inference. For example, it can be used to train an AI model and then use that model for inference. The non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to the RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the non-real-time RIC delivers the inference results to the DU, which then forwards them to the RU.

[0106] The near real-time RIC and non-real-time RIC can also be set up as separate network elements. Optionally, the near real-time RIC and non-real-time RIC can also be part of other devices. For example, the near real-time RIC can be set in the RAN node (e.g., in CU, DU), while the non-real-time RIC can be set in the OAM, cloud server, core network device, or other network device.

[0107] The following uses a single-input single-output (SISO) communication system as an example to introduce the technical solution protected in this application. An SISO communication system refers to a communication system with only one transmitting antenna and one receiving antenna.

[0108] Assuming that in a certain RE, the data signal S and the pilot signal P are superimposed according to the power allocation coefficient A, then the superimposed signal X transmitted by the base station can be expressed by formula (2), specifically:

[0109] When the base station sends the superimposed signal X to the UE, the superimposed signal X is affected by multipath fading, multipath delay and other factors after being transmitted through channel H, resulting in distortion and attenuation of the superimposed signal X. At this time, the superimposed signal Y received by the UE can be expressed by formula (3), specifically:

[0110] In the traditional scheme, after the UE receives the superimposed signal Y, it processes it in three steps: channel estimation, equalization, and demodulation. During the coarse channel estimation process, the LS algorithm can be used, which can be expressed by formula (4), specifically:

[0111] Due to data interference, the UE has relatively low confidence in the initial value of the channel estimate, so the UE will use iterative channel estimation. That is, the UE can use H... est Equalization and demodulation are performed to obtain the data bits. Then, the UE can utilize the error correction capability of the coding system to improve the decoding accuracy of the data bits. Next, the UE can reconstruct the estimated data signal S based on the data bits. est The reconstructed data signal S est After estimating channel H est After that, new data signals are obtained. Finally, the UE can subtract the new data signal from the superimposed signal Y. The new superimposed signal Y′ is obtained as follows:

[0112] The UE can again use the LS algorithm to perform a coarse channel estimation on the new superimposed signal Y′, and obtain the estimated channel H after iteration. est It can be expressed by formula (6), specifically:

[0113] Typically, the estimated channel H after iteration est Less than or equal to the estimated channel H before iteration est That is to say:

[0114] Theoretically, the UE can achieve good channel estimation performance after multiple iterations. However, if the power allocation coefficient A is too small, noise and data interference will be amplified abnormally, severely affecting the initial value of the channel estimation. Inaccurate initial values ​​of the channel estimation will affect the equalization performance, leading to inaccurate estimated data signals and impacting subsequent decoding. If the data bits estimated by the UE are severely distorted, the reconstructed data signal S will also be affected. est The discrepancy between the actual transmitted data signal and the estimated channel H after iteration will cause problems. est The estimated channel H is greater than that before the iteration. est This results in the performance of the iteration increasing rather than decreasing. In other words:

[0115] Therefore, for non-AI iterative channel estimation, the UE requires a larger power allocation coefficient A to obtain a better initial channel estimate, enabling continuous performance improvement during iterations. For AI channel estimation, the UE does not need multiple iterations and can directly perform channel estimation under interference conditions using the received superimposed signal Y and DMRS. Thus, AI channel estimation by the UE is independent of the initial channel estimation value, meaning it does not require a large power allocation coefficient A.

[0116] Based on this, if a UE supports the second reception mode, the minimum power allocation coefficient of power allocation coefficient A will be too large, and the difference between the maximum and minimum power allocation coefficients of power allocation coefficient A will be too small, that is: A∈[A min A max A min >A 阈值1 A max -A min 阈值2

[0117] The smaller difference in the power allocation coefficient A is because after the power of A is allocated to the pilot signal, the power of 1-A is allocated to the data signal. In other words, the value of 1-A affects the equivalent data signal-to-noise ratio during data demodulation. If the value of A is too large, it will cause 1-A to be too small, which will affect the data demodulation performance. Therefore, A... max -A min It will be on the smaller side.

[0118] If a UE supports the third reception mode, the minimum power allocation coefficient of the power allocation coefficient A will be too small, and the difference in power allocation coefficient A will be too large.

[0119] If a UE supports the first reception mode, the minimum power allocation coefficient of power allocation coefficient A is 1 or null, and the difference in power allocation coefficient A does not exist. ​

[0120] Therefore, the base station can request the minimum power allocation coefficient or the difference between the power allocation coefficients A and the power allocation coefficients A reported by the UE, and determine the reception mode supported by the UE by using the minimum power allocation coefficient or the difference between the power allocation coefficients A and the power allocation coefficients A reported by the UE.

[0121] The following describes the implementation process of the above-mentioned protection technical solution through a flowchart.

[0122] Figure 7 is a flowchart of the method for identifying the reception mode between a network device and a terminal device provided in this embodiment of the application. As shown in Figure 7, this method is jointly implemented by the network device and the terminal device. The network device refers to the base station in Figures 3-6 above, and the terminal device refers to the UE in Figures 3-6 above. The following describes the process of the network device identifying the reception mode of the terminal device in a downlink scenario as an example:

[0123] In step S701, the network device can send a request command to the terminal device.

[0124] The request command is used to obtain either the minimum power allocation coefficient or the difference between the power allocation coefficients of the terminal device. The power allocation coefficient of the terminal device refers to the power allocation coefficient when the pilot signal and data signal were superimposed previously. The minimum power allocation coefficient of the terminal device refers to the lowest power allocation coefficient among the multiple times the pilot signal and data signal were superimposed previously. The difference between the power allocation coefficients of the terminal device refers to the difference between the maximum and minimum power allocation coefficients when the pilot signal and data signal were superimposed previously.

[0125] In this embodiment, the request instruction can be a new instruction specifically used to obtain the minimum power allocation coefficient or the difference between the power allocation coefficients of the terminal device from the terminal device. The request instruction can be a capability reporting request issued by the network device when training an AI model deployed on the terminal device. If the request instruction is a capability reporting request, a new instruction can be added to the capability reporting request, enabling the terminal device to report not only its AI capabilities and non-AI capabilities, but also the minimum power allocation coefficient or the difference between the power allocation coefficients of the terminal device.

[0126] In step S702, the terminal device sends a response command to the network device according to the request command.

[0127] Upon receiving a request command, the terminal device can obtain the power allocation coefficients from the pilot signal superimposed on the data signal previously. The terminal device can obtain power allocation coefficients from multiple superimposition operations. In one scenario, if the request command specifies obtaining the terminal device's minimum power allocation coefficient, the terminal device can select the minimum power allocation coefficient from multiple options and send it to the network device.

[0128] In another scenario, when the request instruction instructs the terminal device to obtain the difference in power allocation coefficients, the terminal device can select the minimum and maximum power allocation coefficients from multiple power allocation coefficients, subtract the minimum power allocation coefficient from the maximum power allocation coefficient to obtain the difference in power allocation coefficients, and then send the difference in power allocation coefficients to the network device.

[0129] In step S703, the network device determines the receiving mode supported by the terminal device based on the response instruction.

[0130] The terminal device supports three reception modes: a first reception mode, a second reception mode, and a third reception mode. Since the minimum power allocation coefficient for the first reception mode is either 1 or empty, or there is no difference in the power allocation coefficient, the minimum power allocation coefficient for the second reception mode is too large and the difference in the power allocation coefficient is too small, and the minimum power allocation coefficient for the third reception mode is too small and the difference in the power allocation coefficient is too large, the network device can set two threshold values: a first threshold value and a second threshold value.

[0131] The first threshold is used to distinguish the minimum power allocation coefficient when the terminal device supports the second reception mode from the minimum power allocation coefficient when the terminal device supports the third reception mode. The first threshold is the minimum power allocation coefficient obtained by de-superimposing superimposed signals using an AI model trained on the channel of the cell served by the network device. Alternatively, the first threshold can be calculated using algorithms such as classification and clustering based on the power allocation coefficients reported by terminal devices within the cell served by the network device, or transmitted by other network devices.

[0132] The minimum power allocation coefficient is obtained by desuperimposing superimposed signals using an AI model trained on the channel.

[0133] The second threshold is used to distinguish between the difference in power allocation coefficients when the terminal device supports the second reception mode and the difference in power allocation coefficients when the terminal device supports the third reception mode. The second threshold is the difference in minimum power allocation coefficients obtained by de-superimposing superimposed signals using an AI model trained on the channel of the cell served by the network device. Alternatively, the second threshold can be calculated using algorithms such as classification and clustering based on the differences in power allocation coefficients reported by terminal devices within the cell served by the network device, or it can be transmitted by other network devices.

[0134] When a network device receives the minimum power allocation coefficient from a terminal device, it can detect whether the minimum power allocation coefficient is 1 or null. In one scenario, if the network device determines the minimum power allocation coefficient is 1 or null, it determines that the terminal device supports a first receiving mode, meaning the terminal device does not support the transmission of superimposed signals. In another scenario, if the network device determines the minimum power allocation coefficient is not 1 or null, it can detect whether the minimum power allocation coefficient is greater than a first preset threshold. In one scenario, if the network device determines the minimum power allocation coefficient is greater than the first preset threshold, it determines that the terminal device supports a second receiving mode, meaning the terminal device does not have the capability to de-superimpose superimposed signals based on an AI model. In another scenario, if the network device determines the minimum power allocation coefficient is less than or equal to the first preset threshold, it determines that the terminal device supports a third receiving mode, meaning the terminal device simultaneously has the capability to de-superimpose superimposed signals based on an AI model and the capability to de-superimpose superimposed signals without using an AI model.

[0135] When a network device receives the difference in power allocation coefficients from a terminal device, it can detect whether this difference exists. In one scenario, if the network device determines that the difference does not exist, it determines that the terminal device supports a first receiving mode, meaning the terminal device does not support the transmission of superimposed signals. In another scenario, if the network device determines that the difference exists, it can detect whether the difference is less than a second preset threshold. In one scenario, if the network device determines that the difference is less than the second preset threshold, it supports a second receiving mode, meaning the terminal device does not have the capability to de-superimpose superimposed signals based on an AI model. In another scenario, if the network device determines that the difference is greater than or equal to the second preset threshold, it determines that the terminal device supports a third receiving mode, meaning the terminal device simultaneously has the capability to de-superimpose superimposed signals based on an AI model and the capability to de-superimpose superimposed signals without using an AI model.

[0136] In this embodiment of the application, the network device may request the terminal device to report the minimum power allocation coefficient or the difference between the power allocation coefficients when it historically superimposed the pilot signal and the data signal, and determine the receiving mode supported by the terminal device based on the reported minimum power allocation coefficient or the difference between the power allocation coefficients.

[0137] In step S704, when the network device determines that the terminal device supports the third receiving mode, it sends service function information to the terminal device.

[0138] When a network device determines that a terminal device supports a third receiving mode, it can send local service function information to the terminal device. This allows the terminal device to execute relevant AI functions or services based on the service function information. The service function information includes the service types and / or function types that each network element supporting the target service can provide. Service types can include training, inference, data acquisition, etc. Function types can include enabling features of the target service such as channel estimation, beam management, mobility management, etc.

[0139] In this embodiment, in the downlink scheme, the AI ​​model is deployed on the terminal device. That is, the terminal device is responsible for AI-based channel estimation, equalization, and demodulation operations performed by the network device in scenarios where data signals and pilot signals are superimposed. The network device can configure DMRS and send DMRS to the terminal device. Compared to related technologies that send DMRS and data signals on different video resource REs, the network device in this application superimposes the DMRS and data signals into a single signal and then transmits it through a single RE.

[0140] After receiving the superimposed signal transmitted through the channel, the terminal device can utilize the power allocation coefficients sent by the network device to perform channel estimation, equalization, and demodulation operations. The AI ​​model deployed on the terminal device can be used for channel estimation, equalization, and demodulation operations, and can also perform two operations jointly, such as combining equalization and demodulation operations.

[0141] In this embodiment, in the uplink scheme, the AI ​​model is deployed on the network device. That is, the network device is responsible for AI-based channel estimation, equalization, and demodulation operations performed by the terminal device in scenarios where data signals and pilot signals are superimposed. The network device can instruct on how to configure DMRS, configure DMRS on the terminal device, and send DMRS to the network device. Compared to related technologies that send DMRS and data signals on different video resource REs, the terminal device in this application superimposes the DMRS and data signals into a single signal and then transmits it through a single RE. After receiving the superimposed signal transmitted through the channel, the network device can utilize the power allocation coefficient sent by the terminal device to perform channel estimation, equalization, and demodulation operations. The AI ​​model deployed on the network device can be used for channel estimation, equalization, and demodulation operations, and can also perform these operations jointly, such as combining equalization and demodulation operations.

[0142] In this embodiment of the application, before using AI functions or AI services, the network device or user equipment needs to determine the receiving mode supported by the other party, and then send appropriate signals based on the receiving mode supported by the other party, so that the network device and the user equipment can support various AI-based functions.

[0143] Figure 8 is a schematic diagram of a receiving mode identification device provided in an embodiment of this application. As shown in Figure 8, the receiving mode identification device 800 can execute the process performed by the network device or terminal device in the embodiments shown in Figures 3-7. Please refer to the relevant description in the above method embodiments for details. The receiving mode identification device 800 can be divided into a transceiver unit 810 and a processing unit 820 according to the execution function.

[0144] The transceiver unit 810 can perform corresponding communication functions. The transceiver unit 810 can also be called a communication interface or a communication module.

[0145] The processing unit 820 is used for data processing.

[0146] Optionally, the receiving mode identification device 800 may further include a storage unit, which may be used to store instructions and / or data. The processing unit 820 may read the instructions and / or data in the storage unit so that the receiving mode identification device 800 implements the aforementioned method embodiment.

[0147] The receiving mode identification device 800 can be used to perform the actions performed by the network device in the above method embodiment. The receiving mode identification device 800 can be a network device or a component configurable on a network device. The processing unit 820 is used to perform processing-related operations on the network device side in the above method embodiment. The transceiver unit 810 is used to perform receiving-related operations on the network device side in the above method embodiment.

[0148] Alternatively, the receiving mode identification device 800 can be used to perform the actions performed by the terminal device in the above method embodiments. The receiving mode identification device 800 can be a component of the terminal device. The processing unit 820 is used to perform processing-related operations on the terminal device side in the above method embodiments. The transceiver unit 810 is used to perform receiving-related operations on the terminal device side in the above method embodiments.

[0149] Optionally, the transceiver unit 810 may include a sending unit and a receiving unit. The sending unit is used to perform the sending operation in the above method embodiments. The receiving unit is used to perform the receiving operation in the above method embodiments.

[0150] It should be noted that the receiving mode identification device 800 may include a transmitting unit but not a receiving unit. Alternatively, the receiving mode identification device 800 may include a receiving unit but not a transmitting unit. Specifically, it depends on whether the above-described scheme executed by the receiving mode identification device 800 includes both transmitting and receiving actions.

[0151] The receiving mode identification device 800 can perform the functions of the network device or terminal device in the embodiments shown in Figures 3-7, specifically:

[0152] The transceiver unit 810 is used to send a request instruction to the second device. The request instruction is used to obtain the minimum power allocation coefficient among the existing multiple power allocation coefficients of the second device, and / or the difference between the maximum and minimum power allocation coefficients among the existing multiple power allocation coefficients of the second device. The transceiver unit 810 is also used to receive a response instruction sent by the second device. The response instruction includes the minimum power allocation coefficient of the second device, and / or the difference. The processing unit 820 is used to determine the reception modes supported by the second device based on the minimum power allocation coefficient of the second device, and / or the difference. The reception modes supported by the second device include a first reception mode, a second reception mode, and a third reception mode. The first reception mode refers to the mode in which the second device does not receive superimposed signals. The second reception mode refers to the mode in which the second device receives superimposed signals that are not desuperimposed based on the AI ​​model. The third reception mode refers to the mode in which the second device receives superimposed signals that are desuperimposed based on the AI ​​model and superimposed signals that are not desuperimposed based on the AI ​​model. Superimposed information refers to the signal obtained by superimposing the pilot signal onto the data signal according to the power allocation coefficient.

[0153] In one embodiment, the processing unit 820 is specifically configured to detect whether the minimum power allocation coefficient of the second device is 1 or empty. Specifically, if the minimum power allocation coefficient of the second device is 1 or empty, the processing unit 820 determines that the second device supports a first receiving mode. Specifically, if the minimum power allocation coefficient of the second device is not 1 or empty, the processing unit 820 detects whether the minimum power allocation coefficient of the second device is greater than a first preset threshold. Specifically, if the minimum power allocation coefficient of the second device is greater than the first preset threshold, the processing unit 820 determines that the second device supports a second receiving mode. If the minimum power allocation coefficient of the second device is equal to or less than the first preset threshold, the processing unit 820 determines that the second device supports a third receiving mode.

[0154] In one implementation, the first set threshold is the minimum power allocation coefficient obtained by de-superimposing superimposed signals from an AI model trained on the channel of the cell served by the network device.

[0155] In one embodiment, the processing unit 820 is specifically configured to detect whether a difference exists in the second device. Specifically, if the difference does not exist in the second device, the processing unit 820 determines that the second device supports a first receiving mode. Specifically, if the difference exists in the second device, the processing unit 820 detects whether the difference is less than a second preset threshold. Specifically, if the difference is less than the second preset threshold, the processing unit 820 determines that the second device supports a second receiving mode. Specifically, if the difference is greater than or equal to the second preset threshold, the processing unit 820 determines that the second device supports a third receiving mode.

[0156] In one implementation, the second set threshold is the difference in minimum power allocation coefficients obtained by de-superimposing superimposed signals from an AI model trained on the channel of the cell served by the network device.

[0157] In one embodiment, the transceiver unit 810 is further configured to send service function information to the second device when the second device supports the third receiving mode.

[0158] Figure 9 is a schematic diagram of another receiving mode identification device provided in an embodiment of this application. As shown in Figure 9, this application also provides a receiving mode identification device 900, which can be a network device or a chip. The receiving mode identification device 900 can be used to perform the operations performed by the network device or terminal device in any of the embodiments shown in Figures 3-7 above.

[0159] When the identification device 900 for this receiving mode is a network device, such as a base station, Figure 9 shows a simplified schematic diagram of a base station structure. The base station includes parts 910, 920, and 930.

[0160] The 910 section is mainly used for baseband processing and controlling the base station; the 910 section is usually the control center of the base station, which can be called the processor, and is used to control the base station to perform the processing operations on the network device side in the above method embodiments.

[0161] Section 920 is mainly used to store computer program code and data. Section 930 is mainly used for transmitting and receiving radio frequency signals and for converting radio frequency signals to baseband signals.

[0162] Section 930 is commonly referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver. The transceiver unit of section 930, also called a transceiver or transceiver, includes antenna 933 and radio frequency (RF) circuitry (not shown in the figure), where the RF circuitry is primarily used for RF processing. Optionally, the device in section 930 that performs the receiving function can be considered a receiver, and the device that performs the transmitting function can be considered a transmitter; that is, section 930 includes receiver 932 and transmitter 931. The receiver can also be called a receiving unit, receiver circuit, or receiving unit, and the transmitter can be called a transmitting unit, transmitter, or transmitting circuit.

[0163] Sections 910 and 920 may include one or more circuit boards, each of which may include one or more processors and one or more memories. The processors are used to read and execute programs from the memories to implement baseband processing functions and control the base station. If multiple circuit boards exist, they can be interconnected to enhance processing capabilities. As an alternative implementation, multiple circuit boards may share one or more processors, multiple circuit boards may share one or more memories, or multiple circuit boards may simultaneously share one or more processors.

[0164] For example, the transceiver module in section 930 is used to execute the transceiver-related processes performed by the network device in any of the embodiments shown in Figures 3-7. The processor in section 910 is used to execute the processing-related processes performed by the network device in any of the embodiments shown in Figures 3-7.

[0165] It should be understood that Figure 9 is merely an example and not a limitation, and the network devices described above, including processors, memory, and transceivers, may not depend on the structure shown in Figure 8.

[0166] When the identification device 900 for the receiving mode is a chip, the chip includes a transceiver, a memory, and a processor. The transceiver can be an input / output circuit or a communication interface; the processor can be an integrated processor, a microprocessor, or an integrated circuit on the chip. In the above method embodiments, the transmitting operation of the network device can be understood as the output of the chip, and the receiving operation of the network device in the above method embodiments can be understood as the input of the chip.

[0167] This application also provides a chip device, including a processor, for calling computer programs or computer instructions stored in the memory, so that the processor executes the method provided in any of the embodiments shown in Figures 3-7 above.

[0168] In one possible implementation, the input of the chip device corresponds to the receiving operation in any of the embodiments shown in Figures 3-7, and the output of the chip device corresponds to the sending operation in any of the embodiments shown in Figures 3-7.

[0169] Optionally, the processor is coupled to the memory via an interface.

[0170] Optionally, the chip device may also include a memory that stores computer programs or computer instructions.

[0171] The processor mentioned above can be a general-purpose central processing unit, a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of a program that controls the methods provided in any of the embodiments shown in Figures 3-7. The memory mentioned above can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, such as random access memory (RAM).

[0172] When the receiving mode identification device 900 is a base station, Figure 10 is a schematic diagram of the structure of a base station provided in an embodiment of this application. As shown in Figure 10, the network device 110 in the service generation method embodiment corresponding to Figures 3-7 above performs the functions. The base station 1000 may include one or more DU 1010 and one or more CU 1020. The DU 1010 may include at least one antenna 1011, at least one radio frequency unit 1012, at least one processor 1013, and at least one memory 1014. The DU 1010 is mainly used for transmitting and receiving radio frequency signals, converting radio frequency signals to baseband signals, and performing some baseband processing. The CU 1020 may include at least one processor 1021 and at least one memory 1022. The CU 1020 and DU 1010 can communicate through an interface, wherein the control plane interface can be Fs-C, such as F1-C, and the user plane interface can be Fs-U, such as F1-U.

[0173] The CU 1020 is mainly used for baseband processing and base station control. The DU 1010 and CU 1020 can be physically installed together or separately, i.e., a distributed base station. The CU 1020 is the control center of the base station, also known as a processing unit, and is mainly used to complete baseband processing functions. For example, the CU 1020 can be used to control the base station 1000 to execute the operation procedures related to network device 110 in the above method embodiment.

[0174] Specifically, baseband processing on the CU and DU can be divided according to the protocol layers of the wireless network. For example, the functions of the Packet Data Convergence Protocol (PDCP) layer and above are located on the CU, while the functions of protocol layers below PDCP, such as the Radio Link Control (RLC) layer and the Media Access Control (MAC) layer, are located on the DU. For another example, the CU implements the functions of the Radio Resource Control (RRC) and Packet Data Convergence Protocol (PDCP) layers, while the DU implements the functions of the Radio Link Control (RLC), Media Access Control (MAC), and Physical (PHY) layers.

[0175] Alternatively, the base station 1000 may include one or more radio frequency units (RUs), one or more DUs, and one or more CUs. A DU may include at least one processor 1013 and at least one memory 1014, an RU may include at least one antenna 1011 and at least one radio frequency unit 1012, and a CU may include at least one processor 1021 and at least one memory 1022.

[0176] In one example, the CU 1020 can be composed of one or more single boards. Multiple single boards can collectively support a single access indication wireless access network (such as a 6G network), or they can each support wireless access networks with different access standards (such as LTE, 6G, or other networks). The processor 1021 and memory 1022 can serve one or more single boards. That is, each single board can have its own memory and processor, or multiple single boards can share the same memory and processor. Furthermore, each single board can also have necessary circuitry. Similarly, the DU 801 can be composed of one or more single boards. Multiple single boards can collectively support a single access indication wireless access network (such as a 6G network), or they can each support wireless access networks with different access standards (such as LTE, 6G, or other networks). The memory 1014 and processor 1013 can serve one or more single boards. That is, each single board can have its own memory and processor, or multiple single boards can share the same memory and processor. Furthermore, each single board can also have necessary circuitry.

[0177] Among them, DU and CU can jointly perform the functions of processor 920 in the receiving mode identification device 900 shown in Figure 9, which will not be described in detail.

[0178] This application also provides a computer-readable storage medium including computer program instructions, wherein when the computer program instructions are executed by a terminal device, the terminal device performs any of the methods described in Figures 3-7 and their corresponding descriptions.

[0179] This application also provides a computer-readable storage medium including computer program instructions, wherein when the computer program instructions are executed by a network device, the network device performs any of the methods described in Figures 3-7 and their corresponding descriptions.

[0180] This application also provides a computer program product containing instructions, characterized in that the computer program product stores instructions, which, when executed by a terminal device, cause the terminal device to perform any of the methods described in Figures 3-7 and their corresponding descriptions.

[0181] This application also provides a computer program product containing instructions, characterized in that the computer program product stores instructions that, when executed by a network device, cause the network device to perform any of the methods described in Figures 3, 5, 6, and 7 and their corresponding descriptions.

[0182] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0183] Furthermore, various aspects or features of the embodiments of this application can be implemented as methods, apparatus, or articles of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" as used in this application encompasses a computer program accessible from any computer-readable device, carrier, or medium. For example, computer-readable media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact discs (CDs), digital versatile discs (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). Additionally, the various storage media described herein may represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0184] In the above embodiments, the receiving mode identification device 800 in FIG8 can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented by software, it can be implemented entirely or partially 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 described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0185] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application.

[0186] 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.

[0187] 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 through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0188] 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.

[0189] If the aforementioned function 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 embodiment, essentially, or the part that contributes to the prior art, or a 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, a server, or an access 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, ROM, RAM, magnetic disks, or optical disks.

[0190] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application.

Claims

1. A method for identifying a receiving pattern, characterized in that, The method includes a first device and a second device, wherein the first device is one of a network device and a terminal device, and the second device is the other of the network device and the terminal device. The second device is deployed with an AI model, and the method is executed by the first device, including: Send a request instruction to the second device; the request instruction is used to obtain the minimum power allocation coefficient among the multiple existing power allocation coefficients of the second device, and / or the difference between the maximum power allocation coefficient and the minimum power allocation coefficient among the multiple existing power allocation coefficients of the second device; Receive a response instruction sent by the second device; the response instruction includes the minimum power allocation coefficient of the second device and / or the difference of the second device; Based on the minimum power allocation coefficient of the second device and / or the difference of the second device, the reception modes supported by the second device are determined; the reception modes supported by the second device include a first reception mode, a second reception mode, and a third reception mode; the first reception mode refers to the mode in which the second device does not receive superimposed signals; the second reception mode refers to the mode in which the second device receives superimposed signals that are not desuperimposed based on the AI ​​model; the third reception mode refers to the mode in which the second device receives superimposed signals that are desuperimposed based on the AI ​​model and superimposed signals that are not desuperimposed based on the AI ​​model; the superimposed information refers to the signal obtained by superimposing the pilot signal on the data signal according to the power allocation coefficient.

2. The method according to claim 1, characterized in that, The step of determining the reception mode supported by the second device based on the minimum power allocation coefficient of the second device and / or the difference between the two devices specifically includes: Check whether the minimum power allocation coefficient of the second device is 1 or empty; If the minimum power allocation coefficient of the second device is 1 or empty, it is determined that the second device supports the first receiving mode; If the minimum power allocation coefficient of the second device is not 1 or is empty, detect whether the minimum power allocation coefficient of the second device is greater than the first set threshold. If the minimum power allocation coefficient of the second device is greater than the first set threshold, it is determined that the second device supports the second receiving mode; If the minimum power allocation coefficient of the second device is equal to or less than the first set threshold, it is determined that the second device supports the third receiving mode.

3. The method according to claim 2, characterized in that, The first set threshold is the minimum power allocation coefficient obtained by de-superimposing superimposed signals based on the AI ​​model trained on the channel of the cell served by the network device.

4. The method according to claim 1, characterized in that, The step of determining the reception mode supported by the second device based on the minimum power allocation coefficient of the second device and / or the difference between the two devices specifically includes: Detect whether the difference exists in the second device; If the difference in the second device is not present, it is determined that the second device supports the first receiving mode; If a difference exists in the second device, it is detected whether the difference in the second device is less than a second preset threshold. If the difference between the second device and the second device is less than the second set threshold, it is determined that the second device supports the second receiving mode. If the difference between the second device and the second device is greater than or equal to the second set threshold, it is determined that the second device supports the third receiving mode.

5. The method according to claim 4, characterized in that, The second set threshold is the difference in minimum power allocation coefficients obtained by de-superimposing superimposed signals from the AI ​​model trained on the channel of the cell served by the network device.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: If the second device supports the third receiving mode, service function information is sent to the second device.

7. A receiving mode identification device, characterized in that, include: The transceiver unit is used to send request commands to the second device; The request instruction is used to obtain the minimum power allocation coefficient among the multiple existing power allocation coefficients of the second device, and / or the difference between the maximum power allocation coefficient and the minimum power allocation coefficient among the multiple existing power allocation coefficients of the second device; The transceiver unit is further configured to receive a response instruction sent by the second device; the response instruction includes the minimum power allocation coefficient of the second device and / or the difference of the second device; The processing unit is configured to determine the reception mode supported by the second device based on the minimum power allocation coefficient of the second device and / or the difference of the second device; The second device supports three reception modes: a first reception mode, a second reception mode, and a third reception mode. The first reception mode refers to the mode in which the second device does not receive superimposed signals. The second reception mode refers to the mode in which the second device receives superimposed signals that are not de-superimposed based on the AI ​​model. The third reception mode refers to the mode in which the second device receives superimposed signals that are de-superimposed based on the AI ​​model and superimposed signals that are not de-superimposed based on the AI ​​model. The superimposed information refers to the signal obtained by superimposing the pilot signal on the data signal according to the power allocation coefficient.

8. The apparatus according to claim 7, characterized in that, The processing unit is specifically used to detect whether the minimum power allocation coefficient of the second device is 1 or a null value. If the minimum power allocation coefficient of the second device is 1 or empty, it is determined that the second device supports the first receiving mode; If the minimum power allocation coefficient of the second device is not 1 or is empty, detect whether the minimum power allocation coefficient of the second device is greater than the first set threshold. If the minimum power allocation coefficient of the second device is greater than the first set threshold, it is determined that the second device supports the second receiving mode; If the minimum power allocation coefficient of the second device is equal to or less than the first set threshold, it is determined that the second device supports the third receiving mode.

9. The apparatus according to claim 8, characterized in that, The first set threshold is the minimum power allocation coefficient obtained by de-superimposing superimposed signals based on the AI ​​model trained on the channel of the cell served by the network device.

10. The apparatus according to claim 7, characterized in that, The processing unit is specifically used to detect whether a difference exists in the second device; If the difference in the second device is not present, it is determined that the second device supports the first receiving mode; If a difference exists in the second device, it is detected whether the difference in the second device is less than a second preset threshold. If the difference between the second device and the second device is less than the second set threshold, it is determined that the second device supports the second receiving mode. If the difference between the second device and the second device is greater than or equal to the second set threshold, it is determined that the second device supports the third receiving mode.

11. The apparatus according to claim 10, characterized in that, The second set threshold is the difference in minimum power allocation coefficients obtained by de-superimposing superimposed signals from the AI ​​model trained on the channel of the cell served by the network device.

12. The apparatus according to any one of claims 7-11, characterized in that, The transceiver unit is further configured to send service function information to the second device when the second device supports the third receiving mode.

13. A network device, characterized in that, include: At least one memory; At least one processor, the processor being configured to execute instructions stored in memory to cause the network device to perform the method as described in any one of claims 1-6.

14. A terminal device, characterized in that, include: At least one memory; At least one processor, the processor being configured to execute instructions stored in a memory to cause the terminal device to perform the method as described in any one of claims 1-6.

15. A computer-readable storage medium, characterized in that, It includes computer program instructions, which, when executed by a network device or a terminal device, enable the network device or the terminal device to perform the method as described in any one of claims 1-6.

16. A computer program product containing instructions, characterized in that, The computer program product stores instructions that, when executed by a network device or a terminal device, cause the network device or the terminal device to perform the method described in any one of claims 1-6.

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