Data transmission method and communication device

Abstract

The application provides a data transmission method and a communication device. The method comprises: a first device sending a target data signal to a second device, the target data signal being generated based on a first data signal and a second data signal, transmission resources occupied by the first data signal comprising a first resource, the first resource also being used for transmitting part or all of the second data signal, the first resource comprising at least two of the following resources: time domain resources, frequency domain resources and space domain resources.

Description

Data transmission method and communication equipment Technical Field

[0001] The present application relates to the field of communication technology, and more specifically, to a data transmission method and communication equipment. Background Art

[0002] In order to improve data transmission efficiency, current data signals can be transmitted using time division multiplexing or frequency division multiplexing. However, this method still has the problem of low transmission resource utilization.

[0003] Summary of the Invention

[0004] The present application provides a data transmission method and a communication device. The following describes several aspects of the embodiments of the present application.

[0005] In a first aspect, a data transmission method is provided, including: a first device sends a target data signal to a second device, wherein the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include first resources, and the first resources are also used to transmit part or all of the data signals in the second data signal, and the first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0006] In a second aspect, a data transmission method is provided, including: a second device receives a target data signal sent by a first device, the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include first resources, the first resources are also used to transmit part or all of the data signals in the second data signal, and the first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0007] According to a third aspect, a communication device is provided, which is a first device and includes: a sending unit for sending a target data signal to a second device, wherein the target data signal is generated based on the first data signal and the second data signal, and the transmission resources occupied by the first data signal include first resources, and the first resources are also used to transmit part or all of the data signals in the second data signal, and the first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0008] In a fourth aspect, a communication device is provided, which is a second device and includes: a receiving unit for receiving a target data signal sent by a first device, wherein the target data signal is generated based on a first data signal and a second data signal, and the transmission resources occupied by the first data signal include first resources, and the first resources are also used to transmit part or all of the data signals in the second data signal, and the first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0009] In a fifth aspect, a communication device is provided, comprising a memory, a processor and a transceiver, wherein the memory is used to store programs, the processor is used to call the programs in the memory, and the transceiver is used to: send a target data signal to a second device, wherein the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include a first resource, and the first resource is also used to transmit part or all of the data signal in the second data signal, and the first resource includes at least two of the following resources: time domain resources, frequency domain resources and spatial domain resources.

[0010] In a sixth aspect, a communication device is provided, comprising a memory, a processor, and a transceiver, wherein the memory is used to store programs, the processor is used to call the programs in the memory, and the transceiver is used to: receive a target data signal sent by a first device, wherein the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include a first resource, and the first resource is also used to transmit part or all of the data signal in the second data signal, and the first resource includes at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0011] In a seventh aspect, a device is provided, comprising a processor, configured to call a program from a memory to execute the method described in the first aspect.

[0012] In an eighth aspect, a device is provided, comprising a processor, configured to call a program from a memory to execute the method described in the second aspect.

[0013] In a ninth aspect, a chip is provided, comprising a processor for calling a program from a memory so that a device equipped with the chip executes the method described in the first aspect.

[0014] In a tenth aspect, a chip is provided, comprising a processor for calling a program from a memory so that a device equipped with the chip executes the method described in the second aspect.

[0015] In an eleventh aspect, a computer-readable storage medium is provided, on which a program is stored, wherein the program enables a computer to execute the method described in the first aspect.

[0016] In a twelfth aspect, a computer-readable storage medium is provided, on which a program is stored, wherein the program enables a computer to execute the method described in the second aspect.

[0017] In a thirteenth aspect, a computer program product is provided, comprising a program, wherein the program enables a computer to execute the method described in the first aspect.

[0018] In a fourteenth aspect, a computer program product is provided, comprising a program, wherein the program enables a computer to execute the method described in the second aspect.

[0019] In a fifteenth aspect, a computer program is provided, which enables a computer to execute the method described in the first aspect.

[0020] In a sixteenth aspect, a computer program is provided, which enables a computer to execute the method described in the second aspect.

[0021] The first data signal and the second data signal in this application can be transmitted on the same first resource (e.g., non-orthogonal transmission). In other words, at least two of the time domain resources, frequency domain resources, and spatial domain resources occupied by the first data signal and the second data signal can overlap. Because in traditional transmission methods, only one of the time domain resources, frequency domain resources, and spatial domain resources occupied by the first data signal and the second data signal can be the same, the solution of this application can improve the utilization of transmission resources compared to time division multiplexing and frequency division multiplexing transmission methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG1 is a wireless communication system 100 used in an embodiment of the present application.

[0023] FIG2 is a schematic diagram of channel estimation and signal recovery applicable to an embodiment of the present application.

[0024] FIG3(a) to FIG3(c) show patterns of data symbols and pilot symbols under different configurations.

[0025] FIG4 shows a neural network model applicable to an embodiment of the present application.

[0026] FIG5 shows a neural network model applicable to an embodiment of the present application.

[0027] FIG6 shows a convolutional neural network applicable to an embodiment of the present application.

[0028] FIG7 shows a long short-term memory (LSTM) model applicable to an embodiment of the present application.

[0029] FIG8 shows a process of performing channel estimation based on a channel estimation module.

[0030] FIG9 is a wireless communication system 900 to which an embodiment of the present application is applicable.

[0031] FIG10 is a schematic flow chart of a data transmission method provided in an embodiment of the present application.

[0032] FIG11 is a schematic diagram of adjusting modulation constellation points associated with data signals in an embodiment of the present application.

[0033] FIG12 shows a solution for transmitting a first data signal and a second data signal based on a linear superposition method provided in an embodiment of the present application.

[0034] 13 to 25 illustrate a solution of a first data signal and a second data signal based on nonlinear superposition provided by an embodiment of the present application.

[0035] FIG26 shows the structure of the first receiver in the second device.

[0036] FIG27 is a schematic diagram of recovering a first data signal and a second data signal provided by an embodiment of the present application.

[0037] FIG28 is a schematic diagram of another method for recovering a first data signal and a second data signal provided by an embodiment of the present application.

[0038] Figure 29 is a schematic block diagram of a communication device provided in an embodiment of the present application.

[0039] Figure 30 is a schematic block diagram of another communication device provided in an embodiment of the present application.

[0040] Figure 31 is a structural diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The technical solution in this application will be described below with reference to the accompanying drawings.

[0042] 1. Signal Transmission Process in Wireless Communication Systems

[0043] Figure 1 is a flow chart of signal transmission in a wireless communication system to which an embodiment of the present application applies. As shown in Figure 1 , the signal transmission process in the wireless communication system can be roughly divided into the various signal processing processes S111 to S118 shown in Figure 1 . Some or all of the signal processing processes shown in Figure 1 can be implemented by a separate AI model. For specific implementation, please refer to the descriptions of Figures 5 to 8 .

[0044] In the channel coding process S111, the transmitter performs channel coding on the information to be transmitted to obtain a coded code stream. The information to be transmitted may be in the form of a bit stream.

[0045] In the modulation process S112 , the code stream is modulated into modulation symbols.

[0046] In the pilot insertion process S113, pilot symbols are inserted into the modulation symbols to form a signal to be transmitted, wherein the pilot symbols can be used by a receiver to perform channel estimation and symbol detection.

[0047] In the transmission signal S114, the above signal is carried on the channel and transmitted to the receiver. In the process of transmitting the signal through the channel, noise is usually superimposed.

[0048] In the channel estimation process S115, the receiver can perform channel estimation based on the pilot signal to obtain channel state information (CSI), and feed the CSI back to the transmitter through a feedback link for the transmitter to adjust channel coding, modulation, precoding, etc.

[0049] In the symbol detection process S116, symbol detection is performed on the received modulation symbols to obtain a detection result.

[0050] In the demodulation process S117, the received modulation symbols are demodulated based on the detection result to obtain a code stream.

[0051] In the channel decoding process S118, the code stream is decoded to obtain restored information, wherein the restored information may be in the form of a bit stream.

[0052] It should be understood that the signal processing processes S111 to S118 shown in Figure 1 are merely examples of common signal processing processes in wireless communication systems. Wireless communication systems may also include signal processing processes such as resource mapping, precoding, interference cancellation, and CSI measurement. These signal processing processes can also be implemented through separate AI models. For the sake of brevity, this application will not go into detail.

[0053] 2. Channel Estimation

[0054] Due to the complexity and time-varying nature of wireless channel environments, in wireless communication systems (e.g., the wireless communication systems described above), a receiver needs to recover received signals based on channel estimation results. Figure 2 is a schematic diagram of channel estimation and signal recovery applicable to embodiments of the present application.

[0055] As shown in FIG2 , in step S210 , the transmitter transmits, in addition to the data signal, a series of pilot signals known to the receiver on the time-frequency resources, such as the channel state information-reference signal (CSI-RS) and the demodulation reference signal (DMRS).

[0056] In step S211, the transmitter transmits the above data signal and pilot signal to the transmitter through the channel.

[0057] The time-frequency resources occupied by the pilot signal are different from the time-frequency resources occupied by the data signal.

[0058] In step S212, after receiving the pilot signal, the receiver may perform channel estimation. In one possible implementation, the receiver may estimate channel information of the channel transmitting the pilot signal based on the pre-stored pilot signal and the received pilot signal using a channel estimation algorithm (e.g., a least squares (LS) channel estimation method).

[0059] In step S213, the receiver may recover the channel information on all time-frequency resources using an interpolation algorithm based on the channel information of the channel transmitting the pilot sequence, for use in subsequent CSI feedback or data recovery.

[0060] Based on the above description in conjunction with Figure 2, it can be seen that the time-frequency resources for transmitting pilot signals are different from the time-frequency resources for transmitting data signals. In addition, some communication protocols (for example, the NR communication protocol) stipulate that the symbols used to transmit pilot signals (hereinafter referred to as "pilot symbols") and the symbols used to transmit data signals (hereinafter referred to as "data symbols") are different. Figures 3(a) to 3(c) show the patterns of data symbols and pilot symbols under different configurations.

[0061] As shown in Figure 3(a), within a resource block (RB), pilot symbols are distributed every other symbol across multiple resource elements (REs) corresponding to symbol 2 within the RB. As shown in Figure 3(b), within an RB, pilot symbols occupy a portion of multiple symbols corresponding to symbols 2 and 10 within the RB. As shown in Figure 3(c), within an RB, pilot symbols occupy multiple groups of REs within symbol 2 within the RB, where each group of REs includes two consecutive REs in the frequency domain.

[0062] Generally, different patterns in the patterns shown in Figures 3(a) to 3(c) can be adapted to different communication environments. In some implementations, when the terminal device is moving at a high speed and the channel characteristics vary rapidly, a pattern with a denser distribution of pilot symbols can be selected to help improve the accuracy of channel quality estimation for the entire RB. For example, the pattern shown in Figure 3(b) can be selected.

[0063] In other implementations, when the terminal device moves slowly and the channel characteristics vary slowly over time, a pattern with a sparser distribution of pilot symbols can be selected, which helps to reduce the overhead generated by transmitting pilot signals while ensuring the accuracy of channel quality estimation for the entire RB.

[0064] 3. Neural Network

[0065] In recent years, artificial intelligence research, exemplified by neural networks, has achieved remarkable success in many fields, and will continue to play a vital role in people's lives and production for a long time to come. A neural network can be understood as a computational model consisting of multiple interconnected neuron nodes. The connections between these nodes represent the weighted values ​​from input signals to output signals, often referred to as parameters. Each node performs a weighted summation of different input signals and outputs the result through a specific activation function.

[0066] As shown in Figure 4, neurons can implement nonlinear mappings based on activation functions. The neuron's input can be denoted as A, with each dimension of the input denoted as aj and the corresponding parameter denoted as wj. Together with summation units (SUs), these neurons enhance or weaken the input. Furthermore, the output of the SU can be fed into an activation function f to produce the output t, where j can take values ​​of 1, 2, ..., n.

[0067] Common neural networks include convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), etc.

[0068] The following describes a neural network applicable to embodiments of the present application in conjunction with FIG5 . The neural network shown in FIG5 can be divided into three categories based on the location of different layers: input layer 510 , hidden layer 520 , and output layer 530 . Generally speaking, the first layer is the input layer 510 , the last layer is the output layer 530 , and the intermediate layers between the first and last layers are all hidden layers 520 .

[0069] The input layer 510 is used to input data, where the input data can be, for example, a received signal received by a receiver. The hidden layer 520 is used to process the input data, for example, decompress the received signal. The output layer 530 is used to output processed output data, for example, a decompressed signal.

[0070] As shown in Figure 5, a neural network consists of multiple layers, each of which contains multiple neurons. The neurons between layers can be fully connected or partially connected. For connected neurons, the output of the neurons in the previous layer can serve as the input of the neurons in the next layer.

[0071] With the continuous advancement of neural network research, deep learning algorithms have been proposed in recent years. These algorithms introduce a large number of hidden layers into neural networks, forming DNNs. More hidden layers allow DNNs to better capture complex real-world situations. Theoretically, a model with more parameters has higher complexity and a greater "capacity," meaning it can handle more complex learning tasks. These neural network models are widely used in pattern recognition, signal processing, optimization and combination, anomaly detection, and other fields.

[0072] CNN is a deep neural network with a convolutional structure, and its structure is shown in FIG6 , which may include an input layer 610 , a convolutional layer 620 , a pooling layer 630 , a fully connected layer 640 , and an output layer 650 .

[0073] Each convolution layer 620 may include a plurality of convolution operators, which are also called kernels. The convolution operator can be regarded as a filter for extracting specific information from the input signal. The convolution operator can essentially be a parameter matrix, which is usually predefined.

[0074] The parameter values ​​in these parameter matrices need to be obtained through a lot of training in practical applications. The parameter matrices formed by the parameter values ​​obtained through training can extract information from the input signal, thereby helping CNN to make correct predictions.

[0075] When CNN has multiple convolutional layers, the initial convolutional layer tends to extract more general features, which can also be called low-level features. As the depth of CNN increases, the features extracted by the subsequent convolutional layers become more and more complex.

[0076] Pooling layer 630 is often needed to reduce the number of training parameters. Therefore, it is often necessary to periodically introduce a pooling layer after the convolution layer. For example, as shown in Figure 6, a convolution layer can be followed by a pooling layer, or multiple convolution layers can be followed by one or more pooling layers. In the signal processing process, the sole purpose of the pooling layer is to reduce the spatial size of the extracted information.

[0077] The fully connected layer 640, after being processed by the convolution layer 620 and the pooling layer 630, is not sufficient for CNN to output the required output information. Because as mentioned above, the convolution layer 620 and the pooling layer 630 only extract features and reduce the parameters brought by the input data. However, in order to generate the final output information (for example, the bit stream of the original information transmitted by the transmitter), CNN also needs to use the fully connected layer 640. Generally, the fully connected layer 640 may include multiple hidden layers, and the parameters contained in the multiple hidden layers may be pre-trained based on relevant training data of a specific task type. For example, the task type may include decoding a data signal received by a receiver. For another example, the task type may also include channel estimation based on a pilot signal received by the receiver.

[0078] Following the multiple hidden layers in the fully connected layer 640, the final layer of the CNN is the output layer 650, which is used to output the results. Typically, this output layer 650 is configured with a loss function (e.g., a loss function similar to categorical cross entropy) to calculate the prediction error, or to evaluate the degree of difference between the CNN model's output (also known as the predicted value) and the ideal result (also known as the true value).

[0079] To minimize the loss function, the CNN model needs to be trained. In some implementations, the CNN model can be trained using a backpropagation algorithm (BP). The BP training process consists of a forward propagation process and a backward propagation process. During the forward propagation process (e.g., the propagation from 610 to 650 in Figure 6 is forward propagation), the input data is fed into the aforementioned layers of the CNN model, processed layer by layer, and transmitted to the output layer. If the output result differs significantly from the ideal result, the aforementioned loss function is minimized as the optimization goal, and the backward propagation process is switched to (e.g., the propagation from 650 to 610 in Figure 6 is backward propagation). The partial derivatives of the optimization goal with respect to each neuron weight are calculated layer by layer, forming the gradient of the optimization goal with respect to the weight vector, which serves as the basis for modifying the model parameters. The CNN training process is completed during the parameter modification process. When the aforementioned error reaches the desired value, the CNN training process ends.

[0080] It should be noted that the CNN shown in Figure 6 is only an example of a convolutional neural network. In specific applications, the convolutional neural network can also exist in the form of other network models, which is not limited in this embodiment of the present application.

[0081] RNNs are designed to process sequential data. In traditional neural network models (for example, CNN models), the layers are fully connected, from the input layer to the hidden layer to the output layer, and the nodes within each layer are disconnected. However, these ordinary neural networks are inadequate for many problems. For example, if you want to predict the next word in a sentence, you generally need to use the previous word, because the previous and next words in a sentence are not independent. RNNs are called recurrent neural networks because the current output of a sequence is also related to the previous output. Specifically, the network remembers the previous information and applies it to the calculation of the current output. That is, the nodes between hidden layers are no longer disconnected but connected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the previous moment. In theory, RNNs can process sequence data of any length.

[0082] Training an RNN is similar to training a traditional ANN (artificial neural network). The same backpropagation error algorithm is used, but there is a slight difference. If the RNN is expanded, the parameters W, U, and V are shared, while traditional neural networks are not. Furthermore, when using the gradient descent algorithm, the output of each step depends not only on the network state at the current step, but also on the state of the network at the previous steps. For example, at t = 4, three steps need to be propagated backward, and various gradients need to be added to the three subsequent steps. This learning algorithm is called backpropagation through time (BPTT).

[0083] Given the existence of artificial neural networks and convolutional neural networks, why do we still need recurrent neural networks? The reason is simple. Both convolutional and artificial neural networks assume that elements are independent of each other, and that input and output are also independent, like cats and dogs. However, in the real world, many elements are interconnected, such as the changes in stock prices over time. For example, someone said, "I love traveling, and my favorite place is Yunnan. I must visit __ someday." Everyone knows to fill in the blank with "Yunnan." This is because we infer this based on the context, but achieving this is quite difficult. Therefore, recurrent neural networks were developed. Their essence is that they possess memory, just like humans. Therefore, their output depends on the current input and memory. To put it simply, an RNN is a unit structure that is reused.

[0084] To address the exploding or vanishing gradient problem of RNNs, a modification of the RNN has been made, resulting in the long short-term memory (LSTM) model. As shown in Figure 7, the LSTM introduces a new memory unit, ct (also known as the "cell state"), which performs linear, cyclic information transfer and simultaneously outputs information to the external state ht of the hidden layer. At each time instant t, ct records the historical information up to the current moment. Unlike RNNs, which only consider the most recent state, the memory unit determines which states should be retained and which should be forgotten, addressing the traditional RNN's shortcomings in long-term memory.

[0085] Continuing with Figure 7, to achieve the above state selection, the memory unit introduces a gate control mechanism to control the path of information transmission, similar to the gate in a data circuit, where "0" represents off and "1" represents on. The memory unit includes a forget gate 710, an input gate 720, and an output gate 730. The forget gate is used to control how much information the memory unit ct-1 needs to forget at the previous moment, and the input gate is used to control the candidate state at the current moment. How much information needs to be stored? The output gate is used to control how much information the memory unit ct at the current moment needs to output to the external state ht.

[0086] 4. Channel Estimation Based on AI Decoder

[0087] The channel estimation based on the AI ​​decoder aims to use the AI-based channel estimation module to process the pilot signal received by the receiver to achieve channel estimation. Figure 8 shows the process of channel estimation based on the channel estimation module. Referring to Figure 8, the signal received by the receiver 800 (such as the pilot signal) is used as the input of the channel estimation module 810. Accordingly, the channel estimation module 810 processes the input pilot signal to output channel information. The input of the channel estimation module 810 is the received signal and pilot symbol corresponding to the pilot symbol RE, and the output information is the result of the channel estimation of the entire PRB.

[0088] In addition, in some implementations, in addition to the pilot signal, other auxiliary information may be added to improve the accuracy of the channel information output by the channel estimation module. For example, the channel estimation module 810 may also be fed with the original sequence of the pilot signal pre-stored in the receiver 800, the energy level of the pilot signal received by the receiver 800, the transmission delay when transmitting the pilot signal, or the noise when transmitting the pilot signal.

[0089] The internal implementation of the channel estimation module 810 can be a neural network such as DNN, CNN, etc., and of course it can also be other neural networks. The embodiment of the present application does not make specific limitations on this.

[0090] The above describes the communication process and terminology involved in the embodiments of the present application in conjunction with Figures 1 to 8, and the following describes the communication system applicable to the embodiments of the present application in conjunction with Figure 9.

[0091] FIG9 illustrates a wireless communication system 900 applicable to an embodiment of the present application. The wireless communication system 900 may include a network device 910. The network device 910 may be a device that communicates with a terminal device 920. The network device 910 may provide communication coverage for a specific geographic area and may communicate with a terminal device 920 located within the coverage area.

[0092] Figure 9 exemplarily shows a network device 910 and two terminal devices 920. Optionally, the wireless communication system 900 may include multiple network devices and each network device may include other numbers of terminal devices within its coverage area, which is not limited in this embodiment of the present application.

[0093] Optionally, the wireless communication system 900 may further include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiment of the present application.

[0094] Optionally, the terminal devices 920 may also communicate directly with each other. For example, two terminal devices 920 may communicate with each other via a device-to-device (D2D) link.

[0095] It should be noted that the following description uses the first device and the second device as examples. In some implementations, the first device may be the aforementioned network device 910, and accordingly, the second device may be the aforementioned terminal device 920. In other implementations, the first device may be the aforementioned terminal device 920, and accordingly, the second device may be the aforementioned network device 910. In still other implementations, the first device may be the aforementioned terminal device 920, and accordingly, the second device may be the aforementioned terminal device 920. This embodiment of the present application does not specifically limit this.

[0096] It should be understood that the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: fifth generation (5G) system or new radio (NR), long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), etc. The technical solutions provided in this application can also be applied to future communication systems, such as the sixth generation mobile communication system, satellite communication system, etc.

[0097] The terminal device in the embodiments of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device. The terminal device in the embodiments of the present application may refer to a device that provides voice and / or data connectivity to a user and can be used to connect people, objects, and machines, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. The terminal device in the embodiments of the present application can be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc. Optionally, the UE can be used to act as a base station. For example, the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc. For example, a cellular phone and a car communicate with each other using sidelink signals. The cellular phone and smart home devices communicate without relaying the communication signal through the base station.

[0098] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. A base station can broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmission point (TP), master station MeNB, secondary station SeNB, multi-standard radio (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), positioning node, etc. A base station can be a macro base station, a micro base station, a relay node, a donor node, or the like, or a combination thereof. A base station can also refer to a communication module, modem, or chip used to be provided in the aforementioned device or apparatus. The base station can also be a mobile switching center and a device that performs base station functions in device-to-device D2D, vehicle-to-everything (V2X), and machine-to-machine (M2M) communications, a network-side device in a 6G network, or a device that performs base station functions in future communication systems. The base station can support networks with the same or different access technologies. The embodiments of this application do not limit the specific technology and specific device form used by the network equipment.

[0099] 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 based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0100] In some deployments, the network device in the embodiments of the present application may refer to a CU or a DU, or the network device may include a CU and a DU. The gNB may also include an AAU.

[0101] The network equipment and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the scenarios in which the network equipment and terminal devices are located.

[0102] It should be understood that the communication devices referred to in this application may be network devices or terminal devices. For example, the first communication device may be a network device, and the second communication device may be a terminal device. In another example, the first communication device may be a terminal device, and the second communication device may be a network device. In another example, both the first communication device and the second communication device may be network devices, or both may be terminal devices.

[0103] It should also be understood that all or part of the functions of the communication device in this application can also be implemented through software functions running on hardware, or through virtualization functions instantiated on a platform (such as a cloud platform).

[0104] Currently, in known communication systems, in order to improve the utilization of transmission resources, multiple data signals can be multiplexed and transmitted on the transmission resources. Current multiplexing transmission includes orthogonal multiplexing transmission on time domain, frequency domain, and code domain resources. In other words, the data signals transmitted on multiple transmission resources are transmitted in an orthogonal manner. Orthogonal transmission can be understood as processing the data signals transmitted on multiple transmission resources into mutually orthogonal data signals for transmission. The orthogonal data signals are transmitted independently of each other and do not interfere with each other. In the scenario of orthogonal transmission, for a certain transmission resource, only one data signal can be used to transmit at a certain time, resulting in low utilization of the transmission resources. On the other hand, if the total transmission resources are fixed, if the number of transmission resources occupied by a certain data signal (hereinafter referred to as the "first data signal") increases, it means that the number of transmission resources available for transmitting other data signals (hereinafter referred to as the "second data signal") decreases, which may cause the other signals to be unable to be transmitted in a timely manner.

[0105] Taking time division multiplexing transmission as an example, the first data signal and the second data signal can be transmitted in the same frequency domain but different time domains, or in other words, the first data signal and the second data signal can occupy the same frequency domain resources but different time domain resources. Taking frequency division multiplexing transmission as an example, the first data signal and the second data signal can be transmitted in the same time domain but different frequency domains, or in other words, the first data signal and the second data signal can occupy the same time domain resources but different frequency domain resources.

[0106] From the above, it can be seen that the transmission resources occupied by the first data signal and the second data signal are only the same in one of the time domain resources, frequency domain resources and spatial domain resources, but not in both resources, which will cause the problem of low resource utilization.

[0107] In response to the above problems, an embodiment of the present application provides a data transmission method, in which the first data signal and the second data signal can be the same on at least two resources among time domain resources, frequency domain resources and spatial domain resources, or in other words, the first data signal and the second data signal can be transmitted non-orthogonally, such as the first data signal and the second data signal are transmitted non-orthogonally on the first resource, thereby improving the utilization of data transmission resources and spectrum efficiency.

[0108] The first data signal and the second data signal in the embodiment of the present application may be data signals transmitted between the first device and the second device.

[0109] In some embodiments, the first device may be a transmitting end and the second device may be a receiving end. In some possible implementations, the first device may be a network device and the second device may be a terminal device, or both the first device and the second device may be terminal devices, or the first device may be a terminal device and the second device may be a network device.

[0110] The wireless communication method according to an embodiment of the present application is described below with reference to Figure 10. The wireless communication method shown in Figure 10 includes step S1010.

[0111] In step S1010 , the first device sends a target data signal to the second device.

[0112] In some embodiments, the target data signal is generated based on the first data signal and the second data signal. In other words, the target data signal includes the first data signal and the second data signal which are non-orthogonal.

[0113] In some embodiments, the target data signal includes data signals for multiple second devices. The second device is one of the multiple second devices. For example, the first data signal and the second data signal may be data signals for different second devices.

[0114] In some embodiments, the transmission resources occupied by the first data signal include the first resource. The first resource is also used to transmit at least a portion of the second data signal, or in other words, the first resource is also used to transmit a portion or all of the second data signal. In some implementations, portions of the first and second data signals are non-orthogonally superimposed on the first resource. In some implementations, the first and second data signals are non-orthogonally superimposed on the first resource. In some embodiments, the first resource may also be referred to as an overlay resource.

[0115] Since the first data signal and the second data signal are transmitted in a non-orthogonal superposition manner, the target data signal in the embodiment of the present application may also be referred to as a superposition signal.

[0116] In some embodiments, the first resource belongs to a transmission resource set, where the transmission resource set may include one or more transmission resources. All transmission resources in the transmission resource set may be used to transmit the second data signal, and correspondingly, some or all transmission resources in the transmission resource set may be used to transmit the first data signal. In other words, some or all transmission resources in the transmission resource set are the aforementioned first resources. The transmission resource set may be, for example, a PRB or RB, and the first resource may be, for example, a PRB, RB, or RE.

[0117] In some embodiments, the first resource may include one or more of the following resources: time domain resources, frequency domain resources, and spatial domain resources. As an implementation, the first resource may include time domain resources and frequency domain resources, or in other words, the first data signal and the second data signal may be transmitted on the same time domain resources and the same frequency domain resources, or in other words, the first data signal and the second data signal are non-orthogonally superimposed on the same time domain resources and the same frequency domain resources. As an implementation, the first resource may include time domain resources and spatial domain resources, or in other words, the first data signal and the second data signal may be transmitted on the same time domain resources and the same spatial domain resources, or in other words, the first data signal and the second data signal are non-orthogonally superimposed on the same time domain resources and the same spatial domain resources. As an implementation, the first resource may include frequency domain resources and spatial domain resources, or in other words, the first data signal and the second data signal may be transmitted on the same frequency domain resources and the same spatial domain resources, or in other words, the first data signal and the second data signal are non-orthogonally superimposed on the same frequency domain resources and the same spatial domain resources. As an implementation method, the first resource may include time domain resources, frequency domain resources and spatial domain resources, or in other words, the first data signal and the second data signal may be transmitted on the same time domain resources, the same frequency domain resources and the same spatial domain resources, or in other words, the first data signal and the second data signal are non-orthogonally superimposed on the same time domain resources, frequency domain resources and spatial domain resources.

[0118] Taking the example of the first resource including a time domain resource, the first resource may be a symbol (also called a "time domain symbol"), a time slot, a subframe, or a frame. Taking the example of the first resource including a frequency domain resource, the first resource may include a subcarrier, a bandwidth portion, a frequency band, etc. Taking the example of the first resource including a spatial domain resource, the first resource may include a codeword, a layer, an antenna port, etc. Taking the example of the first resource including both a time domain resource and a frequency domain resource, the first resource may include any one of a PRB, RE, and RB.

[0119] In some implementations, the first data signal and the second data signal may be data signals for the same user, in which case the first resource may include a single-stream transmission resource. In other implementations, the first data signal and the second data signal may be data signals for different users, in which case the first resource may include a multi-stream transmission resource.

[0120] In some embodiments, the target data signal may be generated by the first device.The first device may generate the target data signal based on the first data signal and the second data signal.

[0121] The embodiments of the present application do not specifically limit the method for generating the target data signal. As an example, the target data signal is generated based on a linear superposition of the first and second data signals. As another example, the target data signal is generated based on a nonlinear superposition of the first and second data signals. The following describes these two implementations in detail.

[0122] Transmission method 1

[0123] The first data signal and the second data signal can be transmitted non-orthogonally in a linear superposition manner. In the embodiments of the present application, performing non-orthogonal transmission based on linear superposition helps, on the one hand, simplify the complexity of non-orthogonal transmission. On the other hand, performing non-orthogonal transmission based on linear superposition helps reduce the complexity of the second device in identifying multiple signals of non-orthogonal transmission.

[0124] In some scenarios, when transmitting signals on transmission resources, there are usually some restrictions on the energy of the transmitted signals. Therefore, in the embodiments of the present application, when the superimposed first data signal and the second data signal are transmitted via the first resource, the energy of the signal transmitted on the first resource can be adjusted using the first parameter and / or the second parameter (or, the power of the signal transmitted on the first resource can be adjusted using the first parameter and / or the second parameter). In other words, the parameters associated with the above-mentioned linear superposition method are determined based on the first parameter and / or the second parameter.

[0125] It should be noted that the parameters associated with the above-mentioned linear superposition method are determined based on the first parameter and / or the second parameter. It can be understood that the parameters associated with the linear superposition method include the first parameter and / or the second parameter, or the parameters associated with the linear superposition method are obtained by calculating the first parameter and / or the second parameter. The embodiments of the present application are not limited to this.

[0126] In some implementations, the first parameter is used to adjust the energy of the first data signal transmitted on the first resource. For example, the first parameter is used to increase the energy of the first data signal transmitted on the first resource. In another example, the first parameter is used to decrease the energy of the first data signal transmitted on the first resource.

[0127] In some implementations, the second parameter is used to adjust the energy of the second data signal transmitted on the first resource. For example, the second parameter is used to increase the energy of the second data signal transmitted on the first resource. In another example, the second parameter is used to decrease the energy of the second data signal transmitted on the first resource.

[0128] In some scenarios, it is stipulated that the signal energy of the signal transmitted on the transmission resource is less than or equal to the energy threshold corresponding to the transmission resource (for example, the energy threshold is 1). Accordingly, in some implementations, the parameters associated with the above-mentioned linear superposition method (for example, the first parameter and / or the second parameter) are used to adjust the sum of the energy of the first data signal and the energy of the second data signal transmitted on the first resource to be less than or equal to the energy threshold corresponding to the first resource.

[0129] In some implementations, the value of the first parameter may be between 0 and 1, that is, the first parameter is greater than 0 and less than 1. The value of the second parameter may be between 0 and 1, that is, the second parameter is greater than 0 and less than 1.

[0130] In some implementations, the value of the first parameter and the value of the second parameter may be equal. For example, the value of the first parameter and the value of the second parameter are both 0.5. In some implementations, the values ​​of the first parameter and the second parameter may not be equal. For example, the value of the first parameter is between 0 and 0.5, and the value of the second parameter is between 0.5 and 1. In other words, the value of the first parameter is greater than 0 and less than 0.5, and the value of the second parameter is greater than 0.5 and less than 1.

[0131] In some implementations, the sum of the value of the first parameter and the value of the second parameter is 1, which can fully utilize the energy corresponding to the first resource and improve the transmission success rate of the first data signal and the second data signal.

[0132] The embodiments of the present application do not limit the energy threshold. In some implementations, the energy threshold may be determined based on the average energy corresponding to the first resource. For example, the energy threshold may be equal to the average energy corresponding to the first resource. In another example, the energy threshold may be less than the average energy corresponding to the first resource.

[0133] In the embodiments of the present application, the energy threshold may be predefined, for example, the energy threshold may be predefined by a communication protocol. Of course, the energy threshold may also be preconfigured, for example, the energy threshold may be configured by a network device. The embodiments of the present application are not limited to this.

[0134] For ease of understanding, the following describes a non-orthogonal transmission scheme based on linear superposition in an embodiment of the present application in combination with the first parameter and the second parameter. FIG11 shows a non-orthogonal transmission scheme based on linear superposition.

[0135] As described above, the first resource belongs to a transmission resource set, and the target data signal transmitted on one or more first resources in the transmission resource set is represented by a matrix S. Accordingly, the matrix S is determined by the formula S = V⊙D1+X⊙D2, or in other words, the matrix S satisfies V⊙D1+X⊙D2. Here, the matrix V represents the first parameter associated with the first resource in the transmission resource set; the matrix X represents the second parameter associated with the first resource in the transmission resource set; the matrix D1 represents the first data signal transmitted on the first resource in the transmission resource set; the matrix D2 represents the second data signal transmitted on the first resource in the transmission resource set; and ⊙ represents the Hadamard product.

[0136] In some implementations, assuming that the energy threshold corresponding to the first resource is 1, the matrix V is determined based on the formula V = sqrt(A), and the matrix X is determined based on the formula X = sqrt(1-A), where the matrix A∈[0,1] and sqrt() represents square root calculation.

[0137] It should be noted that in the embodiment of the present application, the number of first resources included in the transmission resource set is not limited. Accordingly, the dimensions of the matrices mentioned above (for example, matrix S, matrix V, matrix D1, matrix D2, matrix A, and matrix X, etc.) are associated with the dimensions (or quantity) of the first resources in the transmission resource set. For example, each element in the matrix may correspond to a transmission resource in the transmission resource set. In some implementations, the dimension of the matrix is ​​the same as the dimension of the first resource in the transmission resource set. Taking the transmission resource set as RB as an example, the RB can be represented as including N rows and M columns of REs, and all REs in the RB are superimposed transmission resources. Accordingly, the matrix mentioned above can be a matrix with N rows and M columns, where M and N are positive integers.

[0138] Typically, the dimensions of the matrix above change when the first resource allocated by the system changes. For example, if the first resource allocated by the system is two RBs, the dimensions of the matrix are the same as the dimensions of the REs in the two RBs. Assuming that an RB can be represented as comprising N rows and M columns of REs, then two RBs comprise 2N rows and 2M columns of REs, and the corresponding dimensions of the two RBs are 2N rows and 2M columns. In this case, if all REs in the two RBs are overlay transmission resources, the dimensions of the matrix mentioned above can be a matrix of 2N rows and 2M columns.

[0139] In some embodiments, the first parameter can be determined based on the first model, or in other words, the first parameter is learnable. The first parameter can be optimized according to the training data during the training process. By optimizing the first parameter through the first model, the flexibility of the first parameter can be increased and the success rate of receiving the data signal can be improved. For example, when the size of the resource block allocated by the system changes, the first parameter (such as the matrix V) can also change in equal dimensions. The first model can be, for example, an AI model or a machine learning model, and the embodiments of the present application do not make specific limitations on this. Taking the first model as an AI model as an example, the embodiments of the present application do not limit the fields to which the AI ​​model is adapted.

[0140] In some embodiments, the first parameter may be non-learnable, or in other words, the first parameter is a preconfigured parameter. By preconfiguring the first parameter, the complexity of linear superposition can be reduced. In some implementations, the first parameter may be preconfigured by the first device (i.e., the transmitting end), or the first parameter may be a parameter predefined in the protocol.

[0141] In some embodiments, the second parameter can be determined based on the second model, or in other words, the second parameter is learnable. The second parameter can be optimized according to the training data during the training process. By optimizing the first parameter through the first model, the flexibility of the first parameter can be increased and the success rate of receiving the data signal can be improved. For example, when the size of the resource block allocated by the system changes, the second parameter (such as matrix X) can change in the same dimension accordingly. The second model can be, for example, an AI model or a machine learning model, and the embodiments of the present application do not specifically limit this. Taking the second model as an AI model as an example, the embodiments of the present application do not limit the fields to which the AI ​​model is adapted.

[0142] In some embodiments, the second parameter may be non-learnable, or in other words, a preconfigured parameter. By preconfiguring the first parameter, the complexity of linear superposition can be reduced. In some implementations, the second parameter may be preconfigured by the first device (i.e., the transmitting end), or the second parameter may be a predefined parameter in the protocol.

[0143] The above describes a method for determining parameters associated with the linear superposition method based on the first parameter and / or the second parameter in an embodiment of the present application. In other implementations, the parameters associated with the linear superposition method can be determined based on a symbol set corresponding to the first data signal and a symbol set corresponding to the second data signal.

[0144] The symbol set associated with the first data signal may include one or more symbols that can be used to transmit the first data signal. In some scenarios, to improve the transmission performance of the first data signal, the first data signal may be modulated. Accordingly, the symbol set associated with the first data signal may include modulation symbols associated with the modulation constellation points of the first data signal. The modulation constellation points of the first data signal are associated with the modulation scheme of the first data signal.

[0145] As previously described, if the first data signal is modulated, the amplitude of the first data signal indicated in the matrix D1 associated with the first data signal may be the modulated amplitude, and / or the phase of the first data signal indicated in the matrix D1 associated with the first data signal may be the modulated phase. In this case, the matrix D1 associated with the first data signal belongs to the set of modulation constellation points associated with the modulation scheme of the first data signal. Taking the modulation scheme of the first data signal as BPSK as an example, the set of modulation constellation points Q1 associated with BPSK can be expressed as Q1 = {-1, 1}. Accordingly, the matrix D1 ∈ Q1 associated with the first data signal. Taking the modulation scheme of the first data signal as QPSK as an example, the set of modulation constellation points Q1 associated with QPSK can be expressed as Q1 = {0.707 + 0.707j, 0.707 - 0.707j, -0.707 + 0.707j, -0.707 - 0.707j}. Accordingly, the matrix D1 ∈ Q1 associated with the first data signal.

[0146] The symbol set associated with the second data signal may include one or more symbols that can be used to transmit the second data signal. In some scenarios, to improve the transmission performance of the second data signal, the second data signal may be modulated. Accordingly, the symbol set associated with the second data signal may include modulation symbols associated with the modulation constellation points of the second data signal. The modulation constellation points of the second data signal are associated with the modulation scheme of the second data signal.

[0147] As previously described, if the second data signal is modulated, the amplitude of the second data signal indicated in the matrix D2 associated with the second data signal can be the modulated amplitude, and / or the phase of the second data signal indicated in the matrix D2 associated with the second data signal can be the modulated phase. In this case, the matrix D2 associated with the second data signal belongs to the set of modulation constellation points associated with the modulation scheme of the second data signal. Taking the modulation scheme of the second data signal as BPSK as an example, the set of modulation constellation points Q2 associated with BPSK can be expressed as Q2 = {-1, 1}. Accordingly, the matrix D2 associated with the second data signal is Q2. Taking the modulation scheme of the second data signal as QPSK as an example, the set of modulation constellation points Q2 associated with QPSK can be expressed as Q2 = {0.707 + 0.707j, 0.707 - 0.707j, -0.707 + 0.707j, -0.707 - 0.707j}. Accordingly, the matrix D2 associated with the first data signal is Q2.

[0148] FIG12 illustrates the training process of the modulation constellation points using the QPSK modulation scheme as an example. Referring to FIG12( a ), the initial set of modulation constellation points associated with the first data signal (or the second data signal) can be expressed as C = {0.707 + 0.707 j, 0.707 - 0.707 j, -0.707 + 0.707 j, -0.707 - 0.707 j}. Accordingly, after learning, the learned set of modulation constellation points associated with the first data signal (or the second data signal) can be expressed as C′ = {(0.707 + x1) + (0.707 + x2) j, (0.707 + x3) - (0.707 + x4) j, - (0.707 + x5) + (0.707 - x6) j, - (0.707 + x7) - (0.707 + x8) j}, as shown in FIG12( b ).

[0149] As shown in Figures 12(a) to (b), the optimization of the modulation constellation point set of the first data signal (or the second data signal) can be understood as a set shaping optimization of the initial modulation constellation point set, which helps to improve the transmission performance of the first data signal (or the second data signal).

[0150] It should be noted that the trained constellation points are only an example, and the actual learning results may vary depending on the training data or initialization settings.

[0151] In some embodiments, the symbol set corresponding to the first data signal (such as matrix D1) can be determined based on the third model, or in other words, the symbol set corresponding to the first data signal is learnable. By optimizing the symbol set corresponding to the first data signal using the third model, it is beneficial to improve the success rate of receiving the data signal. The third model can be, for example, an AI model or a machine learning model, which is not specifically limited in this embodiment of the present application. Taking the third model as an AI model as an example, the embodiment of the present application does not limit the field to which the AI ​​model is adapted.

[0152] In some implementations, the first data signal can be directly processed using the third model to obtain a symbol set corresponding to the first data signal. In other implementations, the first data signal can be modulated first, and then the modulated first data signal can be processed using the third model. The modulation method can include BPSK modulation and / or QPSK modulation described above. The modulated symbols are symbols in a modulation constellation point set. For example, the modulated symbols can be symbols in a modulation constellation point set set set by the system. For example, the symbols in the modulation constellation point set can be initialized using the third model, and then the initialized symbols can be geometrically shaped and optimized based on the third model to obtain a symbol set corresponding to the first data signal.

[0153] In some embodiments, the symbol set corresponding to the first data signal (e.g., matrix D1) may be non-learnable, or in other words, the symbol set corresponding to the first data signal is a preset symbol set. For example, the symbol set corresponding to the first data signal is a modulation constellation point set, or in other words, the symbols corresponding to the first data signal belong to a modulation constellation point set. The modulation constellation point set may be a modulation constellation point set set set by the system. By setting the symbol set corresponding to the first data signal to a preset symbol set, the complexity of non-orthogonal transmission can be reduced.

[0154] In some embodiments, the symbol set corresponding to the second data signal (such as matrix D2) can be determined based on the fourth model, or in other words, the symbol set corresponding to the second data signal is learnable. By using the fourth model to optimize the symbol set corresponding to the second data signal, it is beneficial to improve the success rate of receiving the data signal. The fourth model can be, for example, an AI model or a machine learning model, which is not specifically limited in this embodiment of the present application. Taking the fourth model as an AI model as an example, the embodiment of the present application does not limit the field to which the AI ​​model is adapted.

[0155] In some implementations, the fourth model can be used to directly process the second data signal to obtain a set of symbols corresponding to the second data signal. In other implementations, the second data signal can be modulated first, and then the modulated second data signal can be processed using the fourth model. The modulation method can include the BPSK modulation and / or QPSK modulation described above. The modulated symbols are symbols in a modulation constellation point set. For example, the modulated symbols can be symbols in a modulation constellation point set set set by the system. For example, the fourth model can be used to initialize the symbols in the modulation constellation point set, and then the initialized symbols can be geometrically shaped and optimized based on the fourth model to obtain a set of symbols corresponding to the second data signal.

[0156] In some embodiments, the symbol set corresponding to the second data signal (e.g., matrix D2) may be non-learnable, or in other words, the symbol set corresponding to the second data signal is a preset symbol set. For example, the symbol set corresponding to the second data signal is a modulation constellation point set, or in other words, the symbols corresponding to the second data signal belong to a modulation constellation point set. The modulation constellation point set may be a modulation constellation point set set set by the system. By setting the symbol set corresponding to the second data signal to a preset symbol set, the complexity of non-orthogonal transmission can be reduced.

[0157] In some embodiments, to improve the success rate of receiving the first data signal and the second data signal, the first data signal and the second data signal may have different first statistical distribution characteristics, and the different statistical distribution characteristics can be used to distinguish the first data signal and the second data signal.

[0158] In some embodiments, the first statistical distribution characteristic may include one or more of the following: a modulation mode, a coding mode, and a source type.

[0159] As an example, the first data signal and the second data signal may have different modulation schemes. The modulation schemes may include QPSK and BPSK. For example, the modulation scheme of the first data signal is QPSK, and the modulation scheme of the second data signal is BPSK.

[0160] As another example, the first data signal and the second data signal may have different coding schemes. The coding scheme may include a code rate and / or a channel coding scheme. The code rate may include 378 / 1024 and 434 / 1024. The channel coding scheme may include a Turbo code coding scheme and an LDPC code coding scheme.

[0161] In some implementations, the first data signal and the second data signal may have different code rates. For example, the code rate corresponding to the first data signal is 378 / 1024, and the code rate corresponding to the second data signal is 434 / 1024. In other words, the first data signal may be encoded using a code rate of 378 / 1024, and the second data signal may be encoded using a code rate of 434 / 1024. When the code rates used by the first data signal and the second data signal are different, the channel coding methods used by the first data signal and the second data signal may be the same or different. For example, the first data signal may be encoded using an LDPC code with a code rate of 378 / 1024, and the second data signal may be encoded using an LDPC code with a code rate of 434 / 1024. Alternatively, the first data signal may be encoded using an LDPC code with a code rate of 378 / 1024, and the second data signal may be encoded using a Turbo code with a code rate of 434 / 1024.

[0162] In some implementations, the first data signal and the second data signal may have different channel coding schemes. For example, the first data signal may be coded using a Turbo code, while the second data signal may be coded using an LDPC code.

[0163] As another example, the first data signal and the second data signal may have different source types. The source type may include one or more of the following: image data, voice data, text data, and CSI. For example, the source of the first data signal may be image data, and the source of the second data signal may be voice data. For another example, the source of the first data signal may be text data, and the source of the second data signal may be CSI.

[0164] Multi-layer transmission or multi-user transmission

[0165] In some embodiments, the first data signal may include multiple first signals, and the multiple first signals may be transmitted in a non-orthogonal superposition with the second data signal. In some implementations, the multiple first signals may correspond to multiple second devices (or multiple users), or in other words, the multiple first signals are signals for multiple second devices (or multiple users). The first device may send multiple first signals to multiple second devices respectively. In some implementations, the multiple first signals may correspond to multiple transmission layers of the first device, or in other words, the multiple first signals are signals for multiple transmission layers. The transmission layer can be understood as the number of transmitted streams. The first device may send multiple first signals through multiple transmission layers.

[0166] In some embodiments, the second data signal may include multiple second signals, and the multiple second signals may be transmitted in a non-orthogonal superposition with the second data signal. In some implementations, the multiple second signals may correspond to multiple second devices (or multiple users), or in other words, the multiple second signals are signals for multiple second devices (or multiple users). The first device may send multiple second signals to multiple second devices respectively. In some implementations, the multiple second signals may correspond to multiple transmission layers of the first device, or in other words, the multiple second signals are signals for multiple transmission layers. The transmission layer can be understood as the number of transmitted streams. The first device may send multiple second signals through multiple transmission layers.

[0167] If the first data signal includes multiple first signals and the second data signal includes multiple second signals, when linearly superimposing the first and second data signals, the linear superposition can be performed as follows: linearly superimpose the first and second signals of the same layer; or linearly superimpose the first and second signals for the same second device. For example, the first signal of the first layer can be linearly superimposed with the second signal of the first layer, the first signal of the second layer can be linearly superimposed with the second signal of the second layer, and so on. For another example, the first signal for user 1 can be linearly superimposed with the second signal for user 1, the first signal for user 2 can be linearly superimposed with the second signal for user 2, and so on.

[0168] To enable the second device to better distinguish different first signals and improve the success rate of first signal reception, the multiple first signals may have different second statistical distribution characteristics. The second statistical distribution characteristics may include one or more of the following: modulation mode, coding mode, signal source type, and first parameter. The first parameter may refer to the first parameter described above, which may be used to adjust the transmission energy of the first signal.

[0169] As an example, the multiple first signals may have different modulation modes. The modulation modes may include QPSK and BPSK. For example, the modulation mode of one of the multiple first signals is QPSK, and the modulation mode of another of the multiple first signals is BPSK. Taking the example that the first data signal includes two layers of first signals, the modulation mode of the first signal of the first layer may be QPSK, and the modulation constellation point set corresponding to the first signal of the first layer may be represented as QPSK. 11 ={0.707+0.707j,0.707-0.707j,-0.707+0.707j,-0.707-0.707j}, the modulation mode of the first signal of the second layer can be BPSK, and the modulation constellation point set corresponding to the first signal of the second layer can be expressed as Q 12={-1,1}. Taking the first data signal including the first signals for two users as an example, the modulation mode of the first signal for the first user can be QPSK, and the modulation constellation point set corresponding to the first signal for the first user can be expressed as Q 11 ={0.707+0.707j, 0.707-0.707j, -0.707+0.707j, -0.707-0.707j}, the modulation mode of the first signal for the second user can be BPSK, and the modulation constellation point set corresponding to the first signal for the second user can be expressed as Q 12 ={-1,1}.

[0170] As another example, the multiple first signals may have different coding schemes. The coding scheme may include a code rate and / or a channel coding scheme. The code rate may include 378 / 1024 and 434 / 1024. The channel coding scheme may include a Turbo code coding scheme and an LDPC code coding scheme.

[0171] In some implementations, multiple first signals may have different code rates. For example, if the first data signal includes two layers of first signals, the code rate corresponding to the first signal of the first layer may be 378 / 1024, and the code rate corresponding to the first signal of the second layer may be 434 / 1024. Alternatively, the first signal of the first layer may be encoded using a code rate of 378 / 1024, and the first signal of the second layer may be encoded using a code rate of 434 / 1024. When the code rates used by the multiple first signals are different, the channel coding schemes used by the multiple first signals may be the same or different. For example, the first signal of the first layer may be encoded using an LDPC code with a code rate of 378 / 1024, and the first signal of the second layer may be encoded using an LDPC code with a code rate of 434 / 1024. Alternatively, the first signal of the first layer may be encoded using an LDPC code with a code rate of 378 / 1024, and the first signal of the second layer may be encoded using a Turbo code with a code rate of 434 / 1024. Taking the example of a first data signal including first signals for two users, the code rate corresponding to the first signal for the first user can be 378 / 1024, and the code rate corresponding to the first signal for the second user can be 434 / 1024. In other words, the first signal for the first user can be encoded using a code rate of 378 / 1024, and the first signal for the second user can be encoded using a code rate of 434 / 1024. When the code rates used by multiple first signals are different, the channel coding methods used by the multiple first signals can be the same or different. For example, the first signal for the first user can be encoded using an LDPC code with a code rate of 378 / 1024, and the first signal for the second user can be encoded using an LDPC code with a code rate of 434 / 1024. Alternatively, the first signal for the first user can be encoded using an LDPC code with a code rate of 378 / 1024, and the first signal for the second user can be encoded using a Turbo code with a code rate of 434 / 1024.

[0172] In some implementations, multiple first signals may have different channel coding schemes. For example, if the first data signal includes two layers of first signals, the coding scheme for the first layer's first signal may be a Turbo code, and the coding scheme for the second layer's first signal may be an LDPC code. For example, if the first data signal includes first signals for two users, the coding scheme for the first user's first signal may be a Turbo code, and the coding scheme for the second user's first signal may be an LDPC code.

[0173] As another example, multiple first signals may have different signal source types. The signal source type may include one or more of the following: image data, voice data, text data, and CSI. Taking the example that the first data signal includes two layers of first signals, the signal source of the first signal of the first layer is image data, and the signal source of the first signal of the second layer is voice data, or the signal source of the first signal of the first layer is text data, and the signal source of the first signal of the second layer is CSI. Taking the example that the first data signal includes first signals for two users, the signal source of the first signal for the first user is image data, and the signal source of the first signal for the second user is voice data, or the signal source of the first signal for the first user is text data, and the signal source of the first signal for the second user is CSI.

[0174] As another example, the values ​​of the first parameters corresponding to multiple first signals are different. For example, if the first data signal includes two layers of first signals, the first parameter corresponding to the first signal of the first layer is V1, and the first parameter corresponding to the first signal of the second layer is V2, where V1 ≠ V2. For example, if the first data signal includes first signals for two users, the first parameter corresponding to the first signal for the first user is V3, and the first parameter corresponding to the first signal for the second user is V4, where V3 ≠ V4.

[0175] Similar to the first data signal, the second data signal may also include multiple second signals. The multiple second signals may be transmitted in a non-orthogonal superposition with the first data signal (such as multiple first signals). In some implementations, the multiple second signals may correspond to multiple second devices (or multiple users), or in other words, the multiple second signals are signals for multiple second devices (or multiple users). The first device may send multiple second signals to multiple second devices respectively. In some implementations, the multiple second signals may correspond to multiple transmission layers of the first device, or in other words, the multiple second signals are signals for multiple transmission layers. The transmission layer can be understood as the number of transmitted streams. The first device may send multiple second signals through multiple transmission layers.

[0176] In order for the second device to better distinguish different second signals and improve the success rate of receiving the second signals, the multiple second signals may have different second statistical distribution characteristics. The second statistical distribution characteristics may include one or more of the following: modulation mode, coding mode, source type, and second parameter. The second parameter may refer to the second parameter described above, which can be used to adjust the transmission energy of the second signal. This second statistical distribution characteristic is similar to the relevant content of the second statistical distribution characteristics corresponding to the multiple first signals described above, and for the sake of brevity, it will not be repeated here.

[0177] Transmission method 2

[0178] In some embodiments, the first and second data signals may be transmitted non-orthogonally in a nonlinear superposition manner. Alternatively, the target data signal may be generated based on the nonlinearly superposed first and second data signals. In embodiments of the present application, generating the target data signal based on nonlinear superposition helps increase flexibility in superimposing the first and second data signals.

[0179] In some implementations, the target data signal is generated by nonlinearly superimposing the first data signal and the second data signal using a fifth model. In other words, the fifth model can be used to nonlinearly superimpose the first data signal and the second data signal. The fifth model can be, for example, an AI model or a machine learning model, which is not specifically limited in this embodiment of the present application. Taking the fifth model as an AI model as an example, this embodiment of the present application does not limit the fields to which the AI ​​model is adapted.

[0180] In some implementations, the nonlinear superposition of the first and second data signals using the fifth model may refer to directly performing nonlinear superposition on the first and second data signals using the fifth model, or may refer to performing nonlinear superposition on processed data signals using the fifth model. The processed data signal may be obtained by performing the first processing operation on the first and second data signals.

[0181] There are many ways to perform the first processing operation, which are not specifically limited in the embodiments of the present application. For example, the first processing operation may include one or more of the following: a splicing operation, linear superposition, sixth model processing, and seventh model processing. The sixth model may be used to process the first data signal, and the seventh model may be used to process the second data signal. The sixth model may be an AI model or a machine learning model. The seventh model may be an AI model or a machine learning model.

[0182] In some embodiments, the first processing operation may include linear superposition. The fifth model may be used to perform nonlinear superposition on the first data signal and the second data signal after the linear superposition. The linear superposition method can be referred to the above description and will not be repeated here for the sake of brevity.

[0183] As shown in Figure 13, assuming that the dimensions of the first data signal and the second data signal are N×M, the matrix D1 associated with the first data signal and the matrix D2 associated with the second data signal can be expressed as N×M matrices, the first parameter can be expressed as an N×M matrix V, and the second parameter can be an N×M matrix X. Accordingly, the matrix S after linear superposition can be expressed as S=V⊙D1+X⊙D2, where the matrix V=sqrt(A)∈[0,1] N×M , matrix X = sqrt(1-A)∈[0,1] N×M , A∈[0,1]N×M Afterwards, the matrix S is input into the fifth model, so as to use the fifth model to perform nonlinear superposition on the linearly superposed matrix S to obtain a matrix Q representing the target data signal.

[0184] In some embodiments, taking the first processing operation including a splicing operation as an example, the fifth model may be used to perform nonlinear superposition on the first data signal and the second data signal after splicing.

[0185] The embodiments of the present application do not specifically limit the splicing method. In some implementations, the splicing method may include splicing the first data signal and the second data signal in the time domain, or splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain, or splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain, or splicing the transmission resources used to transmit the first data signal and the transmission resources used to transmit the second data signal in the time domain.

[0186] For example, splicing in the time domain dimension may include that the last time domain resource corresponding to the transmission resource occupied by the first data signal is earlier than the first time domain resource corresponding to the transmission resource occupied by the second data signal, and the last time domain resource corresponding to the first data signal is adjacent to the first time domain resource corresponding to the second data signal in the time domain. In other words, the transmission resource occupied by the first data signal and the transmission resource occupied by the second data signal are continuous in the time domain, and the last time domain resource occupied by the transmission resource occupied by the first data signal is earlier than the first time domain resource corresponding to the transmission resource occupied by the second data signal, as shown in Figure 14.

[0187] As shown in Figure 14, taking the symbols corresponding to REs as time domain resources as an example, the transmission resources occupied by the first signal include RB1, and the transmission resources occupied by the second signal include RB2. Accordingly, the transmission resources occupied by the first signal and the transmission resources occupied by the second signal are spliced ​​in the time domain. This can be understood as splicing the REs in RB1 with the REs in RB2 in the time domain. In other words, the last symbol in RB1 is spliced ​​with the first symbol in RB2, so that the last symbol in RB1 is the previous adjacent symbol of the first symbol in RB2.

[0188] In other implementations, the splicing method may include splicing the first data signal and the second data signal in the frequency domain, or in other words, splicing the transmission resources occupied by the second data signal and the transmission resources occupied by the first data signal in the frequency domain dimension.

[0189] For example, the first frequency domain resource among the transmission resources occupied by the first data signal is the frequency domain resource with the highest frequency among the transmission resources occupied by the first data signal, and the second frequency domain resource among the transmission resources occupied by the second data signal is the frequency domain resource with the lowest frequency among the transmission resources occupied by the second data signal. Accordingly, the splicing in the frequency domain dimension may include the frequency of the first frequency domain resource being lower than the frequency of the second frequency domain resource, and the frequency of the first frequency domain resource being continuous with the frequency of the second frequency domain resource.

[0190] As shown in Figure 15, the transmission resources occupied by the first signal include RB1, and the first frequency domain resource is RE1 with the highest frequency in RB1. The transmission resources occupied by the second signal include RB2, and the second frequency domain resource is RE2 with the lowest frequency in RB2. Accordingly, in the frequency domain, the transmission resources occupied by the first signal and the transmission resources occupied by the second signal are spliced ​​together. This can be understood as splicing RE1 and RE2 in the frequency domain, so that the spliced ​​RE1 and RE2 are continuous in the frequency domain, and the frequency corresponding to RE1 is lower than the frequency corresponding to RE2.

[0191] In other implementations, the splicing method may include splicing based on an input channel of the first data signal and an input channel of the second signal, where the input channel is an input channel of the fifth model. In other words, the input channel of the fifth model may include an input channel of the first data signal and an input channel of the second data signal. Accordingly, the splicing method may include splicing the first data signal input through the input channel of the first data signal with the second data signal input through the input channel of the second data signal.

[0192] It should be noted that, in the above-mentioned process of splicing based on input channels, signals input through different input channels can be associated with different weights. Of course, signals input through different input channels can be associated with the same weight, and this embodiment of the present application does not limit this. Taking the weight associated with the input channel of the first data signal as weight 1 and the weight associated with the input channel of the second data signal as weight 2 as an example, the spliced ​​signal can be determined based on the weight associated with input channel 1, the first data signal, the weight associated with input channel 2, and the second data signal. For example, the spliced ​​signal can be determined based on the sum of the processed first data signal and the processed second data signal, wherein the processed first data signal can be determined based on the first weight and the first data signal, and the processed second data signal can be determined based on the second weight and the second data signal.

[0193] 16 , the first data signal can be input into the fifth model via input channel 1, and the second data signal can be input into the fifth model via input channel 2. Accordingly, before nonlinearly superimposing the first and second data signals using the fifth model, the first and second data signals can be spliced ​​based on the input channels to obtain a spliced ​​signal.

[0194] The embodiments of the present application do not specifically limit the splicing method based on input channels. In some implementations, the spliced ​​signal can be determined based on the sum of the processed first data signal and the processed second data signal, where the processed first data signal can be determined based on a first weight associated with the first input channel and the first data signal, and the processed second data signal can be determined based on a second weight associated with the second input channel and the second data signal.

[0195] In the embodiments of the present application, the method for generating the spliced ​​signal is not specifically limited. For example, the spliced ​​signal may be equal to the sum of the processed first data signal and the processed second data signal. For another example, the spliced ​​signal may be obtained by processing the sum of the processed first data signal and the processed second data signal.

[0196] In addition, the processed first data signal described above is determined based on the first weight and the first data signal, and may include, for example, the processed first data signal being equal to the product of the first weight and the first data signal. For another example, the processed first data signal may be obtained by processing the product of the first weight and the first data signal. Accordingly, the processed second data signal described above is determined based on the second weight and the second data signal, and may include, for example, the processed second data signal being equal to the product of the second weight and the second data signal. For another example, the processed second data signal may be obtained by processing the product of the second weight and the second data signal.

[0197] In some implementations, after the nonlinear superposition of the first data signal and the second data signal based on the fifth model, the size of the transmission resource set may be exceeded, or in other words, the dimension of the first data signal and the second data signal after the nonlinear superposition is greater than the dimension corresponding to the transmission resource set. Therefore, the fifth model can process the first data signal and the second data signal to match the size of the transmission resource set. In some implementations, the fifth model can include a downsampling calculation process, and accordingly, the fifth model can use the downsampling calculation process to process the first data signal and the second data signal to match the size of the transmission resource set. For example, if the fifth model is a CNN, and the first data signal and the second data signal are spliced ​​in the time domain dimension or the frequency domain dimension, the fifth model can use the downsampling calculation process in the convolution processing performed on the first data signal and the second data signal, so that the nonlinear superposition result of the first data signal and the second data signal output by the fifth model matches the size of the transmission resource set (or matches the dimension required by the target data signal). For another example, if the fifth model is a CNN, and the first data signal and the second data signal are spliced ​​based on the input channel, then the number of convolution channels in the fifth model can be adjusted so that the nonlinear superposition result of the first data signal and the second data signal output by the fifth model matches the size of the transmission resource set (or matches the dimension required by the target data signal).

[0198] In some embodiments, the first processing operation in Figure 17 may include sixth model processing and seventh model processing. In some implementations, the first data signal may be processed using the sixth model to obtain a processed first data signal, and the second data signal may be processed using the seventh model to obtain a processed second data signal. Furthermore, the processed first data signal and the processed second data signal may be nonlinearly superimposed using the fifth model. By processing the first data signal using the sixth model and processing the second data signal using the seventh model, the adaptability between the signal to be transmitted and the characteristics of the wireless environment can be improved.

[0199] In some implementations, the sixth model may be used to adjust the symbol set of the first data signal, and the seventh model may be used to adjust the symbol set of the second data signal. Taking the symbol set as an adjusted constellation point set as an example, the sixth model may be used to adjust the modulation constellation points of the first data signal, and the seventh model may be used to adjust the modulation constellation points of the second data signal.

[0200] In some embodiments, the sixth model may be used to learn the correlation between the first data signal in the frequency domain or the time domain, so that the first data signal processed by the sixth model is more suitable for a subsequent linear superposition process or a nonlinear superposition process, and is more adapted to the characteristics of the wireless environment corresponding to the current training data. The seventh model may be used to learn the correlation between the second data signal in the frequency domain or the time domain, so that the second data signal processed by the seventh model is more suitable for a subsequent linear superposition process or a nonlinear superposition process, and is more adapted to the characteristics of the wireless environment corresponding to the current training data.

[0201] In some embodiments, the first processing operation may include a splicing operation, sixth model processing, and seventh model processing. The splicing operation may include one or more of time domain splicing, frequency domain splicing, and channel splicing. In some implementations, the first data signal may be processed using the sixth model to obtain a processed first data signal, and the second data signal may be processed using the seventh model to obtain a processed second data signal. A splicing operation is performed on the processed first data signal and the processed second data signal to obtain a spliced ​​data signal. The fifth model may be used to perform nonlinear superposition on the spliced ​​processed signals.

[0202] Taking Figure 18 as an example, the first processing operation shown in Figure 18 includes frequency domain splicing, sixth model processing, and seventh model processing. For example, the first data signal is processed using the sixth model to obtain a processed first data signal, and the second data signal is processed using the seventh model to obtain a processed second data signal. The processed first data signal and the processed second data signal are frequency-domain spliced ​​to obtain a spliced ​​data signal. The fifth model can be used to perform nonlinear superposition on the spliced ​​processed signals.

[0203] Taking Figure 19 as an example, the first processing operation shown in Figure 19 includes time domain splicing, sixth model processing, and seventh model processing. For example, the first data signal is processed using the sixth model to obtain a processed first data signal, and the second data signal is processed using the seventh model to obtain a processed second data signal. The processed first data signal and the processed second data signal are spliced ​​in the time domain to obtain a spliced ​​data signal. The fifth model can be used to perform nonlinear superposition on the spliced ​​processed signals.

[0204] Taking Figure 20 as an example, the first processing operation shown in Figure 20 includes channel splicing, sixth model processing, and seventh model processing. For example, the first data signal is processed using the sixth model to obtain a processed first data signal, and the second data signal is processed using the seventh model to obtain a processed second data signal. The processed first data signal and the processed second data signal are channel spliced ​​to obtain a spliced ​​data signal. The fifth model can be used to perform nonlinear superposition on the spliced ​​processed signals.

[0205] In some embodiments, the first processing operation may include linear superposition, sixth model processing, and seventh model processing. For example, taking Figure 21 as an example, the first data signal is processed using the sixth model to obtain a processed first data signal, and the second data signal is processed using the seventh model to obtain a processed second data signal. The processed first data signal and the processed second data signal are linearly superimposed to obtain a superimposed data signal. The fifth model can be used to perform nonlinear superposition on the superimposed data signals. The linear superposition method can be any of the superposition methods described above, and for the sake of brevity, it is not repeated here.

[0206] In some embodiments, the target data signal can be obtained by performing the sixth model processing, the seventh model processing, and linear superposition on the first and second data signals. For example, as shown in FIG22 , the first data signal is processed using the sixth model to obtain a processed first data signal, and the second data signal is processed using the seventh model to obtain a processed second data signal. The processed first and second data signals are linearly superimposed to obtain a superimposed data signal.

[0207] As can be seen above, the first data signal includes multiple first signals, and the second data signal includes multiple second signals. The above-mentioned first processing operation can be performed on multiple first signals and / or multiple second signals. For example, the sixth model can be used to process multiple first signals to obtain multiple processed first signals. For another example, the seventh model can be used to process multiple second signals to obtain multiple processed second signals.

[0208] The following description will be made by taking the first processing operation including the sixth model processing, the seventh model processing and the splicing operation as an example.

[0209] If multiple first signals and multiple second signals are spliced, the first signals and second signals of the same type (such as the same layer or for the same user) can be spliced. For example, the first signal and the second signal of the same layer can be spliced. Taking the example that the first data signal includes two layers of first signals and the second data signal includes two layers of second signals, the first signal of the first layer can be spliced ​​with the second signal of the first layer, and the first signal of the second layer can be spliced ​​with the second signal of the second layer. For another example, the first signal and the second signal for the same user can be spliced. Taking the example that the first data signal includes the first signal for two users and the second data signal includes the second signal for two users, the first signal for the first user can be spliced ​​with the second signal for the first user, and the first signal for the second user can be spliced ​​with the second signal for the second user. The splicing operation below is also similar splicing, and for the sake of brevity, it will not be repeated below.

[0210] In some embodiments, the first processing operation may include sixth model processing, seventh model processing, and frequency domain splicing. For example, the sixth model may be used to process multiple first signals to obtain multiple processed first signals, and the seventh model may be used to process multiple second signals to obtain multiple processed second signals. The multiple processed first signals and the multiple processed second signals may be spliced ​​in the frequency domain to obtain multiple spliced ​​signals. The fifth model may be used to perform nonlinear superposition on the multiple spliced ​​signals to obtain multiple nonlinear superpositioned signals.

[0211] The above-mentioned frequency domain splicing can refer to frequency domain splicing of the first signal and the second signal of the same type (such as the same layer or for the same user). As shown in Figure 23, taking multi-layer transmission as an example, assuming that the first data signal includes two layers of first signals and the second data signal includes two layers of second signals, the first signal of the first layer processed by the sixth model can be frequency domain spliced ​​with the second signal of the first layer processed by the seventh model to obtain a first spliced ​​signal. The first signal of the second layer processed by the sixth model can be frequency domain spliced ​​with the second signal of the second layer processed by the seventh model to obtain a second spliced ​​signal. The fifth model can be used to process the first spliced ​​signal and the second spliced ​​signal. For example, the fifth model can be used to process the first spliced ​​signal to obtain a nonlinear superposition signal of the first layer. The fifth model can be used to process the second spliced ​​signal to obtain a nonlinear superposition signal of the second layer.

[0212] In some embodiments, the first processing operation may include sixth model processing, seventh model processing, and time domain splicing. For example, the sixth model may be used to process multiple first signals to obtain multiple processed first signals, and the seventh model may be used to process multiple second signals to obtain multiple processed second signals. The multiple processed first signals and the multiple processed second signals may be spliced ​​in the time domain to obtain multiple spliced ​​signals. The fifth model may be used to perform nonlinear superposition on each of the multiple spliced ​​signals to obtain multiple nonlinearly superposed signals.

[0213] The above-mentioned time domain splicing can refer to the time domain splicing of the first signal and the second signal of the same type (such as the same layer or for the same user). As shown in Figure 24, taking multi-layer transmission as an example, assuming that the first data signal includes two layers of first signals and the second data signal includes two layers of second signals, the first signal of the first layer processed by the sixth model can be time-domain spliced ​​with the second signal of the first layer processed by the seventh model to obtain a first spliced ​​signal. The first signal of the second layer processed by the sixth model can be time-domain spliced ​​with the second signal of the second layer processed by the seventh model to obtain a second spliced ​​signal. The fifth model can be used to process the first spliced ​​signal and the second spliced ​​signal. For example, the fifth model can be used to process the first spliced ​​signal to obtain a nonlinear superposition signal of the first layer. The fifth model can be used to process the second spliced ​​signal to obtain a nonlinear superposition signal of the second layer.

[0214] In some embodiments, the first processing operation may include sixth model processing, seventh model processing, and channel splicing. For example, the sixth model may be used to process multiple first signals to obtain multiple processed first signals, and the seventh model may be used to process multiple second signals to obtain multiple processed second signals. Channel splicing may be performed on the multiple processed first signals and the multiple processed second signals to obtain multiple spliced ​​signals. The fifth model may be used to perform nonlinear superposition on the multiple spliced ​​signals to obtain multiple nonlinear superpositioned signals.

[0215] The above-mentioned channel splicing can refer to the channel splicing of the first signal and the second signal of the same type (such as the same layer or for the same user). As shown in Figure 25, taking multi-layer transmission as an example, assuming that the first data signal includes two layers of first signals and the second data signal includes two layers of second signals, the first signal of the first layer processed by the sixth model can be channel spliced ​​with the second signal of the first layer processed by the seventh model to obtain a first spliced ​​signal. The first signal of the second layer processed by the sixth model can be channel spliced ​​with the second signal of the second layer processed by the seventh model to obtain a second spliced ​​signal. The fifth model can be used to process the first spliced ​​signal and the second spliced ​​signal. For example, the fifth model can be used to process the first spliced ​​signal to obtain a nonlinear superposition signal of the first layer. The fifth model can be used to process the second spliced ​​signal to obtain a nonlinear superposition signal of the second layer.

[0216] The above description uses multi-layer transmission as an example to describe the processing of multiple first signals and multiple second signals. It will be appreciated that multi-user transmission and multi-layer transmission are similar. For the sake of brevity, further details are omitted here. For example, the multi-layer first signals described above can be replaced with multi-user first signals, and the multi-layer second signals can be replaced with multi-user second signals.

[0217] Receiver

[0218] In some embodiments, the second device may include a first receiver. The second device may receive a target data signal via the first receiver. The first receiver may be configured to recover the first data signal and the second data signal from the target data signal. For example, the first receiver may be configured to process the target data signal to obtain the first data signal and the second data signal.

[0219] The embodiment of the present application does not specifically limit the type of the first receiver. For example, the first receiver may be an AI receiver. For another example, the first receiver may be an ML receiver.

[0220] In some implementations, the first receiver may be configured to recover the first data signal and the second data signal based on the first configuration information.

[0221] In some embodiments, the input of the first receiver may include first configuration information and a target data signal, as shown in FIG26 . The output of the first receiver may be determined based on the functionality of the model in the first receiver. For example, the output of the first receiver may be a log-likelihood ratio or a received bit stream.

[0222] The embodiments of the present application do not specifically limit the first configuration information. The first configuration information is related to the superposition method of the first data signal and the second data signal. For example, the first configuration information may include one or more of the following information: the number of transmission layers, the number of users transmitted, the transmission bandwidth, the first parameter (or the index of the first parameter), the second parameter (or the index of the second parameter), the third statistical distribution characteristic corresponding to the first data signal, and the fourth statistical distribution characteristic corresponding to the second data signal.

[0223] The first parameter may be the first parameter described above, that is, the first parameter may be used to adjust the transmission energy of the first data signal; the second parameter may be the second parameter described above, that is, the second parameter may be used to adjust the transmission energy of the second data signal.

[0224] In some embodiments, the third statistical distribution characteristic may include one or more of the following: modulation mode, coding mode, and information source type. The fourth statistical distribution characteristic may include one or more of the following: modulation mode, coding mode, and information source type.

[0225] In some implementations, if the first data signal and the second data signal are linearly superimposed, the first configuration information may include the third statistical distribution characteristic and the fourth statistical distribution characteristic. If the first data signal and the second data signal are not linearly superimposed, the first configuration information may not include the third statistical distribution characteristic and the fourth statistical distribution characteristic. Of course, in some implementations, regardless of whether the first data signal and the second data signal are linearly superimposed, the first configuration information may include the third statistical distribution characteristic and the fourth statistical distribution characteristic. This allows the first configuration information to adapt to different scenarios, unify the input of the first receiver, and reduce the complexity of the first receiver.

[0226] In some embodiments, the first configuration information may include two types of information. The first type of information may affect the model structure and output dimension in the first receiver, while the second type of information may not affect the model structure and output dimension. The first type of information may include, for example, one or more of the modulation mode, number of transmission layers, number of transmission users, and transmission bandwidth. The second type of information may include one or more of the first parameter, the second parameter, the coding mode, and the source type.

[0227] In some embodiments, the first receiver may process the target data signal based on preconfigured parameters to obtain a first processed signal (such as step S2610 in Figure 26), and process the first processed signal based on the first configuration information to obtain a second processed signal (such as step S2620 in Figure 26). The second device may recover the first data signal and the second data signal based on the second processed signal. The dimension of the second processed signal is smaller than the dimension of the first processed signal. In some implementations, processing the first processed signal can be understood as cropping the first processed signal. For example, the first receiver may crop the first processed signal based on the first configuration information.

[0228] In some embodiments, the preconfigured parameters may include the above-mentioned first type of information. The preconfigured parameters may be the maximum parameters of the system configuration. For example, the preconfigured parameters may include one or more of the following: one or more of the maximum number of transmission layers, the maximum bandwidth, and the maximum modulation order corresponding to the modulation method (such as MCS). The first receiver may process the target data signal based on the first configuration information, and output it according to the maximum parameters (such as the maximum number of transmission layers, the maximum bandwidth, and the maximum modulation order corresponding to the MCS) to obtain a first processed signal. Furthermore, the first receiver may cut the first processed signal according to the first configuration information to obtain demodulation information that conforms to the first configuration information, so that the first receiver can adapt to data signals with different numbers of layers, different bandwidths, and different modulation methods, and can generalize the design of the first receiver to reduce the complexity of the design of the first receiver.

[0229] The following describes in detail the processing method of the first receiver by taking the case where the first configuration information is the MCS and the number of transmission layers as examples.

[0230] FIG27 shows a solution in which the first configuration information includes MCS. Assume that the resource unit allocated by the system is N subcarriers × M time domain symbols (or OFDM symbols), the modulation order corresponding to the pre-configured MCS is m, and the number of transmission layers is L. The model structure of the first receiver can be shown in FIG27. The structure can have a convolution kernel number D and a residual block number N. block Of course, the first receiver can also use the network structure described above or other network structures as the basic framework, and this embodiment of the application does not make specific limitations on this.

[0231] The input of the model may include an MCS index m indicating the coding and modulation scheme. The index m can be used as auxiliary information to guide the model to process the signal of the target MCS configuration. The scalar m is copied and rolled out as an MCS information tensor M∈C N×M×1In order to facilitate the signal processing of the model, the complex received signal (ie, the target data signal) can be converted into a real tensor Y∈C N×M×2Nr The MCS information tensor and the received signal tensor are concatenated to obtain the feature map T∈C N×M×(2Nr+1) And send it to the subsequent residual convolution network for processing. The first receiver processes the signal and outputs the log-likelihood ratio tensor using V∈C N×M×L×Qmax , where Q max Indicates the maximum number of bits per symbol corresponding to the maximum modulation order among all possible MCS types supported by the system. In addition, V∈C N×M×L×Qmax The last dimension of the tensor is clipped according to the currently set MCS (such as the MCS in the first configuration information) to obtain the final output log-likelihood ratio tensor V out ∈C N×M×L×Q And sent to the subsequent channel decoding module, where Q represents the number of bits per symbol corresponding to the modulation order of the configured MCS.

[0232] FIG28 shows a scheme in which the first configuration information includes the number of transmission layers. Assume that the resource unit allocated by the system is N subcarriers × M time domain symbols (or OFDM symbols), the modulation order corresponding to the pre-configured MCS is m, and the number of transmission layers is L. The model structure of the first receiver can be shown in FIG28. The structure can have a convolution kernel number D and a residual block number N. block Of course, the first receiver can also use the network structure described above or other network structures as the basic framework, and this embodiment of the application does not make specific limitations on this.

[0233] The input of the model can include the number of transmission layers L configured by the system, which can be used as auxiliary information to guide the model to process the signal of the target transmission layer. The scalar L is copied and rolled out as the layer information tensor L∈C N×M×1 To facilitate the signal processing of the model, the complex received signal (such as the target data signal) is converted into a real tensor Y∈C N×M×2Nr In addition, the layer information tensor and the received signal tensor can be concatenated to obtain the feature map T∈C N×M×(2Nr+1) The feature map is sent to the subsequent residual network for processing. The first receiver processes the signal and outputs the log-likelihood ratio tensor using V∈C N×M×Lmax×Q , where Q represents the number of bits per symbol corresponding to the MCS configured by the system, and L max Indicates the maximum number of transmission layers that the system can support. In some implementations, V∈C N×M×Lmax ×Q The third dimension of the tensor can be cut according to the number of transmission layers L in the first configuration information to obtain the final output log-likelihood ratio tensor V out∈C N×M×L×Q And sent to the subsequent channel decoding module.

[0234] It should be noted that, in some embodiments, the first parameter may be replaced by the first weight, and the second parameter may be replaced by the second weight.

[0235] The method embodiment of the present application is described in detail above in conjunction with Figures 1 to 28. The device embodiment of the present application is described in detail below in conjunction with Figures 29 to 31. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment. Therefore, for parts not described in detail, reference can be made to the above method embodiment.

[0236] Figure 29 is a schematic block diagram of a communication device provided in an embodiment of the present application. The communication device 2900 shown in Figure 29 can be any of the first devices described above. The communication device 2900 includes a sending unit 2910.

[0237] Sending unit 2910 is used to send a target data signal to a second device, where the target data signal is generated based on a first data signal and a second data signal. The transmission resources occupied by the first data signal include first resources, and the first resources are also used to transmit part or all of the data signals in the second data signal. The first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0238] In some possible implementations, the target data signal is generated based on linearly superimposed first data signal and second data signal.

[0239] In some possible implementations, the first data signal and the second data signal are linearly superimposed based on one or more of the following: a set of symbols corresponding to the first data signal; a set of symbols corresponding to the second data signal; a first parameter, the first parameter is used to adjust the transmission energy of the first data signal; and a second parameter, the second parameter is used to adjust the transmission energy of the second data signal.

[0240] In some possible implementations, the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.

[0241] In some possible implementations, the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.

[0242] In some possible implementations, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

[0243] In some possible implementations, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

[0244] In some possible implementations, the first data signal and the second data signal have different first statistical distribution characteristics.

[0245] In some possible implementations, the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a signal source type.

[0246] In some possible implementations, the first data signal includes multiple first signals, and the multiple first signals correspond to multiple second devices, or the multiple first signals correspond to multiple transmission layers of the first device; the second data signal includes multiple second signals, and the multiple second signals correspond to multiple second devices, or the multiple second signals correspond to multiple transmission layers of the first device; the first data signal and the second data signal are linearly superimposed in the following manner: linearly superimposing the first signal and the second signal of the same layer; or linearly superimposing the first signal and the second signal for the same second device.

[0247] In some possible implementations, the multiple first signals have different second statistical distribution characteristics.

[0248] In some possible implementations, the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, first parameter, and second parameter, wherein the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

[0249] In some possible implementations, the target data signal is generated based on the nonlinear superposition of the first data signal and the second data signal.

[0250] In some possible implementations, the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.

[0251] In some possible implementations, the fifth model is used to perform nonlinear superposition on processed data signals, where the processed data signals are obtained by performing a first processing operation on the first data signal and the second data signal.

[0252] In some possible implementations, the first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

[0253] In some possible implementations, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; and splicing based on the input channel of the first data signal and the input channel of the second data signal.

[0254] In some possible implementations, the first data signal includes multiple first signals, the second data signal includes multiple second signals, the sixth model is used to process the multiple first signals, and the seventh model is used to process the multiple second signals.

[0255] Figure 30 is a schematic block diagram of a communication device provided in an embodiment of the present application. The communication device 3000 shown in Figure 30 can be any of the second devices described above. The communication device 3000 includes a receiving unit 3010.

[0256] The receiving unit 3010 is used to receive a target data signal sent by a first device, where the target data signal is generated based on a first data signal and a second data signal. The transmission resources occupied by the first data signal include first resources, and the first resources are also used to transmit part or all of the data signals in the second data signal. The first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0257] In some possible implementations, the target data signal is generated based on linearly superimposed first data signal and second data signal.

[0258] In some possible implementations, the first data signal and the second data signal are linearly superimposed based on one or more of the following: a set of symbols corresponding to the first data signal; a set of symbols corresponding to the second data signal; a first parameter, the first parameter is used to adjust the transmission energy of the first data signal; and a second parameter, the second parameter is used to adjust the transmission energy of the second data signal.

[0259] In some possible implementations, the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.

[0260] In some possible implementations, the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.

[0261] In some possible implementations, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

[0262] In some possible implementations, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

[0263] In some possible implementations, the first data signal and the second data signal have different first statistical distribution characteristics.

[0264] In some possible implementations, the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a signal source type.

[0265] In some possible implementations, the first data signal includes multiple first signals, and the multiple first signals correspond to multiple second devices, or the multiple first signals correspond to multiple transmission layers of the first device; the second data signal includes multiple second signals, and the multiple second signals correspond to multiple second devices, or the multiple second signals correspond to multiple transmission layers of the first device; the first data signal and the second data signal are linearly superimposed in the following manner: linearly superimposing the first signal and the second signal of the same layer; or linearly superimposing the first signal and the second signal for the same second device.

[0266] In some possible implementations, the multiple first signals have different second statistical distribution characteristics.

[0267] In some possible implementations, the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, first parameter, and second parameter, wherein the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

[0268] In some possible implementations, the target data signal is generated based on the nonlinear superposition of the first data signal and the second data signal.

[0269] In some possible implementations, the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.

[0270] In some possible implementations, the fifth model is used to perform nonlinear superposition on processed data signals, where the processed data signals are obtained by performing a first processing operation on the first data signal and the second data signal.

[0271] In some possible implementations, the first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

[0272] In some possible implementations, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; and splicing based on the input channel of the first data signal and the input channel of the second data signal.

[0273] In some possible implementations, the first data signal includes multiple first signals, the second data signal includes multiple second signals, the sixth model is used to process the multiple first signals, and the seventh model is used to process the multiple second signals.

[0274] In some possible implementations, the receiving unit is configured to: receive the target data signal using a first receiver, where the first receiver is configured to recover the first data signal and the second data signal in the target data signal.

[0275] In some possible implementations, the first receiver is configured to recover the first data signal and the second data signal based on first configuration information.

[0276] In some possible implementations, the communication device further includes: a processing unit for processing the target data signal based on preconfigured parameters to obtain a first processed signal, and processing the first processed signal to obtain a second processed signal, wherein the dimension of the second processed signal is smaller than the dimension of the first processed signal; and a recovery unit for recovering the first data signal and the second data signal based on the second processed signal.

[0277] In some possible implementations, the first configuration information includes one or more of the following: the number of transmission layers, the number of transmitted users, the transmission bandwidth, a first parameter, a second parameter, and a third statistical distribution characteristic; wherein the first parameter is used to adjust the transmission energy of the first data signal, and the second parameter is used to adjust the transmission energy of the second data signal.

[0278] In some possible implementations, the third statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a signal source type.

[0279] Figure 31 is a schematic block diagram of a communication device (or communication equipment) according to an embodiment of the present application. The dashed lines in Figure 31 indicate that the unit or module is optional. The device 3100 may be used to implement the method described in the above method embodiment. The device 3100 may be a chip, a communication device, a first device, or a second device.

[0280] In some embodiments, the apparatus 3100 shown in FIG31 may be a first device. The apparatus may include a memory, a processor, and a transceiver, wherein the memory is used to store a program, the processor is used to call the program in the memory, and the transceiver is used to send a target data signal to a second device, wherein the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include first resources, the first resources are further used to transmit part or all of the data signal in the second data signal, and the first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0281] In some possible implementations, the target data signal is generated based on linearly superimposed first data signal and second data signal.

[0282] In some possible implementations, the first data signal and the second data signal are linearly superimposed based on one or more of the following: a set of symbols corresponding to the first data signal; a set of symbols corresponding to the second data signal; a first parameter, the first parameter is used to adjust the transmission energy of the first data signal; and a second parameter, the second parameter is used to adjust the transmission energy of the second data signal.

[0283] In some possible implementations, the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.

[0284] In some possible implementations, the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.

[0285] In some possible implementations, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

[0286] In some possible implementations, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

[0287] In some possible implementations, the first data signal and the second data signal have different first statistical distribution characteristics.

[0288] In some possible implementations, the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a signal source type.

[0289] In some possible implementations, the first data signal includes multiple first signals, and the multiple first signals correspond to multiple second devices, or the multiple first signals correspond to multiple transmission layers of the first device; the second data signal includes multiple second signals, and the multiple second signals correspond to multiple second devices, or the multiple second signals correspond to multiple transmission layers of the first device; the first data signal and the second data signal are linearly superimposed in the following manner: linearly superimposing the first signal and the second signal of the same layer; or linearly superimposing the first signal and the second signal for the same second device.

[0290] In some possible implementations, the multiple first signals or the multiple second signals have different second statistical distribution characteristics.

[0291] In some possible implementations, the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, first parameter, and second parameter, wherein the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

[0292] In some possible implementations, the target data signal is generated based on the nonlinear superposition of the first data signal and the second data signal.

[0293] In some possible implementations, the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.

[0294] In some possible implementations, the fifth model is used to perform nonlinear superposition on processed data signals, where the processed data signals are obtained by performing a first processing operation on the first data signal and the second data signal.

[0295] In some possible implementations, the first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

[0296] In some possible implementations, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; and splicing based on the input channel of the first data signal and the input channel of the second data signal.

[0297] In some possible implementations, the first data signal includes multiple first signals, the second data signal includes multiple second signals, the sixth model is used to process the multiple first signals, and the seventh model is used to process the multiple second signals.

[0298] In some embodiments, the apparatus 3100 shown in FIG31 may be a second device. The apparatus may include a memory, a processor, and a transceiver, wherein the memory is used to store a program, the processor is used to call the program in the memory, and the transceiver is used to: receive a target data signal sent by a first device, wherein the target data signal is generated based on a first data signal and a second data signal, wherein the transmission resources occupied by the first data signal include first resources, the first resources are further used to transmit part or all of the data signals in the second data signal, and the first resources include at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

[0299] In some possible implementations, the target data signal is generated based on linearly superimposed first data signal and second data signal.

[0300] In some possible implementations, the first data signal and the second data signal are linearly superimposed based on one or more of the following: a set of symbols corresponding to the first data signal; a set of symbols corresponding to the second data signal; a first parameter, the first parameter is used to adjust the transmission energy of the first data signal; and a second parameter, the second parameter is used to adjust the transmission energy of the second data signal.

[0301] In some possible implementations, the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.

[0302] In some possible implementations, the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.

[0303] In some possible implementations, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

[0304] In some possible implementations, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

[0305] In some possible implementations, the first data signal and the second data signal have different first statistical distribution characteristics.

[0306] In some possible implementations, the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a signal source type.

[0307] In some possible implementations, the first data signal includes multiple first signals, and the multiple first signals correspond to multiple second devices, or the multiple first signals correspond to multiple transmission layers of the first device; the second data signal includes multiple second signals, and the multiple second signals correspond to multiple second devices, or the multiple second signals correspond to multiple transmission layers of the first device; the first data signal and the second data signal are linearly superimposed in the following manner: linearly superimposing the first signal and the second signal of the same layer; or linearly superimposing the first signal and the second signal for the same second device.

[0308] In some possible implementations, the multiple first signals have different second statistical distribution characteristics.

[0309] In some possible implementations, the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, first parameter, and second parameter, wherein the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

[0310] In some possible implementations, the target data signal is generated based on the nonlinear superposition of the first data signal and the second data signal.

[0311] In some possible implementations, the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.

[0312] In some possible implementations, the fifth model is used to perform nonlinear superposition on processed data signals, where the processed data signals are obtained by performing a first processing operation on the first data signal and the second data signal.

[0313] In some possible implementations, the first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

[0314] In some possible implementations, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; and splicing based on the input channel of the first data signal and the input channel of the second data signal.

[0315] In some possible implementations, the first data signal includes multiple first signals, the second data signal includes multiple second signals, the sixth model is used to process the multiple first signals, and the seventh model is used to process the multiple second signals.

[0316] In some possible implementations, the second device receives the target data signal sent by the first device, including: the second device receives the target data signal using a first receiver, and the first receiver is used to recover the first data signal and the second data signal in the target data signal.

[0317] In some possible implementations, the first receiver is configured to recover the first data signal and the second data signal based on first configuration information.

[0318] In some possible implementations, the processor is used to: process the target data signal based on preconfigured parameters to obtain a first processed signal; process the first processed signal based on the first configuration information to obtain a second processed signal, wherein the dimension of the second processed signal is smaller than the dimension of the first processed signal; and restore the first data signal and the second data signal based on the second processed signal.

[0319] In some possible implementations, the first configuration information includes one or more of the following: the number of transmission layers, the number of transmitted users, the transmission bandwidth, the first parameter, the second parameter, the third statistical distribution characteristic corresponding to the first data signal, and the fourth statistical distribution characteristic corresponding to the second data signal; wherein the first parameter is used to adjust the transmission energy of the first data signal, and the second parameter is used to adjust the transmission energy of the second data signal.

[0320] In some possible implementations, the third statistical distribution characteristic and / or the fourth statistical distribution characteristic include one or more of the following: a modulation mode, a coding mode, and an information source type.

[0321] Continuing with reference to FIG31 , the device 3100 may include one or more processors 3110. The processor 3110 may support the device 3100 in implementing the method described in the foregoing method embodiment. The processor 3110 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0322] The apparatus 3100 may further include one or more memories 3120. The memories 3120 store programs that can be executed by the processor 3110, causing the processor 3110 to perform the methods described in the above method embodiments. The memories 3120 may be independent of the processor 3110 or integrated into the processor 3110.

[0323] The apparatus 3100 may further include a transceiver 3130. The processor 3110 may communicate with other devices or chips via the transceiver 3130. For example, the processor 3110 may transmit and receive data with other devices or chips via the transceiver 3130.

[0324] The present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to the first device or the second device provided in the present application, and the program causes a computer to execute the method performed by the first device or the second device in each embodiment of the present application.

[0325] The present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the first device or the second device provided in the present application, and the program causes a computer to execute the method performed by the first device or the second device in each embodiment of the present application.

[0326] The present application also provides a computer program that can be applied to the first device or the second device provided in the present application, and enables a computer to execute the method performed by the first device or the second device in each embodiment of the present application.

[0327] It should be understood that the terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of this application and are not intended to limit this application. The terms "first", "second", "third", and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0328] In the embodiments of this application, the term "indication" may refer to a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" may refer to a direct indication of B, e.g., B can obtain information through A; it may refer to an indirect indication of B, e.g., A indicates C, e.g., B can obtain information through C; or it may refer to an association between A and B.

[0329] In the embodiments of this application, the term "include" can refer to direct inclusion or indirect inclusion. Alternatively, the term "include" in the embodiments of this application can be replaced with "indicates" or "is used to determine." For example, "A includes B" can be replaced with "A indicates B" or "A is used to determine B."

[0330] In the embodiment of the present application, "B corresponding to A" means that B is associated with A and B can be determined based on A. However, it should be understood that determining B based on A does not mean determining B based solely on A, but B can also be determined based on A and / or other information.

[0331] In the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and indication, configuration and configuration, etc.

[0332] In the embodiments of the present application, "pre-definition" or "pre-configuration" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., a terminal device and a network device). The present application does not limit the specific implementation method. For example, pre-definition may refer to information defined in a protocol.

[0333] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communications field, for example, it may include an LTE protocol, an NR protocol, and related protocols used in future communication systems, and the present application does not limit this.

[0334] In the embodiments of this application, the term "and / or" is simply a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0335] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the embodiments of the present application.

[0336] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0337] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0338] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0339] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. 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 a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0340] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A data transmission method, characterized in that, it includes: The first device sends a target data signal to the second device, the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include a first resource, and the first resource is also used to transmit some or all of the data signals in the second data signal, and the first resource includes at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

2. The method according to claim 1, characterized in that, the target data signal is generated based on the linearly superimposed first data signal and the second data signal.

3. The method according to claim 2, characterized in that, the first data signal and the second data signal perform the linear superposition based on one or more of the following: a symbol set corresponding to the first data signal; a symbol set corresponding to the second data signal; a first parameter for adjusting the transmission energy of the first data signal; a second parameter for adjusting the transmission energy of the second data signal.

4. The method according to claim 3, characterized in that, the first parameter is determined based on a first model, or the first parameter is a pre-configured parameter.

5. The method according to claim 3 or 4, characterized in that, the second parameter is determined based on a second model, or the second parameter is a pre-configured parameter.

6. The method according to any one of claims 3-5, characterized in that, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

7. The method according to any one of claims 3-6, characterized in that, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

8. The method according to any one of claims 2-7, characterized in that, the first data signal and the second data signal have different first statistical distribution characteristics.

9. The method according to claim 8, characterized in that, the first statistical distribution characteristics include one or more of the following: modulation method, coding method, and source type.

10. The method according to any one of claims 2-9, characterized in that, the first data signal includes a plurality of first signals, the plurality of first signals correspond to a plurality of the second devices, or the plurality of first signals correspond to a plurality of transmission layers of the first device; the second data signal includes a plurality of second signals, the plurality of second signals correspond to a plurality of the second devices, or the plurality of second signals correspond to a plurality of transmission layers of the first device; the first data signal and the second data signal perform the linear superposition in the following manner: linearly superpose the first signal and the second signal of the same layer; or linearly superpose the first signal and the second signal for the same second device.

11. The method according to claim 10, characterized in that, The multiple first signals or the multiple second signals have different second statistical distribution characteristics.

12. The method according to claim 11, wherein, the second statistical distribution characteristics include one or more of the following: modulation method, coding method, source type, first parameter, second parameter, the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

13. The method according to claim 1, wherein, the target data signal is generated based on the first data signal and the second data signal that are non-linearly superimposed.

14. The method according to claim 13, wherein, the target data signal is generated by non-linearly superimposing the first data signal and the second data signal using a fifth model.

15. The method according to claim 14, wherein, the fifth model is used to non-linearly superimpose the processed data signals, and the processed data signals are obtained after the first data signal and the second data signal undergo a first processing operation.

16. The method according to claim 15, wherein, the first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein, the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

17. The method according to claim 16, wherein, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; splicing based on the input channels of the first data signal and the input channels of the second data signal.

18. The method according to claim 16 or 17, wherein, the first data signal includes multiple first signals, the second data signal includes multiple second signals, the sixth model is used to process the multiple first signals, and the seventh model is used to process the multiple second signals.

19. A data transmission method, wherein, comprising: a second device receives a target data signal sent by a first device, the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include a first resource, and the first resource is also used to transmit some or all of the data signals in the second data signal, and the first resource includes at least two of the following resources: time domain resources, frequency domain resources, and space domain resources.

20. The method according to claim 19, wherein, the target data signal is generated based on the first data signal and the second data signal that are linearly superimposed.

21. The method according to claim 20, wherein, The first data signal and the second data signal perform the linear superposition based on one or more of the following: The symbol set corresponding to the first data signal; The symbol set corresponding to the second data signal; A first parameter for adjusting the transmission energy of the first data signal; A second parameter for adjusting the transmission energy of the second data signal.

22. The method according to claim 21, wherein, the first parameter is determined based on a first model, or the first parameter is a pre-configured parameter.

23. The method according to claim 21 or 22, wherein, the second parameter is determined based on a second model, or the second parameter is a pre-configured parameter.

24. The method according to any one of claims 21-23, wherein, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

25. The method according to any one of claims 21-24, wherein, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

26. The method according to any one of claims 20-25, wherein, the first data signal and the second data signal have different first statistical distribution characteristics.

27. The method according to claim 26, wherein, the first statistical distribution characteristics include one or more of the following: modulation method, coding method, and source type.

28. The method according to any one of claims 20-25, wherein, the first data signal includes a plurality of first signals, the plurality of first signals correspond to a plurality of the second devices, or the plurality of first signals correspond to a plurality of transmission layers of the first device; the second data signal includes a plurality of second signals, the plurality of second signals correspond to a plurality of the second devices, or the plurality of second signals correspond to a plurality of transmission layers of the first device; the first data signal and the second data signal perform the linear superposition in the following manner: linearly superposing the first signal and the second signal of the same layer; or linearly superposing the first signal and the second signal for the same second device.

29. The method according to claim 28, wherein, the plurality of first signals have different second statistical distribution characteristics.

30. The method according to claim 29, wherein, the second statistical distribution characteristics include one or more of the following: modulation method, coding method, source type, first parameter, second parameter, the first parameter for adjusting the transmission energy of the first signal, and the second parameter for adjusting the transmission energy of the second signal.

31. The method according to claim 19, wherein, the target data signal is generated based on the first data signal and the second data signal that are non-linearly superposed.

32. The method according to claim 31, It is characterized in that the target data signal is generated by non-linearly superimposing the first data signal and the second data signal using a fifth model.

33. The method according to claim 32, It is characterized in that the fifth model is used to perform non-linear superposition on the processed data signal, and the processed data signal is obtained by performing a first processing operation on the first data signal and the second data signal.

34. The method according to claim 33, It is characterized in that the first processing operation includes one or more of the following: splicing operation, linear superposition, processing by a sixth model, and processing by a seventh model; wherein, the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

35. The method according to claim 34, It is characterized in that the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; splicing based on the input channels of the first data signal and the input channels of the second data signal.

36. The method according to claim 34 or 35, It is characterized in that the first data signal includes a plurality of first signals, the second data signal includes a plurality of second signals, the sixth model is used to process the plurality of first signals, and the seventh model is used to process the plurality of second signals.

37. The method according to any one of claims 19-36, It is characterized in that the second device receives the target data signal sent by the first device, including: the second device uses a first receiver to receive the target data signal, and the first receiver is used to recover the first data signal and the second data signal in the target data signal.

38. The method according to claim 37, It is characterized in that the first receiver is used to recover the first data signal and the second data signal based on first configuration information.

39. The method according to claim 38, It is characterized in that the method further includes: the first receiver in the second device processes the target data signal based on pre-configured parameters to obtain a first processed signal; the first receiver in the second device processes the first processed signal based on the first configuration information to obtain a second processed signal, and the dimension of the second processed signal is smaller than the dimension of the first processed signal; the second device recovers the first data signal and the second data signal based on the second processed signal.

40. The method according to claim 38 or 39, It is characterized in that The first configuration information includes one or more of the following: the number of transmission layers, the number of users being transmitted, the transmission bandwidth, a first parameter, a second parameter, the third statistical distribution characteristic corresponding to the first data signal, and the fourth statistical distribution characteristic corresponding to the second data signal; Wherein, the first parameter is used to adjust the transmission energy of the first data signal, and the second parameter is used to adjust the transmission energy of the second data signal.

41. The method according to claim 40, characterized in that, The third statistical distribution characteristic and / or the fourth statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, and source type.

42. A communication device, characterized in that, The communication device is a first device, including: A sending unit, configured to send a target data signal to a second device, where the target data signal is generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal include a first resource, and the first resource is also used to transmit some or all of the data signals in the second data signal, and the first resource includes at least two of the following resources: time domain resource, frequency domain resource, and space domain resource.

43. The communication device according to claim 42, characterized in that, The target data signal is generated based on the linearly superimposed first data signal and second data signal.

44. The communication device according to claim 43, characterized in that, The first data signal and the second data signal are linearly superimposed based on one or more of the following: The symbol set corresponding to the first data signal; The symbol set corresponding to the second data signal; A first parameter, where the first parameter is used to adjust the transmission energy of the first data signal; A second parameter, where the second parameter is used to adjust the transmission energy of the second data signal.

45. The communication device according to claim 44, characterized in that, The first parameter is determined based on a first model, or the first parameter is a pre-configured parameter.

46. The communication device according to claim 44 or 45, characterized in that, The second parameter is determined based on a second model, or the second parameter is a pre-configured parameter.

47. The communication device according to any one of claims 44-46, characterized in that, The symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

48. The communication device according to any one of claims 44-47, characterized in that, The symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

49. The communication device according to any one of claims 43-48, characterized in that, The first data signal and the second data signal have different first statistical distribution characteristics.

50. The communication device according to claim 49, characterized in that, The first statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, and source type.

51. The communication device according to any one of claims 43 - 48, characterized in that, the first data signal includes a plurality of first signals, the plurality of first signals corresponding to a plurality of the second devices, or the plurality of first signals corresponding to a plurality of transport layers of the first device; the second data signal includes a plurality of second signals, the plurality of second signals corresponding to a plurality of the second devices, or the plurality of second signals corresponding to a plurality of transport layers of the first device; the first data signal and the second data signal are linearly superposed in the following manner: linearly superposing the first signal and the second signal of the same layer; or linearly superposing the first signal and the second signal for the same second device.

52. The communication device according to claim 51, characterized in that, the plurality of first signals have different second statistical distribution characteristics.

53. The communication device according to claim 52, characterized in that, the second statistical distribution characteristics include one or more of the following: modulation method, coding method, source type, first parameter, second parameter, the first parameter being used to adjust the transmission energy of the first signal, and the second parameter being used to adjust the transmission energy of the second signal.

54. The communication device according to claim 42, characterized in that, the target data signal is generated based on the first data signal and the second data signal that are non - linearly superposed.

55. The communication device according to claim 54, characterized in that, the target data signal is generated by non - linearly superposing the first data signal and the second data signal using a fifth model.

56. The communication device according to claim 55, characterized in that, the fifth model is used to non - linearly superpose the processed data signals, and the processed data signals are obtained after the first data signal and the second data signal undergo a first processing operation.

57. The communication device according to claim 56, characterized in that, the first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein, the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

58. The communication device according to claim 57, characterized in that, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; splicing based on the input channels of the first data signal and the input channels of the second data signal.

59. The communication device according to claim 57 or 58, characterized in that, The first data signal includes a plurality of first signals, the second data signal includes a plurality of second signals, the sixth model is used to process the plurality of first signals, and the seventh model is used to process the plurality of second signals.

60. A communication device, characterized in that, the communication device is a second device, including: a receiving unit, configured to receive a target data signal sent by a first device, the target data signal being generated based on a first data signal and a second data signal, the transmission resources occupied by the first data signal including a first resource, and the first resource is also used to transmit some or all of the data signals in the second data signal, and the first resource includes at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

61. The communication device according to claim 60, characterized in that, the target data signal is generated based on the linearly superimposed first data signal and the second data signal.

62. The communication device according to claim 61, characterized in that, the first data signal and the second data signal perform the linear superposition based on one or more of the following: a symbol set corresponding to the first data signal; a symbol set corresponding to the second data signal; a first parameter, the first parameter being used to adjust the transmission energy of the first data signal; a second parameter, the second parameter being used to adjust the transmission energy of the second data signal.

63. The communication device according to claim 62, characterized in that, the first parameter is determined based on a first model, or the first parameter is a pre-configured parameter.

64. The communication device according to claim 62 or 63, characterized in that, the second parameter is determined based on a second model, or the second parameter is a pre-configured parameter.

65. The communication device according to any one of claims 62-64, characterized in that, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

66. The communication device according to any one of claims 62-65, characterized in that, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

67. The communication device according to any one of claims 62-66, characterized in that, the first data signal and the second data signal have different first statistical distribution characteristics.

68. The communication device according to claim 67, characterized in that, the first statistical distribution characteristics include one or more of the following: modulation method, coding method, and source type.

69. The communication device according to any one of claims 62-66, characterized in that, The first data signal includes a plurality of first signals, where the plurality of first signals correspond to a plurality of the second devices, or the plurality of first signals correspond to a plurality of transmission layers of the first device; the second data signal includes a plurality of second signals, where the plurality of second signals correspond to a plurality of the second devices, or the plurality of second signals correspond to a plurality of transmission layers of the first device; The first data signal and the second data signal are linearly superposed in the following manner: Linearly superpose the first signal and the second signal of the same layer; or Linearly superpose the first signal and the second signal for the same second device.

70. The communication device according to claim 69, wherein, The plurality of first signals have different second statistical distribution characteristics.

71. The communication device according to claim 70, wherein, The second statistical distribution characteristics include one or more of the following: modulation method, coding method, source type, first parameter, second parameter, where the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

72. The communication device according to claim 60, wherein, The target data signal is generated based on the first data signal and the second data signal that are non-linearly superposed.

73. The communication device according to claim 72, wherein, The target data signal is generated by non-linearly superposing the first data signal and the second data signal using a fifth model.

74. The communication device according to claim 73, wherein, The fifth model is used to non-linearly superpose the processed data signals, and the processed data signals are obtained after the first data signal and the second data signal undergo a first processing operation.

75. The communication device according to claim 74, wherein, The first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; wherein, the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

76. The communication device according to claim 75, wherein, The splicing operation includes one or more of the following: Splice the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; Splice the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; Splice based on the input channels of the first data signal and the input channels of the second data signal.

77. The communication device according to claim 75 or 76, wherein, The first data signal includes a plurality of first signals, the second data signal includes a plurality of second signals, the sixth model is used to process the plurality of first signals, and the seventh model is used to process the plurality of second signals.

78. The communication device according to any one of claims 60-77, characterized in that, the receiving unit is configured to: receive the target data signal by using a first receiver, and the first receiver is configured to recover the first data signal and the second data signal in the target data signal.

79. The communication device according to claim 78, characterized in that, the first receiver is configured to recover the first data signal and the second data signal based on first configuration information.

80. The communication device according to claim 79, characterized in that, the communication device further comprises: a processing unit, configured to process the target data signal based on pre-configured parameters to obtain a first processed signal, and process the first processed signal to obtain a second processed signal, where the dimension of the second processed signal is smaller than that of the first processed signal; a recovery unit, configured to recover the first data signal and the second data signal based on the second processed signal.

81. The communication device according to claim 79 or 80, characterized in that, the first configuration information includes one or more of the following: number of transmission layers, number of users to be transmitted, transmission bandwidth, a first parameter, a second parameter, and a third statistical distribution characteristic; wherein, the first parameter is used to adjust the transmission energy of the first data signal, and the second parameter is used to adjust the transmission energy of the second data signal.

82. The communication device according to claim 81, characterized in that, the third statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, and source type.

83. A communication device, characterized in that, the communication device is a first device, and includes a memory, a processor, and a transceiver. The memory is configured to store a program, the processor is configured to call the program in the memory, and the transceiver is configured to: send a target data signal to a second device, where the target data signal is generated based on a first data signal and a second data signal. The transmission resources occupied by the first data signal include a first resource, and the first resource is further used to transmit some or all of the data signals in the second data signal. The first resource includes at least two of the following resources: time domain resource, frequency domain resource, and spatial domain resource.

84. The communication device according to claim 83, characterized in that, the target data signal is generated based on linearly superimposing the first data signal and the second data signal.

85. The communication device according to claim 84, characterized in that, the first data signal and the second data signal perform the linear superposition based on one or more of the following: a symbol set corresponding to the first data signal; a symbol set corresponding to the second data signal; a first parameter, where the first parameter is used to adjust the transmission energy of the first data signal; a second parameter, where the second parameter is used to adjust the transmission energy of the second data signal.

86. The communication device according to claim 85, characterized in that, The first parameter is determined based on a first model, or the first parameter is a pre-configured parameter.

87. The communication device according to claim 85 or 86, wherein, The second parameter is determined based on a second model, or the second parameter is a pre-configured parameter.

88. The communication device according to any one of claims 85 - 87, wherein, The symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

89. The communication device according to any one of claims 85 - 88, wherein, The symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

90. The communication device according to any one of claims 84 - 89, wherein, The first data signal and the second data signal have different first statistical distribution characteristics.

91. The communication device according to claim 90, wherein, The first statistical distribution characteristics include one or more of the following: modulation method, coding method, and source type.

92. The communication device according to any one of claims 84 - 91, wherein, The first data signal includes a plurality of first signals, and the plurality of first signals correspond to a plurality of the second devices, or the plurality of first signals correspond to a plurality of transmission layers of the first device; the second data signal includes a plurality of second signals, and the plurality of second signals correspond to a plurality of the second devices, or the plurality of second signals correspond to a plurality of transmission layers of the first device; The first data signal and the second data signal are linearly superimposed in the following manner: Linearly superimpose the first signal and the second signal of the same layer; or Linearly superimpose the first signal and the second signal for the same second device.

93. The communication device according to claim 92, wherein, The plurality of first signals or the plurality of second signals have different second statistical distribution characteristics.

94. The communication device according to claim 93, wherein, The second statistical distribution characteristics include one or more of the following: modulation method, coding method, source type, first parameter, second parameter, where the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

95. The communication device according to claim 83, wherein, The target data signal is generated based on the first data signal and the second data signal that are non-linearly superimposed.

96. The communication device according to claim 95, wherein, The target data signal is generated by non-linearly superimposing the first data signal and the second data signal using a fifth model.

97. The communication device according to claim 96, wherein, The fifth model is used for non-linearly superimposing the processed data signal, and the processed data signal is obtained by subjecting the first data signal and the second data signal to a first processing operation.

98. The communication device according to claim 97, wherein, the first processing operation includes one or more of the following: splicing operation, linear superposition, processing by a sixth model, and processing by a seventh model; wherein, the sixth model is used for processing the first data signal, and the seventh model is used for processing the second data signal.

99. The communication device according to claim 98, wherein, the splicing operation includes one or more of the following: splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; splicing based on the input channels of the first data signal and the input channels of the second data signal.

100. The communication device according to claim 98 or 99, wherein, the first data signal includes a plurality of first signals, the second data signal includes a plurality of second signals, the sixth model is used for processing the plurality of first signals, and the seventh model is used for processing the plurality of second signals.

101. A communication device, wherein, the communication device is a second device, including a memory, a processor, and a transceiver. The memory is used for storing programs, the processor is used for calling the programs in the memory, and the transceiver is used for: receiving a target data signal sent by a first device, the target data signal being generated based on a first data signal and a second data signal. The transmission resources occupied by the first data signal include a first resource, and the first resource is also used for transmitting some or all of the data signals in the second data signal. The first resource includes at least two of the following resources: time domain resources, frequency domain resources, and spatial domain resources.

102. The communication device according to claim 101, wherein, the target data signal is generated based on the linearly superimposed first data signal and second data signal.

103. The communication device according to claim 102, wherein, the first data signal and the second data signal perform the linear superposition based on one or more of the following: the symbol set corresponding to the first data signal; the symbol set corresponding to the second data signal; a first parameter for adjusting the transmission energy of the first data signal; a second parameter for adjusting the transmission energy of the second data signal.

104. The communication device according to claim 103, wherein, the first parameter is determined based on a first model, or the first parameter is a pre-configured parameter.

105. The communication device according to claim 103 or 104, wherein, the second parameter is determined based on a second model, or the second parameter is a pre-configured parameter.

106. The communication device according to any one of claims 103 - 105, wherein, the symbol set corresponding to the first data signal is determined based on a third model, or the symbol set corresponding to the first data signal is a preset symbol set.

107. The communication device according to any one of claims 103 - 106, wherein, the symbol set corresponding to the second data signal is determined based on a fourth model, or the symbol set corresponding to the second data signal is a preset symbol set.

108. The communication device according to any one of claims 102 - 107, wherein, the first data signal and the second data signal have different first statistical distribution characteristics.

109. The communication device according to claim 108, wherein, the first statistical distribution characteristics include one or more of the following: modulation method, coding method, and source type.

110. The communication device according to any one of claims 102 - 107, wherein, the first data signal includes a plurality of first signals, the plurality of first signals correspond to a plurality of the second devices, or the plurality of first signals correspond to a plurality of transport layers of the first device; the second data signal includes a plurality of second signals, the plurality of second signals correspond to a plurality of the second devices, or the plurality of second signals correspond to a plurality of transport layers of the first device; the first data signal and the second data signal are linearly superimposed in the following manner: linearly superimposing the first signal and the second signal of the same layer; or linearly superimposing the first signal and the second signal for the same second device.

111. The communication device according to claim 110, wherein, the plurality of first signals have different second statistical distribution characteristics.

112. The communication device according to claim 111, wherein, the second statistical distribution characteristics include one or more of the following: modulation method, coding method, source type, first parameter, second parameter, the first parameter is used to adjust the transmission energy of the first signal, and the second parameter is used to adjust the transmission energy of the second signal.

113. The communication device according to claim 101, wherein, the target data signal is generated based on the first data signal and the second data signal that are non - linearly superimposed.

114. The communication device according to claim 113, wherein, the target data signal is generated by non - linearly superimposing the first data signal and the second data signal using a fifth model.

115. The communication device according to claim 114, wherein, the fifth model is used to non - linearly superimpose the processed data signals, and the processed data signals are obtained after the first data signal and the second data signal undergo a first processing operation.

116. The communication device according to claim 115, wherein, The first processing operation includes one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing; Wherein, the sixth model is used to process the first data signal, and the seventh model is used to process the second data signal.

117. The communication device according to claim 116, Characterized in that, The splicing operation includes one or more of the following: Splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the time domain; Splicing the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal in the frequency domain; Splicing based on the input channels of the first data signal and the input channels of the second data signal.

118. The communication device according to claim 116 or 117, Characterized in that, The first data signal includes a plurality of first signals, the second data signal includes a plurality of second signals, the sixth model is used to process the plurality of first signals, and the seventh model is used to process the plurality of second signals.

119. The communication device according to any one of claims 101-118, Characterized in that, The second device receives the target data signal sent by the first device, including: The second device uses a first receiver to receive the target data signal, and the first receiver is used to recover the first data signal and the second data signal in the target data signal.

120. The communication device according to claim 119, Characterized in that, The first receiver is used to recover the first data signal and the second data signal based on the first configuration information.

121. The communication device according to claim 120, Characterized in that, The processor is used to: Process the target data signal based on preconfigured parameters to obtain a first processed signal; Process the first processed signal based on the first configuration information to obtain a second processed signal, and the dimension of the second processed signal is smaller than the dimension of the first processed signal; Recover the first data signal and the second data signal based on the second processed signal.

122. The communication device according to claim 120 or 121, Characterized in that, The first configuration information includes one or more of the following: number of transmission layers, number of users transmitted, transmission bandwidth, first parameter, second parameter, third statistical distribution characteristic corresponding to the first data signal, and fourth statistical distribution characteristic corresponding to the second data signal; Wherein, the first parameter is used to adjust the transmission energy of the first data signal, and the second parameter is used to adjust the transmission energy of the second data signal.

123. The communication device according to claim 122, Characterized in that, The third statistical distribution characteristic and / or the fourth statistical distribution characteristic includes one or more of the following: modulation method, coding method, and source type.

124. A device, Characterized in that, comprising a processor for invoking a program from a memory to execute the method according to any one of claims 1-18.

125. An apparatus, characterized in that it comprises a processor for invoking a program from a memory to execute the method according to any one of claims 19-41.

126. A chip, characterized in that it comprises a processor for invoking a program from a memory such that a device installed with the chip executes the method according to any one of claims 1-18.

127. A chip, characterized in that it comprises a processor for invoking a program from a memory such that a device installed with the chip executes the method according to any one of claims 19-41.

128. A computer-readable storage medium, characterized in that a program is stored thereon, and the program causes a computer to execute the method according to any one of claims 1-18.

129. A computer-readable storage medium, characterized in that a program is stored thereon, and the program causes a computer to execute the method according to any one of claims 19-41.

130. A computer program product, characterized in that it comprises a program, and the program causes a computer to execute the method according to any one of claims 1-18.

131. A computer program product, characterized in that it comprises a program, and the program causes a computer to execute the method according to any one of claims 19-41.

132. A computer program, characterized in that the computer program causes a computer to execute the method according to any one of claims 1-18.

133. A computer program, characterized in that the computer program causes a computer to execute the method according to any one of claims 19-41.

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