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WO2025118274A1 - Procédé de transmission de données et dispositif de communication - Google Patents

Procédé de transmission de données et dispositif de communication Download PDF

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Publication number
WO2025118274A1
WO2025118274A1 PCT/CN2023/137459 CN2023137459W WO2025118274A1 WO 2025118274 A1 WO2025118274 A1 WO 2025118274A1 CN 2023137459 W CN2023137459 W CN 2023137459W WO 2025118274 A1 WO2025118274 A1 WO 2025118274A1
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Prior art keywords
data signal
parameter
signal
model
communication device
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PCT/CN2023/137459
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English (en)
Chinese (zh)
Inventor
肖寒
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Priority to PCT/CN2023/137459 priority Critical patent/WO2025118274A1/fr
Publication of WO2025118274A1 publication Critical patent/WO2025118274A1/fr
Pending legal-status Critical Current
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path

Definitions

  • the present application relates to the field of communication technology, and more specifically, to a data transmission method and a communication device.
  • the present application provides a data transmission method and a communication device. Several aspects involved in the embodiments of the present application are introduced below.
  • a data transmission method 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, 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 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.
  • a data transmission method 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 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.
  • a communication device which is a first device and includes: a sending unit, 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, 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 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.
  • a communication device which is a second device and includes: a receiving unit, 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, 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 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.
  • a communication device 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 first resources, and the first resources are also 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.
  • a communication device comprising a memory, a processor and a transceiver, the memory being used to store programs, the processor being used to call programs in the memory, and the transceiver being used 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 comprising a first resource, the first resource being also used to transmit part or all of the data signal in the second data signal, the first resource comprising at least two of the following resources: time domain resources, frequency domain resources and spatial domain resources.
  • a device comprising a processor, configured to call a program from a memory to execute the method described in the first aspect.
  • a device comprising a processor, configured to call a program from a memory to execute the method described in the second aspect.
  • a chip 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.
  • a chip 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.
  • a computer-readable storage medium on which a program is stored, wherein the program enables a computer to execute the method described in the first aspect.
  • a computer-readable storage medium on which a program is stored, wherein the program enables a computer to execute the method described in the second aspect.
  • a computer program product comprising a program, wherein the program enables a computer to execute the method described in the first aspect.
  • a computer program product comprising a program, wherein the program enables a computer to execute the method described in the second aspect.
  • a computer program is provided, wherein the computer program enables a computer to execute the method described in the first aspect.
  • a computer program is provided, wherein the computer program enables a computer to execute the method described in the second aspect.
  • the first data signal and the second data signal in the present application may be transmitted on the same first resource (eg, non-orthogonal transmission), or 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 may overlap. Since in the traditional transmission mode, 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 may be the same, therefore, compared with the transmission modes of time division multiplexing and frequency division multiplexing, the scheme of the present application can improve the utilization rate of transmission resources.
  • first resource eg, non-orthogonal transmission
  • FIG. 1 is a wireless communication system 100 to which an embodiment of the present application is applied.
  • FIG. 2 is a schematic diagram of channel estimation and signal recovery applicable to an embodiment of the present application.
  • FIG. 3( a ) to FIG. 3( c ) show patterns of data symbols and pilot symbols under different configurations.
  • FIG4 shows a neural network model applicable to the embodiment of the present application.
  • FIG5 shows a neural network model applicable to the embodiment of the present application.
  • FIG6 shows a convolutional neural network applicable to an embodiment of the present application.
  • Figure 7 shows a long short-term memory (LSTM) model applicable to an embodiment of the present application.
  • LSTM long short-term memory
  • FIG8 shows a process of performing channel estimation based on a channel estimation module.
  • FIG. 9 is a wireless communication system 900 to which an embodiment of the present application is applicable.
  • FIG10 is a schematic flow chart of a data transmission method provided in an embodiment of the present application.
  • FIG. 11 is a schematic diagram of adjusting modulation constellation points associated with a data signal in an embodiment of the present application.
  • FIG. 12 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.
  • 13 to 25 show a scheme of a first data signal and a second data signal based on nonlinear superposition provided by an embodiment of the present application.
  • FIG. 27 is a schematic diagram of recovering a first data signal and a second data signal provided in an embodiment of the present application.
  • FIG. 28 is a schematic diagram of another method of recovering a first data signal and a second data signal provided in an embodiment of the present application.
  • Figure 29 is a schematic block diagram of a communication device provided in an embodiment of the present application.
  • Figure 30 is a schematic block diagram of another communication device provided in an embodiment of the present application.
  • Figure 31 is a structural diagram of a communication device provided in an embodiment of the present application.
  • FIG1 is a flow chart of transmitting signals in a wireless communication system to which an embodiment of the present application is applicable.
  • the signal transmission process in the wireless communication system can be roughly divided into a plurality of signal processing processes S111 to S118 shown in FIG1 .
  • Part or all of the signal processing processes shown in FIG1 can be implemented by a separate AI model, and its specific implementation can be seen in the introduction of FIGS. 5 to 8 .
  • the transmitter performs channel coding on the information to be transmitted to obtain a coded code stream, wherein the information to be transmitted may be in the form of a bit stream.
  • the code stream is modulated into modulation symbols.
  • pilot symbols are inserted into the above modulation symbols to form a signal to be transmitted, wherein the pilot symbols can be used by the receiver to perform channel estimation and symbol detection.
  • the above signal is carried on the channel and transmitted to the receiver.
  • noise is usually superimposed.
  • the receiver can perform channel estimation based on the pilot signal to obtain channel state information (channel state information-reference signal, CSI), and feed back the CSI to the transmitter through a feedback link so that the transmitter can adjust channel coding, modulation, precoding, etc.
  • channel state information channel state information-reference signal, CSI
  • symbol detection is performed on the received modulation symbols to obtain a detection result.
  • the received modulation symbols are demodulated based on the detection result to obtain a code stream.
  • the code stream is decoded to obtain restored information, wherein the restored information may be in the form of a bit stream.
  • signal processing processes S111 to S118 shown in FIG1 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.
  • Signal processing processes can also be implemented through a separate AI model. For the sake of brevity, this application will not go into details.
  • the receiver needs to recover the received signal based on the channel estimation result. Schematic diagram of the recovery.
  • the transmitter in addition to transmitting data signals, the transmitter also transmits a series of pilot signals known to the receiver on time-frequency resources, such as channel state information-reference signal (CSI-RS), demodulation reference signal (DMRS), etc.
  • CSI-RS channel state information-reference signal
  • DMRS demodulation reference signal
  • step S211 the transmitter transmits the above data signal and pilot signal to the transmitter through the channel.
  • the time-frequency resources occupied by the pilot signal are different from the time-frequency resources occupied by the data signal.
  • the receiver may perform channel estimation after receiving the pilot signal.
  • the receiver may estimate the channel information of the channel transmitting the pilot signal based on the pre-stored pilot signal and the received pilot signal through a channel estimation algorithm (e.g., least squares method (LS) channel estimation).
  • LS least squares method
  • the receiver may restore the channel information on the full time-frequency resources using an interpolation algorithm according to the channel information of the channel transmitting the pilot sequence, for subsequent CSI feedback or data recovery.
  • the time-frequency resources for transmitting pilot signals and the time-frequency resources for transmitting data signals are different time-frequency resources.
  • pilot symbols symbols used to transmit pilot signals
  • data symbols symbols used to transmit data signals
  • Figures 3(a) to 3(c) show the patterns of data symbols and pilot symbols under different configurations.
  • pilot symbols are distributed at intervals of one symbol in multiple resource elements (REs) corresponding to symbol 2 in the RB.
  • REs resource elements
  • pilot symbols occupy part of multiple symbols corresponding to symbol 2 and symbol 10 in the RB.
  • pilot symbols occupy multiple groups of REs in symbol 2 in the RB, wherein each group of REs includes two REs that are consecutive in the frequency domain.
  • different patterns can be adapted to different communication environments.
  • a pattern with denser distribution of pilot symbols can be selected to help improve the accuracy of channel quality estimation for the entire RB.
  • the pattern shown in FIG. 3(b) can be selected.
  • a pattern with a sparser distribution of pilot symbols can be selected to help reduce the overhead generated by transmitting pilot signals while ensuring the accuracy of channel quality estimation for the entire RB.
  • a neural network can be understood as a computing model composed of multiple interconnected neuron nodes, where the connection between nodes can represent the weighted value from the input signal to the output signal, usually called a parameter. Each node performs a weighted summation of different input signals and outputs them through a specific activation function.
  • neurons can rely on activation functions to achieve nonlinear mapping, where the input of the neuron can be recorded as A, each dimension of the input is recorded as aj, and the corresponding parameter is recorded as wj, which is used together with summation units (SU) to enhance or weaken the input.
  • the output of SU can be input into the activation function f to obtain the output t, where the value of j is 1, 2, ..., n.
  • CNN convolutional neural network
  • RNN recurrent neural network
  • DNN deep neural network
  • the neural network shown in FIG5 can be divided into three categories according to the positions of different layers: input layer 510, hidden layer 520 and output layer 530.
  • the first layer is the input layer 510
  • the last layer is the output layer 530
  • the intermediate layers between the first layer and the last layer are all hidden layers 520.
  • the input layer 510 is used to input data, where the input data may be, for example, a received signal received by a receiver.
  • the hidden layer 520 is used to process the input data, for example, to decompress the received signal.
  • the output layer 530 is used to output processed output data, for example, to output a decompressed signal.
  • the neural network includes multiple layers, each layer includes multiple neurons, and the neurons between layers can be fully connected or partially connected. For connected neurons, the output of the neurons in the previous layer can be used as the input of the neurons in the next layer.
  • neural network deep learning algorithms have been proposed in recent years. More hidden layers are introduced into the neural network to form DNN. More hidden layers allow DNN to better describe complex situations in the real world. Theoretically, the more parameters a model has, the higher its complexity and the greater its "capacity", which means it can complete more complex learning tasks.
  • This neural network model is widely used in pattern recognition, signal processing, optimization combination, anomaly detection and other aspects.
  • 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 .
  • 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 an input signal.
  • the convolution operator can essentially be a parameter matrix, which is usually predefined.
  • 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.
  • the initial convolutional layer often extracts 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.
  • Pooling layer 630 because it is often necessary to reduce the number of training parameters, it is often necessary to periodically introduce a pooling layer after the convolution layer.
  • a convolution layer may be followed by a pooling layer as shown in FIG6, or multiple convolution layers may be followed by one or more pooling layers.
  • the only purpose of the pooling layer is to reduce the spatial size of the extracted information.
  • 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 will 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.
  • the fully connected layer 640 may include multiple hidden layers, and the parameters contained in the multiple hidden layers may be pre-trained according to the relevant training data of the specific task type.
  • the task type may include decoding the data signal received by the receiver.
  • the task type may also include channel estimation based on the pilot signal received by the receiver.
  • the output layer 650 is used to output the result.
  • the output layer 650 is provided with a loss function (e.g., a loss function similar to the classification cross entropy) for calculating the prediction error, or for evaluating the difference between the result output by the CNN model (also called the predicted value) and the ideal result (also called the true value).
  • a loss function e.g., a loss function similar to the classification cross entropy
  • the CNN model needs to be trained.
  • the backpropagation algorithm can be used to train the CNN model.
  • the BP training process consists of a forward propagation process and a backpropagation process.
  • the forward propagation process for example, the propagation from 610 to 650 in Figure 6 is forward propagation
  • the input data is input into the above layers of the CNN model, processed layer by layer and transmitted to the output layer.
  • the minimization of the above loss function is used as the optimization goal, and the backpropagation is turned into (for example, the propagation from 650 to 610 in Figure 6 is backpropagation), and the partial derivatives of the optimization target to the weight of each neuron are obtained layer by layer, forming the gradient of the optimization target to the weight vector, which is used as the basis for modifying the model parameters.
  • the training process of CNN is completed during the parameter modification process. When the above error reaches the expected value, the training process of CNN ends.
  • the CNN shown in FIG6 is only an example of a convolutional neural network.
  • the convolutional neural network may also exist in the form of other network models, which is not limited in the embodiments of the present application.
  • RNNs The purpose of RNNs is to process sequence data.
  • traditional neural network models for example, CNN models
  • the layers are fully connected from the input layer to the hidden layer and then to the output layer, and the nodes between each layer are disconnected.
  • this ordinary neural network is powerless 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.
  • RNNs can process sequence data of any length.
  • the training of RNN is the same as the training of traditional ANN (artificial neural network).
  • BPTT back propagation through time
  • LSTM long short-term memory
  • ct also known as "cell state”
  • the memory unit will decide which states should be kept and which states should be forgotten, solving the defects of traditional RNN in long-term memory.
  • the memory unit introduces a gate control mechanism to control the path of information transmission, which is similar to the gate in the data circuit.
  • "0" means closed and "1" means open.
  • 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
  • the input gate is used to control the current moment.
  • 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.
  • the channel estimation based on the AI decoder aims to realize channel estimation by processing the pilot signal received by the receiver using the AI-based channel estimation module.
  • Figure 8 shows the process of channel estimation based on the channel estimation module.
  • the signal received by the receiver 800 (such as the pilot signal) is used as the input of the channel estimation module 810, and 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 the pilot symbol corresponding to the pilot symbol RE, and the output information is the result of the entire PRB channel estimation.
  • auxiliary information may be added to improve the accuracy of the channel information output by the channel estimation module.
  • the original sequence of the pilot signal pre-stored by 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, etc. may also be input to the channel estimation module 810.
  • the internal implementation of the channel estimation module 810 may be a neural network such as DNN, CNN, etc., and of course may also be other neural networks, which is not specifically limited in the embodiments 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 geographical area, and may communicate with a terminal device 920 located in the coverage area.
  • FIG9 exemplarily shows a network device 910 and two terminal devices 920.
  • the wireless communication system 900 may include multiple network devices and each network device may include another number of terminal devices within its coverage area, which is not limited in the embodiments of the present application.
  • the wireless communication system 900 may also include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiments of the present application.
  • the terminal devices 920 may also communicate directly with each other.
  • two terminal devices 920 may communicate with each other via a device-to-device (D2D) link.
  • D2D device-to-device
  • the first device and the second device are used as examples for description.
  • the first device may be the network device 910, and accordingly, the second device may be the terminal device 920.
  • the first device may be the terminal device 920, and accordingly, the second device may be the network device 910.
  • the first device may be the terminal device 920, and accordingly, the second device may be the terminal device 920.
  • the embodiments of the present application do not specifically limit this.
  • the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: the fifth generation (5th generation, 5G) system or new radio (new radio, NR), long term evolution (long term evolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) system, LTE time division duplex (time division duplex, TDD), etc.
  • 5G fifth generation
  • NR new radio
  • long term evolution long term evolution
  • LTE long term evolution
  • LTE frequency division duplex frequency division duplex
  • FDD frequency division duplex
  • TDD time division duplex
  • future communication systems such as the sixth generation mobile communication system, satellite communication system, etc.
  • the terminal device in the embodiment 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 embodiment of the present application may be 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 embodiment of the present application can be a mobile phone, a tablet computer, a laptop, 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 smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.
  • the UE can be used to act as a base station.
  • the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc.
  • a cellular phone and a car communicate with each other using sidelink signals.
  • the cellular phone and the smart home device communicate with each other without relaying the communication signal through the base station.
  • the network device in the embodiment of the present application may be a device for communicating with a terminal device, and the network device may also be referred to as an access network device or a wireless access network device, such as a base station.
  • the network device in the embodiment of the present application may refer to a wireless access network (RAN) node (or device) that connects a terminal device to a wireless network.
  • RAN wireless access network
  • the base station may broadly cover the following various names, or be replaced with the following names, such as: Node B (NodeB), evolved NodeB (evolved NodeB, eNB), next generation NodeB (next generation NodeB, gNB), relay station, access point, transmission point (transmitting and receiving point, TRP), transmission point (transmitting point, TP), master station MeNB, secondary station SeNB, multi-standard wireless (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.
  • NodeB Node B
  • eNB evolved NodeB
  • next generation NodeB next generation NodeB
  • relay station access point
  • transmission point transmission point
  • TRP transmission point
  • TRP transmission point
  • TP transmission point
  • the 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.
  • the base station can also refer to a communication module, a modem or a chip used to be set in the aforementioned device or apparatus.
  • the base station can also be a mobile switching center and a device to device D2D, vehicle-to-everything (V2X), a device that assumes the function of a base station in machine-to-machine (M2M) communication, a network side device in a 6G network, a device that assumes the function of a base station in a future communication system, etc.
  • V2X vehicle-to-everything
  • M2M machine-to-machine
  • the base station can support networks with the same or different access technologies.
  • the embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.
  • Base stations can be fixed or mobile.
  • 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.
  • a helicopter or drone can be configured to act as a device that communicates with another base station.
  • the network device in the embodiments of the present application may refer to a CU or a DU, or the network device includes a CU and a DU.
  • the gNB may also include an AAU.
  • the network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air.
  • the embodiments of the present application do not limit the scenarios in which the network equipment and terminal equipment are located.
  • the communication devices involved in the present application may be network devices or terminal devices.
  • the first communication device is a network device and the second communication device is a terminal device.
  • the first communication device is a terminal device and the second communication device is a network device.
  • the first communication device and the second communication device are both network devices or both terminal devices.
  • multiple data signals can be multiplexed and transmitted on transmission resources.
  • the current multiplexed transmission includes orthogonal multiplexed transmission on time domain, frequency domain and code domain resources.
  • the data signals transmitted on multiple transmission resources will be transmitted in an orthogonal transmission manner.
  • Orthogonal transmission can be understood as processing the data signals transmitted on multiple transmission resources into mutually orthogonal data signals for transmission, and the orthogonal data signals are transmitted independently of each other and do not interfere with each other.
  • orthogonal transmission for a certain transmission resource, it can only be used to transmit one data signal at a certain moment, resulting in low utilization of transmission resources.
  • first data signal the number of transmission resources occupied by a certain data signal
  • second data signal the number of transmission resources that can be used to transmit other data signals
  • the first data signal and the second data signal can be transmitted in the same frequency domain and different time domains, or the first data signal and the second data signal can occupy the same frequency domain resources and different time domain resources.
  • the first data signal and the second data signal can be transmitted in the same time domain and different frequency domains, or the first data signal and the second data signal can occupy the same time domain resources and different frequency domain resources.
  • the transmission resources occupied by the first data signal and the second data signal will only be 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.
  • 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 of the 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 the spectrum efficiency.
  • 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.
  • the first device may be a transmitting end, and the second device may be a receiving end.
  • 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.
  • the following describes a wireless communication method according to an embodiment of the present application in conjunction with Fig. 10.
  • the wireless communication method shown in Fig. 10 includes step S1010.
  • step S1010 the first device sends a target data signal to the second device.
  • the target data signal is generated based on the first data signal and the second data signal.
  • the target data signal includes the first data signal and the second data signal which are non-orthogonal.
  • the target data signal includes data signals for multiple second devices.
  • the second device is one of the multiple second devices.
  • the first data signal and the second data signal may be data signals for different second devices.
  • the transmission resource occupied by the first data signal includes the first resource.
  • the first resource is also used to transmit at least part of the data signal in the second data signal, or in other words, the first resource is also used to transmit part or all of the data signal in the second data signal.
  • part of the first data signal and the second data signal are non-orthogonally superimposed on the first resource.
  • the first data signal and the second data signal are non-orthogonally superimposed on the first resource.
  • the first resource may also be referred to as a superimposed resource.
  • the target data signal in the embodiment of the present application may also be referred to as a superposition signal.
  • the first resource belongs to a transmission resource set, wherein 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 above-mentioned first resources.
  • the transmission resource set may be, for example, a PRB or an RB, and the first resource may be, for example, a PRB, an RB, or an RE.
  • the first resource may include one or more of the following resources: time domain resources, frequency domain resources, and spatial domain resources.
  • the first resource may include time domain resources and frequency domain resources, or 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 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.
  • the first resource may include time domain resources and spatial domain resources, or 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 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.
  • the first resource may include frequency domain resources and spatial domain resources, or 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 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.
  • 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.
  • the first resource may be a symbol (also called a "time domain symbol"), a time slot, a subframe, or a frame, etc.
  • the first resource may include a subcarrier, a bandwidth part, a frequency band, etc.
  • the first resource may include a codeword, a layer, an antenna port, etc.
  • the first resource may include any one of a PRB, a RE, and a RB.
  • 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.
  • 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.
  • the embodiment of the present application does not specifically limit the method for generating the target data signal.
  • the target data signal is generated based on the linearly superimposed first data signal and the second data signal.
  • the target data signal is generated based on the nonlinearly superimposed first data signal and the second data signal. The following describes these two implementations in detail.
  • the first data signal and the second data signal can be transmitted non-orthogonally in a linear superposition manner.
  • the non-orthogonal transmission based on the linear superposition manner helps to simplify the complexity of the non-orthogonal transmission.
  • the non-orthogonal transmission based on the linear superposition manner helps to reduce the complexity of the second device identifying multiple signals of the non-orthogonal transmission.
  • the energy of the signal transmitted on the first resource can be adjusted by the first parameter and/or the second parameter (or the power of the signal transmitted on the first resource can be adjusted by the first parameter and/or the second parameter).
  • the parameters associated with the above-mentioned linear superposition method are determined based on the first parameter and/or the second parameter.
  • 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.
  • the first parameter is used to adjust the energy of the first data signal transmitted on the first resource.
  • the first parameter is used to increase the energy of the first data signal transmitted on the first resource.
  • the first parameter is used to reduce the energy of the first data signal transmitted on the first resource.
  • the second parameter is used to adjust the energy of the second data signal transmitted on the first resource.
  • the second parameter is used to increase the energy of the second data signal transmitted on the first resource.
  • the second parameter is used to reduce the energy of the second data signal transmitted on the first resource.
  • 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 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.
  • 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.
  • the value of the first parameter and the value of the second parameter may be equal.
  • the value of the first parameter and the value of the second parameter are both 0.5.
  • the values of the first parameter and the second parameter may not be equal.
  • the value of the first parameter is between 0 and 0.5
  • the value of the second parameter is between 0.5 and 1.
  • the value of the first parameter is greater than 0 and less than 0.5
  • the value of the second parameter is greater than 0.5 and less than 1.
  • 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.
  • 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. For another example, the energy threshold may be less than the average energy corresponding to the first resource.
  • the energy threshold may be predefined, for example, the energy threshold may be predefined by a communication protocol.
  • the energy threshold may also be preconfigured, for example, the energy threshold may be configured by a network device. The embodiment of the present application does not limit this.
  • FIG11 shows a non-orthogonal transmission scheme based on linear superposition.
  • the first resource belongs to the 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.
  • 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 D 1 represents the first data signal transmitted on the first resource in the transmission resource set
  • the matrix D 2 represents the second data signal transmitted on the first resource in the transmission resource set
  • ⁇ represents the Hadamard product.
  • the number of first resources included in the transmission resource set is not limited. Accordingly, the dimensions of the matrices mentioned above (e.g., matrix S, matrix V, matrix D 1 , matrix D 2 , matrix A, and matrix X, etc.) are associated with the dimensions (or the number) of the first resources in the transmission resource set.
  • each element in the matrix may correspond to a transmission resource in the transmission resource set.
  • the dimension of the matrix is the same as the dimension of the first resource in the transmission resource set.
  • RB can be represented as including N rows and M columns of REs, and all REs in RB are superimposed transmission resources.
  • the matrix mentioned above can be a matrix of N rows and M columns, where M and N are positive integers.
  • the dimension of the above matrix when the first resource allocated by the system changes, the dimension of the above matrix also changes. For example, if the first resource allocated by the system is 2 RBs, the dimension of the matrix is the same as the dimension of the REs in the 2 RBs. Assuming that RB can be represented as including N rows and M columns of REs, then 2 RBs include 2N rows and 2M columns of REs, and the dimension corresponding to the 2 RBs is 2N rows and 2M columns. At this time, if all REs in the 2 RBs are superimposed transmission resources, the dimension of the matrix involved above can be a matrix of 2N rows and 2M columns.
  • 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 by 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 change in equal dimensions accordingly.
  • the first 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 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.
  • the first parameter may also be non-learnable, or the first parameter is a pre-configured parameter. By pre-configuring the first parameter, the complexity of linear superposition can be reduced.
  • the first parameter may be pre-configured by the first device (i.e., the transmitting end), or the first parameter is a parameter predefined in the protocol.
  • 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 by 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 equal dimensions accordingly.
  • the second model can be, for example, an AI model or a machine learning model, which is not specifically limited in the embodiments of the present application. Taking the second model as an AI model as an example, the embodiments of the present application do not limit the field to which the AI model is adapted.
  • the second parameter may also be non-learnable, or the second parameter is a pre-configured parameter.
  • the first parameter By pre-configuring the first parameter, the complexity of linear superposition can be reduced.
  • the second parameter may be pre-configured by the first device (i.e., the transmitting end), or the second parameter is a parameter predefined in the protocol.
  • the above describes a scheme for determining parameters associated with the linear superposition method based on the first parameter and/or the second parameter in the embodiment of the present application.
  • the parameters associated with the linear superposition method can be based on the symbol set corresponding to the first data signal, the second data signal, and the like. The symbol set corresponding to the signal is determined.
  • 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.
  • the first data signal in order 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 mode of the first data signal.
  • 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.
  • 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.
  • the second data signal may be modulated.
  • 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 mode of the second data signal.
  • the amplitude of the second data signal indicated in the matrix D2 associated with the second data signal may be the modulated amplitude, and/or the phase of the second data signal indicated in the matrix D2 associated with the second data signal may be the modulated phase.
  • FIG12 takes the QPSK modulation mode as an example to illustrate the training process of the modulation constellation points.
  • 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).
  • the trained constellation points are only an example, and the actual learning results may vary depending on the training data or initialization settings.
  • the symbol set corresponding to the first data signal (such as the 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.
  • the third model can be, for example, an AI model or a machine learning model, which is not specifically limited in the embodiments of the present application. Taking the third model as an AI model as an example, the embodiments of the present application do not limit the field to which the AI model is adapted.
  • the first data signal may be directly processed using the third model to obtain a symbol set corresponding to the first data signal.
  • the first data signal may be modulated first, and then the modulated first data signal may be processed using the third model.
  • the modulation method may 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 may be symbols in a modulation constellation point set set by the system.
  • the symbols in the modulation constellation point set may be initialized using the third model, and then the initialized symbols may be geometrically shaped and optimized based on the third model, thereby obtaining a symbol set corresponding to the first data signal.
  • the symbol set corresponding to the first data signal may be unlearnable, or in other words, the symbol set corresponding to the first data signal is a preset symbol set.
  • the symbol set corresponding to the first data signal is a modulation constellation point set, or in other words, the symbol corresponding to the first data signal belongs to the modulation constellation point set.
  • the modulation constellation point set may be a modulation constellation point set set set by the system.
  • the symbol set corresponding to the second data signal (such as the matrix D 2 ) can be determined based on the fourth model, or in other words, the symbol set corresponding to the second data signal is learnable.
  • the fourth model can be, for example, an AI model or a machine learning model, which is not specifically limited in the embodiments of the present application. Taking the fourth model as an AI model as an example, the embodiments of the present application do not limit the field to which the AI model is adapted.
  • the fourth model may be used to directly process the second data signal to obtain a set of symbols corresponding to the second data signal.
  • the second data signal may be modulated first, and then the modulated second data signal may be processed using the fourth model.
  • the modulation method may 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 may be symbols in a modulation constellation point set set by the system.
  • the fourth model may be used to initialize the symbols in the modulation constellation point set, and then the initialized symbols may be geometrically shaped and optimized based on the fourth model, thereby obtaining a set of symbols corresponding to the second data signal.
  • the symbol set corresponding to the second data signal may be unlearnable, or in other words, the symbol set corresponding to the second data signal is a preset symbol set.
  • 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 the modulation constellation point set.
  • the modulation constellation point set may be a modulation constellation point set set set by the system.
  • 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 from the second data signal.
  • the first statistical distribution characteristic may include one or more of the following: a modulation mode, a coding mode, and an information source type.
  • the first data signal and the second data signal may have different modulation modes.
  • the modulation modes may include QPSK and BPSK.
  • the modulation mode of the first data signal is QPSK
  • the modulation mode of the second data signal is BPSK.
  • the first data signal and the second data signal may have different coding modes.
  • the coding mode may include a code rate and/or a channel coding mode.
  • the code rate may include 378/1024 and 434/1024.
  • the channel coding mode may include a Turbo code coding mode and an LDPC code coding mode.
  • the first data signal and the second data signal may have different code rates.
  • the code rate corresponding to the first data signal is 378/1024
  • the code rate corresponding to the second data signal is 434/1024.
  • the first data signal may be encoded at a code rate of 378/1024
  • the second data signal may be encoded at a code rate of 434/1024.
  • the channel coding methods used by the first data signal and the second data signal may be the same or different.
  • the first data signal may be encoded using an LDPC code with a code rate of 378/1024
  • the second data signal may be encoded using an LDPC code with a code rate of 434/1024
  • the first data signal may be encoded using an LDPC code with a code rate of 378/1024
  • the second data signal may be encoded using a Turbo code with a code rate of 434/1024.
  • the first data signal and the second data signal may have different channel coding modes.
  • the first data signal may be coded using a Turbo code
  • the second data signal may be coded using an LDPC code.
  • the first data signal and the second data signal may have different information source types.
  • the information source type may include one or more of the following: image data, voice data, text data, and CSI.
  • the information source of the first data signal is image data
  • the information source of the second data signal is voice data
  • the information source of the first data signal is text data
  • the information source of the second data signal is CSI.
  • 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.
  • 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.
  • 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 streams transmitted.
  • the first device may send multiple first signals through multiple transmission layers.
  • 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.
  • 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.
  • 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 streams transmitted.
  • the first device may send multiple second signals through multiple transmission layers.
  • the linear superposition can be performed 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.
  • 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
  • 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.
  • 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, source type, and first parameter.
  • the first parameter may refer to the first parameter described above, and the first parameter may be used to adjust the transmission energy of the first signal.
  • multiple first signals may have different modulation modes.
  • the modulation modes may include QPSK and BPSK.
  • the modulation mode of one of the multiple first signals is QPSK
  • the modulation mode of another of the multiple first signals is BPSK.
  • the modulation mode of the first signal of the second layer may be BPSK
  • the plurality of first signals may have different coding modes.
  • the coding mode may include a code rate and/or a channel coding mode.
  • the code rate may include 378/1024 and 434/1024.
  • the channel coding mode may include a Turbo code coding mode and an LDPC code coding mode.
  • multiple first signals may have different code rates.
  • the code rate corresponding to the first signal of the first layer may be 378/1024
  • the code rate corresponding to the first signal of the second layer may be 434/1024.
  • the first signal of the first layer may be encoded at a code rate of 378/1024
  • the first signal of the second layer may be encoded at a code rate of 434/1024.
  • the channel coding methods used by the multiple first signals may be the same or different.
  • the first signal of the first layer may be encoded using an LDPC code with a code rate of 378/1024
  • the first signal of the second layer may be encoded using an LDPC code with a code rate of 434/1024.
  • the first signal of the first layer may be encoded using an LDPC code with a code rate of 378/1024
  • the first signal of the second layer may be encoded using a Turbo code with a code rate of 434/1024.
  • the code rate corresponding to the first signal for the first user may be 378/1024
  • the code rate corresponding to the first signal for the second user may be 434/1024.
  • the first signal for the first user may be encoded at a code rate of 378/1024
  • the first signal for the second user may be encoded at a code rate of 434/1024
  • the channel coding methods used by the multiple first signals may be the same or different.
  • the first signal for the first user may be encoded using an LDPC code with a code rate of 378/1024
  • the first signal for the second user may be encoded using an LDPC code with a code rate of 434/1024
  • the first signal for the first user may be encoded using an LDPC code with a code rate of 378/1024
  • the first signal for the second user may be encoded using a Turbo code with a code rate of 434/1024.
  • the multiple first signals may have different channel coding methods.
  • the coding method of the first signal of the first layer may be a Turbo code coding method
  • the coding method of the first signal of the second layer may be an LDPC code coding method.
  • the coding method of the first signal for the first user may be a Turbo code coding method
  • the coding method of the first signal for the second user may be an LDPC code coding method.
  • first signals may have different source types.
  • the source type may include one or more of the following: image data, voice data, text data, and CSI.
  • the source of the first signal of the first layer is image data
  • the source of the first signal of the second layer is voice data
  • the source of the first signal of the first layer is text data
  • the source of the first signal of the second layer is CSI.
  • the first data signal includes first signals for two users
  • the source of the first signal for the first user is image data
  • the source of the first signal for the second user is voice data
  • the source of the first signal for the first user is text data
  • the source of the first signal for the second user is CSI.
  • the values of the first parameters corresponding to the multiple first signals are different.
  • the first parameter corresponding to the first signal of the first layer is V 1
  • the first parameter corresponding to the first signal of the second layer is V 2 , where V 1 ⁇ V 2
  • the first data signal includes first signals for two users
  • the first parameter corresponding to the first signal for the first user is V 3
  • the first parameter corresponding to the first signal for the second user is V 4 , where V 3 ⁇ V 4 .
  • 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).
  • 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.
  • 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 may be understood as the number of transmission streams.
  • the first device may send multiple second signals through multiple transmission layers.
  • 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, and the second parameter may be used to adjust the transmission energy of the second signal.
  • the second statistical distribution characteristic is similar to the second statistical distribution characteristics corresponding to the above-mentioned multiple first signals, and will not be described again for the sake of brevity.
  • the first data signal and the second data signal may be transmitted non-orthogonally in a non-linear superposition manner.
  • the target data signal may be generated based on the non-linearly superposed first data signal and the second data signal.
  • generating the target data signal based on the non-linear superposition manner helps to improve the flexibility of superimposing the first data signal and the second data signal.
  • the target data signal is generated by nonlinearly superimposing the first data signal and the second data signal using the fifth model, or 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 the embodiments of the present application. Taking the fifth model as an AI model as an example, the embodiments of the present application do not limit the field to which the AI model is adapted.
  • the nonlinear superposition of the first data signal and the second data signal using the fifth model may refer to directly using the fifth model to nonlinearly superpose the first data signal and the second data signal, or may refer to using the fifth model to nonlinearly superpose the processed data signal.
  • the processed data signal may be obtained by performing the first processing operation on the first data signal and the second data signal.
  • the first processing operation may include one or more of the following: splicing operation, linear superposition, sixth model processing, and seventh model processing.
  • the sixth model can be used to process the first data signal
  • the seventh model can be used to process the second data signal.
  • the sixth model can be an AI model or a machine learning model.
  • the seventh model can be an AI model or a machine learning model.
  • 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 refer to the above description, and will not be repeated here for the sake of brevity.
  • the matrix D1 associated with the first data signal and the matrix D2 associated with the second data signal can be expressed as an N ⁇ M matrix
  • the first parameter can be expressed as an N ⁇ M matrix V
  • the second parameter can be an N ⁇ M matrix X.
  • the matrix S is input into the fifth model, so as to use the fifth model to perform nonlinear superposition on the matrix S after linear superposition, and obtain the matrix Q representing the target data signal.
  • the fifth model may be used to perform nonlinear superposition on the first data signal and the second data signal after splicing.
  • 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 dimension, 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 dimension.
  • 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.
  • the transmission resources occupied by the first data signal and the transmission resources occupied by the second data signal are continuous in the time domain, and the last time domain resource occupied by the transmission resource of 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.
  • the transmission resources occupied by the first signal include RB1
  • the transmission resources occupied by the second signal include RB2.
  • the transmission resources occupied by the first signal and the transmission resources occupied by the second signal are spliced in the time domain, which can be understood as splicing the RE in RB1 and the RE in RB2 in the time domain. That is to say, 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.
  • the above-mentioned 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.
  • the first frequency domain resource in the transmission resources occupied by the first data signal is the frequency domain resource with the highest frequency in the transmission resources occupied by the first data signal
  • the second frequency domain resource in the transmission resources occupied by the second data signal is the frequency domain resource with the lowest frequency in the transmission resources occupied by the second data signal.
  • the splicing in the frequency domain dimension may include that the frequency of the first frequency domain resource is lower than the frequency of the second frequency domain resource, and the frequency of the first frequency domain resource is continuous with the frequency of the second frequency domain resource.
  • 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, the transmission resources occupied by the first signal and the transmission resources occupied by the second signal are spliced in the frequency domain, which 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.
  • the splicing method may include splicing based on an input channel of the first data signal and an input channel of the second signal, wherein the input channel is an input channel of the fifth model. That is, 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, and 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.
  • signals input through different input channels can be associated with different weights.
  • signals input through different input channels can be associated with the same weight, which is not limited in this embodiment of the present application.
  • 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.
  • 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.
  • the first data signal may be input into the fifth model through input channel 1, and the second data signal may be input into the fifth model through input channel 2. Accordingly, before the first data signal and the second data signal are nonlinearly superimposed using the fifth model, the first data signal and the second data signal may be spliced based on the input channel to obtain a spliced signal.
  • 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 associated with the first input channel and the first data signal, and the processed second data signal can be determined based on the second weight associated with the second input channel and the second data signal.
  • the generation method of the spliced signal is not specifically limited.
  • the spliced signal may be equal to the sum of the processed first data signal and the processed second data signal.
  • the spliced signal may be obtained by processing the sum of the processed first data signal and the processed second data signal.
  • the first data signal processed above is determined based on the first weight and the first data signal, for example, the processed first data signal may be equal to the product of the first weight and the first data signal.
  • the processed first data signal may be obtained by processing the product of the first weight and the first data signal.
  • the second data signal processed above is determined based on the second weight and the second data signal, for example, the processed second data signal may be equal to the product of the second weight and the second data signal.
  • the processed second data signal may be obtained by processing the product of the second weight and the second data signal.
  • the fifth model can process the first data signal and the second data signal to match the size of the transmission resource set.
  • the fifth model may 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.
  • 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).
  • 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).
  • the first processing operation may include sixth model processing and seventh model processing.
  • the first data signal may be processed using the sixth model to obtain a processed first data signal
  • the second data signal may be processed using the seventh model to obtain a processed second data signal.
  • the processed first data signal and the processed second data signal may be nonlinearly superimposed using the fifth model.
  • 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.
  • the sixth model may be used to adjust the modulation constellation point of the first data signal, and the seventh model may be used to adjust the modulation constellation point of the second data signal.
  • the sixth model can 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 has characteristics that are more suitable for the subsequent linear superposition process or the nonlinear superposition process, and is more adapted to the characteristics of the wireless environment corresponding to the current training data.
  • the seventh model can 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 has characteristics that are more suitable for the subsequent linear superposition process or the nonlinear superposition process, and is more adapted to the characteristics of the wireless environment corresponding to the current training data.
  • the first processing operation may include a splicing operation, a sixth model processing, and a seventh model processing.
  • the splicing operation may include one or more of time domain splicing, frequency domain splicing, and channel splicing.
  • the sixth model may be used to process the first The data signal is processed 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 to obtain a spliced data signal.
  • the fifth model can be used to perform nonlinear superposition on the spliced processed signals.
  • the first processing operation shown in FIG. 18 includes frequency domain splicing, sixth model processing, and seventh model processing.
  • the first data signal is processed using the sixth model to obtain a processed first data signal
  • 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 frequency domain to obtain a spliced data signal.
  • the fifth model can be used to perform nonlinear superposition on the spliced processed signal.
  • the first processing operation shown in FIG. 19 includes time domain splicing, sixth model processing, and seventh model processing.
  • the first data signal is processed using the sixth model to obtain a processed first data signal
  • 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 signal.
  • the first processing operation shown in FIG. 20 includes channel splicing, sixth model processing, and seventh model processing.
  • the first data signal is processed using the sixth model to obtain a processed first data signal
  • 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 signal.
  • the first processing operation may include linear superposition, sixth model processing and seventh model processing.
  • the first data signal is processed using the sixth model to obtain a processed first data signal
  • 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 signal.
  • the linear superposition method can be any of the superposition methods described above, and for the sake of brevity, it will not be repeated here.
  • the target data signal can be obtained by performing the sixth model processing, the seventh model processing and linear superposition on the first data signal and the second data signal.
  • the first data signal is processed by the sixth model to obtain the processed first data signal
  • the second data signal is processed by the seventh model to obtain the processed second data signal.
  • the processed first data signal and the processed second data signal are linearly superimposed to obtain the superimposed data signal.
  • the first data signal includes multiple first signals
  • the second data signal includes multiple second signals.
  • the above-mentioned first processing operation can be for multiple first signals and/or multiple second signals.
  • the sixth model can be used to process multiple first signals to obtain multiple processed first signals.
  • the seventh model can be used to process multiple second signals to obtain multiple processed second signals.
  • the following is an example in which the first processing operation includes the sixth model processing, the seventh model processing and the splicing operation.
  • the first signals and second signals of the same type can be spliced.
  • the first signal and the second signal of the same layer can be spliced.
  • 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
  • the first signal of the second layer can be spliced with the second signal of the second layer.
  • the first signal and the second signal for the same user can be spliced.
  • the first signal for the first user can be spliced with the second signal for the first user
  • the first signal for the second user can be spliced with the second signal for the second user.
  • the splicing operation below is also a similar splicing, and for the sake of brevity, it will not be repeated below.
  • the first processing operation may include sixth model processing, seventh model processing, and frequency domain splicing.
  • the sixth model may be used to process multiple first signals to obtain multiple processed first signals
  • 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 are spliced in the frequency domain to obtain multiple spliced signals.
  • the fifth model may be used to perform nonlinear superposition on multiple spliced signals respectively to obtain multiple nonlinear superpositioned signals.
  • the above-mentioned frequency domain splicing may 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).
  • the first signal of the first layer processed by the sixth model and the second signal of the first layer processed by the seventh model may be frequency domain spliced to obtain a first spliced signal.
  • the first signal of the second layer processed by the sixth model and the second signal of the second layer processed by the seventh model may be frequency domain spliced to obtain a second spliced signal.
  • the fifth model may be used to process the first spliced signal and the second spliced signal.
  • the fifth model may be used to process the first spliced signal to obtain a nonlinear superposition signal of the first layer.
  • the fifth model may be used to process the second spliced signal to obtain a nonlinear superposition signal of the second layer.
  • the first processing operation may include sixth model processing, seventh model processing, and time domain splicing.
  • the sixth model may be used to process multiple first signals to obtain multiple processed first signals
  • the seventh model may be used to process multiple second signals to obtain multiple processed second signals.
  • the fifth model can be used to perform nonlinear superposition on the multiple spliced signals to obtain multiple nonlinear superpositioned signals.
  • the above-mentioned time domain splicing may refer to 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).
  • the first signal of the first layer processed by the sixth model and the second signal of the first layer processed by the seventh model may be time domain spliced to obtain a first spliced signal.
  • the first signal of the second layer processed by the sixth model and the second signal of the second layer processed by the seventh model may be time domain spliced to obtain a second spliced signal.
  • the fifth model may be used to process the first spliced signal and the second spliced signal.
  • the fifth model may be used to process the first spliced signal to obtain a nonlinear superposition signal of the first layer.
  • the fifth model may be used to process the second spliced signal to obtain a nonlinear superposition signal of the second layer.
  • the first processing operation may include sixth model processing, seventh model processing, and channel splicing.
  • the sixth model may be used to process multiple first signals to obtain multiple processed first signals
  • the seventh model may be used to process multiple second signals to obtain multiple processed second signals.
  • Channel splicing is 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 respectively to obtain multiple nonlinear superpositioned signals.
  • the above-mentioned channel splicing may refer to channel splicing of the first signal and the second signal of the same type (such as the same layer or for the same user).
  • the first signal of the first layer processed by the sixth model and the second signal of the first layer processed by the seventh model may be channel spliced to obtain a first spliced signal.
  • the first signal of the second layer processed by the sixth model and the second signal of the second layer processed by the seventh model may be channel spliced to obtain a second spliced signal.
  • the fifth model may be used to process the first spliced signal and the second spliced signal.
  • the fifth model may be used to process the first spliced signal to obtain a nonlinear superposition signal of the first layer.
  • the fifth model may be used to process the second spliced signal to obtain a nonlinear superposition signal of the second layer.
  • the above takes multi-layer transmission as an example to introduce the processing method of multiple first signals and multiple second signals. It can be understood that the scheme of multi-user transmission is similar to that of multi-layer transmission. For the sake of brevity, it is not repeated here.
  • the multi-layer first signal in the above text can be replaced by the first signal for multiple users, and the multi-layer second signal can be replaced by the second signal for multiple users.
  • the second device may include a first receiver.
  • the second device may receive the target data signal through the first receiver.
  • the first receiver may be used to recover the first data signal and the second data signal in the target data signal.
  • the first receiver may be used to process the target data signal to obtain the first data signal and the second data signal.
  • the embodiment of the present application does not specifically limit the type of the first receiver.
  • the first receiver may be an AI receiver.
  • the first receiver may be an ML receiver.
  • the first receiver may be configured to recover the first data signal and the second data signal based on the first configuration information.
  • the input of the first receiver may include the first configuration information and the target data signal, as shown in Figure 26.
  • the output of the first receiver may be determined according to the function of the model in the first receiver.
  • the output of the first receiver may be a log-likelihood ratio or a received bit stream.
  • the embodiment of the present application does 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.
  • 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.
  • 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.
  • 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.
  • 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, so that the first configuration information can be adapted to different scenarios, the input of the first receiver is unified, and the complexity of the first receiver is reduced.
  • 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, and 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, the number of transmission layers, the number of transmission users, and the transmission bandwidth.
  • the second type of information may include the first parameter, the second parameter, and the second parameter. One or more of number, encoding method and source type.
  • 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 restore 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.
  • processing the first processed signal may be understood as cropping the first processed signal.
  • the first receiver may crop the first processed signal based on the first configuration information.
  • the preconfigured parameters may include the above-mentioned first category of information.
  • the preconfigured parameters may be the maximum parameters of the system configuration.
  • 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.
  • 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 of 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.
  • the following describes in detail the processing method of the first receiver by taking the first configuration information being the MCS and the number of transmission layers as examples.
  • Figure 27 shows a solution in which the first configuration information includes MCS.
  • 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
  • the number of transmission layers is L.
  • the model structure of the first receiver may be as shown in Figure 27.
  • the structure may have a residual convolutional network with a number of convolution kernels of D and a number of residual blocks of N blocks as the main skeleton.
  • the first receiver may also use the network structure described above or other network structures as the basic skeleton, and the embodiments of the present application do not specifically limit this.
  • the input of the model may include an MCS index m indicating the coding and modulation mode, which 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 spread out as an MCS information tensor M ⁇ CN ⁇ M ⁇ 1 .
  • the complex received signal i.e., the target data signal
  • the target data signal can be converted into a real tensor Y ⁇ CN ⁇ M ⁇ 2Nr .
  • the MCS information tensor and the received signal tensor are concatenated to obtain a feature map T ⁇ CN ⁇ M ⁇ (2Nr+1) and sent to the subsequent residual convolution network for processing.
  • the first receiver uses V ⁇ CN ⁇ M ⁇ L ⁇ Qmax to output the log-likelihood ratio tensor after signal processing, where Q max represents the maximum number of bits per symbol corresponding to the maximum modulation order among all possible configured MCS types supported by the system.
  • Q max represents the maximum number of bits per symbol corresponding to the maximum modulation order among all possible configured MCS types supported by the system.
  • the last dimension of the V ⁇ C N ⁇ M ⁇ L ⁇ Qmax tensor can be cropped 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 send it to the subsequent channel decoding module, where Q represents the number of bits per symbol corresponding to the modulation order according to the configured MCS.
  • Figure 28 shows a solution in which the first configuration information includes the number of transmission layers.
  • 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
  • the number of transmission layers is L.
  • the model structure of the first receiver may be as shown in Figure 28.
  • the structure may have a residual convolutional network with a number of convolution kernels of D and a number of residual blocks of N blocks as the main skeleton.
  • the first receiver may also use the network structure described above or other network structures as the basic skeleton, and the embodiments of the present application do not specifically limit this.
  • the input of the model may include the number of transmission layers L configured in 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 spread out as a layer information tensor L ⁇ CN ⁇ M ⁇ 1 .
  • the complex form of the received signal (such as the target data signal) is converted into a real form tensor Y ⁇ CN ⁇ M ⁇ 2Nr .
  • the layer information tensor and the received signal tensor can be concatenated to obtain a feature map T ⁇ CN ⁇ M ⁇ (2Nr+1) .
  • the feature map is sent to the subsequent residual network for processing.
  • the first receiver uses V ⁇ CNN ⁇ M ⁇ Lmax ⁇ Q to output the log-likelihood ratio tensor after signal processing, where Q represents the number of bits per symbol corresponding to the MCS configured in the system, and Lmax represents the maximum number of transmission layers that the system can support.
  • the third dimension of the tensor V ⁇ CN ⁇ M ⁇ Lmax ⁇ Q may be cropped according to the number of transmission layers L in the first configuration information to obtain the final output log-likelihood ratio tensor Vout ⁇ CN ⁇ M ⁇ L ⁇ Q and send it to a subsequent channel decoding module.
  • the first parameter may be replaced by the first weight
  • the second parameter may be replaced by the second weight
  • FIG29 is a schematic block diagram of a communication device provided in an embodiment of the present application.
  • the communication device 2900 shown in FIG29 can be any of the first devices described above.
  • the communication device 2900 includes a sending unit 2910.
  • the sending unit 2910 is configured to send a target data signal to a second device, where the target data signal is generated based on the first data signal and the second data signal, where 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, where the first resources include at least two of the following resources: time domain resources, frequency domain resources and airspace resources.
  • the target data signal is generated based on the linear superposition of the first data signal and the second data signal.
  • 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.
  • the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.
  • the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.
  • 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.
  • 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.
  • the first data signal and the second data signal have different first statistical distribution characteristics.
  • the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a source type.
  • 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.
  • the plurality of first signals have different second statistical distribution characteristics.
  • the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, a first parameter, and a 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.
  • the target data signal is generated based on the nonlinearly superimposed first data signal and the second data signal.
  • the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.
  • 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.
  • the first processing operation includes one or more of the following: a splicing operation, a linear superposition, a sixth model processing, and a 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.
  • 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 channel of the first data signal and the input channel of the second data signal.
  • 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
  • the seventh model is used to process the multiple second signals.
  • FIG30 is a schematic block diagram of a communication device provided in an embodiment of the present application.
  • the communication device 3000 shown in FIG30 can be any second device described above.
  • the communication device 3000 includes a receiving unit 3010.
  • 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 signal 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.
  • the target data signal is generated based on the linear superposition of the first data signal and the second data signal.
  • the first data signal and the second data signal are based on one or more of the following: The linear superposition: 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; a second parameter, the second parameter is used to adjust the transmission energy of the second data signal.
  • the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.
  • the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.
  • 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.
  • 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.
  • the first data signal and the second data signal have different first statistical distribution characteristics.
  • the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a source type.
  • 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.
  • the plurality of first signals have different second statistical distribution characteristics.
  • the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, a first parameter, and a 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.
  • the target data signal is generated based on the nonlinearly superimposed first data signal and the second data signal.
  • the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.
  • 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.
  • the first processing operation includes one or more of the following: a splicing operation, a linear superposition, a sixth model processing, and a 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.
  • 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 channel of the first data signal and the input channel of the second data signal.
  • 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
  • the seventh model is used to process the multiple second signals.
  • the receiving unit is used to: receive 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.
  • the first receiver is used to recover the first data signal and the second data signal based on first configuration information.
  • the communication device also includes: a processing unit, used to process the target data signal based on preconfigured parameters to obtain a first processed signal, and to process 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, used to recover the first data signal and the second data signal based on the second processed signal.
  • a processing unit used to process the target data signal based on preconfigured parameters to obtain a first processed signal, and to process 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
  • a recovery unit used to recover the first data signal and the second data signal based on the second processed signal.
  • the first configuration information includes one or more of the following: the number of transmission layers, the number of users transmitted, 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.
  • the third statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and an information source type.
  • FIG31 is a schematic structural diagram of a communication device (or communication equipment) of an embodiment of the present application.
  • the dotted line in FIG31 indicates that the unit or module is optional.
  • the device 3100 can be used to implement the method described in the above method embodiment.
  • the device 3100 can be a chip, a communication device, a first device, or a second device.
  • 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, wherein 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: a time domain resource, a frequency domain resource, and a spatial domain resource.
  • the target data signal is generated based on the linear superposition of the first data signal and the second data signal.
  • 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.
  • the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.
  • the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.
  • 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.
  • 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.
  • the first data signal and the second data signal have different first statistical distribution characteristics.
  • the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a source type.
  • 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.
  • the plurality of first signals or the plurality of second signals have different second statistical distribution characteristics.
  • the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, a first parameter, and a 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.
  • the target data signal is generated based on the nonlinearly superimposed first data signal and the second data signal.
  • the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.
  • 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.
  • the first processing operation includes one or more of the following: a splicing operation, a linear superposition, a sixth model processing, and a 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.
  • 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 channel of the first data signal and the input channel of the second data signal.
  • 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
  • the seventh model is used to process the multiple second signals.
  • the apparatus 3100 shown in FIG31 may be a second device.
  • the apparatus may include a memory, a processor, and a transceiver.
  • the memory is used to store programs
  • the processor is used to call the programs in the memory
  • the transceiver is used to: receive 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.
  • the target data signal is generated based on the linear superposition of the first data signal and the second data signal.
  • 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.
  • the first parameter is determined based on a first model, or the first parameter is a preconfigured parameter.
  • the second parameter is determined based on a second model, or the second parameter is a preconfigured parameter.
  • 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.
  • 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.
  • the first data signal and the second data signal have different first statistical distribution characteristics.
  • the first statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and a source type.
  • 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.
  • the plurality of first signals have different second statistical distribution characteristics.
  • the second statistical distribution characteristic includes one or more of the following: modulation mode, coding mode, source type, a first parameter, and a 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.
  • the target data signal is generated based on the nonlinearly superimposed first data signal and the second data signal.
  • the target data signal is generated by performing nonlinear superposition on the first data signal and the second data signal using a fifth model.
  • 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.
  • the first processing operation includes one or more of the following: a splicing operation, a linear superposition, a sixth model processing, and a 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.
  • 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 channel of the first data signal and the input channel of the second data signal.
  • 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
  • the seventh model is used to process the multiple second signals.
  • the second device receives a 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.
  • the first receiver is used to recover the first data signal and the second data signal based on first configuration information.
  • the processor is used to: process the target data signal based on preconfigured parameters to obtain to a first processed signal; based on the first configuration information, 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; based on the second processed signal, restoring the first data signal and the second data signal.
  • the first configuration information includes one or more of the following: the number of transmission layers, the number of users transmitted, the transmission bandwidth, a first parameter, a second parameter, a third statistical distribution characteristic corresponding to the first data signal, and a 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.
  • the third statistical distribution characteristic and/or the fourth statistical distribution characteristic includes one or more of the following: a modulation mode, a coding mode, and an information source type.
  • the device 3100 may include one or more processors 3110.
  • the processor 3110 may support the device 3100 to implement the method described in the method embodiment described above.
  • the processor 3110 may be a general-purpose processor or a special-purpose processor.
  • the processor may be a central processing unit (CPU).
  • the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • DSP digital signal processor
  • ASIC application-specific integrated circuits
  • FPGA field programmable gate arrays
  • a general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
  • the apparatus 3100 may further include one or more memories 3120.
  • the memory 3120 stores a program, which can be executed by the processor 3110, so that the processor 3110 executes the method described in the above method embodiment.
  • the memory 3120 may be independent of the processor 3110 or integrated in the processor 3110.
  • the apparatus 3100 may further include a transceiver 3130.
  • the processor 3110 may communicate with other devices or chips through the transceiver 3130.
  • the processor 3110 may transmit and receive data with other devices or chips through the transceiver 3130.
  • the embodiment of 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 embodiment of the present application, and the program enables the computer to execute the method performed by the first device or the second device in each embodiment of the present application.
  • the embodiment of 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 embodiment of the present application, and the program enables the computer to execute the method performed by the first device or the second device in each embodiment of the present application.
  • the present application also provides a computer program.
  • the computer program can be applied to the first device or the second device provided in the present application, and the computer program enables a computer to execute the method performed by the first device or the second device in each embodiment of the present application.
  • the "indication" mentioned can be a direct indication, an indirect indication, or an indication of an association relationship.
  • a indicates B which can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association relationship between A and B.
  • the "include” mentioned may refer to direct inclusion or indirect inclusion.
  • the “include” mentioned in the embodiments of the present application may be replaced with “indicate” or “used to determine”.
  • a includes B which may be replaced with A indicates B, or A is used to determine B.
  • B corresponding to A means that B is associated with A, and B can be determined according to A.
  • determining B according to A does not mean determining B only according to A, and B can also be determined according to A and/or other information.
  • the term "corresponding" may indicate that there is a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship of indication and being indicated, configuration and being configured, etc.
  • pre-definition or “pre-configuration” can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in a device (for example, including a terminal device and a network device), and the present application does not limit the specific implementation method.
  • pre-definition can refer to what is defined in the protocol.
  • the “protocol” may refer to a standard protocol in the communication 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.
  • the term "and/or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships.
  • a and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone.
  • the character "/" in this article generally indicates that the associated objects before and after are in an "or" relationship.
  • the order of execution of the above-mentioned processes does not necessarily mean the order in which they are executed.
  • the sequence 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.
  • the disclosed systems, devices and methods can be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • 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.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
  • the computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.
  • the computer-readable storage medium may 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.
  • the available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disk (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.
  • a magnetic medium e.g., a floppy disk, a hard disk, a magnetic tape
  • an optical medium e.g., a digital versatile disk (DVD)
  • DVD digital versatile disk
  • SSD solid state disk

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente demande concerne un procédé de transmission de données et un dispositif de communication. Le procédé comprend les étapes suivantes : un premier dispositif envoie un signal de données cible à un second dispositif, le signal de données cible étant généré sur la base de premiers signaux de données et de seconds signaux de données, des ressources de transmission occupées par les premiers signaux de données comprenant des premières ressources, les premières ressources étant en outre utilisées pour transmettre une partie ou la totalité des signaux de données parmi les seconds signaux de données et les premières ressources comprenant au moins deux ressources parmi une ressource de domaine temporel, une ressource de domaine fréquentiel et une ressource de domaine spatial.
PCT/CN2023/137459 2023-12-08 2023-12-08 Procédé de transmission de données et dispositif de communication Pending WO2025118274A1 (fr)

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PCT/CN2023/137459 WO2025118274A1 (fr) 2023-12-08 2023-12-08 Procédé de transmission de données et dispositif de communication

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190099964A (ko) * 2018-02-20 2019-08-28 세종대학교산학협력단 다중 셀 비직교 다중 액세스 네트워크의 다수 사용자 스케줄링을 위한 사용자 클러스터링 기법
CN110418297A (zh) * 2019-09-12 2019-11-05 安徽大学 一种基于误码率公平的功率域noma协作传输方法及其装置
CN113382414A (zh) * 2021-03-12 2021-09-10 厦门大学 基于网络切片的非正交多址接入系统资源分配方法及装置
WO2023129698A1 (fr) * 2021-12-31 2023-07-06 Ofinno, Llc Estimation de qualité pour communication cellulaire

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190099964A (ko) * 2018-02-20 2019-08-28 세종대학교산학협력단 다중 셀 비직교 다중 액세스 네트워크의 다수 사용자 스케줄링을 위한 사용자 클러스터링 기법
CN110418297A (zh) * 2019-09-12 2019-11-05 安徽大学 一种基于误码率公平的功率域noma协作传输方法及其装置
CN113382414A (zh) * 2021-03-12 2021-09-10 厦门大学 基于网络切片的非正交多址接入系统资源分配方法及装置
WO2023129698A1 (fr) * 2021-12-31 2023-07-06 Ofinno, Llc Estimation de qualité pour communication cellulaire

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