Communication method and apparatus

Abstract

Embodiments of the present application disclose a communication method and apparatus. The method comprises: configuring X reference signals corresponding to N transmit beams within one reference signal period, each reference signal among the X reference signals comprising first indication information and second indication information, the first indication information being used for indicating a receive beam corresponding to each reference signal among the X reference signals, the second indication information being used for indicating switching of M receive beams within one reference signal period to scan the N transmit beams, each of N and M being an integer greater than 1, and X being an integer greater than N and M; and transmitting the X reference signals to a terminal device by means of the N transmit beams. Using the embodiments of the present application can reduce the delay of reference signal configuration and beam scanning, eliminate the impact of a state change of a terminal device that occurs in a process of switching receive beams across reference signal configuration periods, and increase the accuracy of beam measurement and / or beam prediction.

Description

Communication method and device

[0001] This application claims priority to the Chinese patent application with application number 202311656708.8 filed with the State Intellectual Property Office of China on December 4, 2023, and priority to the Chinese patent application with the invention name “A Communication Method and Device”, all contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and in particular to a communication method and device. Background Art

[0003] With the development of fifth-generation mobile communication (5G) systems, the spectrum used is gradually evolving toward higher frequencies. Due to physical transmission characteristics, spatial transmission loss and penetration loss in high-frequency bands are significantly higher than in low-frequency bands. To compensate for these drawbacks, larger antenna arrays are often used in high-frequency bands to combat the attenuation of received signals caused by these losses. Antenna beamforming concentrates energy into a narrow beam, effectively improving network coverage and user experience.

[0004] In existing technical solutions, the overhead of beam scanning can be reduced by methods such as layered scanning, that is, first scanning a wide beam, and then scanning a small number of narrow beams under the wide beam, so as to achieve the goal of reducing overhead. At the same time, the artificial intelligence (AI) technology that has emerged in recent years has also achieved remarkable results in reducing beam scanning overhead. However, in AI-based beam pair prediction, only after the switching of all receiving beams is completed will it be recorded as an input for AI prediction, resulting in an increase in the reference signal configuration delay, as well as an increase in the scanning and prediction delay. At the same time, if the state of the user equipment (UE) changes during the receiving beam switching process (for example, the user equipment rotates), the final prediction performance will also be affected. Summary of the Invention

[0005] The embodiments of the present application provide a communication method and apparatus, which reduce the delay of reference signal configuration and beam scanning, eliminate the impact of changes in the terminal device state during the process of switching the receiving beam across the reference signal configuration period, and improve the accuracy of beam measurement and / or beam prediction.

[0006] In a first aspect, an embodiment of the present application provides a communication method, which is applied to a network device, or a chip or circuit configured in the network device, including:

[0007] X reference signals corresponding to N transmit beams are configured within a reference signal period, where each of the X reference signals includes first indication information and second indication information, where the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within a reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M; and the X reference signals are sent to a terminal device through the N transmit beams.

[0008] By configuring the first indication information and the second indication information of each reference signal, the reference signal of the N transmitting beams configured for the M receiving beams can be switched by the terminal device within one reference signal period to complete the scanning of all transmitting beams, thereby reducing the delay of the reference signal configuration and beam scanning, eliminating the impact of the change in the terminal device state during the switching of the receiving beam across the reference signal configuration period, and improving the accuracy of beam measurement and / or beam prediction.

[0009] In one possible design, capability information sent by the terminal device is received, where the capability information includes at least one of the following information: a maximum number of the reference signals or the number of receive beams that can be scanned within a reference signal period; and, based on the capability information, the X reference signals corresponding to the N transmit beams within a reference signal period are configured. Based on the capability information of the terminal device, the X reference signals corresponding to the N transmit beams within a reference signal period are configured to ensure that the terminal device can complete measurement of the X reference signals corresponding to the N transmit beams.

[0010] In one possible design, a scanning result sent by the terminal device is received, where the scanning result indicates measurement values ​​of all beam pairs between the N transmit beams and the M receive beams scanned within a reference signal period. The scanning results collected within a reference signal period are input into the AI ​​model for prediction, thereby improving the efficiency of the AI ​​model prediction.

[0011] In one possible design, the scanning result includes M reference signal received power (RSRP) sets, and each of the M RSRP sets includes a measurement value after the receiving beam completes scanning the N transmitting beams.

[0012] In one possible design, the scan results are input into an artificial intelligence (AI) model for prediction, where the AI ​​model outputs K optimal beam pairs, where K is an integer greater than or equal to 1. Using the AI ​​model to predict the scan results ensures the accuracy of selecting the optimal beam pair.

[0013] In one possible design, it is determined whether there is an optimal beam pair among the K optimal beam pairs corresponding to the same transmit beam; if so, Y reference signals corresponding to at least two of the same transmit beams within a reference signal period are sent to the terminal device, each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate the switching of the receive beam corresponding to the same transmit beam within a reference signal period to scan the same transmit beam, and Y is an integer greater than 1.

[0014] For the case where the output of the AI ​​model is multiple optimal beam pairs, if there is an optimal beam pair corresponding to the same transmit beam among the K optimal beam pairs, then by configuring the third indication information and the fourth indication information for each reference signal, the reference signals of at least two identical transmit beams are configured within one reference signal period, and the terminal device can switch the receiving beam within one reference signal period to complete the scanning of at least two identical transmit beams, thereby further reducing the delay of reference signal configuration and beam scanning.

[0015] In one possible design, the first indication information includes an identifier of the receiving beam and an identifier of the antenna array surface. The receiving beam corresponding to each reference signal is determined by the identifier of the receiving beam and the identifier of the antenna array surface.

[0016] In one possible design, each reference signal further includes an identifier of the reference signal.

[0017] In a second aspect, an embodiment of the present application provides a communication method, which is applied to a terminal device, or a chip or circuit configured in the terminal device, including:

[0018] Receive X reference signals corresponding to N transmit beams within a reference signal period sent by a network device, where each of the X reference signals includes first indication information and second indication information, the first indication information being used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information being used to instruct switching of M receive beams to scan the N transmit beams within the one reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M; and scan the N transmit beams based on the first indication information and the second indication information.

[0019] By configuring the first indication information and the second indication information of each reference signal, the reference signal of the N transmitting beams configured for the M receiving beams can be switched by the terminal device within one reference signal period to complete the scanning of all transmitting beams, thereby reducing the delay of the reference signal configuration and beam scanning, eliminating the impact of the change in the terminal device state during the switching of the receiving beam across the reference signal configuration period, and improving the accuracy of beam measurement and / or beam prediction.

[0020] In one possible design, capability information is sent to the network device, where the capability information is used to indicate the X reference signals corresponding to the N transmit beams configured within a reference signal period, wherein the capability information includes at least one of the following information: the maximum number of the reference signals that can be scanned within a reference signal period, or the number of the receive beams. Based on the capability information of the terminal device, the X reference signals corresponding to the N transmit beams within a reference signal period are configured to ensure that the terminal device can complete the measurement of the X reference signals corresponding to the N transmit beams.

[0021] In one possible design, a scanning result is sent to the network device, where the scanning result indicates measurement values ​​of all beam pairs between the N transmit beams and the M receive beams scanned within one reference signal period. This allows the network device to input the scanning results collected within one reference signal period into the AI ​​model for prediction, thereby improving the efficiency of the AI ​​model's prediction.

[0022] In one possible design, the scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam completes scanning the N transmitting beams.

[0023] In one possible design, Y reference signals corresponding to at least two transmit beams sent by the network device within a reference signal period are received, and the at least two transmit beams are the same transmit beams corresponding to the optimal beam pair among the K optimal beam pairs output by the AI ​​model, and each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate the switching of the receive beams corresponding to the at least two transmit beams within a reference signal period to scan the at least two transmit beams, and Y is an integer greater than 1; based on the third indication information and the fourth indication information, the at least two transmit beams are scanned.

[0024] For the case where the output of the AI ​​model is multiple optimal beam pairs, if there is an optimal beam pair corresponding to the same transmit beam among the K optimal beam pairs, then by configuring the third indication information and the fourth indication information for each reference signal, the reference signals of at least two identical transmit beams are configured within one reference signal period, and the terminal device can switch the receiving beam within one reference signal period to complete the scanning of at least two identical transmit beams, thereby further reducing the delay of reference signal configuration and beam scanning.

[0025] In one possible design, the first indication information includes an identifier of the receiving beam and an identifier of the antenna array surface. The receiving beam corresponding to each reference signal is determined by the identifier of the receiving beam and the identifier of the antenna array surface.

[0026] In one possible design, each reference signal further includes an identifier of the reference signal.

[0027] In a third aspect, an embodiment of the present application provides a communication device, including:

[0028] a processing module, configured to configure X reference signals corresponding to N transmit beams within a reference signal period, where each of the X reference signals includes first indication information and second indication information, the first indication information being used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information being used to instruct switching of M receive beams to scan the N transmit beams within the reference signal period, where both N and M are integers greater than 1, and X is an integer greater than N and M;

[0029] A sending module is used to send the X reference signals to the terminal device through the N transmit beams.

[0030] In one possible design, a receiving module is used to receive capability information sent by the terminal device, where the capability information includes at least one of the following information: the maximum number of the reference signals that can be scanned within one reference signal period, or the number of the receiving beams; the processing module is further used to configure the X reference signals corresponding to the N transmitting beams within one reference signal period based on the capability information.

[0031] In one possible design, a receiving module is used to receive a scanning result sent by the terminal device, and the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams scanned within a reference signal period.

[0032] In one possible design, the scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam completes scanning the N transmitting beams.

[0033] In one possible design, the processing module is further used to input the scanning results into an artificial intelligence (AI) model for prediction, wherein the output of the AI ​​model is K optimal beam pairs, and K is an integer greater than or equal to 1.

[0034] In one possible design, the processing module is also used to determine whether there is an optimal beam pair among the K optimal beam pairs corresponding to the same transmitting beam; if so, Y reference signals corresponding to at least two of the same transmitting beams within a reference signal period are sent to the terminal device, each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receiving beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate the switching of the receiving beam corresponding to the same transmitting beam within a reference signal period to scan the same transmitting beam, and Y is an integer greater than 1.

[0035] The operations and beneficial effects performed by the communication device can refer to the method and beneficial effects described in the first aspect above, and the repeated parts will be omitted.

[0036] In a fourth aspect, an embodiment of the present application provides a communication device, including:

[0037] a receiving module, configured to receive X reference signals corresponding to N transmit beams within a reference signal period sent by a network device, where each of the X reference signals includes first indication information and second indication information, the first indication information being used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information being used to instruct switching of M receive beams to scan the N transmit beams within the one reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M;

[0038] A processing module is used to scan the N transmission beams based on the first indication information and the second indication information.

[0039] In one possible design, a sending module is used to send capability information to the network device, where the capability information is used to indicate the X reference signals corresponding to the N transmit beams configured within a reference signal period, wherein the capability information includes at least one of the following information: the maximum number of the reference signals that can be scanned within a reference signal period, or the number of the receive beams.

[0040] In one possible design, a sending module is used to send a scanning result to the network device, where the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams scanned within a reference signal period.

[0041] In one possible design, the scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam completes scanning the N transmitting beams.

[0042] In one possible design, the receiving module is further used to receive Y reference signals corresponding to at least two transmit beams sent by the network device within a reference signal period, where the at least two transmit beams are the same transmit beams corresponding to the optimal beam pair among the K optimal beam pairs output by the AI ​​model, and each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate switching the receive beams corresponding to the at least two transmit beams within a reference signal period to scan the at least two transmit beams, and Y is an integer greater than 1; the processing module is further used to scan the at least two transmit beams based on the third indication information and the fourth indication information.

[0043] The operations and beneficial effects performed by the communication device can refer to the method and beneficial effects described in the second aspect above, and the repeated parts will be omitted.

[0044] In a fifth aspect, the present application provides a communication device, which includes a processor and a memory, wherein the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the communication device performs the method as described in any one of the first aspects.

[0045] In a sixth aspect, the present application provides a communication device, comprising a processor and a memory, wherein the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory so that the communication device performs a method as described in any one of the second aspects.

[0046] In a seventh aspect, the present application provides a communication device, which may be a network device, a device in a network device, or a device that can be used in conjunction with a network device. The communication device may also be a chip system. The communication device may execute the method described in the first aspect. The functions of the communication device may be implemented by hardware, or by hardware executing corresponding software implementations. The hardware or software includes one or more modules corresponding to the above functions. The module may be software and / or hardware. The operations and beneficial effects performed by the communication device may refer to the method and beneficial effects described in the first aspect above, and any repetitions will not be repeated.

[0047] In an eighth aspect, the present application provides a communication device, which may be a terminal device, a device in a terminal device, or a device that can be used in conjunction with a terminal device. The communication device may also be a chip system. The communication device may execute the method described in the second aspect. The functions of the communication device may be implemented by hardware or by hardware executing corresponding software implementations. The hardware or software includes one or more modules corresponding to the above functions. The module may be software and / or hardware. The operations and beneficial effects performed by the communication device may refer to the methods and beneficial effects described in the second aspect above, and any repetitions will not be repeated.

[0048] In a ninth aspect, the present application provides a computer-readable storage medium for storing a computer program. When the computer program is executed, the method described in any one of the first and second aspects is implemented.

[0049] In a tenth aspect, the present application provides a computer program product comprising a computer program, which, when executed, enables the method described in any one of the first and second aspects to be implemented.

[0050] In the eleventh aspect, an embodiment of the present application provides a communication system, which includes at least one terminal device and at least one network device, the terminal device is used to execute the steps in the above-mentioned first aspect, and the network device is used to execute the steps in the above-mentioned second aspect.

[0051] In a twelfth aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is used to communicate with an external device or an internal device, and the processor is used to implement the methods in the above aspects.

[0052] In one possible design, the chip may further include a memory storing a computer program or instructions, and the processor is configured to execute the computer program or instructions stored in the memory, or other programs or instructions. When the computer program or instructions are executed, the processor is configured to implement the aforementioned various aspects of the method.

[0053] In one possible design, the chip can be integrated into a terminal device or a network device. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] FIG1 is a schematic diagram of the architecture of a communication system 100 provided in an embodiment of the present application;

[0055] FIG2 is a schematic diagram of a beam;

[0056] FIG3 is a schematic diagram of a beam management process;

[0057] FIG4 is a schematic diagram of a neuron structure;

[0058] FIG5 is a schematic diagram of a neural network structure;

[0059] FIG6 is a schematic diagram of a process for downlink transmit beam prediction based on AI;

[0060] FIG7 is a schematic diagram of a process of beam pair prediction based on AI;

[0061] FIG8 is a schematic diagram of a beam pair scanning;

[0062] FIG9 is a flow chart of a communication method provided in an embodiment of the present application;

[0063] FIG10 is a schematic diagram of a beam pair scanning;

[0064] FIG11 is a flow chart of another communication method provided in an embodiment of the present application;

[0065] FIG12 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0066] FIG13 is a schematic structural diagram of another communication device provided in an embodiment of the present application;

[0067] FIG14 is a schematic diagram of the structure of a network device provided in an embodiment of the present application;

[0068] FIG15 is a schematic structural diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following is an explanation of the key terms involved in this application:

[0070] (1) Artificial Intelligence: Giving machines human intelligence and applying computer hardware and software to simulate certain intelligent human behaviors, including machine learning and many other methods.

[0071] (2) Machine learning: learning models or rules from raw data. There are many different machine learning methods, such as neural networks (NN), decision trees, support vector machines, etc.

[0072] (3) AI model: refers to a function model that maps input of a certain dimension to output of a certain dimension, and its model parameters are obtained through machine learning training. For example, f(x) = ax 2 +b is a quadratic function model, which can be regarded as an AI model. a and b correspond to the parameters of the model and can be obtained through machine learning training.

[0073] (4) Neural network: refers to artificial neural network, which is a mathematical model that imitates the behavioral characteristics of animal neural networks and performs distributed parallel information processing. It is a special form of AI model.

[0074] (5) Dataset: Data used for model training, verification, and testing in machine learning. The quantity and quality of data will affect the effectiveness of machine learning.

[0075] (6) Model training: By selecting a suitable loss function and using an optimization algorithm to train the model parameters, the loss function value is minimized.

[0076] (7) Hyperparameters: parameters such as the number of neural network layers, the number of neurons, activation function, loss function, etc.

[0077] (8) Loss function: used to measure the difference between the model's predicted value and the true value.

[0078] (9) Model application: Use the trained model to solve practical problems.

[0079] As shown in Figure 1, Figure 1 is a schematic diagram of the architecture of a communication system 100 provided in an embodiment of the present application. The communication system 100 may include a network device 110 and terminal devices 101 to 106. It should be understood that the communication system 100 to which the method of the embodiment of the present application can be applied may include more or fewer network devices or terminal devices. The network device or terminal device can be hardware, or functionally divided software, or a combination of the two. The network device and the terminal device can communicate through other devices or network elements. In the communication system 100, the network device 110 can send downlink data to the terminal devices 101 to 106. Of course, the terminal devices 101 to 106 can also send uplink data to the network device 110. Data can be carried on physical channels, such as the physical downlink control channel (PDCCH), the physical downlink shared channel (PDSCH), the physical uplink shared channel (PUSCH) or the physical uplink control channel (PUCCH), and further examples include the physical sidelink control channel (PSCCH) and the physical sidelink shared channel (PSSCH).

[0080] Terminal devices 101 to 106 may be cellular phones, smart phones, portable computers, handheld communication devices, handheld computing devices, satellite radio devices, global positioning systems, personal digital assistants (PDAs), and / or any other suitable devices for communicating on wireless communication system 100. Network device 110 may be an LTE and / or NR network device, specifically a base station (NodeB), an evolved base station (eNodeB), a base station in a 5G mobile communication system, a next generation mobile communication base station (gNB), a base station in a future mobile communication system, or an access node in a Wi-Fi system.

[0081] The communication system 100 may adopt a public land mobile network (PLMN), a device-to-device (D2D) network, a machine-to-machine (M2M) network, an Internet of Things (IoT), or other networks. In addition, terminal devices 104 to 106 may also form a communication system. In this communication system, terminal device 105 may send downlink data to terminal device 104 or terminal device 106. The method in the embodiment of the present application may be applied to the communication system 100 shown in FIG. 1 .

[0082] Figure 2 shows a schematic diagram of a beam. A base station can transmit both wide and narrow beams. Narrow beams have a spotlight-like effect, concentrating limited transmission energy in a narrow direction, significantly improving base station coverage. While large antennas with narrow beams improve coverage, they also incur significant beam management overhead. Base stations require more narrow beams to cover the entire space. To select the most suitable beam, terminal devices must traverse and measure a large number of candidate beams, resulting in significant beam management overhead. This is primarily due to hardware limitations at base stations, which often cannot simultaneously transmit multiple beams to cover the entire cell. Specifically, a base station can transmit one beam direction at a time, but can then transmit different beams at multiple times to cover the required directions for the entire cell. To address this issue, existing technologies reduce beam scanning overhead through methods such as layered scanning. This involves first scanning a wide beam, then scanning a smaller number of narrow beams within the wide beam, thereby reducing overhead. Furthermore, the recent rise of artificial intelligence (AI) technology has also achieved significant results in reducing beam scanning overhead.

[0083] 1. Beam management

[0084] Establish and maintain a suitable set of beam pairs between network devices and terminal devices. For downlink transmission, the network device needs to select an appropriate transmit beam, and the terminal device needs to select an appropriate receive beam. Together, these two beam pairs form a set to maintain a good wireless connection. Figure 3 illustrates the beam management process. The beam management process can be further subdivided into beam training, beam maintenance, and beam failure recovery.

[0085] Beam selection is mainly achieved through reference signals and corresponding beam measurements. Specifically, reference signals mainly include synchronization signal blocks (SSBs) and channel state information-reference signals (CSI-RSs). SSBs are cell broadcast signals, including primary synchronization signals (PSSs), secondary synchronization signals (SSSs), physical broadcast channels (PBCHs), and demodulation reference signals (DMRSs). SSBs are sent periodically according to the cell configuration, and their functions are not only used for beam management, but also for initial access, time-frequency synchronization, etc. In simple terms, SSB signals can be considered as wide-beam signals. Correspondingly, CSI-RS signals are user-level signals, and network equipment configures one or more groups of CSI-RS resources for terminal devices based on actual conditions. Similarly, CSI-RS signals are not only used for beam management, but can also be used for channel quality measurement, etc., and CSI-RS signals can be simply understood as narrow-beam signals.

[0086] The purpose of beam management is to establish and maintain a set of suitable beam pairs, that is, to select a suitable transmit beam (direction) at the transmitter and a suitable receive beam (direction) at the receiver. Together, they form a set of beam pairs to maintain a good wireless connection. This process can be called service beam selection. In some scenarios, due to environmental changes, the originally established beam pairs may be blocked, and the network equipment and terminal equipment do not have enough time to adjust the beams. At this time, beam failure recovery can be used to quickly select and establish another set of beam pairs. Since this application is mainly aimed at service beam selection scenarios, the beam recovery process will not be described in detail.

[0087] 2. Artificial Intelligence and Machine Learning

[0088] Machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning. Among them:

[0089] Supervised learning uses a machine learning algorithm to learn the mapping relationship between sample values ​​and sample labels based on collected sample values ​​and sample labels. This learned mapping relationship is then expressed using a machine learning model. The process of training a machine learning model is the process of learning this mapping relationship. For example, in signal detection, a noisy received signal is a sample, and the true constellation point corresponding to this signal is the label. Through training, machine learning aims to learn the mapping relationship between samples and labels, essentially enabling the machine learning model to become a signal detector. During training, the model parameters are optimized by calculating the error between the model's predicted values ​​and the true labels. Once the mapping relationship is learned, it can be used to predict the label of each new sample. The mapping relationship learned by supervised learning can include linear and nonlinear mappings. Learning tasks can be categorized into classification and regression based on the type of label.

[0090] Unsupervised learning relies solely on collected sample values, using algorithms to discover inherent patterns within them. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0091] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal decision-making actions. However, because the labels for "correct actions" are not available in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

[0092] Deep neural networks (DNNs) are a specific implementation of machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, DNN-based deep learning communication systems can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0093] The idea of ​​DNN comes from the neuron structure of the brain. As shown in Figure 4, Figure 4 is a schematic diagram of the neuron structure. Each neuron performs a weighted sum operation on its input value, and the weighted sum result is used to generate an output through a nonlinear function. Assume that the input of the neuron is x = [x0,…,x n ], the weight corresponding to the input is d=[d0,…,d n ], the bias of the weighted sum is b, and the form of nonlinear function can be diversified. An example is the max{0,x} maximum function. Then the effect of the execution of a neuron can be Figure 5 is a schematic diagram of a neural network structure. A DNN typically has multiple layers, with each layer containing multiple neurons. The input layer processes the received values ​​through neurons and then passes them to the intermediate hidden layer. Similarly, the hidden layer then passes the calculation results to the final output layer, generating the DNN's final output.

[0094] DNNs typically have more than one hidden layer, which directly impacts their ability to extract information and fit functions. Increasing the number of hidden layers or increasing the width of each layer can improve the DNN's function-fitting capabilities. The weighted values ​​in each neuron are the parameters of the DNN network model. These parameters are optimized through training, enabling the DNN network to extract data features and express mapping relationships. DNNs typically use supervised or unsupervised learning strategies to optimize model parameters.

[0095] Based on the network construction method, DNNs can be divided into feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The neural network shown in Figure 5 is an FNN network. Its characteristic is that neurons in adjacent layers are fully connected. This means that FNNs generally require a large amount of storage space and lead to high computational complexity.

[0096] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0097] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0098] The above-mentioned FNN, CNN, and RNN are common neural network structures, which are all constructed based on neurons. As mentioned above, each neuron performs a weighted sum operation on its input values, and the weighted summation result generates an output through a nonlinear function. We call the weights of the weighted summation operation of neurons in the neural network and the nonlinear function the parameters of the neural network. Taking the neuron with max{0,x} as the nonlinear function as an example, The parameters of the neuron to be operated are weights d=[d0,…,d n ], the weighted sum bias is b, and the nonlinear function max{0,x}. The parameters of all neurons in a neural network constitute the parameters of the neural network.

[0099] As shown in Figure 6, Figure 6 is a flow chart of AI-based downlink transmit beam prediction. In the existing AI-based beam management process, it is usually necessary to configure two sets of reference signal resources. The first set of configured reference signal resources is used to support the first round of sparse beam scanning. The terminal device obtains the measurement value obtained by the first round of sparse beam scanning, such as the reference signal received power (RSRP), and inputs it into the AI ​​to perform AI-based beam prediction. Usually, the prediction output of AI can be the probability of each beam becoming the optimal beam, or directly predict the RSRP corresponding to the beam. Through screening, the K beams that are most likely to become the optimal beam can be selected for the second round of scanning to find the optimal beam.

[0100] The solution in Figure 6 is mainly aimed at predicting downlink transmit beams. Each time, the AI ​​input is the measurement value fed back by the corresponding receive beam after scanning the configured sparse transmit beams. The optimal beam predicted by the AI ​​is also the best 1 or K optimal transmit beams corresponding to the receive beam. After the prediction for a transmit beam is completed, the terminal device switches the receive beam, and the network device reconfigures the transmit beam, performs the next round of beam scanning, and predicts the optimal transmit beam for the receive beam.

[0101] As shown in Figure 7, Figure 7 is a flowchart of beam pair prediction based on AI. Each input of AI is the measurement value of all sparse beam pairs (corresponding to each configured sparse transmit beam and all receive beams) collected after scanning all receive beams, and the prediction of AI is also the optimal one or K beam pairs. Among them, the transmit beams corresponding to the beam pairs may be the same but the receive beams are different. The specific process of beam pair prediction is that the network device configures the corresponding sparse transmit beam, and the terminal device switches to receive beam 1 for beam scanning to obtain the measurement value; after completion, the terminal device switches to receive beam 2, the network device configures the sparse transmit beam, and the terminal device scans to obtain the measurement value; repeat this step until all M receive beams of the terminal device are scanned. There are two ways to report the scanned measurement values:

[0102] (1) The terminal device reports once each time it completes a scan. (2) The terminal device reports once after completing the scan of all receive beams.

[0103] After the terminal device completes the reporting and the network device obtains the measurement values ​​of all sparse beam pairs, it is input into the AI ​​model to predict the optimal beam pair. The output of the AI ​​prediction is the optimal one or K beam pairs.

[0104] For the support of beam management, the NZP-CSI-RS-ResourceSet entry is defined and explained in 38.214 and 38.331, which defines the configuration information such as the identification (ID) of the CSI-RS resource. Among them, the repetition entry is related to beam management. This entry is used to indicate whether the terminal device identifies that all reference signals corresponding to this reference signal period correspond to the same transmit beam. When repetition is on, all CSI-RS resources in a reference signal period correspond to the same transmit beam. At this time, the terminal device needs to switch the receive beam in order to find the optimal receive beam. When repetition is off, all CSI-RS resources in a reference signal period correspond to different transmit beams. At this time, the terminal device does not need to switch the receive beam. Switching the receive beam requires crossing the period.

[0105] AI-based beam pair prediction is an optimization solution based on AI-based downlink transmit beam prediction, reducing reference signal configuration overhead. For example, for downlink transmit beam prediction, each prediction requires 16 reference signal resources. If the terminal device has four receive beams, a total of 64 reference signal resources are required. However, for beam pair prediction, each receive beam scan only requires fewer transmit beams than the downlink transmit beam prediction to achieve similar performance.

[0106] However, there are also certain problems with AI-based beam pair prediction. For example, for downlink transmit beam prediction, each AI prediction input is fixed to one receive beam, that is, it will switch to the next receive beam only after completing the prediction for one receive beam, resulting in each beam scan and prediction being an independent process. For beam pair prediction, it is necessary to configure the corresponding reference signal (transmit beam) in each independent reference signal period, which is the repetition off case mentioned above. Only after all receive beams are switched, will it be recorded as an AI prediction input. In this process, because it is necessary to configure the reference signal period across periods, scanning and prediction delays will be caused. At the same time, if the terminal device state changes during the receive beam switching process (for example, the terminal device rotates), the final prediction performance will also be affected.

[0107] In AI-based beam pair prediction, the reference signal configuration and beam scanning delay required to collect AI input is: T_period (reference signal configuration period) * M (number of receive beams). Figure 8 shows a schematic diagram of beam pair scanning. If there are four receive beams, four receive beam switches are required. After each switch, the four transmit beams need to be scanned to collect AI input. The reference signal configuration and beam scanning delay is: 20ms * 4 = 80ms.

[0108] In order to solve the above technical problems, the embodiments of the present application provide the following solutions.

[0109] As shown in FIG9 , FIG9 is a flow chart of a communication method provided in an embodiment of the present application. The method includes at least the following steps:

[0110] S901: A network device configures X reference signals corresponding to N transmit beams within a reference signal period.

[0111] Each of the X reference signals includes first indication information and second indication information, wherein the first indication information is used to indicate the receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within one reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M.

[0112] Among them, the first indication information includes the identifier of the receiving beam and the identifier of the antenna array. For example, Rx-Spatial-Filter can be used to indicate the identifier of the receiving beam, and Rx-Panel is used to indicate the identifier of the antenna array of the terminal device. The receiving beam on an antenna array corresponding to each reference signal is jointly indicated by Rx-Spatial-Filter and Rx-Panel. The second indication information may be repetition on, and repetition on is used to indicate that the receiving beam is switched within a reference signal period to complete all transmitting beam scans. Optionally, each of the X reference signals also includes the identifier of the reference signal, and the identifier of the reference signal may be a reference signal resource indicator (CSI-RS resource indicator, CRI).

[0113] Based on the first indication information and the second indication information, the terminal device can determine that different reference signals corresponding to the same transmit beam require the terminal device to switch the receive beam. That is, within one reference signal period, the network device needs to configure M reference signals, corresponding to M different receive beams respectively. In addition, within the reference signal period, the network device also needs to send N different transmit beams, and the way to distinguish different transmit beams is: different reference signal IDs corresponding to different transmit beams. However, different transmit beams can correspond to the same receive beam (for example, RX-Spatial-Filter, Rx-Panel). In this case, the terminal device can determine that the reference signal corresponds to different transmit beams, and the same receive beam can be used for scanning. In summary, all reference signals can be configured within one reference signal period, and the terminal device can distinguish them based on the first indication information, the second indication information and the identifier of the reference signal.

[0114] Optionally, the network device can configure X reference signals corresponding to N transmit beams within a reference signal period based on the sparse beam pattern predicted by the AI ​​beam pair. The sparse beam pattern is used to indicate that some of the beams in all beams can be beam scanned so as to serve as input for the AI. If the number of transmit beams in the sparse beam pattern is N and the number of receive beams is M, the number of reference signals that the network device needs to configure is N*M. Among the configured N*M reference signals, each N reference signals corresponds to N different transmit beams and the same receive beam, and M groups are repeatedly configured. The M groups of reference signals can be distinguished by the receive beam ID and the array ID.

[0115] For example, as shown in Table 1, if the number of transmit beams is N = 4 and the number of receive beams is M = 4, the network device can configure 16 reference signals within one reference signal period. Transmit beam 1 corresponds to CSI-0, CSI-1, CSI-2, and CSI-3, which correspond to receive beams Rx-0, Rx-1, Rx-2, and Rx-3, respectively. Repetition: on is also configured. This means that the terminal device needs to switch four receive beams to scan transmit beam 1 in order to measure CSI-0, CSI-1, CSI-2, and CSI-3, respectively. Transmit beam 2 corresponds to CSI-4, CSI-5, CSI-6, and CSI-7, which correspond to receive beams Rx-0, Rx-1, Rx-2, and Rx-3, respectively. Repetition:on is also configured. This means the terminal device needs to switch four receive beams to scan transmit beam 2 to measure CSI-4, CSI-5, CSI-6, and CSI-7. Transmit beam 3 corresponds to CSI-8, CSI-9, CSI-10, and CSI-11, which correspond to receive beams Rx-0, Rx-1, Rx-2, and Rx-3, respectively. Repetition:on is also configured. This means the terminal device needs to switch four receive beams to scan transmit beam 3 to measure CSI-8, CSI-9, CSI-10, and CSI-11. Transmit beam 4 corresponds to CSI-12, CSI-13, CSI-14, and CSI-15, which correspond to receive beams Rx-0, Rx-1, Rx-2, and Rx-3, respectively. Repetition: on is configured at the same time. That is, the terminal device needs to switch the four receive beams to scan transmit beam 4 in order to measure CSI-12, CSI-13, CSI-14, and CSI-15 respectively.

[0116] Table 1

[0117] Optionally, the terminal device may send capability information to the network device, where the capability information includes at least one of the following information: the maximum number of reference signals or the number of receive beams that can be scanned within a reference signal period; the network device may configure the X reference signals corresponding to the N transmit beams within a reference signal period based on the capability information. Further, optionally, the network device may send third indication information to the terminal device, where the third indication information is used to instruct the terminal device to report the capability information.

[0118] S902: The network device sends the X reference signals to the terminal device through the N transmit beams.

[0119] S903: The terminal device scans the N transmitting beams based on the first indication information and the second indication information.

[0120] Specifically, the terminal device can scan the N transmit beams respectively by switching the M receive beams to obtain a scan result. For example, for the reference signal configured by the network device as shown in Table 1, the terminal device can first switch to receive beam 1, determine the four different transmit beams corresponding to receive beam 1, and complete the scan. Then, it can switch to receive beam 2, determine the four different transmit beams corresponding to receive beam 2, and complete the scan. It can then switch again to receive beam 3, determine the four different transmit beams corresponding to receive beam 3, and complete the scan. Finally, it can switch to receive beam 4, determine the four different transmit beams corresponding to receive beam 4, and complete the scan.

[0121] As shown in Figure 10, Figure 10 is a schematic diagram of beam pair scanning. For each transmit beam, first indication information (indicating the receive beam corresponding to each reference signal) and second indication information (indicating switching between receive beam scanning and transmit beam scanning) are configured to complete all beam pair scanning within one reference signal period. Compared to the beam pair scanning scheme across multiple reference signal periods shown in Figure 8, the saved reference signal configuration and beam scanning delay is: T_period*(M-1), that is, the reference signal configuration and beam scanning are completed within only one reference signal period, reducing the reference signal configuration and beam scanning delay.

[0122] The scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmit beams and the M receive beams after scanning within one reference signal period. For example, the measurement value may be RSRP, reference signal receiving quality (RSRQ) or signal to interference plus noise ratio (SINR). Optionally, the scanning result may include M RSRP sets, each of the M RSRP sets including a measurement value after the receive beam scans the N transmit beams. For example, the M RSRP sets may be expressed as [RSRP_Rx1, RSRP_Rx2, ..., RSRP_RxM], where RSRP_rx1 represents the RSRP set after receive beam 1 scans all transmit beams, RSRP_rx2 represents the RSRP set after receive beam 2 scans all transmit beams, ..., RSRP_rx2 represents the RSRP set after receive beam M scans all transmit beams.

[0123] Optionally, the embodiment of the present application may further include the following steps:

[0124] S904, the terminal device sends a scanning result to the network device, where the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams that have been scanned within one reference signal period.

[0125] S905: The network device inputs the scanning result into an artificial intelligence (AI) model for prediction, wherein the output of the AI ​​model is K optimal beam pairs, where K is an integer greater than or equal to 1.

[0126] Optionally, the network device can pre-train the AI ​​model. Specifically, the network device configures the reference signals of all transmit beams to perform full codebook scanning. Among them, the reference signal can be a synchronization signal block (SSB) or a CSI reference signal (CSI-RS), etc. The terminal device can obtain the measurement value of the transmit beam, such as RSRP, RSRQ or SINR, by scanning the transmit beam. The measurement values ​​of each position corresponding to the sparse beam pattern are used as the input of the AI ​​model. At the same time, by comparing the sizes of each measurement value, the label of the classification training method is obtained, that is, the ID of the optimal beam pair, and the measurement values ​​of all transmit beams can also be used as the label of the AI ​​regression training method. After determining the input of the AI ​​model and the label of the AI ​​training, the AI ​​model is trained. The process of full codebook scanning can be repeated many times until the AI ​​model is fully converged.

[0127] Among them, the output of the AI ​​model can be the ID of the optimal beam pair or the RSRP corresponding to all beam pairs. If the training of the AI ​​model is based on the regression method, the output of the AI ​​model is the RSRP corresponding to all beam pairs. If the training of the AI ​​model is based on the classification training method, the output of the AI ​​model is the probability that each beam pair is the optimal beam pair.

[0128] In an embodiment of the present application, by configuring the first indication information and the second indication information of each reference signal, the reference signal of the N transmit beams configured for the M receive beams can be switched within one reference signal period by the terminal device to complete the scanning of all transmit beams, thereby reducing the delay of reference signal configuration and beam scanning, eliminating the impact of changes in the terminal device state during the switching of receive beams across the reference signal configuration period, and improving the accuracy of beam measurement and / or beam prediction. In addition, the scanning results collected within one reference signal period are input into the AI ​​model for prediction, thereby improving the efficiency and accuracy of the AI ​​model prediction.

[0129] In the previous embodiment, the output of the AI ​​model may include one or more optimal beam pairs. When the output of the AI ​​model is an optimal beam pair, the network device can directly configure the reference signal for data transmission based on the prediction result. When the prediction result is multiple optimal beam pairs, the network device needs to reconfigure the reference signal corresponding to the transmit beam for beam scanning, so as to determine the optimal or most suitable beam pair among the multiple optimal beam pairs. When the output of the AI ​​model is a Top-K optimal beam pair, there are K beam pairs corresponding to different transmit beams and receive beams. If the reference signal is configured across periods, it may cause delays in reference signal configuration and beam scanning. In the following embodiment, by configuring multiple reference signals within a reference signal period, the scanning of the Top-K beam pairs is completed within a reference signal period, and by comparing the RSRP obtained by the scan, the optimal or most suitable beam pair among the K beams is determined.

[0130] As shown in FIG11 , FIG11 is a flow chart of another communication method provided in an embodiment of the present application. The method includes at least the following steps:

[0131] S1101: A network device configures X reference signals corresponding to N transmit beams within a reference signal period.

[0132] Each of the X reference signals includes first indication information and second indication information, wherein the first indication information is used to indicate the receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within one reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M.

[0133] S1102: The network device sends the X reference signals to the terminal device through the N transmit beams.

[0134] S1103: The terminal device scans the N transmitting beams based on the first indication information and the second indication information.

[0135] S1104, the terminal device sends a scanning result to the network device, where the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams that have been scanned within one reference signal period.

[0136] S1105: The network device inputs the scanning result into an artificial intelligence (AI) model for prediction, wherein the output of the AI ​​model is K optimal beam pairs, where K is an integer greater than or equal to 1.

[0137] The specific implementation of S1101-S1105 is the same as that of S901-S905 in the previous embodiment. The specific implementation of S1101-S1105 can refer to S901-S905 and will not be repeated here.

[0138] S1106: The network device determines whether there is an optimal beam pair among the K optimal beam pairs that corresponds to the same transmit beam.

[0139] It should be noted that after the AI ​​model outputs K optimal beam pairs, these K optimal beam pairs may occur in the following situations: the same transmit beam appears multiple times (less than K) corresponding to different receive beams, or the same receive beam appears multiple times corresponding to different transmit beams. Network devices can configure reference signals based on the different situations of the K beam pairs.

[0140] S1107: If so, the network device sends Y reference signals corresponding to at least two of the same transmission beams within a reference signal period to the terminal device.

[0141] Each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receiving beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate switching the receiving beam corresponding to the same transmitting beam within a reference signal period to scan the same transmitting beam, and Y is an integer greater than 1.

[0142] Among them, the third indication information includes the identifier of the receiving beam and the identifier of the antenna array. For example, Rx-Spatial-Filter can be used to indicate the identifier of the receiving beam, and Rx-Panel is used to indicate the identifier of the antenna array of the terminal device. The receiving beam on an antenna array corresponding to each reference signal is jointly indicated by Rx-Spatial-Filter and Rx-Panel. The fourth indication information may be repetition on, and repetition on is used to indicate that the receiving beam corresponding to the same transmitting beam is switched within a reference signal period for beam scanning. Optionally, each of the Y reference signals also includes the identifier of the reference signal, and the identifier of the reference signal may be CRI.

[0143] If there is an optimal beam pair among the K optimal beam pairs corresponding to the same transmit beam, and there are at least two identical transmit beams, the reference signal corresponding to each transmit beam can be configured as follows: CSI-n[CRI-n, Rx-m, Repetition: on], CSI-n+1[CRI-N+1, Rx-m+1, Repetition: on].

[0144] For example, if the AI ​​model outputs four optimal beam pairs, receive beam 1 corresponds to transmit beam 1 and transmit beam 2, and receive beam 2 also corresponds to transmit beam 1 and transmit beam 2. Therefore, the network device can configure four reference signals within one reference signal period. Transmit beam 1 corresponds to CSI-16 and CSI-17, corresponding to receive beams Rx-0 and Rx-1, respectively, and Repetition:on is configured. This means that the terminal device needs to switch between two receive beams to scan transmit beam 1 in order to measure CSI-16 and CSI-17. Transmit beam 2 corresponds to CSI-18 and CSI-19, corresponding to receive beams Rx-0 and Rx-1, respectively, and Repetition:on is configured. This means that the terminal device needs to switch between two receive beams to scan transmit beam 2 in order to measure CSI-18 and CSI-19.

[0145] Table 2

[0146] If an optimal beam pair among the K optimal beam pairs corresponds to the same transmit beam, and there is one identical transmit beam, then according to the existing solution, a reference signal corresponding to the identical transmit beam can be configured within one reference signal period without configuring the third indication information and the fourth indication information. The terminal device can complete scanning of the identical transmit beam by switching multiple receive beams within one reference signal period.

[0147] Optionally, if it is determined that none of the K optimal beam pairs correspond to the same transmit beam. For example, the same receive beam appears multiple times and corresponds to different transmit beams, or there are K completely different optimal beam pairs. The terminal device may switch the receive beam, configure, for each receive beam, a reference signal corresponding to the transmit beam within each reference signal period, and send the reference signal to the terminal device.

[0148] Optionally, the terminal device may scan the transmit beams corresponding to the K optimal beam pairs and send another scan result to the network device. The network device determines the RSRP state based on the other scan result and selects the optimal or most suitable beam pair from the K optimal beam pairs for data transmission.

[0149] In an embodiment of the present application, for the case where the output of the AI ​​model is multiple optimal beam pairs, if there is an optimal beam pair corresponding to the same transmit beam among the K optimal beam pairs, then by configuring the third indication information and the fourth indication information for each reference signal, the reference signals of at least two identical transmit beams configured are within one reference signal period, and the terminal device can switch the receiving beam within one reference signal period to complete the scanning of at least two identical transmit beams, thereby further reducing the delay of the reference signal configuration and the beam scanning.

[0150] It can be understood that in the above-mentioned method embodiments, the methods and operations implemented by the terminal device can also be implemented by components that can be used for the terminal device (such as chips or circuits), and the methods and operations implemented by the network device can also be implemented by components that can be used for the network device (such as chips or circuits).

[0151] In the embodiment of the present application, the terminal device or network device can be divided into functional modules according to the above method example. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation. The following is an example of dividing each functional module corresponding to each function.

[0152] The method provided in the embodiment of the present application is described in detail above in conjunction with Figures 9 and 11. Below, the communication device provided in the embodiment of the present application is described in detail in conjunction with Figures 12 and 13. It should be understood that the description of the device embodiment corresponds to the description of the method embodiment. Therefore, for matters not described in detail, please refer to the method embodiment above. For the sake of brevity, it will not be repeated here.

[0153] 12 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application. The communication device may include a receiving module 1201, a processing module 1202, and a sending module 1203.

[0154] The communication device can implement the steps or processes corresponding to those performed by the network device in the above method embodiments. For example, it can be a network device, or a chip or circuit configured in the network device. The receiving module 1201 and the sending module 1203 are used to perform the transmission and reception related operations on the network device side of the above method embodiments, and the processing module 1202 is used to perform the processing related operations of the network device in the above method embodiments.

[0155] A processing module 1202 is configured to configure X reference signals corresponding to N transmit beams within a reference signal period, where each of the X reference signals includes first indication information and second indication information, where the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to instruct switching of M receive beams to scan the N transmit beams within the reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M.

[0156] The sending module 1203 is configured to send the X reference signals to the terminal device through the N transmit beams.

[0157] Optionally, the receiving module 1201 is configured to receive capability information sent by the terminal device, where the capability information includes at least one of the following information: a maximum number of the reference signals that can be scanned within a reference signal period, or a maximum number of the receive beams;

[0158] The processing module 1202 is further configured to configure the X reference signals corresponding to the N transmit beams within a reference signal period according to the capability information.

[0159] Optionally, the receiving module 1201 is used to receive a scanning result sent by the terminal device, where the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams scanned within a reference signal period.

[0160] Optionally, the scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam completes scanning the N transmitting beams.

[0161] Optionally, the processing module 1202 is further used to input the scanning results into an artificial intelligence (AI) model for prediction, wherein the output of the AI ​​model is K optimal beam pairs, and K is an integer greater than or equal to 1.

[0162] Optionally, the processing module 1202 is also used to determine whether there is an optimal beam pair among the K optimal beam pairs corresponding to the same transmitting beam; if so, Y reference signals corresponding to at least two of the same transmitting beams within a reference signal period are sent to the terminal device, each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receiving beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate the switching of the receiving beam corresponding to the same transmitting beam within a reference signal period to scan the same transmitting beam, and Y is an integer greater than 1.

[0163] It should be noted that the implementation of each module may also correspond to the corresponding description of the method embodiment shown in Figure 9 or Figure 11, and execute the methods and functions executed by the network device in the above embodiment.

[0164] 13 is a schematic diagram of the structure of another communication device provided in an embodiment of the present application. The communication device may include a receiving module 1301, a processing module 1302, and a sending module 1303.

[0165] The communication device can implement the steps or processes corresponding to those performed by the terminal device in the above method embodiments, and can be, for example, a terminal device, or a chip or circuit configured in the terminal device. The receiving module 1301 and the sending module 1303 are used to perform the transmission and reception related operations on the terminal device side of the above method embodiments, and the processing module 1302 is used to perform the processing related operations of the terminal device in the above method embodiments.

[0166] A receiving module 1301 is configured to receive X reference signals corresponding to N transmit beams within a reference signal period sent by a network device, where each of the X reference signals includes first indication information and second indication information, where the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to instruct switching of M receive beams to scan the N transmit beams within the reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M.

[0167] The processing module 1302 is configured to scan the N transmit beams based on the first indication information and the second indication information.

[0168] Optionally, the sending module 1303 is used to send capability information to the network device, where the capability information is used to indicate the X reference signals corresponding to the N transmit beams configured within a reference signal period, wherein the capability information includes at least one of the following information: the maximum number of the reference signals that can be scanned within a reference signal period, or the number of the receive beams.

[0169] Optionally, the sending module 1303 is used to send a scanning result to the network device, where the scanning result is used to indicate that the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams are scanned and completed within one reference signal period.

[0170] Optionally, the scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam completes scanning the N transmitting beams.

[0171] Optionally, the receiving module 1301 is further used to receive Y reference signals corresponding to at least two transmit beams sent by the network device within a reference signal period, where the at least two transmit beams are the same transmit beam corresponding to the optimal beam pair in the K optimal beam pairs output by the AI ​​model, and each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receive beam corresponding to each reference signal in the Y reference signals, and the fourth indication information is used to indicate switching the receive beams corresponding to the at least two transmit beams within one reference signal period to scan the at least two transmit beams, where Y is an integer greater than 1;

[0172] The processing module 1302 is further configured to scan the at least two transmit beams based on the third indication information and the fourth indication information.

[0173] It should be noted that the implementation of each module can also correspond to the corresponding description of the method embodiment shown in Figure 9 or Figure 11, and execute the methods and functions executed by the terminal device in the above embodiment.

[0174] Figure 14 is a schematic diagram of the structure of a network device provided in an embodiment of the present application. The network device can be applied to the system shown in Figure 1 to perform the functions of the network device in the above method embodiment, or to implement the steps or processes performed by the network device in the above method embodiment.

[0175] As shown in Figure 14, the network device includes a processor 1401 and a transceiver 1402. Optionally, the network device also includes a memory 1403. The processor 1401, transceiver 1402, and memory 1403 can communicate with each other via internal connection paths to transmit control and / or data signals. The memory 1403 is used to store computer programs, and the processor 1401 is used to call and execute the computer programs from the memory 1403 to control the transceiver 1402 to transmit and receive signals. Optionally, the network device may also include an antenna for transmitting uplink data or uplink control signaling output by the transceiver 1402 via wireless signals.

[0176] The processor 1401 and the memory 1403 may be combined into a processing device, and the processor 1401 is configured to execute program code stored in the memory 1403 to implement the aforementioned functions. In a specific implementation, the memory 1403 may also be integrated into the processor 1401 or independent of the processor 1401. The processor 1401 may correspond to the processing module in FIG12 .

[0177] The transceiver 1402 may correspond to the receiving module and transmitting module in FIG12 , and may also be referred to as a transceiver unit or transceiver module. The transceiver 1402 may include a receiver (or receiver, receiving circuit) and a transmitter (or transmitter, transmitting circuit). The receiver is used to receive signals, and the transmitter is used to transmit signals.

[0178] It should be understood that the network device shown in FIG14 is capable of implementing each process related to the network device in the method embodiments shown in FIG9 or FIG11. The operations and / or functions of each module in the network device are respectively for implementing the corresponding processes in the above method embodiments. For details, please refer to the description of the above method embodiments. To avoid repetition, detailed description is omitted here.

[0179] The processor 1401 can be used to execute the actions implemented within the network device described in the previous method embodiments, while the transceiver 1402 can be used to execute the actions of the network device sending to or receiving from the terminal device described in the previous method embodiments. For details, please refer to the description of the previous method embodiments, which will not be repeated here.

[0180] Processor 1401 may be a central processing unit (CPU), a general-purpose processor (GPOR), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device (PLD), a transistor logic device (TLD), a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 1401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. Communication bus 1404 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industrial Standard Architecture (EISA) bus. These buses may be categorized as address buses, data buses, control buses, and so on. For ease of illustration, FIG14 shows only one bold line, but this does not imply that there is only one bus or type of bus. Communication bus 1404 is used to facilitate communication between these components. In the embodiment of this application, transceiver 1402 is used to communicate signaling or data with other node devices. The memory 1403 may include volatile memory, such as nonvolatile dynamic random access memory (NVRAM), phase change random access memory (PRAM), magnetoresistive random access memory (MRAM), etc. It may also include non-volatile memory, such as at least one disk storage device, electrically erasable programmable read-only memory (EEPROM), flash memory devices, such as NOR flash memory or NAND flash memory, semiconductor devices, such as solid state disks (SSDs), etc. The memory 1403 may optionally be at least one storage device located away from the aforementioned processor 1401. The memory 1403 may optionally also store a set of computer program code or configuration information. Optionally, the processor 1401 may also execute the program stored in the memory 1403. The processor may cooperate with the memory and the transceiver to execute any one of the methods and functions of the network device in the above-mentioned application embodiments.

[0181] Figure 15 is a schematic diagram of the structure of a terminal device provided in an embodiment of the present application. The terminal device can be applied to the system shown in Figure 1 to perform the functions of the terminal device in the above method embodiment, or to implement the steps or processes performed by the terminal device in the above method embodiment.

[0182] As shown in Figure 15 , the terminal device includes a processor 1501 and a transceiver 1502. Optionally, the terminal device also includes a memory 1503. The processor 1501, transceiver 1502, and memory 1503 can communicate with each other via internal connection paths to transmit control and / or data signals. The memory 1503 is used to store computer programs, and the processor 1501 is used to call and execute the computer programs from the memory 1503 to control the transceiver 1502 to transmit and receive signals. Optionally, the terminal device may also include an antenna for transmitting uplink data or uplink control signaling output by the transceiver 1502 via wireless signals.

[0183] The processor 1501 and the memory 1503 may be combined into a processing device, and the processor 1501 is configured to execute program code stored in the memory 1503 to implement the aforementioned functions. In a specific implementation, the memory 1503 may also be integrated into the processor 1501 or independent of the processor 1501. The processor 1501 may correspond to the processing module in FIG13 .

[0184] The transceiver 1502 may correspond to the receiving module and transmitting module in FIG13 , and may also be referred to as a transceiver unit or transceiver module. The transceiver 1502 may include a receiver (or receiver, receiving circuit) and a transmitter (or transmitter, transmitting circuit). The receiver is used to receive signals, and the transmitter is used to transmit signals.

[0185] It should be understood that the terminal device shown in FIG15 is capable of implementing the various processes related to the terminal device in the method embodiments shown in FIG9 or FIG11. The operations and / or functions of the various modules in the terminal device are respectively for implementing the corresponding processes in the above method embodiments. For details, please refer to the description of the above method embodiments. To avoid repetition, detailed description is omitted here.

[0186] The processor 1501 can be used to execute the actions implemented within the terminal device described in the previous method embodiments, while the transceiver 1502 can be used to execute the actions of the terminal device sending to or receiving from the network device described in the previous method embodiments. For details, please refer to the description of the previous method embodiments and will not be repeated here.

[0187] The processor 1501 can be any of the aforementioned types of processors. The communication bus 1504 can be a PCI bus or an EISA bus. These buses can be classified as address buses, data buses, and control buses. For ease of illustration, Figure 15 shows only one thick line, but this does not imply a single bus or type of bus. The communication bus 1504 is used to enable communication between these components. The transceiver 1502 of the device in the embodiments of the present application is used to communicate signaling or data with other devices. The memory 1503 can be any of the aforementioned types of memory. The memory 1503 can optionally be at least one storage device located remotely from the processor 1501. The memory 1503 stores a set of computer program code or configuration information, and the processor 1501 executes the program in the memory 1503. The processor can cooperate with the memory and transceiver to perform any of the methods and functions of the terminal device in the aforementioned embodiments.

[0188] An embodiment of the present application also provides a chip system, which includes a processor for supporting a terminal device or a network device to implement the functions involved in any of the above embodiments, such as generating or processing the reference signal involved in the above method.

[0189] In one possible design, the chip system may also include a memory for storing computer programs and data necessary for the terminal device or network device. The chip system may consist of a single chip or may include a chip and other discrete components. The inputs and outputs of the chip system correspond to the receive and transmit operations of the terminal device or network device in the method embodiment, respectively.

[0190] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which includes: a computer program, which, when running on a computer, enables the computer to execute the method of any one of the embodiments shown in Figure 9 or Figure 11.

[0191] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable medium, which stores a computer program. When the computer program runs on a computer, the computer executes the method of any one of the embodiments shown in Figure 9 or Figure 11.

[0192] According to the method provided in the embodiment of the present application, the present application also provides a communication system, which includes one or more terminal devices and one or more network devices as mentioned above.

[0193] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. 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. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0194] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that: The method comprises: X reference signals corresponding to N transmit beams are configured within a reference signal period, where each of the X reference signals includes first indication information and second indication information, where the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within one reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M; The X reference signals are sent to the terminal device via the N transmit beams.

2. The method according to claim 1, characterized in that The method further comprises: Receiving capability information sent by the terminal device, the capability information including at least one of the following information: a maximum number of the reference signals that can be scanned within a reference signal period, or a maximum number of the receiving beams; The X reference signals corresponding to the N transmit beams in one reference signal period are configured according to the capability information.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Receive a scanning result sent by the terminal device, where the scanning result is used to indicate measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams that have been scanned within a reference signal period.

4. The method according to claim 3, characterized in that The scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam scans the N transmitting beams.

5. The method according to claim 3 or 4, characterized in that The method further comprises: The scanning result is input into an artificial intelligence AI model for prediction, wherein the output of the AI ​​model is K optimal beam pairs, and K is an integer greater than or equal to 1.

6. The method according to claim 5, characterized in that The method further comprises: Determine whether there is an optimal beam pair among the K optimal beam pairs corresponding to the same transmit beam; If so, Y reference signals corresponding to at least two of the same transmit beams within a reference signal period are sent to the terminal device, and each of the Y reference signals includes third indication information and fourth indication information, and the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate the switching of the receive beam corresponding to the same transmit beam within a reference signal period to scan the same transmit beam, and Y is an integer greater than 1.

7. The method according to any one of claims 1 to 6, characterized in that: The first indication information includes an identifier of the receiving beam and an identifier of the antenna array surface.

8. The method according to any one of claims 1 to 7, characterized in that: Each of the reference signals further includes an identifier of the reference signal.

9. A communication method, characterized in that: The method comprises: Receiving X reference signals corresponding to N transmit beams within a reference signal period sent by a network device, where each of the X reference signals includes first indication information and second indication information, where the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within the one reference signal period, where N and M are both integers greater than 1, and X is an integer greater than N and M; Based on the first indication information and the second indication information, the N transmit beams are scanned.

10. The method according to claim 9, characterized in that The method further comprises: Send capability information to the network device, where the capability information is used to indicate the X reference signals corresponding to the N transmit beams configured in one reference signal period, wherein the capability information includes at least one of the following information: the maximum number of the reference signals that can be scanned within one reference signal period, or the number of the receive beams.

11. The method according to claim 9 or 10, characterized in that The method further comprises: A scanning result is sent to the network device, where the scanning result is used to indicate that measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams are scanned and completed within one reference signal period.

12. The method according to claim 11, characterized in that The scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam scans the N transmitting beams.

13. The method according to any one of claims 9 to 12, characterized in that: The method further comprises: Receive Y reference signals corresponding to at least two transmit beams within a reference signal period sent by the network device, where the at least two transmit beams are the same transmit beams corresponding to the optimal beam pair among the K optimal beam pairs output by the AI ​​model, each of the Y reference signals includes third indication information and fourth indication information, the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate that the receive beams corresponding to the at least two transmit beams are switched within a reference signal period to scan the at least two transmit beams, and Y is an integer greater than 1; Based on the third indication information and the fourth indication information, the at least two transmit beams are scanned.

14. The method according to any one of claims 9 to 13, characterized in that: The first indication information includes an identifier of the receiving beam and an identifier of the antenna array surface.

15. The method according to any one of claims 9 to 14, characterized in that: Each of the reference signals further includes an identifier of the reference signal.

16. A communication device, characterized in that: The device comprises: a processing module, configured to configure X reference signals corresponding to N transmit beams within a reference signal period, where each of the X reference signals includes first indication information and second indication information, where the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within one reference signal period, where both N and M are integers greater than 1, and X is an integer greater than N and M; A sending module is used to send the X reference signals to the terminal device through the N transmit beams.

17. The device according to claim 16, characterized in that The device also includes: A receiving module, configured to receive capability information sent by the terminal device, wherein the capability information includes at least one of the following information: a maximum number of the reference signals that can be scanned within a reference signal period, or a maximum number of the receiving beams; The processing module is further used to configure the X reference signals corresponding to the N transmit beams within a reference signal period according to the capability information.

18. The device according to claim 16 or 17, characterized in that The device also includes: A receiving module is used to receive a scanning result sent by the terminal device, and the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams scanned within a reference signal period.

19. The device according to claim 18, characterized in that The scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam scans the N transmitting beams.

20. The device according to claim 18 or 19, characterized in that The processing module is further used to input the scanning results into an artificial intelligence AI model for prediction, wherein the output of the AI ​​model is K optimal beam pairs, and K is an integer greater than or equal to 1.

21. The device according to claim 20, characterized in that The processing module is further used to determine whether there is an optimal beam pair among the K optimal beam pairs corresponding to the same transmission beam; If so, Y reference signals corresponding to at least two of the same transmit beams within a reference signal period are sent to the terminal device, and each of the Y reference signals includes third indication information and fourth indication information, and the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate the switching of the receive beam corresponding to the same transmit beam within a reference signal period to scan the same transmit beam, and Y is an integer greater than 1.

22. A communication device, characterized in that: The device comprises: a receiving module, configured to receive X reference signals corresponding to N transmit beams within a reference signal period sent by a network device, wherein each of the X reference signals includes first indication information and second indication information, wherein the first indication information is used to indicate a receive beam corresponding to each of the X reference signals, and the second indication information is used to indicate switching of M receive beams to scan the N transmit beams within the one reference signal period, wherein both N and M are integers greater than 1, and X is an integer greater than N and M; A processing module is used to scan the N transmission beams based on the first indication information and the second indication information.

23. The device according to claim 22, characterized in that The device also includes: A sending module is used to send capability information to the network device, where the capability information is used to indicate the X reference signals corresponding to the N transmit beams configured within a reference signal period, wherein the capability information includes at least one of the following information: the maximum number of the reference signals that can be scanned within a reference signal period, or the number of the receive beams.

24. The device according to claim 22 or 23, characterized in that The device also includes: A sending module is used to send a scanning result to the network device, and the scanning result is used to indicate the measurement values ​​of all beam pairs between the N transmitting beams and the M receiving beams scanned within a reference signal period.

25. The device according to claim 24, characterized in that The scanning result includes M RSRP sets, and each RSRP set in the M RSRP sets includes a measurement value after the receiving beam scans the N transmitting beams.

26. The device according to any one of claims 22 to 25, characterized in that The receiving module is further used to receive Y reference signals corresponding to at least two transmit beams sent by the network device within a reference signal period, wherein the at least two transmit beams are the same transmit beams corresponding to the optimal beam pair among the K optimal beam pairs output by the AI ​​model, and each of the Y reference signals includes third indication information and fourth indication information, wherein the third indication information is used to indicate the receive beam corresponding to each of the Y reference signals, and the fourth indication information is used to indicate that the receive beams corresponding to the at least two transmit beams are switched within a reference signal period to scan the at least two transmit beams, and Y is an integer greater than 1; The processing module is further used to scan the at least two transmitting beams based on the third indication information and the fourth indication information.

27. A communication device, characterized in that: The device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the communication device to perform the method according to any one of claims 1 to 8.

28. A communication device, characterized in that: The communication device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the communication device to perform the method according to any one of claims 9 to 15.

29. A computer-readable storage medium, characterized in that: The computer-readable storage medium comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 15 is implemented.

30. A chip, characterized in that: The chip includes a processor and a communication interface, wherein the communication interface is used to communicate with an external device or an internal device, and the processor is used to implement the method according to any one of claims 1-15.

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