Communication methods and related apparatuses

Abstract

The application provides a communication method and related device, and relates to the technical field of wireless communication. The method comprises the following steps: a network device receives a pilot signal from a terminal device; a channel parameter estimation result is obtained based on the pilot signal and first angle information; wherein the first angle information is determined based on a reflected echo signal of a sensing signal from at least one sensing target, and comprises angle information of the at least one sensing target relative to the network device. The application uses the physical association between the communication link and the sensing link generated by the common scatterer, uses the angle information obtained by sensing as prior information to assist the communication channel estimation, introduces additional observation information for the channel estimation, thereby improving the precision of the channel parameter estimation and improving the overall performance of the system.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a communication method and related apparatus. Background Technology

[0002] As one of the key enabling technologies for 6G networks, the integrated sensing and communication (ISAC) system achieves deep integration and collaborative operation of communication and sensing functions by sharing hardware platforms, signal waveforms and spectrum resources.

[0003] Parameter estimation is a core component of the ISAC system for achieving environmental perception and information acquisition. Its main task is to accurately extract key parameters such as the angle, distance, and channel gain corresponding to the perceived target and communication user from the received signal. By accurately estimating these key parameters, functions such as target detection, user localization, trajectory tracking, and environmental reconstruction can be further realized. Simultaneously, the parameter estimation results also serve as an important basis for beam management and network optimization in the communication system.

[0004] However, the accuracy of the parameter estimation results of the ISAC system is not high at present, which limits the further improvement of the overall system performance. Summary of the Invention

[0005] This application provides a communication method and related apparatus that can improve the accuracy of ISAC system parameter estimation results, thereby enhancing the overall system performance.

[0006] Firstly, a communication method is provided. This method can be executed by a network device (such as an ISAC base station), or by a component configured in the network device (such as a circuit, chip, or chip system), or by a logic module or software capable of implementing all or part of the functions of the network device. This application does not limit this approach. The following description uses a network device as an example.

[0007] The method includes: a network device receiving a pilot signal from a terminal device; obtaining a channel parameter estimation result based on the pilot signal and a first angle information; wherein the first angle information is determined based on the echo signal reflected by the sensing signal through at least one sensing target, including the angle information of at least one sensing target relative to the network device.

[0008] In channel estimation, network devices additionally utilize the first angle information determined based on the sensing echo, that is, they utilize the physical correlation between the communication link and the sensing link caused by the common scatterer, which introduces additional prior information and observation information into the communication channel estimation, thereby improving the accuracy of communication channel parameter estimation and thus improving the overall system performance.

[0009] In one possible implementation, before receiving the pilot signal, the process further includes: the network device sending a sensing signal, receiving the echo signal, and determining the first angle information based on the echo signal. This demonstrates that the sensing process can be unified with the communication channel estimation process, laying the foundation for improving the accuracy of ISAC system parameter estimation results and thus enhancing the overall system performance.

[0010] In one possible implementation, determining the first angle information based on the echo signal includes: discretizing the angle space to obtain M candidate angles, where M is a positive integer; and determining the angle of the at least one sensing target relative to the network device from the M candidate angles based on the echo signal. The discretization process makes the first angle information discrete, which can be directly used for communication channel estimation.

[0011] In one possible implementation, the first angle information is used to indicate the candidate communication channel angle. The communication link and the sensing link are in the same physical propagation environment, and the sensed angle overlaps with the communication channel angle. The angle search range for communication channel estimation is narrowed from the entire angle space to the candidate angle indicated by the first angle information, thereby reducing the search space, reducing the estimation complexity, and improving the estimation accuracy.

[0012] In one possible implementation, the pilot signal is a pilot signal transmitted by the terminal device within T1 time slots, where T1 is an integer greater than 1. This lays the foundation for joint estimation using the block correlation of the same candidate angle across multiple time slots when performing subsequent channel parameter estimation, which is beneficial for improving the equivalent signal-to-noise ratio.

[0013] In one possible implementation, obtaining the channel parameter estimation result based on the pilot signal and the first angle information includes: discretizing the angle space to obtain a candidate angle set, the candidate angle set including M candidate angles, where M is a positive integer; estimating the channel coefficients corresponding to each candidate angle based on the pilot signal received within the T1 time slots, wherein the channel coefficients corresponding to the same candidate angle within the T1 time slots are jointly estimated. The T1 time slots are located within the channel coherence time, the angle of the communication channel remains approximately unchanged within the T1 time slots, and the channel coefficients corresponding to the same candidate angle are simultaneously zero or simultaneously non-zero in each time slot; by jointly estimating the channel coefficients corresponding to the same candidate angle using the observation information of the T1 time slots, the observations of multiple time slots jointly constrain the same sparsity variable, thereby improving the equivalent signal-to-noise ratio of the system.

[0014] In one possible implementation, joint estimation of the channel coefficients corresponding to the same candidate angle within the T1 time slots includes: assigning the same hyperparameter to the equivalent transmitted signal corresponding to the same candidate angle within the T1 time slots, and iteratively updating the hyperparameters corresponding to each candidate angle based on the pilot signal. The hyperparameters corresponding to the same candidate angle are jointly determined by the posterior information of the equivalent transmitted signal in the T1 time slots, which is equivalent to estimating based on the joint observations of the T1 time slots. This improves the equivalent signal-to-noise ratio of the system, thereby improving the estimation accuracy under conditions of low signal-to-noise ratio and limited observation resources.

[0015] In one possible implementation, obtaining the channel parameter estimation result based on the pilot signal and the first angle information includes: discretizing the angle space to obtain an angle candidate set, the angle candidate set including M candidate angles, where M is a positive integer; estimating the channel parameters of the communication channel based on the pilot signal and the prior distribution corresponding to each of the candidate angles, to obtain the channel parameter estimation result, wherein the prior distribution corresponding to the candidate angle indicated by the first angle information is determined based on a first initial value, and the prior distribution corresponding to the candidate angle not indicated by the first angle information is determined based on a second initial value, and the probability that the channel coefficient of the corresponding candidate angle is non-zero is greater than the probability that the channel coefficient of the candidate angle is non-zero in the prior distribution corresponding to the first initial value. The angle information obtained in the sensing stage is transformed into a probability tilt towards the candidate angles, which is equivalent to the sensing result informing the estimation process which candidate angles are more likely to have real communication channel angles, thereby highlighting angles more likely to have real multipath in the estimation and improving the estimation accuracy.

[0016] Secondly, a communication method is provided, which can be executed by a terminal device, or by a component (such as a circuit, chip, or chip system) configured in the terminal device, or by a logic module or software capable of implementing all or part of the functions of the terminal device. This application does not limit this approach. The following description uses a terminal device as an example.

[0017] The method includes: a terminal device sending a pilot signal to a network device, the pilot signal and first angle information being used by the network device to obtain channel parameter estimation results, the first angle information being determined based on the echo signal reflected by at least one sensing target from the sensing signal, and the first angle information including the angle information of the at least one sensing target relative to the network device.

[0018] The second aspect is the implementation on the terminal device side, which corresponds to the first aspect. The explanations, supplements, and descriptions of the beneficial effects of the first aspect also apply to the second aspect, and will not be repeated here.

[0019] Thirdly, a communication device is provided, comprising a processing module and a transceiver module. The transceiver module is used to receive pilot signals from a terminal device; the processing module is used to obtain channel parameter estimation results based on the pilot signals and first angle information, wherein the first angle information is determined based on echo signals reflected by at least one sensing target from a sensing signal, and the first angle information includes the angle information of the at least one sensing target relative to the network device.

[0020] Fourthly, a communication device is provided, comprising a transceiver module. The transceiver module is used to transmit a pilot signal to a network device. The pilot signal and first angle information are used by the network device to obtain channel parameter estimation results. The first angle information is determined based on echo signals reflected by at least one sensing target from a sensing signal. The first angle information includes the angle information of the at least one sensing target relative to the network device.

[0021] The third and fourth aspects are the implementation on the device side corresponding to the first and second aspects. The explanations, supplements, and descriptions of the beneficial effects of the first and second aspects also apply to the third and fourth aspects, and will not be repeated here.

[0022] Fifthly, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0023] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0024] In another implementation, the communication device is a chip configured in a network device such as an ISAC base station. When the communication device is a chip configured in a network device, the communication interface can be an input / output interface.

[0025] In a sixth aspect, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the second aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0026] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0027] In another implementation, the communication device is a chip configured in a terminal device. When the communication device is a chip configured in a terminal device, the communication interface can be an input / output interface.

[0028] In a seventh aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute a method in any possible implementation of any aspect.

[0029] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0030] Eighthly, a communication device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory, receive signals via a receiver, and transmit signals via a transmitter to execute the method in any possible implementation of any of the preceding aspects.

[0031] Optionally, the processor may be one or more, and the memory may be one or more.

[0032] Ninthly, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code or instructions) that, when the computer program is run, causes a computer to perform a method in any possible implementation of any of the above aspects.

[0033] In a tenth aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the method in any possible implementation of any of the above aspects.

[0034] Eleventhly, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0035] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0036] In a twelfth aspect, a communication system is provided, including the aforementioned terminal device and network device. Optionally, the communication system may further include other devices that communicate with the terminal device and / or the network device. For example, the communication system is used to perform the above-described method, such as the terminal device transmitting a pilot signal to the network device, and the network device obtaining a channel parameter estimation result based on the pilot signal and first angle information, wherein the first angle information is determined based on the echo signal reflected by at least one sensing target, and the first angle information includes the angle information of the at least one sensing target relative to the network device. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of a communication system used in an embodiment of this application; Figure 2 This is a schematic diagram of the ISAC system architecture; Figure 3 A schematic diagram of the block structure of multi-timeslot uplink pilot signals; Figure 4 This is a diagram illustrating the dependencies between variables in the SBSBL algorithm. Figure 5 A flowchart illustrating a communication method provided in an embodiment of this application; Figure 6 A flowchart illustrating yet another communication method provided in an embodiment of this application; Figure 7 A schematic block diagram of a communication device provided in an embodiment of this application; Figure 8 This is a schematic block diagram of a communication device provided in an embodiment of this application. Detailed Implementation

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

[0039] The technical solutions provided in this application can be applied to various communication systems, such as: Global System for Mobile Communications (GSM) systems, General Packet Radio Service (GPRS), Wireless Local Area Network (WLAN), Long Term Evolution (LTE) systems, LTE Frequency Division Duplex (FDD) systems, LTE Time Division Duplex (TDD) systems, sidelink communication systems, Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) communication systems, non-terrestrial network (NTN) communication systems, 5th generation (5G) mobile communication systems, or new radio access technology (NR). Among these, 5G mobile communication systems can include non-standalone (NSA) and / or standalone (SA) networking. The technical solutions provided in this application can also be applied to future communication systems. This application does not limit the scope of these applications.

[0040] Figure 1 This is a schematic diagram of a communication system 100 used in an embodiment of this application. The communication system 100 may include network devices, such as... Figure 1 The network device 110 is shown. The communication system 100 may also include terminal devices, such as... Figure 1 The terminal device 120 shown. The network device 110 and the terminal device 120 can communicate via a wireless link.

[0041] Figure 1 An exemplary network device 110 and a terminal device 120 are shown. Optionally, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0042] The network equipment in this application can be network-side equipment such as access network equipment and core network equipment. Access network equipment is sometimes also called access node. Access network equipment has wireless transceiver capabilities and is used to communicate with terminals. Access network equipment includes, but is not limited to, base stations, evolved NodeBs (eNodeBs), transmission reception points (TRPs) in the above-mentioned communication systems, next-generation NodeBs (gNBs) in 5G mobile communication systems, access network equipment or modules of access network equipment in open RAN (ORAN) systems, satellites in NTN communication systems, base stations in future mobile communication systems, or access nodes in WiFi systems, ISAC base stations, etc. Access network equipment can also be modules or units that can implement some of the functions of a base station. Access network equipment can be macro base stations, micro base stations or indoor stations, relay nodes or donor nodes, or wireless controllers in cloud radio access network (CRAN) scenarios. Optionally, access network equipment can also be servers, wearable devices, or vehicle-mounted equipment, etc. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). Multiple access network devices in a communication system can be base stations of the same type or different types. Base stations can communicate with terminals directly or via relay stations. Terminals can communicate with multiple base stations using different access technologies. The embodiments of this application do not limit the specific technology or device form used in the access network equipment. In this application, the access network equipment is referred to as a network device.

[0043] In this application, the means for implementing the functions of a network device can be a network device itself, or a means capable of supporting the network device in implementing those functions, such as a processor, circuit, chip, or chip system. This means can be installed in or connected to the network device. In the technical solutions provided in this application, the example of a network device being used to implement the functions of a network device is used to describe the technical solutions provided in this application.

[0044] The terminal device in this application can be a wireless terminal device capable of receiving network device scheduling and instruction information. The wireless terminal device can be a device providing voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. For example, the terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, or satellite communication, etc. The terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, aircraft (such as drone, helicopter, airplane), hot air balloon, ship, robot, robotic arm, or smart home device, etc. The embodiments of this application do not limit the form of the terminal device.

[0045] In this application, the apparatus for implementing the functions of a terminal device can be the terminal device itself, or any apparatus capable of supporting the terminal device in implementing those functions, such as a processor, circuit, chip, or chip system. This apparatus can be installed in or connected to the terminal device. In the technical solutions provided in this application, the example of a terminal device being used to implement the functions of a terminal device is used to describe the technical solutions provided in this application.

[0046] Access network equipment and / or terminal equipment can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. This application does not limit the application scenarios of the access network equipment and terminal equipment. They can be deployed in the same or different scenarios; for example, both can be deployed on land simultaneously; or the access network equipment can be deployed on land while the terminal equipment is deployed on water, etc., and so on.

[0047] In practical applications, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing a portion of the base station's functions. For example, network devices can be central units (CUs), distributed units (DUs), CUs (control planes, CPs), CUs (user planes, UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0048] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0049] To facilitate understanding of the embodiments of this application, the terminology used in this application will be briefly explained first. Optionally, the explanation of some terms may also refer to the explanations in the 3rd Generation Partnership Project (3GPP) standard protocol.

[0050] 1. ISAC: This technology integrates communication base stations and sensing nodes, enabling the wireless network not only to transmit data but also to function as a giant distributed sensor network. By analyzing signal reflection, attenuation, and multipath effects in the environment, it can acquire information such as the target's position, velocity, motion state, and environmental structure. The resulting environmental information model provides the wireless network with rich spatial cognitive capabilities, supporting the development of new application scenarios such as intelligent transportation, autonomous driving, smart cities, industrial automation, drone collaborative control, and immersive interaction. Simultaneously, the widely deployed communication nodes can form a large-scale collaborative sensing network. Through high-speed links, sharing and fusing sensing data from each node can overcome the line-of-sight limitations of single-point sensing, achieving wide-area, high-resolution tracking and imaging.

[0051] Figure 2 This is a schematic diagram of the ISAC system architecture, including: ISAC base station, targets 1, 2, ..., l, ..., L0, and central users (CUs) 1, ..., k, ..., K. The ISAC base station can send sensing pilot signals to each target, and each target reflects the echo signal. Each CU can send communication uplink pilot signals. The common path of the communication channel refers to the path of the communication uplink pilot signal sent by the CU after reflection or scattering by the sensing target (e.g., Target1) to the base station. The existence of this path proves that there is a common scatterer in the sensing channel (i.e., the sensing target is also a reflector of the communication multipath). The free path of the communication channel refers to the path of the communication uplink pilot signal sent by the CU that does not pass through the sensing target, but directly reaches the base station through other environmental scatterers, or directly to the base station. This path information is unique to pure communication and cannot be obtained from the sensing stage.

[0052] CU refers to the terminal equipment that needs to be served in the communication system. Please refer to the above description of terminal equipment.

[0053] It should be understood that the technical terms used in this application are for illustrative purposes only and not as limiting. For example, as technology evolves, technical terms may also change, and other technical terms that have the same technical meaning should also apply to this application.

[0054] The IMT-2030 (6G) Promotion Group white paper requires that the positioning accuracy of the 6G integrated sensing system reach the centimeter level, the distance resolution of target perception reach the centimeter level, and the angular resolution reach the 0.1 degree level, which means that it needs to have both highly reliable communication and high-precision environmental perception capabilities.

[0055] However, the inventors discovered that the current ISAC system has low accuracy in parameter estimation for obtaining positioning, distance, and angle information, which results in the inability to obtain high-reliability communication and high-precision environmental perception capabilities, thus limiting the further improvement of the overall system performance.

[0056] The inventors also discovered that the reasons for the low accuracy of the parameter estimation results include at least the following: 1. Traditional parameter estimation methods often fail to fully utilize the inherent relationship between communication signals and sensing signals. In other words, communication and sensing information processing are separate, so it is difficult to improve the accuracy of parameter estimation, especially the accuracy of channel parameter estimation results.

[0057] 2. Parameter recovery relies solely on observation information from a single task, resulting in insufficient observation information and limited accuracy.

[0058] The inventors discovered through research that communication and sensing links typically exist in similar physical propagation environments, and both are affected by the same scatterers during signal propagation, thus exhibiting a natural correlation in spatial structural characteristics. If the characteristics of these shared scatterers can be effectively modeled and transformed into a usable mathematical structure, it is expected to provide additional prior information support for communication-sensing fusion parameter estimation, thereby improving the accuracy and reliability of parameter recovery.

[0059] Therefore, the embodiments of this application need to solve the following fundamental problems in order to address the insufficient accuracy of parameter estimation and achieve an overall improvement in system performance: 1. How to fully explore the physical correlation characteristics between communication and sensing links caused by common scatterers, establish a mathematical model that can accurately describe the inherent coupling relationship of the sensing channel, and transform it into usable prior information to provide a theoretical basis and modeling basis for subsequent sensing fusion parameter estimation.

[0060] 2. Based on the established mathematical model of sensory fusion, how to design a perception-assisted communication channel parameter estimation method so that the target information obtained by the perception channel estimation can effectively assist in the recovery of communication user parameters.

[0061] In view of this, this application provides a communication method, and the solution provided by this application will be described in detail below with reference to the corresponding flowchart. It is understood that the illustrative flowchart provided in this application mainly uses different devices (such as terminal devices and network devices) as examples of the execution subjects of the interaction to illustrate the method, but this application does not limit the execution subjects of the interaction. For example, the device (such as terminal device or network device) in the illustrative flowchart can also be a chip, chip system, or processor that supports the device in implementing the method, or it can be a logic module or software that can implement all or part of the functions of the device.

[0062] As a general statement, the message or signaling interactions involved in the interaction process of this application embodiment can be standard messages or signaling or newly introduced messages or signaling. This application embodiment does not make specific limitations on this.

[0063] The research and development approach and the resulting theoretical derivation will be explained below: Since communication and sensing links are usually in similar physical propagation environments, the two links will be affected by the same scatterer during signal propagation. That is, there is a common scatterer in the sensing channel. Therefore, the inventors have found that the sensing target angle information obtained by estimating the sensing channel parameters can be used as prior information for estimating the communication channel to improve the accuracy of the communication channel estimation.

[0064] However, continuous angle values ​​in real-world scenarios cannot be directly used as usable mathematical priors (because sensing and communication algorithms operate in discrete index spaces). Therefore, this application transforms the sensing estimation problem into a sparse signal recovery problem on a discrete grid. This transformation necessarily requires constructing a linear observation matrix, introducing a hierarchical Bayesian model, and designing a matching variational expectation-maximization (VEM) iterative algorithm. This results in a two-stage synesthesia fusion parameter estimation scheme based on sparse Bayesian learning (SBL) and sensing-assisted block sparse Bayesian learning (SBSBL).

[0065] The following section will provide a detailed theoretical derivation and explanation of the above research and development approach.

[0066] SBL is used in the sensing channel estimation stage. Its core idea is to automatically learn the variance of each component by introducing sparse priors and using hierarchical Bayesian inference to fit the probability distribution of the unknown signal. Assuming the ISAC base station has One transmitting antenna and There are 10 receiving antennas, which are uniformly arranged in a uniform linear array (ULA) with half-wavelength intervals.

[0067] Base station transmitters and users are allocated non-orthogonal frequency resources to perform sensing and communication channel estimation separately. (In time slots) Within this timeframe, once the base station completes the entire process of sensing channel estimation, the sensing echo signal received by the base station can be expressed as: (1.1), In equation (1.1), It is a sensing pilot signal sent by the base station. It is additive white Gaussian noise transmitted by the base station to the sensing channel; It is the first From the perspective of perceiving the target It is the first The reflectance coefficient of a perceived target; It is the turning vector sent by the base station. It is the turning vector received by the base station. It is the total number of perceived targets. This indicates the conjugate transpose operation.

[0068] Due to the actual number of perceived targets L 0 M ( M To quantize the number of grid cells, i.e., to satisfy sparsity, therefore, in Execute within the scope Point quantization to obtain the angle candidate set (1.2).

[0069] Assuming the quantization is sufficiently precise and the true angles (referring to the angles of the sensing target and the multipath channel of the communication user in the real-world scenario) fall within the candidate angle set (i.e., the angle candidate set), the sensing echo signal received by the base station can be re-represented as a vector form: (1.3), In equation (1.3), Equivalent sensing channel matrix Equivalent sensing transmits pilot signals ; It is the receiver steering vector matrix, and its first... Column representation ; It is the equivalent discrete transmission steering vector matrix. When the... From a quantitative perspective When it is not the angle of the sensed target (referring to the position angle information of the sensed target detected by transmitting the sensed pilot signal in the sense channel), that is... ( (For the set containing all truly perceived target angles), we have The aforementioned "real angle" comprises two parts: the perceived target angle and the communication user's multipath channel angle. Here, "real perceived target angle" refers to the perceived target angle portion of the "real angle".

[0070] It should be noted that the purpose of discretizing the angle space is not only to transform the continuous parameter estimation problem into a solvable sparse signal recovery problem, but also to make the output of the estimation result a discrete candidate angle (or its index), thereby directly interfacing with the communication channel estimation operating on the discrete candidate angle space, and providing an interface for the sensing results to serve as prior information to assist in communication channel estimation. Optionally, the value of M can be determined based on a trade-off between estimation accuracy and computational complexity; the larger M is, the higher the angle resolution, but the greater the corresponding computational load.

[0071] Thus, the continuous angle estimation problem has been transformed into a sparse signal recovery problem on a discrete grid. As a linear observation matrix, it can be used in subsequent parameter estimation processes.

[0072] The inventors discovered that because the SBL framework is suitable for sparse scenarios and does not require prior knowledge of the number of targets, it is applicable to scenarios with... Figure 2 Taking the example scenario where the number of vehicles on the road changes constantly and the output format is a discrete support index set adapted to the communication side, this application constructs the following hierarchical SBL model. Typically, the number of sensing targets is finite, i.e., the number of multipaths in the channel is finite; therefore, most elements in matrix S are zero, satisfying sparsity. Based on this, we define... ,in It is Gaussian noise. If the standard deviation is such that, under the assumption of independent and identically distributed Gaussian noise, we have: (1.4), In equation (1.4), for The first in Each element.

[0073] Therefore, the base station receiver senses the echo signal. The likelihood function can be expressed as (1.5), In equation (1.5), yes The first in One element, yes The first in Each element.

[0074] To describe the matrix The sparsity of the SBL framework is assumed. If the prior distribution follows a zero-mean Gaussian distribution, then: (1.6) In equation (1.6), express Stacking of the variances of each element.

[0075] The hyperparameters represent the prior variance of the parameters to be estimated corresponding to each candidate angle. During the iteration process, the hyperparameters corresponding to candidate angles without sensing targets are driven to approach zero, causing the estimated values ​​of the corresponding parameters to approach zero, thereby achieving automatic learning of the sparse structure. Therefore, this embodiment of the application can automatically identify candidate angles with sensing targets from the received signal without knowing the number of sensing targets in advance, making it suitable for scenarios where the number of sensing targets changes over time (e.g., the number of vehicles on the road changes constantly).

[0076] Therefore, according to Bayes' theorem, we can obtain... The posterior distribution: (1.7) Due to probability Since it is not easy to find, this scheme will use the variational expectation-maximization (VEM) algorithm to estimate the optimal combination of hyperparameters.

[0077] The VEM algorithm is an iterative algorithm for maximum likelihood estimation problems with latent variables or missing data. It mainly consists of two processes: E-step and M-step. The main goal of the E-step is to calculate the expected value of the latent variables given the observed data and the current parameter estimates. The M-step, on the other hand, uses the expected value obtained in the E-step to maximize the likelihood function of the parameters, thereby updating the parameter estimates. The E-step and M-step are performed alternately until the parameter estimates meet the convergence condition and no longer change significantly.

[0078] Compared to the expectation-maximization (EM) algorithm, the VEM algorithm requires additional input to the hidden variables. and Assign hyperparameters and Make it conform to the Gamma prior distribution: (1.8) (1.9) Applying the idea of ​​the VEM algorithm to estimate hyperparameters And assume The variables are independent of each other, and hidden variables can be obtained. and known quantities The expected expression under the current parameters: (1.10) In equation (1.10), This indicates that the logarithmic posterior distribution of the input latent variables is calculated.

[0079] The VEM algorithm assigns a Gamma prior distribution to hyperparameters, which can avoid overfitting and improve the robustness of parameter estimation.

[0080] In E-step, it is necessary to obtain Therefore, it is necessary to retain variables. Related terms and variables Finding its expectation and simplifying it, we know that it follows a complex Gaussian distribution: (1.11), its mean and expectations Represented as: (1.12) (1.13) Can be used as The estimated value, i.e. .

[0081] In the M-step, it is necessary to obtain and .for , retain and variable Related terms and variables Finding the expected value and simplifying it, we know that it follows a Gamma distribution: (1.14), its parameters and expectations Represented as: (1.15) (1.16) (1.17) in Representing vectors The first in One element, Representation matrix The Line 1 The elements of the column.

[0082] for , retain and variable Related terms and variables Finding the expectation and simplifying it, we can see that it follows a complex Gaussian distribution. (1.18), its parameters and expectations Represented as: (1.19) (1.20) (1.21), in This indicates that the trace of the input matrix is ​​being sought.

[0083] After iterative convergence, Extract the one with the largest absolute value The index of each element, and then... The indexes form a set, which constitutes the perceptual perspective support set. : (1.22), (1.23) (1.24). Thus, at least one angle of the sensing target relative to the base station is determined from the M candidate angles, i.e., the first angle information.

[0084] SBSBL is used in the communication channel estimation stage, improving upon the use of sensing angle support sets. Beyond serving as prior information, it also involves: leveraging the block correlation across multiple time slots to share hyperparameters from the same angle across time slots, thereby improving the equivalent signal-to-noise ratio. In the time slot Within the system, the base station completes the entire process of sensing-assisted communication channel estimation. Orthogonal frequency resources are allocated among the users to separate their signals at the receiver. Then the base station receives the signal of the [user name missing]. The uplink pilot signal for a single user can be represented as: (2.1) In equation (2.1), It is the first Communication pilots sent by individual users It is additive white Gaussian noise in the communication channel; It is the first The number of multipath channels per user channel It is the first In the user channel, the first The angle of the path, It is the first In the user channel, the first The reflection coefficient of the path. Similarly, in Execute within the scope Point quantization to obtain the angle candidate set Assuming the quantization is sufficiently fine and the true angle falls within the candidate angle set, then the angle received by the base station is the... The uplink pilot signal of an individual user can be re-represented in vector form: (2.2) In equation (2.2), , , ; These are discrete communication channel coefficients. When the... From a quantitative perspective Not from the perspective of communication channel At that time, that is ,have Conversely, when That is, the true perspective is the first From the perspective of the first quantization, the vectorized first... Uplink pilot signal for individual users The time slot is non-zero, such as Figure 3 As shown, the quantization angle M represents a total of M discrete angle grids (m=1, 2, ..., M), corresponding to the multipath angles of arrival that may exist in the communication channel, and the time slot T1 represents a total of T1 consecutive time slots. The two-dimensional matrix represents the above... The arrow indicates that the two-dimensional matrices on the left are stacked into a long vector as shown on the right. From the vectorized result on the right (the long vector), it can be seen that the circles mark two consecutive... The length blocks, each filled with diagonal lines (representing non-zero values) and separated by spaces (representing zero), precisely illustrate the meaning of block sparsity: in a vector X of length MT1, non-zero elements are not randomly scattered, but rather arranged in a sequence of length MT1. The sequence appears in consecutive blocks, each block corresponding to a quantization angle m. Either the entire block is non-zero (this angle is the actual angle of the communication channel, i.e., the communication channel angle), or the entire block is zero (this angle has no signal).

[0085] The physical basis for the block-sparse structure is that: T1 time slots are located within the channel coherence time, and the angle of the communication channel remains approximately constant within T1 time slots. Therefore, the channel coefficient corresponding to the same candidate angle is either zero or non-zero simultaneously in each time slot. In other words, in the vectorized equivalent transmitted signal, the non-zero elements are not randomly scattered, but appear in groups of continuous blocks with a length equal to the number of time slots: each block corresponds to a candidate angle, and the entire block is non-zero when the candidate angle is the actual communication channel angle; otherwise, the entire block is zero.

[0086] Therefore, equivalent transmission signals belonging to the same quantization angle but different time slots are given... ( for The first in (each element) is assigned the same hyperparameter This improves the equivalent signal-to-noise ratio, thereby enhancing the system's estimation performance. (2.3) in It is used for regulation The precision vector of block sparsity, , This indicates that the determinant of the input matrix is ​​calculated.

[0087] It can be understood that assigning the same hyperparameter to the equivalent transmitted signals corresponding to the same candidate angle in different time slots is equivalent to estimating the channel coefficient corresponding to the candidate angle by combining the observation information of T1 time slots. The observations of multiple time slots jointly constrain the same sparsity variable, thereby improving the equivalent signal-to-noise ratio of the system, thus achieving the effect of improving the equivalent signal-to-noise ratio and improving the system estimation performance.

[0088] Based on the principle of the variational expectation maximization algorithm, we define... ,in It is Gaussian noise. If the standard deviation is such that, under the assumption of independent and identically distributed Gaussian noise, we have: (2.4), in for The first in One element. Therefore, the uplink pilot signal at the base station receiver. The likelihood function can be expressed as (2.5) in and They are and The first in Each element. Additionally, hidden variables. and Hyperparameters also need to be assigned and Make it conform to the Gamma prior distribution: (2.6) (2.7) Assumption The variables are independent of each other, and hidden variables can be obtained. and known quantities The expected expression under the current parameters: (2.8), where This indicates that the logarithmic posterior distribution of the input latent variables is calculated.

[0089] For hidden variables Hyperparameters in the assigned Gamma distribution , The shape parameter determines the overall shape of the curve, and It is a scale parameter, characterizing the effect of curve along... The distribution of axes can be stretched or compressed. Furthermore, because there are common scatterers in the sensing channel (i.e., some sensing target angles coincide with the communication channel angles), the sensing angle support set previously obtained during the sensing channel estimation stage can be utilized. Give hyperparameters Assign a large value to promote The smaller value and Obtaining non-zero solutions is used to characterize There is a greater probability that it is from the perspective of a communication channel. This process can be represented as: (2.9) After the above modifications, the hyperparameters It follows the new Gamma prior distribution: (2.10) in It is the Gamma function.

[0090] The mechanism of the above design is that the larger the scale parameter, the smaller the prior mean of the accuracy, that is, the larger the variance of the channel coefficient of the corresponding candidate angle, the easier it is for the channel coefficient to take a non-zero value, and the higher the posterior probability corresponding to the candidate angle during the iteration process.

[0091] Therefore, the angle information obtained in the perception stage is transformed into a probability tilt for a possible real communication channel. This is equivalent to the perception result informing the estimation process of the communication channel: the angle indicated by the first angle information is more likely to be the angle of a real (called candidate) communication channel. This is the key mechanism of this application embodiment to use the perception result to assist in the estimation of the communication channel and improve the estimation accuracy.

[0092] Since not all communication channel angles are selected from the perspective of the sensing target (i.e., some...) Therefore, hyperparameters are given. Assigning fixed values ​​lacks the ability to extract the true set of communication channel angles. Flexibility. Therefore, It needs to be divided into and The two parts are updated iteratively. express The complement and satisfying .for Its parameters are considered not to contain prior angle information, therefore their values ​​should remain unchanged and be assigned very small values, i.e. .for It is modeled as a random variable with a Gamma distribution and the following probability density: (2.11) in These are the shape and scale hyperparameters of the Gamma distribution. For the hidden variables... The posterior probability can be reformulated as: (2.12), where .

[0093] It should be noted that if a fixed prior configuration is assigned to all candidate angles indiscriminately, free paths that do not pass through the sensing target will be missed, and false alarm angles introduced during the sensing stage will be mistakenly retained. In the embodiments of this application, the hyperparameters corresponding to the candidate angles indicated by the first angle information participate in the iterative update, enabling the observation data to correct the bias in the prior; the hyperparameters corresponding to candidate angles not indicated by the first angle information are kept at the second initial value to avoid being contaminated by the prior. This design balances the utilization of sensing priors with the flexible extraction of the true angle set, ensuring the robustness of the estimation. This design is not a simple superposition of sensing results and channel estimation.

[0094] The update is explained in detail below. The process: renew By retaining variables Related terms and variables To find the expected value, we have: (2.13) in This represents the expectation of each element in the input vector. Note that it follows a Gaussian distribution. mean and variance for: (2.14) (2.15) renew : with calculation The method is similar, retaining variables Related terms and variables To find the expected value, we can get: (2.16) in and They represent vectors respectively The first in Elements and matrices The Middle Line 1 The elements in the column. Note that they follow a Gamma distribution. Its parameters and expectations They are respectively: (2.17) (2.18) (2.19) renew Approximate posterior probability It can be calculated as follows: (2.20) It can be observed that it follows a Gamma distribution. Its parameters and expectations They are respectively: (2.21), (2.22) (2.23) in This indicates that the trace of the input matrix is ​​being sought.

[0095] renew Approximate posterior probability It can be represented as: (2.24) Similarly, it follows a Gamma distribution. Its parameters and expectations They are respectively: (2.25) (2.26) (2.27).

[0096] by Figure 4 For example, the dependencies between variables in the SBSBL algorithm are illustrated: The diamond-shaped nodes represent the input, such as the uplink pilot signal received by the base station. Circular nodes represent variables, such as the equivalent transmitted signal X and noise accuracy. and the hyperparameters corresponding to the candidate angles Square nodes represent hyperparameters, including a, p, q, c, d, etc.

[0097] The connections between nodes correspond to the conditional dependencies of variables in the aforementioned theoretical derivation: received signal The equivalent transmitted signal X is constrained by the observation model, and the block sparsity of X is determined by the hyperparameters. adjust; It follows a Gamma prior distribution, and its scale parameter Further constrained by hyperparameters p and q; noise accuracy Constrained by hyperparameters c and d.

[0098] Figure 4 The right-hand box illustrates the multi-slot block correlation characteristics in the factor plot: equivalent transmitted signals x belonging to the same candidate angle m but not in the same slot. m,1 x m,2 ... x m,T1 They are all connected to the same hyperparameter node. This means that the same candidate angle shares the same hyperparameter within T1 time slots, and the observation information from multiple time slots jointly constrains the same sparse variable, thereby improving the estimation performance.

[0099] also, Figure 4 In The hyperparameters corresponding to candidate angles not indicated by the first angle information remain at the second initial value and do not participate in iterative updates. , It participates in iterative updates to avoid contaminating the estimated results corresponding to the true angle with erroneous first-angle information. During the iteration process, posterior information follows... Figure 4 The direction indicated by the middle arrow propagates back and forth between the variable node and the hyperparameter node until the convergence condition is met.

[0100] Based on the above theoretical derivation, the embodiments of this application provide the following communication methods.

[0101] Figure 5 This is a schematic diagram illustrating a communication method according to an embodiment of this application. It can be understood that... Figure 5 The terminal device in the middle can be Figure 1 Any terminal device in the context of network equipment can refer to any component within that terminal device (such as a processor, chip, or chip system). Network equipment can be... Figure 1 Any access network device (such as an ISAC base station) can refer to any component within the access network device (such as a processor, chip, or chip system). Figure 5 As shown, the method includes the following steps: S11. The terminal device sends an uplink pilot signal, and the network device receives the uplink pilot signal accordingly.

[0102] For definitions and examples of uplink pilot signals, please refer to existing technologies.

[0103] For example, the pilot signal is the pilot signal sent by the terminal device in T1 time slots, where T1 is an integer greater than 1. The purpose is to lay the foundation for improving the signal-to-noise ratio by using the block correlation of the same candidate angle in multiple time slots for joint estimation when performing channel parameter estimation later.

[0104] S12. The network device obtains the channel parameter estimation results based on the uplink pilot signal and sensing angle information.

[0105] The sensing angle information, also known as the first angle information, includes the angle of at least one sensing target relative to the network device. It can also be called the sensing angle set or the sensing angle support set. The method of obtaining the first angle information will be described in detail in the following embodiments.

[0106] For example, the perceived angle information is used to indicate the candidate communication channel angle. In this step, through differentiated prior configuration, the probability that the channel coefficient of the candidate angle indicated by the perceived angle information is non-zero is greater than the probability that the channel coefficient of the candidate angle not indicated by the perceived angle information is non-zero, thereby highlighting the angles that are more likely to have real multipath in the estimation.

[0107] In existing technologies, communication channel estimation only utilizes uplink pilot signals, and the information processing of communication and sensing is separated. However, in this embodiment, the network device additionally utilizes sensing angle information to assist in communication channel estimation. That is, it utilizes the physical correlation between the communication link and the sensing link generated by the common scatterer, which introduces additional prior information into the communication channel estimation, thereby improving the accuracy of communication channel estimation and thus enhancing the overall system performance.

[0108] Figure 6 This is another communication method provided by an embodiment of the present application. Compared with the above embodiments, it adds a process for obtaining sensing parameters such as first angle information, including the following steps: S21 and ISAC base stations send sensing signals, and correspondingly, the sensing target reflects the sensing echo signal.

[0109] S22 and ISAC base stations receive sensing echo signals.

[0110] S23 and ISAC base stations estimate sensing channel parameters and obtain sensing angle sets based on sensing echo signals.

[0111] This step is based on the SBL algorithm principle described above, and specifically includes the following steps: A1. Initialization.

[0112] For example, initialization includes: setting the number of iterations. Based on equations (1.12) and (1.13), the initial values ​​of the posterior distribution parameters are calculated. , ,in, Indicates to The conjugate transpose operation. , , It is the receiver steering vector matrix, and its first... Column representation Therefore, for each candidate angle The receiver steering vector can be calculated (the calculation method can be found in existing technology), and then all M steering vectors are arranged in columns to form a... , To sense echo signals.

[0113] Since equations (1.12) and (1.13) cannot be solved directly, a set of initialization hyperparameters needs to be provided. For example, all values ​​are taken as the minimum positive values ​​as the starting point for iteration.

[0114] As can be seen, this step is based on the principle that the number of perceived targets satisfies sparsity, which lays the foundation for the target positions output by the subsequent algorithm to be directly represented as discrete indices.

[0115] After initialization, the system enters an alternating iteration between E-step and M-step.

[0116] A2. Update hyperparameters : t=t+1, using and And calculate the distribution based on equation (1.14). Among them, such as equations (1.15) and (1.16). , .

[0117] This step corresponds to the M-step update in the theoretical section above. .

[0118] It should be noted that the hyperparameters updated in this step represent the prior variance of the parameters to be estimated corresponding to each candidate angle: as the iteration proceeds, the hyperparameters corresponding to the candidate angles of the unperceived target are driven to approach zero, and the estimated values ​​of the corresponding parameters to be estimated also approach zero, thereby realizing automatic learning of sparse structures without needing to know the number of perceived targets in advance.

[0119] A3. Update hyperparameters :use and And calculate the distribution based on equation (1.18). Among them, as shown in equations (1.19) and (1.20), the hyperparameters , .in, This represents the number of time slots.

[0120] This step corresponds to the M-step update in the theoretical section above. .

[0121] A4. Using distribution and Expectations , ,renew and .

[0122] when When the time is reached, the SBL algorithm stops iterating; otherwise, it continues to iterate (i.e., continues to execute) steps A2-A4.

[0123] After stopping the iteration, output the final estimated value. And calculate the set of perceived target angles that need to be recovered. and the set of reflection coefficients : , .

[0124] in It is a set of perception angle support, which stores The largest absolute value The index position of each element.

[0125] As can be seen, the sensing angle support set output in this step indicates the angle of the sensing target in the form of discrete indices, that is, the first angle information is in discrete form: the angle of at least one sensing target relative to the network device belongs to the angle candidate set. For example, the first angle information may include the quantized angle value of the candidate angle, or it may include the index corresponding to the angle of at least one sensing target in the angle candidate set; this embodiment does not limit this. Since the channel parameter estimation on the communication side is also performed based on the angle candidate set, the discrete form of the first angle information can directly correspond to the candidate angle space used by the communication side for estimation, thus allowing the sensing estimation result to be used as prior information for communication channel estimation without additional transformation.

[0126] Because some sensing targets can be considered as scatterers in the uplink communication link, the first... The set of common scatterers in the central user and sensing channel is:

[0127] in It is the number of overlapping angles and satisfies the condition. , It is the first The number of multipath communication channels for each central user. Due to the set of sensing target angles. Some angles in the middle satisfy the conditions ( Indicates the first The first central user's The communication channel angle of each path), that is, the angle of some sensed targets is consistent with the communication channel angle, therefore the sensed angle support set Some of the index values ​​in equation (1.24) also support the communication channel angle, and this information can be used to help improve the communication channel estimation performance in the next stage.

[0128] Therefore, the first angle information can be used to indicate the angle of the candidate communication channel. The physical basis for this is that the communication link and the sensing link are in the same physical propagation environment, and at least one sensing target in the sensing channel is also a scatterer of the communication channel between the network device and the terminal device. Therefore, the sensed angle overlaps with the angle of the communication channel. The inventors have discovered that in the prior art, communication channel estimation and environmental target perception are treated as two independent problems, without discovering or utilizing the aforementioned physical correlation. The embodiments of this application transform this physical correlation into usable prior information, narrowing the angle search range for communication channel estimation from the entire angle space to the candidate position indicated by the first angle information (i.e., the candidate communication channel angle), thereby reducing the search space and improving estimation accuracy.

[0129] S24. The terminal device sends an uplink pilot signal, and the network device receives the uplink pilot signal accordingly.

[0130] For definitions and examples of uplink pilot signals, please refer to existing technologies.

[0131] For example, the pilot signal is the pilot signal sent by the terminal device in T1 time slots, where T1 is an integer greater than 1. The purpose is to use the block correlation of the same candidate angle in multiple time slots for joint estimation when performing channel parameter estimation in the future, so as to lay the foundation for improving the equivalent signal-to-noise ratio.

[0132] S25. The network device obtains the channel parameter estimation results based on the uplink pilot signal and sensing angle information.

[0133] For example, the SBSBL process is as follows: B1. Set the number of iterations Using the perspective of perception to support the set According to the rules Assign prior initial values ​​to and initialize parameters .

[0134] This step corresponds to the processing of equations (2.9) and (2.10) in the aforementioned theoretical derivation. The above-mentioned perceptual perspective support set According to the rules The specific process for allocating prior initial values ​​is as follows: Discretize the angle space to obtain an angle candidate set (the same angle candidate set used in the sensing channel estimation stage). Assign a first initial value (e.g., 1) to the hyperparameters corresponding to the candidate angles indicated by the first angle information in the angle candidate set. Assign a second initial value (e.g., 1) to the hyperparameters corresponding to candidate angles not indicated by the first angle information. In this context, the probability that the channel coefficient of the corresponding candidate angle will be non-zero is greater than the probability corresponding to the second initial value, according to the prior distribution corresponding to the first initial value. For example, when the hyperparameter is the scale parameter of the Gamma prior distribution, the first initial value is a larger scale parameter value, and the second initial value is a smaller scale parameter value: the larger the scale parameter, the smaller the prior mean of the accuracy, the larger the variance of the channel coefficient of the corresponding candidate angle, and the easier it is for the channel coefficient to be non-zero. Therefore, the angle information acquired in the sensing stage is transformed into a probability bias towards the candidate angle, which is equivalent to the sensing result informing the estimation process: the candidate angle position indicated by the first angle information is more likely to have a real multipath communication.

[0135] B2, use and renew ， It is a constant value.

[0136] This step corresponds to the posterior distribution update (M-step) shown in equations (2.16) to (2.23) in the aforementioned theoretical derivation: based on the posterior distribution parameters of the equivalent transmitted signal, update the hyperparameters and noise accuracy corresponding to each candidate angle.

[0137] In this step, This reflects that the hyperparameters corresponding to the same candidate angle are jointly determined by the posterior information of the equivalent transmitted signals in T1 time slots, which is the specific implementation of assigning the same hyperparameters to the equivalent transmitted signals corresponding to the same candidate angle in T1 time slots.

[0138] B3. Use renew , It is a constant value.

[0139] This step corresponds to the hyperparameter update (M-step) shown in equations (2.24) to (2.27) in the aforementioned theoretical derivation. It should be noted that, since the same hyperparameter is assigned to the equivalent transmitted signal corresponding to the same candidate angle within T1 time slots, the observation information from multiple time slots jointly constrains the same sparse variable, which is equivalent to estimating the system by jointly observing the T1 time slots. The equivalent signal-to-noise ratio of the system is improved, thereby improving the estimation accuracy under conditions of low signal-to-noise ratio and limited observation resources.

[0140] B4, Use and renew , ,in , This corresponds to the updates shown in equations (2.14) to (2.15) in the aforementioned theoretical derivation. Different processing methods are used for updating hyperparameters (corresponding to equation (2.11)): the hyperparameters corresponding to the candidate angles indicated by the first angle information participate in the iterative update, enabling the observation data to correct biases in the perception prior (e.g., false alarm angles introduced during the perception stage); the hyperparameters corresponding to candidate angles not indicated by the first angle information remain at the second initial value to avoid prior contamination. This balances the gain from prior utilization with the flexibility of extracting the true angle set, ensuring the robustness of the estimation.

[0141] when At this point, the SBSBL algorithm stops iterating and outputs the final estimated value. And calculate the set of communication channel angles that need to be recovered. and channel coefficient set :

[0142]

[0143] in It is storage The largest absolute value A collection of elements.

[0144] The output channel parameter estimation results include the angle and channel coefficients of the communication channel, where the angle is output as candidate angles (or their indices) from the angle candidate set. Optionally, the convergence condition is that the difference between the estimates of two adjacent iterations is less than a preset threshold.

[0145] As can be seen, the SBSBL algorithm first assigns the same hyperparameter to the equivalent transmitted signals at the same quantization angle but in different time slots based on the block correlation characteristics of multi-time slot transmitted signals, thereby improving the equivalent signal-to-noise ratio when the system performs parameter recovery. Then, it introduces the sensing angle support set as prior information and marks the potential communication channel angle index position based on the physical characteristic that there is a common scatterer in the sensing channel. During the iteration process, it improves the posterior probability value, making the communication channel estimation result more accurate.

[0146] The communication method provided in this embodiment involves a network device first obtaining first angle information by sending sensing signals and receiving echo signals. Then, based on the first angle information and pilot signals sent by the terminal device within T1 time slots, communication channel estimation is performed. Corresponding to the problems discovered by the inventors above: on the one hand, addressing the issues of fragmented communication and sensing information processing and insufficient observation information, this embodiment utilizes the physical correlation between sensing channels generated by common scatterers, introducing the angle information obtained in the sensing stage as prior information into communication channel estimation. Differentiated prior configurations allow candidate angles more likely to have true multipath propagation to obtain higher posterior probabilities, introducing additional observation information for communication channel estimation. On the other hand, addressing the problem of limited estimation accuracy under low signal-to-noise ratio and limited observation resources, this embodiment utilizes the block correlation of multi-time-slot pilot signals for joint estimation, improving the system's equivalent signal-to-noise ratio. Therefore, the accuracy of communication channel parameter estimation is improved, providing an accurate parameter basis for beam management, user positioning, target detection, etc., thereby achieving an overall improvement in system performance. Furthermore, the estimation during the perception phase does not require prior knowledge of the number of perceived targets, making it suitable for real-world scenarios where the number of targets changes over time; the divide-and-conquer update mechanism of hyperparameters ensures the robustness of the estimation results even when false alarms exist in the perception prior.

[0147] It should be understood that Figures 1 to 6 The flowcharts or scene diagrams shown are for illustrative purposes only and are not intended to limit the embodiments of this application to the examples illustrated. In fact, those skilled in the art can interpret the embodiments based on... Figures 1 to 6 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0148] The above text combined Figures 1 to 6 This document describes in detail the communication method provided in the embodiments of this application. The following will combine... Figures 7 to 8 The device embodiments of this application are described in detail below. It should be understood that the communication device of this application embodiment can execute the various communication methods of the foregoing embodiments of this application, that is, the specific working processes of the various products below can be referred to the corresponding processes in the foregoing method embodiments.

[0149] In the embodiments described above, the terminal device may execute some or all of the steps in each embodiment; the network device may execute some or all of the steps in each embodiment. These steps or operations are merely examples, and the embodiments of this application may also perform other operations or variations thereof. Furthermore, the steps may be executed in different orders as presented in the embodiments, and it is not necessary to execute all the operations in the embodiments of this application. Moreover, the sequence number of each step does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0150] Figure 7 This is a schematic block diagram of a communication device provided in an embodiment of this application. Figure 7 As shown, the communication device 700 may include a communication module 720. The communication module 720 can implement corresponding communication functions, which can be internal communication functions of the communication device 700 or communication functions between the communication device 700 and other devices. Optionally, the communication module 720 may also be referred to as a communication interface or transceiver module. Optionally, the communication device 700 further includes a processing module 710. The processing module 710 can implement corresponding processing functions.

[0151] Optionally, the communication device 700 further includes a storage module, which can be used to store instructions and / or data; the processing module 710 can read the instructions and / or data in the storage module so that the communication device 700 can implement the aforementioned method embodiments.

[0152] In one possible design, the communication device 700 may correspond to the network device in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the network device. The communication device 700 can be used to perform the steps or processes performed by the network device in any of the above method embodiments.

[0153] For example, the communication module 720 is used to receive pilot signals from the terminal device; Processing module 710 is used to obtain channel parameter estimation results based on the pilot signal and the first angle information. The first angle information is determined based on the echo signal reflected by the sensing signal through at least one sensing target. The first angle information includes the angle information of the at least one sensing target relative to the network device. The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0154] In one possible design, the communication device 700 may correspond to the terminal device in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the terminal device. The communication device 700 can be used to perform the steps or processes performed by the terminal device in any of the above method embodiments.

[0155] For example, The communication module 720 is used to send a pilot signal to the network device. The pilot signal and the first angle information are used by the network device to obtain channel parameter estimation results. The first angle information is determined based on the echo signal reflected by the sensing signal through at least one sensing target. The first angle information includes the angle information of the at least one sensing target relative to the network device.

[0156] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0157] Figure 8 This is another schematic block diagram of the communication device 800 provided in the embodiments of this application. The communication device 800 may be a chip, chip system, or processor, etc., in a terminal device or network device that implements the above-described methods. The communication device 800 can be used to implement the methods described in the above-described method embodiments; for details, please refer to the descriptions in the above-described method embodiments.

[0158] like Figure 8 As shown, the communication device 800 may include one or more processors 810, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 810 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 800 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0159] In an alternative design, the processor 810 may also store instructions and / or data that can be executed by the processor 810 to cause the communication device 800 to perform the methods described in the above method embodiments.

[0160] In another alternative design, the communication device 800 may include a communication interface 820 for implementing receiving and transmitting functions. For example, the communication interface 820 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0161] Optionally, the communication device 800 may include one or more memories 830, which may store instructions that can be executed on the processor 810, causing the communication device 800 to perform the methods described in the above method embodiments. Optionally, the memories 830 may also store data. Optionally, the processor 810 may also store instructions and / or data. The processor 810 and the memories 830 may be provided separately or integrated together.

[0162] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0163] In one implementation, the communication device 800 may correspond to the terminal device in the above method embodiments and may be used to execute the various steps and / or processes executed by the terminal device in the above method embodiments. The processor 810 may be used to execute instructions stored in the memory 830, and when the processor 810 executes the instructions stored in the memory, the processor 810 is used to execute the various steps and / or processes of the above method embodiments corresponding to the terminal device.

[0164] In another implementation, the communication device 800 may correspond to the network device in the above method embodiments and may be used to execute the various steps and / or processes executed by the network device in the above method embodiments. The processor 810 may be used to execute instructions stored in the memory 830, and when the processor 810 executes the instructions stored in the memory, the processor 810 is used to execute the various steps and / or processes of the above method embodiments corresponding to the network device.

[0165] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0166] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0167] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0168] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0169] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned network device and terminal device. For example, the communication system is used to perform the above method, whereby the terminal device sends a pilot signal to the network device, the network device receives the pilot signal from the terminal device, and based on the pilot signal and first angle information, obtains a channel parameter estimation result. The first angle information is determined based on the echo signal reflected by at least one sensing target from the sensing signal, and includes the angle information of the at least one sensing target relative to the network device.

[0170] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or terminal device in any of the foregoing method embodiments.

[0171] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or terminal device in any of the foregoing method embodiments.

[0172] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0173] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0174] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0176] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0177] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A communication method, characterized in that, include: The network device receives pilot signals from the terminal device, wherein the pilot signals are pilot signals transmitted by the terminal device within T1 time slots, and T1 is an integer greater than 1; The network device obtains channel parameter estimation results based on the pilot signal and the first angle information. The first angle information is determined based on the echo signal reflected by at least one sensing target from the sensing signal. The first angle information includes the angle information of the at least one sensing target relative to the network device. Obtaining the channel parameter estimation results based on the pilot signal and the first angle information includes: discretizing the angle space to obtain an angle candidate set, the angle candidate set including M candidate angles, where M is a positive integer; estimating the channel coefficients corresponding to each candidate angle based on the pilot signal received within the T1 time slots, wherein the channel coefficients corresponding to the same candidate angle within the T1 time slots are jointly estimated.

2. The method according to claim 1, characterized in that, Before receiving the pilot signal from the terminal device, the method further includes: The network device sends the sensing signal; The network device receives the echo signal; The network device determines the first angle information based on the echo signal.

3. The method according to claim 2, characterized in that, Determining the first angle information based on the echo signal includes: Discretize the angle space to obtain M candidate angles, where M is a positive integer; Based on the echo signal, the angle of the at least one sensing target relative to the network device is determined from the M candidate angles.

4. The method according to any one of claims 1-3, characterized in that, The first angle information is used to indicate the angle of the candidate communication channel.

5. The method according to claim 1, characterized in that, The joint estimation of the channel coefficients corresponding to the same candidate angle within the T1 time slots includes: Assign the same hyperparameter to the equivalent transmitted signal corresponding to the same candidate angle within the T1 time slots, and iteratively update the hyperparameter corresponding to each candidate angle based on the pilot signal.

6. The method according to claim 1 or 2, characterized in that, The estimation of the channel coefficients corresponding to each candidate angle based on the pilot signals received within the T1 time slots includes: Based on the pilot signals received within the T1 time slots and the prior distributions corresponding to each candidate angle, the channel parameters of the communication channel are estimated to obtain the channel parameter estimation results. The prior distributions corresponding to the candidate angles indicated by the first angle information are determined based on a first initial value, and the prior distributions corresponding to the candidate angles not indicated by the first angle information are determined based on a second initial value. The probability that the channel coefficient of the corresponding candidate angle is non-zero is greater than the probability that the channel coefficient of the candidate angle is non-zero.

7. A communication method, characterized in that, include: The terminal device sends pilot signals to the network device within T1 time slots, where T1 is an integer greater than 1. The pilot signals and first angle information are used by the network device to obtain channel parameter estimation results. The first angle information is determined based on the echo signals reflected by at least one sensing target from the sensing signal. The first angle information includes the angle information of the at least one sensing target relative to the network device. The use of the pilot signals and first angle information to obtain channel parameter estimation results by the network device includes: the pilot signals received within the T1 time slots are used by the network device to estimate the channel coefficients corresponding to each candidate angle. Among them, the channel coefficients corresponding to the same candidate angle within the T1 time slots are jointly estimated. Each candidate angle is M candidate angles included in the angle candidate set, where M is a positive integer. The angle candidate set is obtained by the network device through discretization processing of the angle space.

8. The method according to claim 7, characterized in that, The first angle information is used to indicate the angle of the candidate communication channel.

9. A communication device, characterized in that, The device includes at least one processor coupled to a memory storing a program or instructions, the processor executing the program or instructions to cause the device to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they cause the computer to perform the method as described in any one of claims 1 to 8.

11. A communication system, characterized in that, Includes the communication device as described in claim 9.

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