Communication method and related equipment

Abstract

The invention provides a communication method and related equipment, the method is applied to first equipment in a communication perception integrated system, and the method comprises the following steps: according to a Cramer-Rao bound threshold, solving an optimization problem which takes a beam forming vector of a transmitted beam as an optimization variable and takes the secrecy and rate of a user as an optimization target, and obtaining a Cramer-Rao bound threshold of the Cramer-Rao bound threshold of the Cramer-Rao bound threshold of the Cramer-Rao bound; obtaining a recommended value of the beamforming vector, the Cramer-Rao bound threshold being set based on a perception performance demand; and sending a communication perception integrated signal according to the recommended value of the beam forming vector. According to the method, a CRB threshold value is set based on a sensing performance requirement for a communication and sensing integrated scene with a secure transmission requirement, beam forming and resource allocation are optimized in combination with the CRB threshold value, and secure communication is realized on the basis of guaranteeing the sensing performance of a communication and sensing integrated system.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a communication method, a communication device, a first device, a communication system, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the continuous increase in wireless communication requirements, wireless communication networks represented by the next-generation communication (Beyond 5G, B5G) and the sixth-generation communication (sixth generation, 6G) of the fifth-generation mobile communication (fifth generation, 5G) are gradually evolving to higher frequency bands. In this case, radar sensing and wireless communication are gradually becoming similar in terms of hardware architecture, channel characteristics, signal processing, etc., effectively promoting the integration of sensing and communication functions, and realizing integrated sensing and communication (ISAC) in one system. Among them, integrated sensing and communication can also be simply referred to as integrated communication and sensing. The integrated communication and sensing system uses a dual-functional base station and integrated waveform design, which not only reduces the hardware cost but also improves the system spectrum efficiency, and has broad application prospects in scenarios such as autonomous driving, low-altitude security, and the Internet of Things.

[0003] In the integrated communication and sensing scenario, to meet the sensing performance requirements of the system, the base station needs to send integrated signals with a relatively high power in the direction of the target. At this time, if the target to be sensed is a potential eavesdropper (Eve), the integrated signal sent by the base station will be eavesdropped, and the secure communication of legitimate users cannot be guaranteed. Therefore, the industry usually uses physical channel characteristics to enhance the security of the communication system. Specifically, by using artificial noise to mask the communication signals of legitimate users, while ensuring the sensing performance of the system, the signal reception of passive eavesdroppers is disrupted, thereby realizing the secure communication of legitimate users. In this case, how to design secure beamforming to maximize the secrecy rate of users while ensuring the sensing performance of the system is crucial for an integrated communication and sensing system with secure communication requirements. Summary of the Invention

[0004] This application provides a communication method and related devices, aiming at the integrated communication and sensing scenario with secure transmission requirements, setting the Cramer-Rao Bound (CRB) threshold based on the sensing performance requirements, and optimizing beamforming and resource allocation in combination with the CRB threshold, so as to achieve secure communication while ensuring the sensing performance of the integrated communication and sensing system.

[0005] To achieve the above object, this application provides the following technical solutions:

[0006] The first aspect of the present application provides a communication method. This method can be applied to a first device. This method can be applied to a communication-sensing integrated system (or simply referred to as a communication-sensing integrated system, integrated system, system), for example, the first device in a communication-sensing integrated system. The first device can include network devices such as base stations. The base station can include but is not limited to gNB, ng-eNB. Among them, the network device can also integrate a sensing function to implement communication-sensing integration. In some examples, the network device can be a dual-functional base station, where the dual function includes a communication function and a sensing function.

[0007] The first device (such as a network device like a base station) in the communication-sensing integrated system can solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the sum secrecy rate (SSR) of the user as the optimization target according to the Cramer-Rao Bound (CRB) threshold, and obtain a recommended value of the beamforming vector. Then the first device can send a communication-sensing integrated signal according to the recommended value of the beamforming vector.

[0008] The communication method of the present application is aimed at a communication-sensing integrated scenario with secure transmission requirements. The first device (such as a dual-functional base station) sets the CRB threshold based on the sensing performance requirements, combines the CRB threshold to solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy rate of the user as the optimization target, and obtains a recommended value of the beamforming vector, so as to optimize beamforming and resource allocation, and achieve secure communication while ensuring the sensing performance of the communication-sensing integrated system.

[0009] In some possible implementation manners, the first device can solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the sum secrecy rate of the user as the optimization target under the constraint that the Cramer-Rao bound corresponding to the angle of the potential eavesdropper is less than or equal to the Cramer-Rao bound threshold, and obtain a recommended value of the beamforming vector.

[0010] By solving the optimization problem with the Cramer-Rao bound threshold as the constraint condition to obtain the recommended value of the beamforming vector and performing beam transmission according to the recommended value of the beamforming vector, secure beamforming can be achieved, so as to maximize the secrecy rate of the user while ensuring the sensing performance of the system.

[0011] In some possible implementation manners, the first device can convert an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the sum secrecy rate of the user as the optimization target into a convex problem, and solve the convex problem according to the Cramer-Rao bound threshold to obtain a recommended value of the beamforming vector. Among them, the convex problem is also called a convex optimization problem or a convex minimization problem, which studies the problem of minimizing a convex function defined in a convex set.

[0012] In a convex problem, the objective function and the constraint conditions in the form of inequalities are both convex functions, and the constraint conditions in the form of equalities are affine functions, such as a linear function plus a constant. Any local optimal solution of a convex problem is a global optimal solution, without local minimum traps. When an optimization problem is transformed into a convex problem, convex optimization can be used to solve the convex problem. Compared with direct solution (such as directly solving a non-convex problem), transforming the original optimization problem into a convex problem for solution can significantly shorten the solution time and improve the solution efficiency.

[0013] In some possible implementation manners, the first device can transform an optimization problem with the beamforming vector of the transmitting beam as the optimization variable and the secrecy sum rate of the user as the optimization objective into a convex problem through successive convex approximation and semidefinite relaxation.

[0014] Among them, successive convex approximation (SCA) is a method for solving non-convex optimization problems. This method decomposes the original non-convex problem into a series of convex sub-problems for solution. By constructing a series of convex functions to approximate the original non-convex function, each convex function is constructed based on the solution of the previous step, and through gradual adjustment, it gradually approaches the global optimal solution. Semidefinite relaxation (SDR) is an important technique for solving complex optimization problems. It relaxes the original problem into a semidefinite programming (SDP) problem and approximately solves the original problem by using the theory and algorithms of convex optimization, so as to achieve a balance between computational complexity and solution quality.

[0015] In some possible implementation manners, the first device can add a penalty term to the objective function of the optimization problem. The penalty term includes a rank-one constraint.

[0016] Among them, after transformation through successive convex approximation or semidefinite relaxation, the objective function and other constraint conditions except the rank-one constraint are all transformed into convex functions. By introducing the rank-one constraint as a penalty term into the objective function, an approximate optimal solution of the original optimization problem can be obtained. In this way, a balance can be achieved between solution efficiency and solution quality.

[0017] In some possible implementation manners, the first device can obtain an upper bound of the rank-one constraint through Taylor expansion. For example, the first device can use first-order Taylor expansion at a given point to obtain an upper bound of the equivalent rank-one constraint.

[0018] By using Taylor expansion to obtain an upper bound of the equivalent rank-one constraint, this method can simplify the problem and contribute to problem solving.

[0019] In some possible implementation manners, the first device may solve the convex problem through convex optimization according to the Cramer-Rao bound threshold, and obtain a recommended value of the beamforming vector. For example, the first device may use the CVX tool in MATLAB to solve the convex problem and obtain a recommended value of the beamforming vector.

[0020] This method can solve the problem by means of a convex optimization tool after converting the problem into a convex problem, which improves the solving efficiency. Moreover, this method can reuse existing convex optimization tools to avoid resource waste.

[0021] In some possible implementation manners, the first device may randomly generate an initial solution according to the constraint conditions. Then, the first device may iteratively update the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector. For example, in the first round of iteration, the first device may, based on the initial solution, iteratively update the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector. When the difference between the updated solution and the initial solution does not meet the set iteration convergence threshold (for example, the iteration convergence accuracy), the next round of iteration is performed. In the next round of iteration, the first device may, based on the updated solution obtained in the previous round, iteratively update the convex problem through successive convex approximation to obtain the updated solution of the current round. If the difference between the updated solution of the current round and the updated node of the previous round does not meet the iteration convergence threshold, the next round of iteration is performed. When the iteration convergence threshold is met, for example, the difference between the updated solution of the current round and the updated solution of the previous round is less than the iteration convergence threshold, the first device may determine the updated solution as the recommended value of the beamforming vector.

[0022] Further, when the iteration convergence threshold is not met, the first device may update the penalty coefficient (or called the penalty factor), record the updated solution of the beamforming vector output currently, and perform the next iteration according to the updated solution and the updated penalty coefficient.

[0023] This method iteratively updates the convex problem through successive convex approximation multiple times until an updated solution that meets the iteration convergence threshold is obtained, and outputs the updated solution as the recommended value of the beamforming vector. In this way, the rapid solution of the optimization problem can be realized, and by sending the communication and sensing integrated signal according to the above recommended value, secure communication can be achieved while ensuring the sensing performance of the communication and sensing integrated system.

[0024] The second aspect of this application provides a communication device. The communication device includes a solving module and a communication module. Among them, the solving module and the communication module may be software modules or hardware modules. Specifically, when implemented, the solving module may be a solver, and the communication module may be a transceiver module. The functions of the solving module and the communication module will be described in detail below.

[0025] The solution module is used to solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secrecy sum rate as the optimization objective according to the Cramer-Rao Bound (CRB) threshold, and obtain a recommended value of the beamforming vector. The CRB threshold can be set based on the sensing performance requirements. For example, a smaller CRB threshold can be set when the sensing performance requirements are high. The communication module is used to send communication and sensing integrated signals according to the recommended value of the beamforming vector.

[0026] For the communication device of this application in the integrated communication and sensing scenario with secure transmission requirements, the CRB threshold is set based on the sensing performance requirements, and combined with the CRB threshold, an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secrecy sum rate as the optimization objective is solved to obtain a recommended value of the beamforming vector, so as to optimize beamforming and resource allocation, and while ensuring the sensing performance of the integrated communication and sensing system, achieve secure communication at the same time.

[0027] In some possible implementation manners, the solution module is specifically used to: solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secrecy sum rate as the optimization objective under the constraint that the Cramer-Rao Bound corresponding to the angle of the potential eavesdropper is less than or equal to the CRB threshold, and obtain a recommended value of the beamforming vector.

[0028] By solving the optimization problem with the CRB threshold as the constraint condition, the communication device obtains a recommended value of the beamforming vector, and performs beam transmission according to the recommended value of the beamforming vector, which can achieve secure beamforming and maximize the secrecy rate of the user while ensuring the sensing performance of the system.

[0029] In some possible implementation manners, the solution module is specifically used to: convert an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secrecy sum rate as the optimization objective into a convex problem; then solve the convex problem according to the CRB threshold to obtain a recommended value of the beamforming vector.

[0030] Among them, any local optimal solution of the convex problem is the global optimal solution, and there is no local minimum trap. When the optimization problem is converted into a convex problem, convex optimization can be used to solve the convex problem. By converting the original optimization problem into a convex problem for solution, the communication device can greatly shorten the solution time and improve the solution efficiency.

[0031] In some possible implementation manners, the solution module is specifically used to: convert an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secrecy sum rate as the optimization objective into a convex problem through successive convex approximation and semidefinite relaxation.

[0032] Among them, successive convex approximation is a technique for solving non-convex optimization problems. This technique decomposes the original non-convex problem into a series of convex sub-problems for solution. By constructing a series of convex functions to approximate the original non-convex function, each convex function is constructed based on the solution of the previous step, and through gradual adjustment, it gradually approaches the global optimal solution. Semidefinite relaxation is an important technique for solving complex optimization problems. It relaxes the original problem into a semidefinite programming problem and approximately solves the original problem using the theory and algorithms of convex optimization, thereby achieving a balance between computational complexity and solution quality.

[0033] In some possible implementation manners, the solving module is further configured to: add a penalty term including a rank-one constraint to the objective function of the optimization problem. By introducing the rank-one constraint as a penalty term into the objective function, the communication device can obtain an approximate optimal solution to the original optimization problem. In this way, a balance can be achieved between the solving efficiency and the solving quality.

[0034] In some possible implementation manners, the solving module is further configured to: obtain an upper bound of the rank-one constraint through Taylor expansion. By using Taylor expansion to obtain the upper bound of the equivalent rank-one constraint, the communication device can simplify the problem, which is helpful for problem solving.

[0035] In some possible implementation manners, the solving module is specifically configured to: solve the convex problem through convex optimization according to the Cramér-Rao bound threshold, and obtain a recommended value of the beamforming vector.

[0036] After converting the problem into a convex problem, the communication device can solve it with the help of convex optimization tools, which improves the solving efficiency. Moreover, the device can reuse existing convex optimization tools to avoid resource waste.

[0037] In some possible implementation manners, the solving module is specifically configured to: randomly generate an initial solution according to the constraint conditions; perform iterative update on the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector; when the iterative convergence threshold is satisfied, determine the updated solution as the recommended value of the beamforming vector.

[0038] The communication device performs multiple iterations on the convex problem through successive convex approximation until an updated solution that satisfies the iterative convergence threshold is obtained, and outputs the updated solution as the recommended value of the beamforming vector. In this way, the rapid solution of the optimization problem can be realized, and by sending the communication and sensing integrated signal according to the above recommended value, secure communication can be achieved while ensuring the sensing performance of the communication and sensing integrated system.

[0039] The third aspect of the present application provides a first device. The first device includes:

[0040] a memory, configured to store computer programs or computer instructions;

[0041] A processor for executing a computer program or computer instructions stored in the memory, so that the first device executes the method described in any implementation manner of the first aspect.

[0042] The fourth aspect of this application provides a communication system. The communication system includes a first device, a second device, and a target to be sensed. The first device is configured to execute the method described in any implementation manner of the first aspect. The second device is configured to extract user demand information according to the received communication and sensing integrated signal. The target to be sensed is configured to send an echo signal to the first device.

[0043] The fifth aspect of this application is a computer storage medium for storing a computer program, which when executed, is used to implement the communication method provided in the first aspect or the second aspect of this application.

[0044] The sixth aspect of this application provides a computer program product containing instructions. When it runs on the first device, it causes the first device to execute the method described in any implementation manner of the above-mentioned first aspect. Description of the Drawings

[0045] Figure 1 It is a schematic structural diagram of a communication and sensing integrated system disclosed in an embodiment of this application;

[0046] Figure 2 It is a schematic diagram of a communication and sensing integrated scenario disclosed in an embodiment of this application;

[0047] Figure 3 It is a flowchart of a communication method disclosed in an embodiment of this application;

[0048] Figure 4 It is a schematic diagram of an algorithm flow for a first device to solve the transformed optimization problem disclosed in an embodiment of this application;

[0049] Figure 5 It is a trade-off diagram of sensing performance and secure communication performance obtained by simulation disclosed in an embodiment of this application;

[0050] Figure 6 It is a transmit beam pattern without sensing performance constraints disclosed in an embodiment of this application;

[0051] Figure 7 It is a transmit beam pattern with sensing performance constraints disclosed in an embodiment of this application;

[0052] Figure 8 It is a transmit beam pattern with enhanced sensing performance constraints disclosed in an embodiment of this application;

[0053] Figure 9 It is a schematic diagram of the structure of a first device disclosed in an embodiment of this application;

[0054] Figure 10 It is a hardware structure diagram of a base station disclosed in an embodiment of the present application;

[0055] Figure 11 It is a hardware structure diagram of a terminal device disclosed in an embodiment of the present application. Specific implementation manners

[0056] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. The terms used in the following embodiments are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are also intended to include the expressions such as "one or more", unless clearly indicated otherwise in the context. It should also be understood that in the embodiments of the present application, "one or more" means one, two or more than two; " / ", describing the association relationship of associated objects, indicates that three relationships can exist; for example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally means an "or" relationship between the associated objects before and after.

[0057] Referring to "one embodiment" or "some embodiments" described in this specification means that specific features, structures or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Thus, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" and the like appearing in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0058] The "multiple" involved in the embodiments of the present application means greater than or equal to two. It should be noted that in the description of the embodiments of the present application, the terms "first", "second", etc. are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0059] This application can be applied to various communication systems, especially integrated sensing and communication (ISAC) systems. The communication in this system includes but is not limited to: long term evolution (LTE), 5th generation (5G), new radio (NR), 5G advanced (5GA), B5G, 6G, radio communication related to the 3rd generation partnership project (3GPP), or a mixture of multiple radio communications, other possible future radio communications, etc.

[0060] Figure 1 Fig. shows a schematic structural diagram of an integrated sensing and communication system. The integrated sensing and communication system 100 includes at least one first device, and the first device may include, for example, Figure 1 the network device 110 shown as follows; the system 100 may further include at least one second device, and the second device may include, for example, Figure 1 the terminal device 120 shown as follows; the system 100 may further include at least one target to be sensed, for example, Figure 1 the target to be sensed 130 shown as follows.

[0061] The network device 110 has a communication function. For example, the network device 110 and the terminal device 120 can communicate through a wireless link and then exchange information. It can be understood that the network device 110 and the terminal device 120 can also be referred to as communication devices. The network device 110 has a sensing function. For example, after the network device 110 sends a sensing signal, it will receive an echo signal of the target to be sensed 130. The network device 110 can obtain the sensing result of the target to be sensed according to the sensing signal and the echo signal of the sensing signal. The sensing result may include but is not limited to the distance, angle, position, moving speed, or external dimension of the target to be sensed 130, etc. In this way, the network device 110 can further use the sensing result to assist communication and improve the quality of communication. It should be noted that the sensing function and the communication function can be implemented by the same network device 110 or by multiple network devices 110 cooperating with each other, which is not limited in the embodiments of this application.

[0062] The integrated sensing and communication system is a system that integrates communication functions and sensing functions. The integration of communication and sensing has the following advantages: sharing hardware for communication and radar sensing functions can save hardware costs; the sensing function can be directly deployed on the existing site, so the deployment is convenient; it is convenient to form a collaborative network, and the sensing result is used to assist communication to improve the quality of communication.

[0063] The network device 110 is a network-side device with wireless transceiver capabilities. For example, the network device 110 can be a base station, an evolved NodeB (eNodeB) in LTE, a next generation NodeB (gNB) in 5G, a transmission reception point (TRP), a base station evolved by 3GPP in the future, an access node in a WiFi system, a wireless relay node, a wireless backhaul node, etc. The network device 110 can include one or more co-located or non-co-located transmission reception points. Among them, the base station can be: a macro base station, a micro base station, a pico base station, a small station, a relay station, or a balloon station, etc.

[0064] Again, for example, the network device 110 can include a central unit (CU), a distributed unit (DU), or both CU and DU. In this way, some functions of the radio access network device can be implemented through multiple network function entities. These network function entities can be network elements in hardware devices, software functions running on dedicated hardware, or virtualized functions instantiated on a platform (such as a cloud platform). Another example is in vehicle to everything (V2X) technology, the network device 110 can be a road side unit (RSU). Multiple network devices 110 in a communication system can be of the same type of base station or different types of base stations. The base station can communicate with the terminal device 120 or communicate with the terminal device 120 through a relay station. The network device 110 in this application can also be a device with sensing capabilities, which can send sensing signals and receive and process the echo signals of the sensed target 130. In the embodiments of this application, the communication device for implementing the functions of the network device 110 can be the network device 110, or a network device 110 with partial base station functions, or a device capable of supporting the network device 110 to implement this function, such as a chip system, and this device can be installed in the network device 110.

[0065] The network device 110 can integrate sensing functions. For example, the network device 110 can be a dual-functional base station. A dual-functional base station (DFBS) refers to a new type of base station that integrates non-communication functions in addition to communication functions. Through the sharing and coordination of software and hardware resources, it realizes the composite ability of "communication + X". Among them, "X" can include, but is not limited to, non-communication functions such as radar detection, environmental sensing, energy harvesting, and positioning and navigation, aiming to improve the resource utilization rate of the base station, the level of network intelligence, and the diversification of service scenarios. Taking the example of a dual-functional base station of "communication + radar detection" as an example, on the one hand, this dual-functional base station can support 5G / 6G wireless communication and provide data transmission services for users. On the other hand, it can achieve target detection and imaging (such as vehicle, pedestrian, and drone monitoring) by transmitting detection signals.

[0066] The terminal device 120 is a user-side device with wireless transceiver functions. It can be a fixed device, a mobile device, a handheld device (such as a mobile phone), a wearable device, a vehicle-mounted device, or a wireless device (such as a communication module, a modem, or a chip system, etc.) built into the above devices. The terminal device 120 is used to connect people, things, machines, etc., and can be widely used in various scenarios, such as: cellular communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine / machine-type communications (M2M / MTC) communication, Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, self-driving, remote medical, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, drones, robots, and other scenarios. Exemplarily, the terminal device 120 can be a handheld terminal in cellular communication, a communication device in D2D, an IoT device in MTC, a surveillance camera in smart transportation and smart city, or a communication device on a drone, etc. The terminal device 120 is sometimes referred to as a user equipment (UE), a user terminal, a user device, a user unit, a user station, a terminal, an access terminal, an access station, a UE station, a remote station, a mobile device, or a wireless communication device, etc.

[0067] The perceived target 130 is a target that can be perceived by the network device 110 with sensing capabilities. For example, it is a stationary far-field point target, including but not limited to a stranded ship, a stationary vehicle, etc. This target can feedback electromagnetic waves to the network device 110. The perceived target can also be referred to as a detected target, a perceived object, a detected object, a perceived device, etc., and the embodiments of the present invention do not make limitations.

[0068] The sensing signal refers to a signal used to sense or detect a target. Or rather, the sensing signal refers to a signal used to sense or detect environmental information. For example, the sensing signal is an electromagnetic wave sent by the network device 110 for sensing environmental information. The sensing signal can also be referred to as a radar signal, a radar sensing signal, a detection signal, a radar detection signal, an environmental sensing signal, etc., and the embodiments of the present application do not make limitations.

[0069] It should be noted that Figure 1 is an example of a communication system architecture applicable to the embodiments of the present application. Figure 1 The naming of each network element included is just a name, and the name does not limit the function of the network element itself. In the 5G network and other future networks, the above-mentioned network elements can also have other names, and the embodiments of the present application do not make specific limitations in this regard. For example, in the 6G network, some or all of the above-mentioned network elements may use the terms in 5G, or may have other names, etc. A unified description is made here and will not be repeated below.

[0070] Before introducing the method of the embodiments of the present application, some technical terms related to the embodiments are first introduced.

[0071] I. Communication User (CU): It is the main body that uses the communication system for information transmission and interaction. In the integrated communication and sensing system, the CU can be a terminal device such as a mobile phone or an Internet of Things device that normally conducts data communication.

[0072] II. Eavesdropper (Eve): It is a third party that attempts to illegally obtain the information transmitted by the communication user. The communication user can transmit sensitive information (such as identity information, location information) or perceived environmental information. The eavesdropper can intercept the signals in the shared spectrum and illegally obtain the above-mentioned sensitive information or environmental information, resulting in information leakage.

[0073] III. Beamforming: A key technology in the field of wireless communication. By weighting the signals of multiple antenna elements in terms of phase and amplitude, the energy is concentrated in a specific direction to form a directional beam, thereby enhancing the signal strength, suppressing interference, and enhancing communication reliability. Applying beamforming technology to concentrate the signal in the direction of legitimate communication users can weaken the signal strength in the direction of eavesdroppers, reducing the possibility of eavesdroppers obtaining the transmission information of legitimate communication users.

[0074] IV. Artificial Noise Technology: According to the acquired channel state information (CSI), an artificial noise is generated and concentrated in the direction (or angle, azimuth) of potential eavesdroppers while minimizing the interference to legitimate communication users, increasing the eavesdropping difficulty.

[0075] V. Physical Channel: Carries data information. For example, the physical channel can be the physical downlink shared channel (PDSCH), physical downlink control channel (PDCCH), physical broadcast channel (PBCH), physical sidelink shared channel (PSSCH), physical sidelink control channel (PSCCH), physical sidelink broadcast channel (PSBCH), physical sidelink feedback channel (PSFCH), physical uplink shared channel (PUSCH), physical uplink control channel (PUCCH), etc. For the subsequent evolved networking forms, new physical channel names may be introduced, and the embodiments of the present invention do not make any restrictions.

[0076] In the integrated communication and sensing scenario, to meet the sensing performance requirements (or called sensing performance demands) of the system, the base station can send a communication and sensing integrated signal (which can also be simply called the integrated signal) with a relatively high power in the direction of the target. At this time, if the target to be sensed is a potential eavesdropper, the sent integrated signal will be eavesdropped, and the secure communication of legitimate communication users cannot be guaranteed. For example, Figure 2As shown below. For this purpose, consider using physical channel characteristics to enhance the security of communication systems. A widely used solution is the artificial noise-assisted physical layer security method based on interference management. Specifically, when the base station transmits confidential data to a user, the base station uses beamforming technology to ensure legitimate communication for the user, and at the same time transmits artificial noise to interfere with eavesdroppers. By using artificial noise to mask the communication signals of legitimate communication users, while ensuring the system sensing performance, the signal reception of passive eavesdroppers is disrupted, thereby realizing the secure communication of legitimate users.

[0077] In this case, how to design secure beamforming to maximize the secrecy sum rate of users while ensuring the system sensing performance is crucial for the integrated communication and sensing system with secure communication requirements.

[0078] In view of this, the present application provides a communication method. This method can be applied to an integrated communication and sensing system (or simply referred to as an integrated communication and sensing system, an integrated system, a system), such as the first device in an integrated communication and sensing system. The first device (such as a network device like a base station) in the integrated communication and sensing system can solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy sum rate (SSR) of the user as the optimization target according to the Cramer-Rao Bound (CRB) threshold, and obtain a recommended value of the beamforming vector. Then the first device can send an integrated communication and sensing signal according to the recommended value of the beamforming vector.

[0079] Among them, CRB is the lower bound of the variance of the unbiased estimation of deterministic parameters, and is used to measure the performance of the estimation method (for example, the best result that the estimation method can achieve). In the integrated communication and sensing scenario, CRB can be used to characterize the sensing performance index. For CRB, the sensing end (such as the first device) can set a threshold according to the sensing performance requirement (for example, the sensing accuracy requirement), which is also called the CRB threshold. The CRB threshold does not need to be obtained in advance from the user side or the target side to be sensed. For example, if the integrated communication and sensing system requires a high sensing measurement accuracy, a smaller CRB threshold is correspondingly set. The secrecy sum rate (SSR) is the core index to measure the physical layer security performance in a multi-user system, and is used to describe the total rate at which all legitimate users can securely transmit information in the presence of eavesdroppers. The essence of the secrecy sum rate is the sum of the secrecy capacities of each communication user, which reflects the overall secure transmission ability of the integrated communication and sensing system when dealing with eavesdropping.

[0080] This method is applicable to the integrated communication and sensing scenario with secure transmission requirements. Based on the sensing performance requirements, the CRB threshold is set, and beamforming and resource allocation are optimized in combination with the CRB threshold. While ensuring the sensing performance of the integrated communication and sensing system, secure communication is achieved simultaneously.

[0081] To make the communication method of this application clearer and easier to understand, the embodiments of the communication method of this application will be introduced in detail below with reference to the accompanying drawings.

[0082] See Figure 3 The flowchart of a communication method shown below, this method includes the following steps:

[0083] S302. Solve the optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy sum rate of the user as the optimization target according to the Cramér-Rao bound threshold, and obtain the recommended value of the beamforming vector.

[0084] This step can be executed by the first device. Among them, the first device can include network devices such as base stations. The base station can include but is not limited to gNB, ng-eNB. Among them, the network device can integrate sensing functions to realize integrated communication and sensing. In some examples, the network device can be a dual-functional base station, where the dual functions include communication functions and sensing functions.

[0085] The Cramér-Rao bound threshold is the threshold set for the Cramér-Rao bound CRB, denoted as . Among them, CRB is the lower bound of the variance of the unbiased estimate of the deterministic parameter, which is used to measure the performance of the estimation method (for example, the best result that the estimation method can achieve). In the integrated communication and sensing scenario, CRB can be used to characterize the sensing performance index. The CRB threshold does not need to be pre-obtained from the user side or the sensed target side. The sensing end (such as the first device) can set the CRB threshold according to the sensing performance requirements (for example, the sensing accuracy requirement). For example, if the integrated communication and sensing system requires a higher sensing measurement accuracy, the first device can set a smaller CRB threshold.

[0086] Beamforming is a key technology in the fields of wireless communication, radar, etc. Its core idea is to enhance the signal in a specific direction (form a beam) by weighting the phase and amplitude of the signals of multiple antenna elements, while suppressing interference or noise in other directions. The beamforming vector is a set of weighting coefficients, usually complex numbers, including amplitude and phase information, which are used to weight and combine the signals of each antenna element to achieve directional transmission or reception of the beam.

[0087] In this application, the beamforming vector can include the beamforming vector of the signal by the first device (such as a base station) of and the beamforming vector of the first device for artificial noise (AN) Suppose there is an array composed of N antenna elements, such as a uniform linear array (ULA), and the transmitted signal is s(t). Then the transmitted signal of the nth antenna can be expressed as: . Among them, is the weighting coefficient (in complex form) of the nth antenna element, is the amplitude weight, is the phase offset. The beamforming vector w can be expressed as .

[0088] S304. Transmit the communication and sensing integrated signal according to the recommended value of the beamforming vector.

[0089] This step can be executed by the first device. Among them, the first device can include network devices such as base stations. In some examples, the network device can be a dual-functional base station, where the dual functions include communication function and sensing function.

[0090] The recommended value of the beamforming vector includes a recommended set of weighting coefficients, and the weighting coefficients can include at least one of amplitude and phase information. The first device can perform weighted combination on the signals of each antenna element according to the recommended value of the beamforming vector, so that the signals are enhanced in a specific direction to form a beam, thereby realizing the directional transmission of the beam.

[0091] Based on the above description, the present application provides a communication method. For the integrated communication and sensing scenario with secure transmission requirements, the first device (such as a dual-functional base station) sets the CRB threshold based on the sensing performance requirements, and combines the CRB threshold to solve the optimization problem with the beamforming vector of the transmitting beam as the optimization variable and the secrecy sum rate of the user as the optimization objective, and obtains the recommended value of the beamforming vector, so as to optimize beamforming and resource allocation, and realize secure communication while ensuring the sensing performance of the communication and sensing integrated system.

[0092] Among them, the optimization problem can be established according to the communication model and the sensing model. Specifically, when implementing, a communication and sensing integrated framework can be established according to the network architecture including network devices (such as base stations), multiple communication users, and one sensed target (such as a potential Eve). Under this communication and sensing integrated framework, a communication model is established according to the secrecy rate of legal communication users. A sensing model is established according to the Cramer-Rao bound of the sensed target.

[0093] First, an introduction to the establishment of the communication integrated framework is given. Consider the downlink security scenario of a communication and sensing integrated system, such as Figure 2As shown in the figure. Among them, the communication and sensing integrated system includes a base station equipped with transmitting antennas and receiving antennas, legitimate communication users with single antennas, and a radar target (potential Eve) located in the far field. It is assumed that the base station uses a uniform linear array and the channel state information of all legitimate communication users is known. The superimposed signal transmitted by the base station is expressed as follows:

[0094] (1)

[0095] Among them, is the information symbol with zero mean and unit power sent to user , is the beamforming vector of the base station for signal , is the artificial noise, which usually includes complex Gaussian random variables with zero mean and unit power, used to counter the eavesdropping of potential eavesdroppers, is the beamforming vector of the base station for artificial noise . Since the base station knows the power distribution and other related characteristics (such as self-transmission and self-reception) of the transmitted artificial noise, the base station can extract the sensing information of the corresponding sensed target from the target echo information.

[0096] In addition, it is assumed that the channel from the base station to user is modeled as Rice fading, then the channel can be correspondingly expressed as:

[0097] (2)

[0098] Among them, represents the line-of-sight component in Rice fading, represents the path loss of the communication user at the reference distance, represents the distance from the base station to the user, represents the path loss exponent, represents the Rice factor. The non-line-of-sight component in Rice fading is then modeled as a circularly symmetric complex Gaussian random variable with zero mean and unit variance.

[0099] Then, the communication model and sensing model are introduced. Specifically, as follows:

[0100] The achievable communication rate of user can be expressed as:

[0101] (3)

[0102] Among them, in denotes conjugate transpose, denotes noise, such as additive white Gaussian noise.

[0103] The channel between the base station and the target to be sensed (potential Eve) is modeled as a LoS channel as follows:

[0104] (4)

[0105] where, represents the path loss of the potential Eve at the reference distance, represents the distance from the base station to the potential Eve, represents the path loss exponent, is the azimuth angle where the potential Eve is located, and its corresponding steering vector is characterized as:

[0106] (5)

[0107] When the target is a potential eavesdropper, the eavesdropping channel capacity corresponding to receiving the information of the th legitimate communication user can be:

[0108] (6)

[0109] In this case, the secrecy rate can be used to characterize the communication performance index of the system as follows:

[0110] (7)

[0111] where, represents .

[0112] Further, a sensing performance index can be constructed, and the construction process will be described in detail below.

[0113] Among them, the reflected echo signal received by the base station from the target to be sensed can be expressed as:

[0114] (8)

[0115] where, represents the reflection coefficient, which is usually related to the Radar Cross Section (RCS) and the round-trip path loss, represents the steering vector of the receiving antenna, represents the steering vector of the transmitting antenna.

[0116] This application uses the CRB corresponding to the angle of the potential Eve to characterize the sensing performance index as follows:

[0117] (9)

[0118] Among them, , represents the frame length, represents the trace of the matrix. Since the communication and sensing integrated system uses the transmitted signal to achieve communication and sensing functions simultaneously, when the frame length of the transmitted signal is large enough, the transmit covariance matrix can be expressed as:

[0119] (10)

[0120] If the communication and sensing integrated system requires a high sensing measurement accuracy, the base station can set a smaller CRB threshold without pre-acquiring it from the user side or the target side to be sensed.

[0121] According to the communication model and sensing model, an optimization problem is established with the beamforming variables of the transmit beam as the optimization traversal and the secrecy sum rate of the user as the optimization objective, and minimum thresholds are set for the minimum secrecy rate of the user and the angle CRB of the target to be sensed, as follows:

[0122]

[0123] s.t. C1: , C2: , C3: , C4: . (11)

[0124] Among them, is the CRB threshold, for example, the threshold of the lower bound of the variance for unbiased estimation of deterministic parameters, represents the secrecy rate threshold of the user, represents the total transmit power of the base station. Constraint C1 ensures the sensing performance of the system, constraint C2 is the equality representation of the transmit covariance matrix, constraint C3 ensures the minimum secrecy rate requirement of legitimate communication users, and constraint C4 represents the total transmit power constraint of the system.

[0125] Correspondingly, the base station can solve the optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy sum rate of the user as the optimization objective, subject to the constraint that the Cramer-Rao bound corresponding to the potential eavesdropper's angle is less than or equal to the Cramer-Rao bound threshold, to obtain the recommended value of the beamforming vector.

[0126] In the optimization problem shown in formula (11), the objective function and the constraint conditions have complex non-convex characteristics, and directly solving the problem is extremely challenging. Among them, the non-convex characteristics can include non-convex sets or non-convex functions. Among them, a non-convex set is a set that does not satisfy the definition of a convex set, such as an annular region or a discrete point set. Non-convex functions can include functions that do not satisfy the definition of a convex function. Common non-convex functions can include non-convex quadratic functions, composite functions containing absolute values and exponents, and objective functions in combinatorial optimization problems. The objective function has multiple local minima or multiple local maxima, and the optimization algorithm is prone to falling into local optima.

[0127] To this end, the first device can convert the optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy sum rate of the user as the optimization objective shown in formula (11) into a convex problem, and then solve the convex problem according to the Cramér-Rao bound threshold to obtain a recommended value of the beamforming vector. In the convex problem, both the objective function and the constraint conditions in the form of inequalities are convex functions, and the constraint conditions in the form of equalities are affine functions, such as linear functions plus constants. Any local optimal solution of the convex problem is the global optimal solution, and there is no local minimum trap. When the optimization problem is converted into a convex problem, convex optimization can be used to solve the convex problem.

[0128] The process of converting the optimization problem into a convex problem will be described in detail below.

[0129] Specifically, the first device can convert the optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy sum rate of the user as the optimization objective into a convex problem through successive convex approximation (SCA) and semidefinite relaxation. Among them, the first device can perform iterative optimization through successive convex approximation to solve complex problems, and transform the constraint conditions (such as C1 in formula (11)) in the optimization problem (as shown in formula (11)) by applying the Schur complement theorem. Accordingly, the original optimization problem can be reconstructed into the following form:

[0130]

[0131] s.t. C1: , C2: , C3: , C4: , C5: , C6: . (12)

[0132] Among them, let , , , , and satisfy , , rank( ) = 1, rank( ) = 1, .

[0133] To simplify the complex fractional structure of the secrecy rate of communication users in the optimization problem (as shown in formula (12)), the first-order Taylor expansion can be used for approximation processing in the SCA iteration, so as to obtain the lower-bound expression of the secrecy rate of user :

[0134] (13)

[0135] Through the above conversion and approximation, the objective function and the constraint conditions C1 to C5 are converted into convex functions. Further, the first device can also perform a conversion for the constraint condition C6. Among them, the constraint condition C6 is a rank-one constraint (Rank-One Constraint), and the rank-one constraint requires that the rank of the matrix is equal to 1, that is, the matrix can be expressed as the outer product of two vectors.

[0136] Specifically, the first device can add a penalty term to the objective function of the optimization problem, and this penalty term includes the rank-one constraint. By introducing the rank-one constraint as a penalty term into the objective function, an approximate optimal solution of the original optimization problem can be obtained. Among them, the equivalent representation of the rank-one constraint is as follows:[[]]

[0137] (14)

[0138] Among them, represents the sum of the singular values of the matrix, which is called the nuclear norm; represents the largest singular value of the matrix, which is defined as the spectral norm. In this way, the first device can obtain the upper bound of the rank-one constraint through Taylor expansion. Among them, the first device can use the first-order Taylor expansion at the given point to obtain the upper bound of the equivalent rank-one constraint:

[0139] (15)

[0140] Among them, represents the eigenvector corresponding to the largest eigenvalue of the matrix . Similarly, the first device can perform a conversion on the rank-one constraint of .

[0141] Based on the above conversion or transformation, the original optimization problem can be relaxed and reconstructed into a solvable optimization problem P3, as follows:

[0142]

[0143] such that C1: , C2: , C3: , C4: , C5: .(16)

[0144] To obtain an approximate optimal solution to the original optimization problem, the penalty coefficient can be multiplied by a constant in each iteration, that is . In this case, the first device can solve the convex problem according to the Cramer-Rao bound threshold through convex optimization (convex, CVX) to obtain a recommended value of the beamforming vector. For example, the first device can use the CVX tool in MATLAB to solve the convex problem to obtain a recommended value of the beamforming vector. Among them, the first device using the CVX tool to solve the convex problem can include using a continuous convex approximation algorithm based on a penalty term for solving.

[0145] The following details the solution process.

[0146] In specific implementation, the first device can randomly generate an initial solution according to the constraint conditions, and then iteratively update the convex problem through continuous convex approximation to obtain an updated solution of the beamforming vector. When the iteration convergence threshold is met, the first device determines the updated solution as the recommended value of the beamforming vector. Among them, the iteration convergence threshold can be pre-configured by the user. Meeting the iteration convergence threshold can be that the difference between the updated solution and the updated solution obtained in the previous iteration is less than the iteration convergence threshold.

[0147] It should be noted that when the iteration convergence threshold is not met, the first device can update the penalty coefficient (or called the penalty factor), record the updated solution of the currently output beamforming vector, and perform the next iteration according to the updated solution and the updated penalty coefficient.

[0148] For ease of understanding, Figure 4 a schematic diagram of the algorithm flow for the first device to solve the transformed optimization problem is also shown. The algorithm input can include the initial solution and the iteration convergence threshold. This initial solution is an initial feasible solution, that is, an initial solution that satisfies the constraint conditions. The iteration convergence threshold is also called the iteration convergence accuracy, which can be in this example. The algorithm output can include the recommended value of the beamforming vector, denoted as , .

[0149] The first device can solve the transformed optimization problem P3, for example, by combining , Update , . Then, the first device can, based on the difference of the objective function When the difference of the objective function is greater than or equal to the iteration convergence threshold, it can update as well as , so as to perform the next iteration.

[0150] To further verify the effectiveness of the proposed secure beamforming method for the integrated communication and sensing system with artificial noise assistance, the following simulation experiments were conducted in this application. The main simulation parameters were set as follows: the number of transmit antennas , the number of receive antennas , and the number of users ; the base station location was set to [0,0], the target location was: in the 0° direction, 30 away from the origin; the location of user 1 was: in the 10° direction, 40 away from the origin, and the location of user 2 was: in the 30° direction, 35 away from the origin; in addition, the channel parameters were set as: , , , and the transmit power and noise power were correspondingly set as: , .

[0151] Figure 5 shows a trade-off diagram of the sensed performance and secure communication performance obtained by simulation. This diagram shows the relationship between the secrecy sum rate of the users and . As the sensed performance requirement is relaxed (i.e., the larger the value of ), the integrated communication and sensing system will allocate more resources to the communication function, resulting in an increase in the secrecy sum rate. In addition, as the sensed performance requirement is relaxed, the secrecy sum rate gain of the users will show a trend of first increasing and then decreasing.

[0152] Figure 6 shows the transmit beam pattern without the sensed performance constraint. When the integrated communication and sensing system has no sensed performance requirement, the resources are all used to ensure the secure communication of the system. At this time, the secrecy sum rate of the communication users is 13.0539, and less energy is allocated in the angular direction of the sensed target (azimuth angle is 0°), and this part of the energy is only used to reduce the eavesdropping channel capacity.

[0153] Figure 7 shows the transmit beam pattern with the sensed performance constraint. When the integrated communication and sensing system has certain sensed performance requirements, the integrated communication and sensing system needs to meet the communication requirements and sensing requirements simultaneously. At this time, compared with Figure 6When there is no awareness performance constraint, more energy will be allocated in the angular direction of the perceived target to ensure the system's perception performance while reducing the listening channel capacity. At this time, , the secrecy sum rate of the user is 11.6287.

[0154] When the requirement for perception performance is further improved ( the smaller the value of ), the allocation of system resources will be extremely tense. At this time, due to the limitation of the total transmission power, the energy of the communication beam (such as the transmission beam) needs to be used for both communication and perception functions simultaneously. The communication beam occupies a large amount of energy in the direction of the perceived target, resulting in a decrease in the secrecy sum rate of the user. The resource allocation and trade-off of the communication and perception integrated system will become even more important. As Figure 8 shown, when, the communication beam occupies a large amount of energy in the direction of the perceived target, and the secrecy sum rate of the user decreases to 9.49475.

[0155] The above theoretical analysis and verification are carried out for a simplified scenario, which can be further extended on the current basis and applied to the following actual scenarios: intelligent transportation, low-altitude security, or the Internet of Things. In the intelligent transportation scenario, a vehicle can be set as the perceived target (i.e., the target that the communication and perception integrated system or the first device in the system needs to perceive), or it can be set as a communication user. Whether the vehicle is set as the perceived target or the communication user can be flexibly changed according to the vehicle's needs. Among them, an illegal vehicle is regarded as a potential eavesdropper. At this time, the solution of the present application can be applied to it to achieve secure communication while ensuring the perception performance. In the low-altitude security scenario, the solution of the present application can be directly applied to it to achieve secure communication while ensuring the perception performance; in the Internet of Things scenario, according to the communication and sensing requirements of different terminal devices, the solution of the present application can be applied to it to achieve secure communication while ensuring the perception performance.

[0156] The above Figures 3 to 8 introduced the communication method of the present application and the simulation effect of this method. Next, a communication device provided by the present application will be introduced.

[0157] Based on Figures 3 to 8 the communication method shown above and its simulation effect, the present application also provides a first device. Next, the first device of the present application will be introduced from the perspective of functional modularization.

[0158] First, referring to Figure 9 the structural schematic diagram of a first device shown, the first device 900 includes:

[0159] A solution module 902 is configured to solve an optimization problem with the beamforming vector of the transmission beam as the optimization variable and the user's secrecy sum rate as the optimization target according to the Cramer-Rao bound threshold, where the Cramer-Rao bound threshold is set based on the sensing performance requirement, so as to obtain a recommended value of the beamforming vector.

[0160] A communication module 904 is configured to send a communication and sensing integrated signal according to the recommended value of the beamforming vector.

[0161] Among them, the solution module 902 may be a solver, and the specific implementation of the solution module 902 may refer to Figure 3 the relevant content description in S302 in the illustrated embodiment. The communication module 904 may include a transceiver module, and the transceiver module is configured to send a communication and sensing integrated signal according to the recommended value of the beamforming vector. Further, the communication module 904 is further configured to receive an echo signal sent by the target to be sensed. The specific implementation of the solution module 902 and the communication module 904 may refer to Figures 3 to 8 the relevant content description, which will not be elaborated here.

[0162] In some possible implementation manners, the solution module 902 is specifically configured to:

[0163] Solve an optimization problem with the beamforming vector of the transmission beam as the optimization variable and the user's secrecy sum rate as the optimization target under the constraint that the Cramer-Rao bound corresponding to the angle of the potential eavesdropper is less than or equal to the Cramer-Rao bound threshold, so as to obtain a recommended value of the beamforming vector.

[0164] In some possible implementation manners, the solution module 902 is specifically configured to:

[0165] Convert an optimization problem with the beamforming vector of the transmission beam as the optimization variable and the user's secrecy sum rate as the optimization target into a convex problem.

[0166] Solve the convex problem according to the Cramer-Rao bound threshold to obtain a recommended value of the beamforming vector.

[0167] In some possible implementation manners, the solution module 902 is specifically configured to:

[0168] Convert an optimization problem with the beamforming vector of the transmission beam as the optimization variable and the user's secrecy sum rate as the optimization target into a convex problem through successive convex approximation and semidefinite relaxation.

[0169] In some possible implementation manners, the solution module 902 is further configured to:

[0170] Add a penalty term to the objective function of the optimization problem, where the penalty term includes a rank-one constraint.

[0171] In some possible implementation manners, the solving module 902 is further configured to:

[0172] Obtain an upper bound of the rank-one constraint through Taylor expansion.

[0173] In some possible implementation manners, the solving module 902 is specifically configured to:

[0174] Solve the convex problem through convex optimization according to the Cramér-Rao bound threshold, and obtain a recommended value of the beamforming vector.

[0175] In some possible implementation manners, the solving module 902 is specifically configured to:

[0176] Randomly generate an initial solution according to the constraint conditions;

[0177] Iteratively update the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector;

[0178] When the iteration convergence threshold is satisfied, determine the updated solution as the recommended value of the beamforming vector.

[0179] Next, the first device 900 will be introduced from the perspective of hardware implementation.

[0180] Figure 10 This is a composition example of a first device 900 provided in an embodiment of this application. The first device 900 may be a network device such as a base station. Figure 10 Taking the first device 900 as an example of a base station for illustration. Figure 10 A simplified schematic diagram of the base station structure is shown. The base station 1000 includes a processor 1010, a memory 1020, and a transceiver 1030. The processor 1010 is mainly used for baseband processing and controlling the base station, etc.; the processor 1010 is usually the control center of the base station and is used to control the base station to execute the processing operations on the first device side in the above method embodiments. The memory 1020 is mainly used for storing computer program codes and data. The transceiver 1030 is mainly used for the transceiver of radio frequency signals and the conversion between radio frequency signals and baseband signals; the transceiver 1030 can usually be referred to as a transceiver module, a transceiver, a transceiver circuit, etc. The transceiver 1030 includes an antenna 1033 and a radio frequency circuit (not shown in the figure), where the radio frequency circuit is mainly used for radio frequency processing. Optionally, the device in the transceiver 1030 for implementing the receiving function is regarded as a receiver, and the device for implementing the sending function is regarded as a transmitter, that is, the transceiver 1030 includes a receiver 1032 and a transmitter 1031. The receiver can also be referred to as a receiving module, a receiver, or a receiving circuit, etc., and the transmitter can be referred to as a transmitting module, a transmitter, or a transmitting circuit, etc.

[0181] The processor 1010 and the memory 1020 may include one or more single boards, and each single board may include one or more processors and one or more memories. The processor is used to read and execute computer programs in the memory to implement baseband processing functions and control of the base station. If there are multiple single boards, they can be interconnected to enhance processing capabilities. As an alternative implementation, it is also possible that multiple single boards share one or more processors, or multiple single boards share one or more memories, or multiple single boards simultaneously share one or more processors.

[0182] In one implementation, the transceiver 1030 is used to perform the transceiver-related processes executed by the base station in the foregoing method embodiments. The processor 1010 is used to perform the processing-related processes executed by the base station in the foregoing method embodiments.

[0183] It should be understood that Figure 10 by way of example only and not limitation, the above network device including a processor, a memory, and a transceiver may not depend on Figure 10 the structure shown.

[0184] In addition, an operating system runs on the above components. For example, a real-time operating system or a general operating system, etc. Application programs can be installed and run on the operating system. Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the explanations and beneficial effects of the relevant content in any of the above terminal devices can refer to the corresponding method embodiments provided above, and will not be elaborated here.

[0185] This application also provides a communication system, which may include a first device 900, a second device, and a target to be sensed. Among them, the first device 900 may be a base station as Figure 10 shown, and the second device may be Figure 11 the terminal device shown.

[0186] Figure 11 This is a composition example of a second device provided in the embodiments of this application. The second device may be a terminal device, such as a CU.

[0187] As Figure 11 shown, the terminal device may include a processor 1110, an external memory interface 1120, an internal memory 1121, a display screen 1130, a camera 1140, an antenna 1, an antenna 2, a mobile communication module 1150, and a wireless communication module 1160, etc.

[0188] It can be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the terminal device. In other embodiments, the terminal device may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0189] The processor 1110 may include one or more processing units. For example, the processor 1110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0190] It can be understood that the interface connection relationships between the modules illustrated in this embodiment are only illustrative and do not constitute a structural limitation on the terminal device. In other embodiments of the present application, the terminal device may also adopt different interface connection methods or a combination of multiple interface connection methods in the above embodiments.

[0191] The external memory interface 1120 may be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the terminal device. The external memory card communicates with the processor 1110 through the external memory interface 1120 to implement the data storage function. For example, files such as music and videos are saved in the external memory card.

[0192] The internal memory 1121 can be used to store computer-executable program codes, and the executable program codes include instructions. The processor 1110 executes various functional applications and data processing of the terminal device by running the instructions stored in the internal memory 1121. The internal memory 1121 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.). The data storage area can store data created during the use of the terminal device (such as audio data, phone book, etc.). In addition, the internal memory 1121 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), etc. The processor 1110 executes various functional applications and data processing of the terminal device by running the instructions stored in the internal memory 1121, and / or the instructions stored in the memory provided in the processor.

[0193] The wireless communication function of the terminal device can be implemented by an antenna 1, an antenna 2, a mobile communication module 1150, a wireless communication module 1160, a modulation and demodulation processor, a baseband processor, etc.

[0194] The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the terminal device can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization rate of the antennas. For example, the antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0195] The mobile communication module 1150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G, etc. applied to the terminal device. The mobile communication module 1150 can include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 1150 can receive electromagnetic waves by the antenna 1, filter, amplify, etc. the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 1150 can also amplify the signal modulated by the modulation and demodulation processor and convert it into electromagnetic waves through the antenna 1 for radiation. In some embodiments, at least some functional modules of the mobile communication module 1150 can be provided in the processor 1110. In some embodiments, at least some functional modules of the mobile communication module 1150 and at least some modules of the processor 1110 can be provided in the same device.

[0196] In some embodiments, the terminal device initiates or receives a call request through the mobile communication module 1150 and the antenna 1.

[0197] In addition, an operating system runs on the above components. For example, iOS operating system, Android operating system, Windows operating system, etc. Application programs can be installed and run on the operating system. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the explanations and beneficial effects of the relevant content in any of the above-mentioned terminal devices can refer to the corresponding method embodiments provided above, and will not be elaborated here.

[0198] Furthermore, an embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies.

[0199] A program is stored on the computer-readable storage medium, and when the program is executed by a processor, it implements the communication method executed on the first device side or the communication method executed in the communication system in the above embodiments.

[0200] An embodiment of the present application also provides a computer program product containing instructions. The computer program product can be software or a program product containing instructions that can run on the first device or the communication system, or be stored in any available medium. When the computer program product runs on the first device or the communication system, the first device or the communication system is caused to execute the above-mentioned communication method.

[0201] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0202] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices, or modules, and can be in electrical, mechanical, or other forms.

[0203] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0204] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0205] If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the part that essentially contributes to the technical solution of the present application or all or part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the processes of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks, or optical discs that can store program codes.

[0206] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of each embodiment of the present application.

Claims

1. A communication method, characterized in that, Applied to a first device, the method includes: Solving an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective according to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector, where the Cramer-Rao bound threshold is set based on sensing performance requirements; Sending a communication and sensing integrated signal according to the recommended value of the beamforming vector.

2. The method according to claim 1, characterized in that, The solving an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective according to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector includes: Solving an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective under the constraint that the Cramer-Rao bound corresponding to the angle of the potential eavesdropper is less than or equal to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector.

3. The method according to claim 1, wherein The solving an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective according to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector includes: Converting an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective into a convex problem; Solving the convex problem according to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector.

4. The method according to claim 3, wherein The converting an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective into a convex problem includes: Converting an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective into a convex problem through successive convex approximation and semidefinite relaxation.

5. The method according to claim 3, wherein The method further includes: Adding a penalty term to the objective function of the optimization problem, where the penalty term includes a rank-one constraint.

6. The method according to claim 5, wherein The method further includes: Obtaining an upper bound of the rank-one constraint through Taylor expansion.

7. The method according to any one of claims 3 to 6, characterized in that, The solving the convex problem according to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector includes: Solving the convex problem by convex optimization according to the Cramer-Rao bound threshold, and obtaining a recommended value of the beamforming vector.

8. The method according to claim 7, wherein The solving the convex problem by convex optimization, and obtaining a recommended value of the beamforming vector includes: Randomly generating an initial solution according to the constraint conditions; Iteratively updating the convex problem through successive convex approximation, and obtaining an updated solution of the beamforming vector; When the iteration convergence threshold is satisfied, determining the updated solution as the recommended value of the beamforming vector.

9. A communication device, characterized in that, The device includes: A solving module, configured to solve an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secure sum rate as the optimization objective according to the Cramer-Rao bound threshold, and obtain a recommended value of the beamforming vector, where the Cramer-Rao bound threshold is set based on sensing performance requirements; A communication module, configured to send a communication and sensing integrated signal according to the recommended value of the beamforming vector.

10. The device according to claim 9, characterized in that, The solving module is specifically configured to: Taking the condition that the Cramér-Rao bound corresponding to the angle of the potential eavesdropper is less than or equal to the Cramér-Rao bound threshold as a constraint, solve the optimization problem with the beamforming vector of the transmitting beam as the optimization variable and the secrecy sum rate of the user as the optimization objective, and obtain the recommended value of the beamforming vector.

11. The device according to claim 9, wherein The solving module is specifically used for: Convert the optimization problem with the beamforming vector of the transmitting beam as the optimization variable and the secrecy sum rate of the user as the optimization objective into a convex problem; Solve the convex problem according to the Cramér-Rao bound threshold to obtain the recommended value of the beamforming vector.

12. The device according to claim 11, characterized in that, The solving module is specifically used for: Convert the optimization problem with the beamforming vector of the transmitting beam as the optimization variable and the secrecy sum rate of the user as the optimization objective into a convex problem through successive convex approximation and semidefinite relaxation.

13. The device according to claim 11, characterized in that, The solving module is further used for: Add a penalty term to the objective function of the optimization problem, and the penalty term includes a rank-one constraint.

14. The device according to claim 13, characterized in that, The solving module is further used for: Obtain the upper bound of the rank-one constraint through Taylor expansion.

15. The device according to any one of claims 11 to 14, characterized in that, The solving module is specifically used for: Solve the convex problem through convex optimization according to the Cramér-Rao bound threshold to obtain the recommended value of the beamforming vector.

16. The device according to claim 15, characterized in that, The solving module is specifically used for: Randomly generate an initial solution according to the constraint conditions; Iteratively update the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector; When the iteration convergence threshold is met, determine the updated solution as the recommended value of the beamforming vector.

17. A first device, characterized in that, The first device includes: A memory for storing computer programs or computer instructions; A processor for executing the computer programs or computer instructions stored in the memory, so that the first device executes the method according to any one of claims 1 to 8.

18. A communication system, characterized in that, The communication system includes a first device, a second device and a target to be sensed, and the first device is used to execute the method according to any one of claims 1 to 8.

19. A computer storage medium, characterized in that, The computer storage medium is used to store a computer program, and when the computer program is executed, it is used to implement the method according to any one of claims 1 to 8.

20. A computer program product, characterized in that, Including computer-readable instructions; the computer-readable instructions are used to implement the method according to any one of claims 1 to 8.

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