A communication method and related device

Abstract

The application provides a communication method and related equipment, the method is applied to a first device in a communication and sensing integrated system, comprising: according to the Cramer-Rao bound threshold, solving an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the user's secrecy and rate as the optimization target, to obtain the recommended value of the beamforming vector, wherein the Cramer-Rao bound threshold is set based on the sensing performance requirement; then transmitting a communication and sensing integrated signal according to the recommended value of the beamforming vector. The method sets the CRB threshold based on the sensing performance requirement for the communication and sensing integrated scene with safety transmission requirements, optimizes the beamforming and resource allocation combined with the CRB threshold, and realizes safe communication on the basis of guaranteeing the sensing performance of the communication and sensing integrated system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, 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

[0002] With the increasing demand for wireless communication, wireless communication networks represented by the next generation of communication (Beyond 5G, B5G) and the sixth generation of mobile communication (6G) gradually evolve to higher frequency bands. In this case, radar sensing and wireless communication gradually tend to be similar in hardware architecture, channel characteristics and signal processing, effectively promoting the integration of sensing and communication functions, and realizing communication sensing integration (ISAC) in one system. Among them, communication sensing integration can also be referred to as sensing integration, and the sensing integration system uses dual-function base stations and integrated waveform design, not only reduces hardware cost, but also improves system spectrum efficiency, and has wide application prospects in automatic driving, low-altitude security and Internet of Things scenarios.

[0003] In the sensing integration scenario, in order to meet the sensing performance requirements of the system, the base station needs to send a large power integrated signal to the target direction. At this time, if the sensed target is an eavesdropper (Eve), the integrated signal sent by the base station will be eavesdropped, and the secure communication of the legitimate user 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 signal of the legitimate user, the signal reception of the passive eavesdropper is destroyed while the system sensing performance is guaranteed, thereby realizing the secure communication of the legitimate user. In this case, how to design a secure beamforming to maximize the secrecy rate of the user while guaranteeing the sensing performance of the system is crucial to the sensing integration system with secure communication requirements. SUMMARY

[0004] The present application provides a communication method and related equipment, aiming at the sensing integration scenario with secure transmission requirements, setting a Cramer-Rao Bound (CRB) threshold based on the sensing performance requirements, optimizing the beamforming and resource allocation combined with the CRB threshold, and realizing secure communication while guaranteeing the sensing performance of the communication sensing integration system.

[0005] In order to achieve the above purpose, the present application provides the following technical solutions:

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

[0007] The first device (such as a network device including a base station) in the communication and perception 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 to obtain a recommended value of the beamforming vector. Then the first device can send a communication and perception integrated signal according to the recommended value of the beamforming vector.

[0008] The communication method of the present application is aimed at a communication and perception integrated scenario with security transmission requirements. The first device (such as a dual-function base station) sets a CRB threshold based on the perception performance requirement, solves 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 combined with the CRB threshold to obtain a recommended value of the beamforming vector, thereby optimizing the beamforming and resource allocation, and realizing secure communication while ensuring the perception performance of the communication and perception 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 with the constraint that the Cramer-Rao Bound of the potential listener corresponding angle is less than or equal to the Cramer-Rao Bound threshold to obtain a recommended value of the beamforming vector.

[0010] The method solves the optimization problem with the Cramer-Rao Bound threshold as the constraint to obtain a recommended value of the beamforming vector, and performs beam transmission according to the recommended value of the beamforming vector, which can realize secure beamforming and maximize the secrecy rate of the user while ensuring the perception performance of the system.

[0011] In some possible implementation manners, the first device can convert the 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 minimization problem of a convex function defined in a convex set.

[0012] In a 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, for example, linear functions plus constants. Any local optimal solution of a convex problem is a global optimal solution, and there is no local minimum trap. When an optimization problem is converted into a convex problem, the convex optimization can be used to solve the convex problem. Compared with direct solving (for example, directly solving a non-convex problem), converting the original optimization problem into a convex problem for solving can greatly shorten the solving time and improve the solving efficiency.

[0013] In some possible implementation ways, the first device can convert, by successive convex approximation and semidefinite relaxation, an optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy and rate of the user as the optimization objective into a convex problem.

[0014] wherein the successive convex approximation (SCA) is a method for solving a non-convex optimization problem, which decomposes the original non-convex problem into a series of convex sub-problems for solving. By constructing a series of convex functions to approximate the original non-convex function, each convex function is constructed on the basis of the solution of the previous step, and the global optimal solution is gradually approached by gradually adjusting. The semidefinite relaxation (SDR) is an important technique for solving complex optimization problems, which relaxes the original problem into a semidefinite programming (SDP) problem, and uses the theory and algorithm of convex optimization to approximately solve the original problem, so as to balance between the computational complexity and the solving quality.

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

[0016] wherein after the conversion by the successive convex approximation or the semidefinite relaxation, the objective function and other constraint conditions except the rank-one constraint are converted into convex functions. By introducing the rank-one constraint as a penalty term into the objective function, the method can obtain an approximate optimal solution of the original optimization problem. In this way, a balance between the solving efficiency and the solving quality can be achieved.

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

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

[0019] In some possible implementation manners, the first device can obtain the recommended value of the beamforming vector by solving the convex problem through convex optimization according to the KKT threshold. For example, the first device can solve the convex problem by using the CVX tool in MATLAB to obtain the recommended value of the beamforming vector.

[0020] The method can be solved by means of a convex optimization tool after converting the problem into a convex problem, improving the solving efficiency, and the method can reuse existing convex optimization tools to avoid resource waste.

[0021] In some possible implementation manners, the first device can randomly generate an initial solution according to the constraint condition. Then the first device can obtain an updated solution of the beamforming vector by iteratively updating the convex problem through successive convex approximation. For example, the first device can first obtain an updated solution of the beamforming vector by iteratively updating the convex problem through successive convex approximation based on the initial solution in the first iteration. When the difference between the updated solution and the initial solution does not satisfy a set iteration convergence threshold (for example, an iteration convergence precision), the next iteration is performed. In the next iteration, the first device can obtain an updated solution of the current round by iteratively updating the convex problem through successive convex approximation based on the updated solution of the previous round. If the difference between the updated solution of the current round and the updated solution of the previous round does not satisfy the iteration convergence threshold, the next iteration is performed. When the iteration convergence threshold is satisfied, 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 can determine the updated solution as the recommended value of the beamforming vector.

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

[0023] The method iteratively updates the convex problem through successive convex approximation for multiple times until an updated solution satisfying the iteration convergence threshold is obtained, and outputs the updated solution as the recommended value of the beamforming vector, so that the optimization problem can be quickly solved, and the communication and sensing integrated signal can be sent according to the recommended value, so that the sensing performance of the communication and sensing integrated system is ensured and safe communication is realized at the same time.

[0024] The second aspect of the present application provides a communication device. The communication device includes a solving module and a communication module. The solving module and the communication module can be software modules or hardware modules. In specific implementation, the solving module can be a solver, and the communication module can be a transceiver module. The functions of the solving module and the communication module are described in detail below.

[0025] The solving module is configured to solve an optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives according to the CRB threshold, and obtain a recommended value of the beamforming vector. The CRB threshold can be set based on the sensing performance requirement. For example, a smaller CRB threshold can be set when the sensing performance requirement is higher. The communication module is configured to send the communication-sensing integrated signal according to the recommended value of the beamforming vector.

[0026] The communication device provided in the present application sets the CRB threshold based on the sensing performance requirement for the communication-sensing integrated scene with the security transmission requirement, solves the optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives in combination with the CRB threshold, and obtains a recommended value of the beamforming vector, so as to optimize the beamforming and resource allocation and realize the secure communication while ensuring the sensing performance of the communication-sensing integrated system.

[0027] In some possible implementation manners, the solving module is specifically configured to solve the optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives, with the constraint condition that the CRB of the angle corresponding to the potential listener is less than or equal to the CRB threshold, and obtain a recommended value of the beamforming vector.

[0028] The communication device solves the optimization problem with the CRB threshold as the constraint condition, thereby obtaining the recommended value of the beamforming vector, and performs beam transmission according to the recommended value of the beamforming vector, so as to realize the 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 solving module is specifically configured to convert the optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives into a convex problem, and then solve the convex problem according to the CRB threshold to obtain the recommended value of the beamforming vector.

[0030] In the convex problem, any local optimal solution is a global optimal solution, and there is no local minimum trap. When the optimization problem is converted into the convex problem, the convex optimization can be used to solve the convex problem. The communication device solves the original optimization problem by converting it into the convex problem, which can greatly shorten the solving time and improve the solving efficiency.

[0031] In some possible implementation manners, the solving module is specifically configured to convert the optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives into a convex problem by means of continuous convex approximation and semi-positive relaxation.

[0032] Among them, continuous convex approximation is a technique for solving non-convex optimization problems, which decomposes the original non-convex problem into a series of convex sub-problems to solve. By constructing a series of convex functions to approximate the original non-convex function, each convex function is constructed on the basis of the solution of the previous step, and the global optimal solution is gradually approached by gradually adjusting. Semi-positive relaxation is an important technique for solving complex optimization problems, which relaxes the original problem into a semi-positive programming problem, and uses the theory and algorithm of convex optimization to approximately solve the original problem, thereby balancing the computational complexity and solution quality.

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

[0034] In some possible implementation ways, the solving module is further configured to obtain an upper bound of the rank-one constraint by Taylor expansion. By using Taylor expansion to obtain the upper bound of the equivalent rank-one constraint, the communication apparatus can simplify the problem and facilitate problem solving.

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

[0036] The communication apparatus can solve the problem by means of a convex optimization tool after converting the problem into a convex problem, thereby improving the solving efficiency, and the apparatus can reuse the existing convex optimization tool, thereby avoiding resource waste.

[0037] In some possible implementation ways, the solving module is specifically configured to randomly generate an initial solution according to the constraint condition; obtain an updated solution of the beamforming vector by iteratively updating the convex problem through continuous convex approximation; and determine the updated solution as the recommended value of the beamforming vector when the iteration convergence threshold is met.

[0038] The communication apparatus iterates the convex problem through continuous convex approximation for 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 optimization problem can be quickly solved, and the communication and perception integrated signal can be sent according to the recommended value, so that the perception performance of the communication and perception integrated system can be ensured, and safe communication can be realized at the same time.

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

[0040] a memory for storing computer programs or computer instructions;

[0041] A processor configured to execute computer programs or computer instructions stored in the memory, so that the first device executes the method according to any one of the implementation manners of the first aspect.

[0042] A fourth aspect of the present application provides a communication system. The communication system comprises a first device configured to execute the method according to any one of the implementation manners of the first aspect, a second device configured to extract user demand information from a received communication and perception integrated signal, and a perceived target configured to send a backhaul signal to the first device.

[0043] A fifth aspect of the present application provides a computer storage medium configured to store a computer program. The computer program is configured to, when executed, implement the communication method according to the first aspect or the second aspect of the present application.

[0044] A sixth aspect of the present application provides a computer program product comprising instructions. When the computer program product is executed on a first device, the first device is caused to execute the method according to any one of the implementation manners of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A structural schematic diagram of a communication and perception integrated system disclosed by an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a communication and perception integrated scene disclosed by an embodiment of the present application;

[0047] Figure 3 A flowchart of a communication method disclosed by an embodiment of the present application;

[0048] Figure 4 An algorithm flowchart of a first device solving a converted optimization problem disclosed by an embodiment of the present application;

[0049] Figure 5 A tradeoff diagram of a simulated perception performance and a secure communication performance disclosed by an embodiment of the present application;

[0050] Figure 6 A transmit beam pattern without a perception performance constraint disclosed by an embodiment of the present application;

[0051] Figure 7 A transmit beam pattern with a perception performance constraint disclosed by an embodiment of the present application;

[0052] Figure 8 A transmit beam pattern with an enhanced perception performance constraint disclosed by an embodiment of the present application;

[0053] Figure 9 A structural schematic diagram of a first device disclosed by an embodiment of the present application;

[0054] Figure 10 A hardware structure diagram of a base station disclosed by an embodiment of the present application is shown in FIG. 1.

[0055] Figure 11 A hardware structure diagram of a terminal device disclosed by an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. The terms used in the following embodiments are only for the purpose of describing the specific embodiments and are not intended to be limiting to the present application. As used in the specification and the appended claims of the present application, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “one or more,” in the embodiments of the present application, refer to one, two, or more than two; “and / or” describes the associated objects in the association relationship, which means that there can be three kinds of relationships; for example, A and / or B can mean that A exists alone, A and B exist together, B exists alone, where A and B can be singular or plural. The character “ / ” generally represents an “or” relationship between the associated objects.

[0057] In the present specification, the reference to “one embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, the appearances of the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in additional embodiments,” and so on, in various places in the specification are not necessarily all referring to the same embodiment, unless otherwise indicated by the context. The terms “comprising,” “including,” “having,” and their variants mean “including but not limited to,” unless otherwise indicated by the context.

[0058] The plurality referred to 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,” and the like are only used for the purpose of distinguishing the description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

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

[0060] Figure 1 A structural schematic diagram of an integrated sensing and communication system is shown. The integrated sensing and communication system 100 includes at least one first device, which can include a network device 110 as shown. Figure 1 The system 100 can also include at least one second device, which can include a terminal device 120 as shown. Figure 1 The system 100 can also include at least one sensed target, for example, a sensed target 130 as shown. Figure 1 The system 100 can also include at least one sensed target, for example, a sensed target 130 as shown.

[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 interact 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 a backwave signal of the sensed target 130. The network device 110 can obtain a sensing result of the sensed target according to the sensing signal and the backwave signal of the sensing signal. The sensing result can include, but is not limited to, the distance, angle, position, moving speed, or size of the sensed target 130, etc. In this way, the network device 110 can further utilize 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 can be implemented by multiple network devices 110 cooperating with each other, and the embodiments of the present application are not limited.

[0062] The integrated sensing and communication system is a system that integrates communication function and sensing function. The integration of communication and sensing has the following advantages: sharing hardware for communication and radar sensing function, which can save hardware cost; deploying sensing function directly on existing site, so it is easy to deploy; it is convenient for collaborative networking, using sensing result to assist communication and improve the quality of communication.

[0063] The network device 110 is a network-side device with wireless transceiving function. 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 in a later evolution of 3GPP, 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-sited or non-co-sited transmission reception points. 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] For another example, the network device 110 can include a central unit (CU), a distributed unit (DU), or a CU and a DU. In this way, part of the function of the wireless access network device can be implemented through multiple network function entities. These network function entities can be network elements in a hardware device, or software functions running on a dedicated hardware, or virtualized functions instantiated on a platform (e.g., a cloud platform). For another example, 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 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 function. The device can send sensing signals, receive and process echo signals of the sensed target 130. In the embodiments of this application, the communication apparatus for implementing the function of the network device 110 can be the network device 110, or a network device 110 with part of the function of the base station, or an apparatus capable of supporting the network device 110 to implement the function, such as a chip system, which 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 additionally integrates non-communication functions on the basis of communication functions, and realizes the composite capability of “communication + X” through sharing and cooperation of software and hardware resources. The “X” can include, but is not limited to, radar detection, environmental sensing, energy collection, positioning and navigation, and other non-communication functions, aiming to improve the resource utilization rate, network intelligent level and diversification of service scenarios of the base station. Taking the dual-functional base station of “communication + radar detection” as an example, the dual-functional base station can support 5G / 6G wireless communication on the one hand, and provide data transmission services for users, and on the other hand, can realize target detection and imaging (such as vehicle, pedestrian and unmanned aerial vehicle monitoring) through the emission of detection signals.

[0066] The terminal device 120 is a user-side device with wireless transceiving function, which 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, 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 wear, smart transportation, smart city, unmanned aerial vehicle, robot, etc. For example, the terminal device 120 can be a handheld terminal in cellular communication, a communication device in D2D, an internet-of-things device in MTC, a monitoring camera in smart transportation and smart city, or a communication device on an unmanned aerial vehicle, etc. The terminal device 120 can also be referred to as a user equipment (UE), a user terminal, a user apparatus, 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 perception function, for example, a static far-field point target, including but not limited to a stranded ship, a static vehicle, etc. The target can feed back an electromagnetic wave to the network device 110. The perceived target can also be referred to as a detected target, a perceived object, a detected object, or a perceived device, etc., and the embodiments of the present application are not limited thereto.

[0068] The perception signal refers to a signal for perceiving a target or detecting a target, or in other words, a signal for perceiving environmental information or detecting environmental information. For example, the perception signal is an electromagnetic wave transmitted by the network device 110 for perceiving environmental information. The perception signal can also be referred to as a radar signal, a radar perception signal, a detection signal, a radar detection signal, an environmental perception signal, etc., and the embodiments of the present application are not limited thereto.

[0069] It should be noted that, Figure 1 An example of a communication system architecture suitable for embodiments of the present application. Figure 1 The names of the various network elements included in the above are only names, and the names do not limit the functions of the network elements themselves. In 5G networks and future other networks, the above various network elements can also be other names, and the embodiments of the present application do not specifically limit this. For example, in a 6G network, part or all of the above network elements can continue to use the terms in 5G, or can be other names, etc., which are uniformly described here and will not be described again below.

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

[0071] I. Communication user (CU): is the subject of information transmission and interaction using a communication system. In the integrated sensing and communication system, the CU can be a mobile phone, an Internet of Things device, etc. terminal device normally performing data communication.

[0072] II. Eavesdropper (Eve): is a third party trying to illegally obtain the transmitted information of the communication user. The communication user can transmit sensitive information (such as identity information, location information) or perceived environmental information. The eavesdropper can illegally obtain the above sensitive information or environmental information by intercepting signals in the shared spectrum, causing information leakage.

[0073] Three, Beamforming: is a key technology in the field of wireless communication, through the phase and amplitude weighting of multiple antenna units, the energy is concentrated in a certain direction, forming a directional beam, so as to improve the signal strength, suppress interference and enhance the communication reliability. Application of beamforming technology will concentrate the signal in the direction of the legal communication user, so that the signal strength in the direction of the listener is weakened, which can reduce the possibility of the listener obtaining the transmission information of the legal communication user.

[0074] Four, Artificial noise technology: According to the obtained channel state information (channel state information, CSI), an artificial noise is generated, which is concentrated in the direction (or angle, direction) of the potential listener, while ensuring that the interference to the legal communication user is minimized, and the listening difficulty is increased.

[0075] Five, Physical channel: carries data information, for example, the physical channel can be physical downlink shared channel (physical downlink shared channel, PDSCH), physical downlink control channel (physical downlink control channel, PDCCH), physical broadcast channel (physical broadcast channel, PBCH), physical sidelink shared channel (physical sidelink shared channel, PSSCH), physical sidelink control channel (physical sidelink control channel, PSCCH), physical sidelink broadcast channel (physical sidelink broadcast channel, PSBCH), physical sidelink feedback channel (physical sidelink feedback channel, PSFCH), physical uplink shared channel (physical uplink shared channel, PUSCH), physical uplink control channel (physical uplink control channel, PUCCH) and the like. For the subsequent evolution of the networking form, new physical channel names may be introduced, and the embodiments of the present application do not make any limitation.

[0076] In the integrated sensing and communication scene, in order to meet the sensing performance requirements (or called sensing performance requirements) of the system, the base station can send a communication sensing integrated signal (also can be simply referred to as integrated signal) with large power to the target direction. At this time, if the sensed target is a potential listener, the integrated signal sent will be listened to, and the safe communication of the legal communication user cannot be guaranteed, such as Figure 2The security of communication systems is illustrated. To this end, the use of physical channel characteristics to enhance the security of communication systems is considered. A widely used approach is the artificial noise assisted physical layer security method based on interference management. Specifically, when the base station end transmits confidential data to the user, the base station end uses beamforming technology to ensure the legitimate communication of the user, while transmitting artificial noise to interfere with the listener. By using artificial noise to mask the communication signals of the legitimate communication user, the signal reception of the passive listener is destroyed while ensuring the system sensing performance, thereby realizing the secure communication of the legitimate user.

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

[0078] Therefore, the present application provides a communication method. The method can be applied to a sensing-integrated communication system (or simply referred to as a sensing-integrated communication system, an integrated system, or a system), such as a first device in a sensing-integrated communication system. The first device (such as a network device such as a base station) in the sensing-integrated communication 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 objective according to the Cramer-Rao Bound (CRB) threshold to obtain a recommended value of the beamforming vector. Then the first device can send a sensing-integrated communication signal according to the recommended value of the beamforming vector.

[0079] Wherein, the 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 sensing-integrated communication scenario, the CRB can be used to represent the sensing performance index. For the CRB, the sensing end (such as the first device) can set a threshold according to the sensing performance requirement (such as 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 sensed target side. For example, if the sensing-integrated communication system requires high sensing measurement accuracy, a smaller CRB threshold is set. The sum secrecy rate (SSR) is a core index for measuring the physical layer security performance of a multi-user system, which is used to describe the total rate at which all legitimate users can safely transmit information in the presence of a listener. The essence of the sum secrecy rate is the sum of the secrecy capacity of each communication user, which reflects the overall secure transmission capability of the sensing-integrated communication system against the listener.

[0080] The method is aimed at a communication-sensing integrated scene with security transmission requirements, sets a CRB threshold based on sensing performance requirements, optimizes beamforming and resource allocation in combination with the CRB threshold, and realizes secure communication while ensuring the sensing performance of the communication-sensing integrated system.

[0081] In order to make the communication method of the present application more clear and easy to understand, the embodiments of the communication method of the present application will be described in detail below in combination with the drawings.

[0082] Referring to Figure 3 the flowchart of a communication method, the method comprises the following steps:

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

[0084] This step can be performed by a first device. The first device can include a network device such as a base station. The base station can include but is not limited to gNB, ng-eNB. The network device can integrate sensing function to realize communication-sensing integration. In some examples, the network device can be a dual-function base station, wherein the dual-function includes communication function and sensing function.

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

[0086] Beamforming is a key technology in the fields of wireless communication, radar, etc., and its core idea is to weight the signals of multiple antenna elements by phase and amplitude, so as to enhance the signals in a specific direction (form a beam), 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, used to weight and combine the signals of each antenna element, so as to realize directional transmission or reception of the beam.

[0087] In this application, the beamforming vector can include the beamforming vector of the first device (such as the base station) to the signal . and a first device's beamforming vector of artificial noise (AN) Suppose there is an array 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: wherein, is the weighting coefficient (complex number) of the nth antenna element, is the amplitude weight, is the phase offset. The beamforming vector w can be expressed as .

[0088] S304, transmitting a communication-sensing integrated signal according to the recommended value of the beamforming vector.

[0089] This step can be performed by a first device. The first device can include a network device such as a base station. In some examples, the network device can be a dual-function base station, wherein the dual-function includes a communication function and a sensing function.

[0090] The recommended value of the beamforming vector includes a recommended set of weighting coefficients, which can include at least one of amplitude and phase information. The first device can weight and combine the signals of each antenna element according to the recommended value of the beamforming vector, so that the signal is enhanced in a certain 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 a communication-sensing integrated scene with security transmission requirements, a first device (such as a dual-function base station) sets a CRB threshold based on the sensing performance requirements, solves an optimization problem with the beamforming vector of the transmitted beam as the optimization variable and the secrecy and rate of the user as the optimization objective in combination with the CRB threshold, obtains the recommended value of the beamforming vector, and thereby optimizes the beamforming and resource allocation, while ensuring the sensing performance of the communication-sensing integrated system and realizing secure communication.

[0092] The optimization problem can be established according to the communication model and the sensing model. In specific implementation, a communication-sensing integrated framework can be established according to a network architecture including a network device (such as a base station), a plurality of communication users, and a sensed target (such as a potential Eve). Under the communication-sensing integrated framework, a communication model is established according to the secrecy rate of the legitimate communication user. A sensing model is established according to the Cramer-Rao bound of the sensed target.

[0093] First, the establishment of a communication integrated framework is introduced. Consider a downlink security scenario of a communication-sensing integrated system, such as Figure 2As shown. The integrated communication and sensing system includes a device equipped with... root transmitting antenna and Base station with root receiving antenna, A single-antenna legitimate communication user and a radar target (potential Eve) located in the far field. Assuming 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 represented as follows:

[0094] (1)

[0095] in, To users The transmitted information symbol has zero mean and unit power. For base station to signal beamforming vector, Artificial noise, typically consisting of a complex Gaussian random variable with zero mean and unit power, is used to counter eavesdropping by potential eavesdroppers. For base stations to block artificial noise The beamforming vector. Since the base station is aware of the power distribution and other relevant 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] Furthermore, assuming base station to user channel If modeled as Ricean fading, the channel can be represented as follows:

[0097] (2)

[0098] in, Represents the line-of-sight component in Rice decay. Represents the path loss of a communication user at a reference distance. Represents the distance from the base station to the user. Represents the road damage index. Represents the Rice factor. The non-line-of-sight component in Rice fading. It is then modeled as a cyclic symmetric complex Gaussian random variable with zero mean and zero unit variance.

[0099] Next, the establishment of the communication model and the perception model will be introduced. Details are as follows:

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

[0101] (3)

[0102] in, In denotes conjugate transpose, denotes noise, e.g., additive white Gaussian noise.

[0103] Channel between base station and perceived target (potential Eve) is modeled as LoS channel, as follows:

[0104] (4)

[0105] where, denotes path loss at reference distance for potential Eve, denotes distance from base station to potential Eve, denotes path loss exponent, denotes azimuth angle of potential Eve, whose corresponding steering vector is denoted as:

[0106] (5)

[0107] The capacity of the eavesdropping channel when the target is a potential eavesdropper can be:

[0108] (6) In this case, the secrecy rate can be used to represent the communication performance indicator of the system, as follows:

[0109]

[0110] (7) where,

[0111] denotes .

[0112] Further, a perceived performance indicator can be constructed, and the construction process is described in detail below.

[0113] where, the received reflected echo signal of the perceived target at the base station can be represented as:

[0114] (8)

[0115] where, denotes reflection coefficient, which is usually related to radar cross section (RCS) and round trip path loss, denotes steering vector of receiving antenna, denotes steering vector of transmitting antenna.

[0116] The CRB of the potential Eve corresponding angle is used to represent the perceived performance indicator, as follows:

[0117] ​(9)

[0118] wherein, , denotes the frame length, denotes the trace of a matrix. Since the communication and perception integrated system utilizes the transmitted signal to simultaneously realize the functions of communication and perception, when the frame length of the transmitted signal is large enough, the transmitted covariance matrix can be represented as:

[0119] (10)

[0120] If the communication and perception integrated system requires high accuracy of perception measurement, the base station can set a smaller CRB threshold value, without the need for prior acquisition from the user side or the perceived target side.

[0121] According to the communication model and the perception model, an optimization problem is established, in which the beamforming variable of the transmitted beam is the optimization variable, and the secrecy rate and the rate of the user are the optimization objectives. A minimum threshold is set for the minimum secrecy rate of the user and the angle CRB of the perceived target, as shown below:

[0122]

[0123] s.t. C1: ,

[0124] C2: ,

[0125] C3: ,

[0126] C4: (11)

[0127] wherein, is the CRB threshold value, for example, the threshold value of the lower bound of the variance of the unbiased estimation of the deterministic parameter, represents the secrecy rate threshold value of the user, denotes the total transmission power of the base station. The constraint C1 guarantees the perception performance of the system, the constraint C2 is the equation representation of the transmitted covariance matrix, the constraint C3 guarantees the minimum secrecy rate requirement of the legitimate communication user, and the constraint C4 represents the total transmission power constraint of the system.

[0128] Correspondingly, the base station can solve the optimization problem with the beamforming vector of the transmitted beam as the optimization variable and the secrecy rate and the rate of the user as the optimization objectives, with the constraint condition that the Cramer-Rao bound of the corresponding angle of the potential listener is less than or equal to the Cramer-Rao bound threshold value, to obtain the recommended value of the beamforming vector.

[0129] In the optimization problem shown in equation (11), the objective function and the constraint conditions have complex non-convex characteristics, and direct solution of the problem is extremely challenging. The non-convex characteristics can include non-convex sets or non-convex functions. The non-convex set is a set that does not satisfy the definition of a convex set, such as a ring-shaped region or a discrete point set. The non-convex function can include a function that does not satisfy the definition of a convex function, and common non-convex functions can include non-convex quadratic functions, composite functions containing absolute values, exponents, objective functions in combinatorial optimization problems. The objective function has multiple local minima or multiple local maxima, and the optimization algorithm is prone to local optimization.

[0130] To this end, the first device can convert the optimization problem shown in equation (11) with the beamforming vector of the transmit beam as the optimization variable and the secrecy and rate of the user as the optimization target into a convex problem, and then solve the convex problem according to the Krammer-Rao threshold to obtain the recommended value of the beamforming vector. In the convex problem, the objective function and the constraint condition in the inequality form are convex functions, and the constraint condition in the equality form is an affine function, for example, a linear function plus a constant. Any local optimal solution of the convex problem is a global optimal solution, and there is no local minimum trap. When the optimization problem is converted into a convex problem, the convex optimization can be used to solve the convex problem.

[0131] The process of converting the optimization problem into a convex problem will be described in detail below.

[0132] Specifically, the first device can convert the optimization problem with the beamforming vector of the transmit beam as the optimization variable and the secrecy and rate of the user as the optimization target into a convex problem through successive convex approximation (SCA) and semi-definite relaxation. The first device can perform iterative optimization through successive convex approximation to solve complex problems, and apply the Schur complement theorem to transform the constraint condition (such as C1 in equation (11)) in the optimization problem (such as equation (11)). Accordingly, the original optimization problem can be reconstructed as follows:

[0133]

[0134] s.t. C1: ,

[0135] C2: ,

[0136] C3: ,

[0137] C4: ,

[0138] C5: ,

[0139] C6: (12)

[0140] Among them, let , , , And satisfy , rank ( ) = 1, rank( ) = 1, .

[0141] To simplify the complex fractional structure of the confidentiality rate of communication users in the optimization problem (as shown in Equation (12)), a first-order Taylor expansion can be used for approximation in the SCA iteration, thereby obtaining the user's confidentiality rate. The lower bound expression for the confidentiality rate is:

[0142] (13)

[0143] Through the above transformations and approximations, the objective function and constraints C1 to C5 are transformed into convex functions. Furthermore, the first device can also transform constraint C6. Constraint C6 is a rank-one constraint, which requires the rank of the matrix to be equal to 1, meaning the matrix can be represented as the outer product of two vectors.

[0144] Specifically, the first device can add a penalty term to the objective function of the optimization problem, which includes a rank-one constraint. By introducing the rank-one constraint as a penalty term into the objective function, an approximate optimal solution to the original optimization problem can be obtained. The equivalent representation of the rank-one constraint is shown below:

[0145] (14)

[0146] in, The sum of the singular values ​​of a matrix is ​​called the nuclear norm; The largest singular value of the matrix is ​​represented by the spectral norm. Thus, the first device can obtain the upper bound of the rank-one constraint through Taylor expansion. Here, the first device can be used at a given point... We use a first-order Taylor expansion to obtain the upper bound of the equivalent rank-one constraint:

[0147] (15)

[0148] in, Representation matrix Similarly, the first device can obtain the eigenvector corresponding to the largest eigenvalue. The rank-one constraint is transformed.

[0149] Based on the above conversion or transformation, the original optimization problem can be relaxed and restructured into a solvable optimization problem P3, as shown below:

[0150]

[0151] s.t. C1: ,

[0152] C2: ,

[0153] C3: ,

[0154] C4: ,

[0155] C5: (16)

[0156] To obtain an approximate optimal solution of the original optimization problem, the penalty coefficient In each iteration, the constant , i.e. In this case, the first device can obtain the recommended value of the beamforming vector by solving the convex problem through convex optimization (CVX) according to the threshold of the Cramér-Rao bound. For example, the first device can use the CVX tool in MATLAB to solve the convex problem and obtain the recommended value of the beamforming vector. Wherein, the first device uses the CVX tool to solve the convex problem can include solving by using a continuous convex approximation algorithm based on a penalty term.

[0157] The solving process is described in detail below.

[0158] In a specific implementation, the first device can randomly generate an initial solution according to the constraint condition, and then update the convex problem through continuous convex approximation iteration 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. Wherein, the iteration convergence threshold can be pre-configured by the user. The iteration convergence threshold can be that the difference between the updated solution and the updated solution obtained in the last iteration is less than the iteration convergence threshold.

[0159] It should be noted that when the iteration convergence threshold is not met, the first device can update the penalty coefficient (or called penalty factor), record the updated solution of the output beamforming vector, and perform the next iteration according to the updated solution and the updated penalty coefficient.

[0160] For ease of understanding, Figure 4An algorithmic flowchart of a first device solving the converted optimization problem is also shown. The algorithmic inputs can include an initial solution and an iteration convergence threshold. The initial solution is an initial feasible solution, i.e., an initial solution that satisfies the constraints. The iteration convergence threshold is also referred to as the iteration convergence accuracy, which in this example can be . The algorithmic outputs can include a recommended value of the beamforming vector, denoted as 、 .

[0161] The first device can solve the converted optimization problem P3, e.g., by combining 、 updating 、 . Then the first device can update when the difference of the objective function is greater than or equal to the iteration convergence threshold. The first device can then update and for the next iteration.

[0162] To further verify the effectiveness of the secure beamforming method of the artificial noise aided sensing-communication integrated system, the following simulation experiments are performed. The main simulation parameters are set as follows: the number of transmit antennas , the number of receive antennas , and the number of users ; the base station position is set to [0, 0], and the target position is 0°, with a distance of 30 from the origin; the user 1 position is 10°, with a distance of 40 from the origin, and the user 2 position is 30°, with a distance of 35 from the origin; in addition, the channel parameters are set as 、 、 , and the transmit power and noise power are set as 、 .

[0163] Figure 5 A simulation obtained tradeoff between sensing performance and secure communication performance is shown, which shows the relationship between the secrecy and rate of the user and . As the sensing performance requirement is relaxed (i.e., the value of is greater), the communication-sensing integrated system allocates more resources to the communication function, resulting in an increase in secrecy and rate. In addition, as the sensing performance requirement is relaxed, the secrecy and rate gain of the user will first increase and then decrease.

[0164] Figure 6The transmit beam pattern without sensing performance constraint is shown, when the communication-sensing integrated system has no sensing performance requirement, the resources are all used to guarantee the safe communication of the system, at this time, the secrecy and rate of the communication user is 13.0539, and a smaller energy is allocated to the angle direction of the sensed target (azimuth angle is 0°), and the part of energy is only used to reduce the capacity of the eavesdropping channel.

[0165] Figure 7 The transmit beam pattern with sensing performance constraint is shown, when the communication-sensing integrated system has a certain sensing performance requirement, the communication-sensing integrated system needs to meet the communication demand and the sensing demand at the same time, at this time, compared with the transmit beam pattern without sensing performance constraint in Figure 6 , more energy will be allocated to the angle direction of the sensed target, which is used to reduce the capacity of the eavesdropping channel while guaranteeing the sensing performance of the system. At this time, , the secrecy and rate of the user is 11.6287.

[0166] When the sensing performance requirement is further improved (the value of is smaller), the allocation of system resources will be extremely nervous, at this time, due to the limitation of total transmit power, the energy of the communication beam (such as transmit beam) needs to be used for communication and sensing functions at the same time, the communication beam occupies a large amount of energy in the direction of the sensed target, which leads to the decrease of the secrecy and rate of the user, and the allocation and trade-off of the resources of the communication-sensing integrated system will be increasingly important. As shown in Figure 8 , , the communication beam occupies a large amount of energy in the direction of the sensed target, and the secrecy and rate of the user decrease to 9.49475.

[0167] The above theoretical analysis and verification are carried out for a simplified scene, which can be further extended on the basis of the current and applied to the following actual scenes: intelligent transportation, low-altitude security or Internet of Things. In the intelligent transportation scene, the vehicle can be set as the sensed target (i.e. the target needed to be sensed by the communication-sensing integrated system or the first device in the system) or the communication user. The vehicle can be set as the sensed target or the communication user flexibly according to the demand of the vehicle. Among them, the illegal vehicle is regarded as a potential eavesdropper, at this time, the scheme of the present application can be applied to it, which realizes safe communication while guaranteeing the sensing performance. In the low-altitude security scene, the scheme of the present application can be directly applied to it, which realizes safe communication while guaranteeing the sensing performance; in the Internet of Things scene, according to the sensing demand of different terminal devices, the scheme of the present application can be applied to it, which realizes safe communication while guaranteeing the sensing performance.

[0168] The above Figures 3 to 8 introduces the communication method of the present application and the simulation effect of the method. The following introduces a communication device provided by the present application.

[0169] Based on Figures 3 to 8 The communication method and its simulation effect are shown, and the application further provides a first device. From the perspective of functional modularization, the first device of the application is introduced as follows.

[0170] First, referring to Figure 9 The structural schematic diagram of a first device is shown, and the first device 900 includes:

[0171] The solving module 902 is configured to solve an optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives according to a Cramér-Rao bound threshold to obtain a recommended value of the beamforming vector, where the Cramér-Rao bound threshold is set based on a sensing performance requirement.

[0172] The communication module 904 is configured to send a communication-sensing integrated signal according to the recommended value of the beamforming vector.

[0173] The solving module 902 can be a solver, and the specific implementation of the solving module 902 can refer to the related content described in the embodiment shown in Figure 3 The communication module 904 can include a transceiver module configured to send a communication-sensing integrated signal according to the recommended value of the beamforming vector. Further, the communication module 904 is further configured to receive a backscatter signal sent by a sensed target. The specific implementation of the solving module 902 and the communication module 904 can refer to the related content described in Figures 3 to 8 , which will not be described here again.

[0174] In some possible implementation manners, the solving module 902 is specifically configured to:

[0175] Solve an optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives, with the constraint that the Cramér-Rao bound of the corresponding angle of a potential listener is less than or equal to the Cramér-Rao bound threshold, to obtain a recommended value of the beamforming vector.

[0176] In some possible implementation manners, the solving module 902 is specifically configured to:

[0177] Convert the optimization problem with the beamforming vector of the transmit beam as an optimization variable and the secrecy and rate of the user as optimization objectives into a convex problem.

[0178] Solve the convex problem according to the Cramér-Rao bound threshold to obtain a recommended value of the beamforming vector.

[0179] In some possible implementation manners, the solving module 902 is specifically configured to:

[0180] By continuous convex approximation and semi-positive relaxation, an optimization problem with the beamforming vector of a transmit beam as an optimization variable and the secrecy and rate of a user as optimization objectives is converted into a convex problem.

[0181] In some possible implementation manners, the solving module 902 is further configured to:

[0182] A penalty term is added in the objective function of the optimization problem, and the penalty term includes a rank-one constraint.

[0183] In some possible implementation manners, the solving module 902 is further configured to:

[0184] An upper bound of the rank-one constraint is obtained by Taylor expansion.

[0185] In some possible implementation manners, the solving module 902 is specifically configured to:

[0186] The convex problem is solved by convex optimization according to a Cramer-Rao bound threshold, and a recommended value of the beamforming vector is obtained.

[0187] In some possible implementation manners, the solving module 902 is specifically configured to:

[0188] An initial solution is randomly generated according to a constraint condition;

[0189] The convex problem is iteratively updated by continuous convex approximation, and an updated solution of the beamforming vector is obtained;

[0190] When an iterative convergence threshold is met, the updated solution is determined as the recommended value of the beamforming vector.

[0191] Next, the first device 900 is introduced from the perspective of hardware materialization.

[0192] Figure 10 An example of a first device 900 is provided for the embodiments of the present application. The first device 900 can be a network device such as a base station. Figure 10 The first device 900 is taken as a base station example for illustration. Figure 10A simplified base station structure diagram 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, controlling the base station, etc.; the processor 1010 is usually the control center of the base station, used to control the base station to perform the processing operations of 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 transceiving radio frequency signals and converting radio frequency signals and baseband signals; the transceiver 1030 can usually be referred to as a transceiving module, a transceiver, a transceiving circuit, etc. The transceiver 1030 includes an antenna 1033 and a radio frequency circuit (not shown in the figure), wherein the radio frequency circuit is mainly used for radio frequency processing. Optionally, the devices for realizing the receiving function in the transceiver 1030 are regarded as receivers, and the devices for realizing the sending function are regarded as transmitters, 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.

[0193] The processor 1010 and the memory 1020 can include one or more single boards, each of which can include one or more processors and one or more memories. The processor is used to read and execute the computer program in the memory to realize the baseband processing function and the control of the base station. If there are multiple single boards, the single boards can be interconnected to enhance the processing capability. As an optional implementation, multiple single boards can also share one or more processors, or multiple single boards can share one or more memories, or multiple single boards can share one or more processors at the same time.

[0194] In an implementation, the transceiver 1030 is used to execute the transceiving-related processes performed by the base station in the above method embodiments. The processor 1010 is used to execute the processing-related processes performed by the base station in the above method embodiments.

[0195] It should be understood that, Figure 10 For example only and not limitation, the network device including the processor, the memory, and the transceiver described above can not depend on Figure 10 The structure shown.

[0196] 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 convenience and brevity of description, the explanations and beneficial effects of the related contents in any of the terminal devices provided above can refer to the corresponding method embodiments provided above, and will not be repeated here.

[0197] The application also provides a communication system, which can include the first device 900, a second device and a perceived target. The first device 900 can be a base station as shown in Figure 10 FIG. 1, and the second device can be a terminal device as shown in Figure 11 FIG. 2.

[0198] Figure 11 A second device is provided in the embodiments of the application. The second device can be a terminal device, for example, a CU.

[0199] As shown in Figure 11 FIG. 2, the terminal device can 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, and the like.

[0200] It can be understood that the structure shown in the embodiments does not constitute a specific limitation on the terminal device. In other embodiments, the terminal device can include more or fewer components than shown, or combine certain components, or split certain components, or different component arrangements. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0201] The processor 1110 can include one or more processing units, for example: the processor 1110 can 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. Different processing units can be independent devices or integrated into one or more processors.

[0202] It can be understood that the interface connection relationship between the modules shown in the embodiments is only illustrative and does not constitute a structural limitation on the terminal device. In other embodiments of the application, the terminal device can also use different interface connection methods or combinations of multiple interface connection methods.

[0203] The external memory interface 1120 can be used to connect an external memory card, such as a Micro SD card, to extend the storage capacity of the terminal device. The external memory card communicates with the processor 1110 through the external memory interface 1120 to implement a data storage function. For example, files such as music and videos are stored in the external memory card.

[0204] The internal memory 1121 can be used to store computer executable program codes, which 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. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like. The data storage area can store data created during use of the terminal device (such as audio data, a phone book, etc.), and the like. In addition, the internal memory 1121 can include a high-speed random access memory, and can further include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like. 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 disposed in the processor.

[0205] The wireless communication function of the terminal device can be implemented through the antenna 1, the antenna 2, the mobile communication module 1150, the wireless communication module 1160, a modem processor, and a baseband processor, and the like.

[0206] 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 of a wireless local area network. In some other embodiments, the antennas can be used in combination with a tuning switch.

[0207] The mobile communication module 1150 can provide a solution for wireless communication including 2G / 3G / 4G / 5G, etc. applied on the terminal device. The mobile communication module 1150 can include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 1150 can receive electromagnetic waves by the antenna 1, and perform filtering, amplification, etc. on the received electromagnetic waves, and transfer to the modem processor for demodulation. The mobile communication module 1150 can also amplify the signal modulated by the modem processor, and convert to electromagnetic waves radiated by the antenna 1. In some embodiments, at least part of the functional modules of the mobile communication module 1150 can be arranged in the processor 1110. In some embodiments, at least part of the functional modules of the mobile communication module 1150 can be arranged in the same device as at least part of the modules of the processor 1110.

[0208] In some embodiments, the terminal device initiates or receives a call request through the mobile communication module 1150 and the antenna 1.

[0209] In addition, on the above components, an operating system is running. 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 brevity of description, the explanation and beneficial effects of the above-mentioned related content in any of the terminal devices can refer to the corresponding method embodiments provided above, which will not be repeated here.

[0210] Further, the embodiments of the present application also provide a computer readable storage medium. The computer readable storage medium includes permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of storage media include, but are not limited to, parameter random access 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.

[0211] The computer readable storage medium stores a program, and the program is executed by the processor to implement the communication method executed at the first device side or the communication method executed at the communication system in the above embodiments.

[0212] The embodiments of the present application further provide a computer program product containing instructions. The computer program product can be software or a program product containing instructions, which 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 executes the above communication method.

[0213] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and module can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0214] In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the modules is only a logical function division. There can be another division manner in actual implementation. For example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the modules can be indirect coupling or communication connection through some interface, device or module, and can be electrical, mechanical or in other forms.

[0215] The modules described as separate components can or can not be physically separate, and the components displayed as modules can or can not be physical modules, that is, they can be located in one place, or can be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0216] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically, or two or more modules can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0217] The integrated module, if implemented in the form of a software function module and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially make contributions to or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of 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 the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0218] The above description and the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents. The modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A communication method characterized by comprising: The method is applied to a communication system, the communication system comprising a first device, a second device and a perceived target, the perceived target being a potential eavesdropper, and the method comprising: solving an optimization problem with a constraint that a Cramér-Rao bound of an angle corresponding to the potential eavesdropper is less than or equal to a Cramér-Rao bound threshold, and with a beamforming vector of a transmit beam as an optimization variable and a secrecy and a rate of a user as optimization objectives, to obtain a recommended value of the beamforming vector, the Cramér-Rao bound threshold being set based on a sensing performance requirement, and the Cramér-Rao bound of the angle corresponding to the potential eavesdropper being determined according to a transmit covariance matrix; transmitting a communication-sensing integrated signal according to the recommended value of the beamforming vector.

2. The method of claim 1, wherein, The solving of the optimization problem with the constraint that the Cramér-Rao bound of the angle corresponding to the potential eavesdropper is less than or equal to the Cramér-Rao bound threshold, and with the beamforming vector of the transmit beam as the optimization variable and the secrecy and the rate of the user as the optimization objectives, to obtain the recommended value of the beamforming vector, comprises: converting the optimization problem with the constraint that the Cramér-Rao bound of the angle corresponding to the potential eavesdropper is less than or equal to the Cramér-Rao bound threshold, and with the beamforming vector of the transmit beam as the optimization variable and the secrecy and the rate of the user as the optimization objectives, into a convex problem; solving the convex problem to obtain the recommended value of the beamforming vector.

3. The method of claim 2, wherein, The converting of the optimization problem with the constraint that the Cramér-Rao bound of the angle corresponding to the potential eavesdropper is less than or equal to the Cramér-Rao bound threshold, and with the beamforming vector of the transmit beam as the optimization variable and the secrecy and the rate of the user as the optimization objectives, into the convex problem, comprises: converting the optimization problem with the constraint that the Cramér-Rao bound of the angle corresponding to the potential eavesdropper is less than or equal to the Cramér-Rao bound threshold, and with the beamforming vector of the transmit beam as the optimization variable and the secrecy and the rate of the user as the optimization objectives, into the convex problem through successive convex approximation and semi-definite relaxation.

4. The method of claim 2, wherein, The method further comprises: adding a penalty term comprising a rank-one constraint in an objective function of the optimization problem.

5. The method of claim 4, wherein, The method further comprises: obtaining an upper bound of the rank-one constraint through Taylor expansion.

6. The method according to any one of claims 2 to 5, characterized in that, The solving of the convex problem to obtain the recommended value of the beamforming vector, comprises: solving the convex problem through convex optimization to obtain the recommended value of the beamforming vector.

7. The method of claim 6, wherein, The solving of the convex problem through convex optimization to obtain the recommended value of the beamforming vector, comprises: randomly generating an initial solution according to the constraint condition; iteratively updating the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector; determining the updated solution as the recommended value of the beamforming vector when an iterative convergence threshold is satisfied.

8. A communication device, characterized by The apparatus is deployed at a first device of a communication system, the communication system comprising the first device, a second device and a perceived target, the perceived target being a potential eavesdropper, and the apparatus comprising: a solving module, configured to solve an optimization problem with a constraint that a Cramér-Rao bound of the potential eavesdropper corresponding angle is less than or equal to a Cramér-Rao bound threshold, and an optimization variable of a beamforming vector of a transmit beam, and an optimization objective of a user's secrecy and rate, to obtain a recommended value of the beamforming vector, the Cramér-Rao bound threshold being set based on a sensing performance requirement; a communication module, configured to send a communication-sensing integrated signal according to the recommended value of the beamforming vector.

9. The apparatus of claim 8, wherein, The solving module is specifically configured to: convert the optimization problem with the constraint that the Cramér-Rao bound of the potential eavesdropper corresponding angle is less than or equal to the Cramér-Rao bound threshold, and the optimization variable of the beamforming vector of the transmit beam, and the optimization objective of the user's secrecy and rate, into a convex problem; solve the convex problem to obtain the recommended value of the beamforming vector.

10. The apparatus of claim 9, wherein, The solving module is specifically configured to: convert the optimization problem with the constraint that the Cramér-Rao bound of the potential eavesdropper corresponding angle is less than or equal to the Cramér-Rao bound threshold, and the optimization variable of the beamforming vector of the transmit beam, and the optimization objective of the user's secrecy and rate, into a convex problem through successive convex approximation and semi-definite relaxation.

11. The apparatus of claim 9, wherein, The solving module is further configured to: add a penalty term including a rank-one constraint in an objective function of the optimization problem.

12. The apparatus of claim 11, wherein, The solving module is further configured to: obtain an upper bound of the rank-one constraint through Taylor expansion.

13. The apparatus of any one of claims 9 to 12, wherein, The solving module is specifically configured to: solve the convex problem through convex optimization to obtain the recommended value of the beamforming vector.

14. The apparatus of claim 13, wherein, The solving module is specifically configured to: randomly generate an initial solution according to the constraint condition; update the convex problem through successive convex approximation to obtain an updated solution of the beamforming vector; when an iterative convergence threshold is met, determine the updated solution as the recommended value of the beamforming vector.

15. A first device, comprising: The first device comprises: a memory, configured to store computer programs or computer instructions; a processor, configured to execute the computer programs or computer instructions stored in the memory, so that the first device performs the method in any one of claims 1 to 7.

16. A communication system, characterized by The communication system comprises a first device, a second device and a sensed target, and the first device is configured to perform the method in any one of claims 1 to 7.

17. A computer storage medium, comprising, The computer storage medium is configured to store computer programs, which are executed to implement the method in any one of claims 1 to 7.

18. A computer program product, characterised in that, The computer storage medium comprises computer readable instructions; the computer readable instructions are configured to implement the method in any one of claims 1 to 7.

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