Method, apparatus and system for communication and sensing integration of full duplex single base station system

Abstract

The application discloses a communication and sensing integrated method, device and system of a full-duplex single base station system, and belongs to the technical field of wireless communication. The method comprises the following steps: by jointly introducing a movable antenna and a reconfigurable intelligent surface in the full-duplex single base station system, the physical space limitation of a traditional fixed antenna is broken through, and link fading in a complex environment is effectively overcome. By dynamically regulating the positions of the transmitting movable antenna and the receiving movable antenna, an artificial line-of-sight link is constructed by using RIS phase shift regulation, the total reachable rate of the system is greatly improved, and the best dynamic balance of the communication and sensing performance is realized under the premise of effectively reducing the high hardware cost of the double base station system. An algorithm framework integrating fractional programming and alternating optimization is designed, a high-dimensional non-convex problem containing transmit-receive beamforming, RIS element phase shift and transmit-receive antenna position is efficiently decoupled, and the algorithm not only greatly improves the overall optimization convergence speed, but also effectively avoids falling into a local optimum.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and more specifically, relates to a communication and sensing integrated method, apparatus and system for a full-duplex single-base system. Background Technology

[0002] With the development of sixth-generation mobile communication technology, emerging applications have placed demands on both high-precision sensing capabilities and high-quality wireless connectivity. However, the separate design of communication and sensing cannot simultaneously meet the needs of these applications. To integrate precise sensing and high-quality communication into a unified wireless system, sensor-integrated communication is considered a transformative paradigm. By sharing spectrum and hardware resources, sensor-integrated communication aims to achieve high-quality communication while simultaneously possessing high-precision sensing capabilities, thus achieving mutual benefit and synergy between these two functions. In recent years, most research on sensor-integrated communication has adopted MIMO systems, which not only provide greater spatial freedom but also synthesize multiple beams simultaneously toward both communication users and sensing targets. However, MIMO-based sensor-integrated communication architectures face two key challenges. On the one hand, in today's complex urban environments, traditional MIMO beams suffer severe path loss due to various obstacles, resulting in insufficient signal strength. On the other hand, traditional MIMO primarily uses fixed-position antenna arrays, which cannot fully utilize the spatial freedom provided by multiple antennas.

[0003] To address the issue of insufficient signal strength along direct paths, reconfigurable smart surfaces are considered an effective technology. By intelligently configuring a large number of reflective elements, reconfigurable smart surfaces can adjust the phase of the incident signal and establish artificial line-of-sight connections, thereby achieving favorable reconfiguration of the radio environment. To address the challenge of insufficient utilization of spatial degrees of freedom caused by fixed-position antenna arrays, movable antennas have been proposed as an effective method. Unlike traditional fixed-position antennas, movable antennas can move flexibly within a given area, fully utilizing spatial degrees of freedom to optimize wireless channel conditions, thereby improving the system's communication and sensing performance. Furthermore, from an implementation perspective, existing integrated sensing and communication systems can be mainly divided into bistatic and monostatic types. To avoid self-interference problems, some integrated sensing and communication research has adopted bistatic systems with separate transmitters and receivers, but this incurs additional hardware costs.

[0004] Although there has been research on integrated sensing systems assisted by individual reconfigurable smart surfaces or movable antennas, modeling and optimizing monostatic integrated sensing systems while reducing hardware costs remains a highly challenging problem. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the prior art, this application provides a communication and sensing integration method, device and system for a full-duplex single-base system, which is used to solve the technical problem of how to model and optimize a single-base integrated communication and sensing system while reducing hardware costs.

[0006] To achieve the above objectives, according to one aspect of this application, a method for integrating communication and sensing in a full-duplex single-base system is provided, comprising: S1: Construct a full-duplex single-base system comprising: a base station, a reconfigurable smart surface, a communication user terminal, and a target to be sensed; the base station is equipped with multiple movable transmitting antennas and multiple movable receiving antennas; S2: The objective function is to maximize the total reachable rate of all communication user terminals in the full-duplex single-base system and the weighted sum of the mutual information obtained by sensing the sensing target; establish a non-convex optimization problem for jointly optimizing the phase shift matrix of the reconfigurable smart surface, the beamforming of each of the transmitting movable antennas and the receiving movable antennas, and the position of each antenna. S3: Transform the non-convex optimization problem containing a logarithmic fractional objective function into multiple easily tractable subproblems of non-fractional form; S4: Solve multiple easily tractable subproblems based on the alternating optimization architecture to obtain the current solution result; S5: Determine whether the objective function corresponding to the current solution result has converged. If it has not converged, return to S4 for the next round of solution. If it has converged, use the current solution result to configure the full-duplex single-base system so that it can realize the integration of communication and sensing.

[0007] Further, S4 includes: S41: Use the KKT conditions to obtain the current optimal beamforming for each transmit movable antenna and each receive movable antenna; S42: Substitute the current optimal beamforming of the transmitting movable antenna and each receiving movable antenna into multiple easily processed subproblems, and then use the semidefinite relaxation method to solve them to obtain the current optimal phase shift matrix of the reconfigurable smart surface. S43: Based on the current phase shift matrix, solve for the current optimal position of each transmitting movable antenna and each receiving movable antenna; The current solution results include: the current optimal beamforming and current optimal position of each transmitting movable antenna and each receiving movable antenna, as well as the current optimal phase shift matrix of the reconfigurable smart surface.

[0008] Further, S43 includes: Phase 1: Within a given movable one-dimensional or two-dimensional closed area of ​​each transmitting and receiving movable antenna, a candidate point set is formed by discretizing the grid according to a preset step size; the legal position combinations of each transmitting and receiving movable antenna within the candidate point set are traversed, and the objective function utility value is calculated. The legal combination that maximizes the utility is selected as the initial update position. The second stage involves using the initial updated position as the starting point of the iteration. The gradient ascent method is used to iteratively calculate the current updated positions of the transmitting and receiving movable antennas along the gradient direction of the objective function with respect to the antenna position coordinate variables. Then, position projection is performed. After each update of the position coordinates based on gradient ascent, it is checked whether the current updated position violates the boundary constraints of the moving area or the minimum safe distance constraint. If it violates the constraint, a pre-constructed projection function is used to forcibly project the out-of-bounds or non-compliant antenna position coordinates onto the boundary of the valid feasible region closest to the current illegal coordinates. If it does not violate the constraint, the corresponding current updated position is taken as the current optimal position.

[0009] Furthermore, the current updated positions of the transmitting movable antenna and the receiving movable antenna are:

[0010]

[0011] in, and This refers to the current updated position and the initial antenna position of the transmitting antenna. and This refers to the current updated position and the initial antenna position of the receiving antenna. Let be the objective function. To learn step length, and The objective functions are respectively For the One transmitting antenna location The real gradient and objective function For the Each receiving antenna position The real gradient.

[0012] Furthermore, the objective function in S2 is expressed as: ; in, The position vectors of the transmitting antennas are: , The position vector of each receiving antenna is ; The beamforming matrix of the transmitting antenna. The beamforming vector of the receiving antenna. This is the RIS phase shift matrix. To balance the weighting factors of communication and sensing, For the first The communication rate per user, where K is the total number of user terminals. The mutual information of the target radar reflected echo.

[0013] Furthermore, the perceived mutual information is: =log2(1+ ); where the perceived mutual information term is The superscript H indicates the conjugate transpose. For the target RCS coefficient, and These represent the guidance vectors from the base station to the target and from the target to the base station, respectively. For path response parameters, To receive noise power; The self-interference channel matrix is ​​obtained by using the following formula: Calculate the first The transmitting antenna and the first The straight-line distance between the receiving antennas Establish a self-interference channel matrix ; where the self-interference channel matrix element in row j and column i All are modeled as including free space path loss parameters and the straight-line distance. The function, , λ is the wavelength.

[0014] Further, S3 includes: Introducing the first auxiliary variable Used to convert the coupling of communication rate with the external variable of the logarithmic function of perceived mutual information; Introducing a second auxiliary vector and the third auxiliary vector The fractional projection within the signal-to-interference-plus-noise ratio (SIR) is used to obtain the transformed equivalent objective function. .

[0015] According to another aspect of this application, a communication and sensing integrated device for a full-duplex single-base system is provided, comprising: A construction module is used to construct a full-duplex single-base system including: a base station, a reconfigurable smart surface, a communication user terminal, and a target to be sensed; the base station is equipped with multiple movable transmitting antennas and receiving antennas; A module is established to maximize the total reachable rate of all communication user terminals in the full-duplex single-base system and the weighted sum of the mutual information obtained from sensing the sensing target as the objective function; and to establish a non-convex optimization problem for jointly optimizing the phase shift matrix of the reconfigurable smart surface, the beamforming of each of the transmitting movable antennas and the receiving movable antennas, and the positions of each antenna. An introduction module is used to transform the non-convex optimization problem, which contains a logarithmic fractional objective function, into multiple easily tractable subproblems in non-fractional form. The solution module is used to solve multiple easily tractable subproblems based on an alternating optimization architecture to obtain the current solution result; The judgment module is used to determine whether the objective function corresponding to the current solution result has converged. If it has not converged, it returns to the solution module for the next round of solution. If it has converged, it uses the current solution result to configure the full-duplex single-base system so that it can realize the integration of communication and sensing.

[0016] According to another aspect of this application, a communication and sensing integrated system for a full-duplex single-base system is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the communication and sensing integrated method for the full-duplex single-base system.

[0017] According to another aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the communication and sensing integration method for the full-duplex single-base system.

[0018] In summary, compared with the prior art, the above-described technical solutions conceived in this application can achieve the following beneficial effects: (1) This invention overcomes the physical space limitations of traditional fixed antennas and effectively overcomes the link fading problem in complex environments by jointly introducing movable antennas and reconfigurable smart surfaces in a full-duplex single-base system. By dynamically adjusting the positions of the transmitting and receiving movable antennas, this invention not only makes full use of spatial degrees of freedom to significantly improve the diversity performance and target perception accuracy of multi-user communication, but also uses the phase shift control of RIS to construct an artificial line-of-sight link, which greatly improves the total reachability of the system. Under the premise of effectively reducing the high hardware cost of the bistatic system, it flexibly achieves the best dynamic balance between communication and perception performance in combination with different service requirements. In addition, this invention has specially designed an algorithm framework that integrates fractional programming and alternating optimization, which efficiently decouples the high-dimensional non-convex problem involving transmit and receive beamforming, RIS element phase shift, and transmit and receive antenna positions. This algorithm not only greatly improves the overall optimization convergence speed and effectively avoids getting trapped in local optima, but also closely approaches the performance limit with lower computational overhead, showing strong superiority and robustness in the actual deployment of complex integrated sensing scenarios.

[0019] (2) This scheme first solves for the current optimal beamforming of each transmit movable antenna and each receive movable antenna; then it solves for the current optimal phase shift matrix of the reconfigurable smart surface; finally, it uses a two-stage method combining coarse-grained search and projected gradient ascent to solve for the current optimal position of each transmit movable antenna and each receive movable antenna. This design, considering the tight coupling between the variables involved, has the advantage of decoupling the variables by using an alternating optimization method, thus better solving the constructed problem.

[0020] (3) This scheme adopts a two-stage method combining coarse-grained search and projected gradient ascent for solving the problem. This design takes into account that the traditional direct gradient ascent method cannot fully utilize the movable range of the movable antenna. The advantage is that by combining coarse-grained search and projected gradient ascent, the movable range of the transmitting and receiving antenna can be effectively utilized.

[0021] (4) The current updated positions of the transmitting movable antenna and the receiving movable antenna mentioned in this scheme are: and This design takes into account the high non-convexity of the objective function with respect to the continuous position coordinates of the antenna, and the difficulty in directly solving for the closed-form optimal solution. The advantage is that by using the gradient ascent method to perform step-size iterative calculations along the real gradient direction of the objective function with respect to the antenna position coordinates to guide the dynamic update of the coordinates, it achieves efficient and accurate optimization of the best position of the transmitting and receiving antennas within a continuous effective spatial region.

[0022] (5) The objective function of this scheme is expressed as: This design takes into account the different priority requirements for communication and sensing performance in different application scenarios. The advantage is that by dynamically adjusting the weighting factor according to actual business needs, the optimal dynamic balance between communication and sensing performance can be achieved, thereby ensuring that the system can achieve excellent overall performance in various application scenarios.

[0023] (6) This plan utilizes Calculate the first The transmitting antenna and the first The straight-line distance between the receiving antennas Establish a self-interference channel matrix Self-interference channel matrix element in row j and column i All are modeled as including free space path loss parameters and the straight-line distance. The function is given by the base station. This design, considering the significant self-interference caused by the transmitting antenna at the base station to the receiving antenna, has the advantage of enabling precise quantification and evaluation of the self-interference intensity by modeling the self-interference channel as a function of the transmitting and receiving antenna positions under near-field channel conditions.

[0024] (7) This scheme introduces the first auxiliary variable. This is used to convert the coupling of variables outside the logarithmic function of the communication rate and the perceived mutual information; a second auxiliary vector is introduced. and the third auxiliary vector The fractional projection within the signal-to-interference-plus-noise ratio (SIR) is used to obtain the transformed equivalent objective function. This design, considering that the original objective function is a non-convex fraction with logarithmic terms that is difficult to handle, has the advantage of simplifying the original objective function by using fractional programming, transforming it into an equivalent objective function in summative form that is relatively easier to handle. Attached Figure Description

[0025] Figure 1 A flowchart of the communication and sensing integration method for a full-duplex single-base system provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the integrated communication and sensing system based on a movable antenna and a reconfigurable smart surface provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of a transmitter and receiver model based on a movable antenna provided in Embodiment 1 of the present invention; Figure 4 The simulation diagram shows the convergence performance of the FP-AO algorithm provided in Embodiment 1 of the present invention.

[0026] Figure 5 This is a graph showing the relationship between system utility and maximum transmit power at the base station, provided in Embodiment 1 of the present invention. Figure 6 This is a graph showing the relationship between system utility and the number of transmitting antennas provided in Embodiment 1 of the present invention; Figure 7 This is a graph showing the relationship between system utility and the maximum movable range of the transmitting antenna provided in Embodiment 1 of the present invention. Figure 8 The graph shows the relationship between the amount of mutual information sensed and the total communication rate provided in Embodiment 1 of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Furthermore, the technical features involved in the various embodiments described below can be combined with each other as long as they do not conflict with each other.

[0028] First, the terms involved in this invention are explained as follows: A movable antenna is a new type of antenna that can move flexibly within a given area and make full use of spatial degrees of freedom to optimize wireless channel conditions.

[0029] Reconfigurable smart surfaces refer to a new type of wireless communication infrastructure that can adjust the phase of incident signals and establish artificial line-of-sight connections.

[0030] Communication and sensing integration refers to a transformative paradigm that achieves high-quality communication and high-precision sensing by sharing spectrum and hardware resources.

[0031] Self-interference refers to the non-negligible interference phenomenon caused by the transmitting antenna at the base station to the receiving antenna.

[0032] Example 1 This embodiment provides an integrated communication and sensing method for a full-duplex single-base system, such as... Figure 1 As shown, it includes S1-S5.

[0033] S1: Construct a full-duplex single-base system including a base station, a reconfigurable smart surface, a communication user terminal, and a target to be sensed; the base station is equipped with multiple transmit movable antennas and multiple receive movable antennas.

[0034] S2: The objective function is to maximize the total reachable rate of all communication user terminals in the full-duplex single-base system and the weighted sum of the mutual information obtained by sensing the sensing target; establish a non-convex optimization problem for jointly optimizing the phase shift matrix of the reconfigurable smart surface, the beamforming of each of the transmitting movable antennas and the receiving movable antennas, and the position of each antenna.

[0035] S3: Transform the non-convex optimization problem containing a logarithmic fractional objective function into multiple non-fractional, easily tractable subproblems.

[0036] S4: Solve multiple easily tractable subproblems based on the alternating optimization architecture to obtain the current solution result.

[0037] S5: Determine whether the objective function corresponding to the current solution result has converged. If it has not converged, return to S4 for the next round of solution. If it has converged, use the current solution result to configure the full-duplex single-base system so that it can realize the integration of communication and sensing.

[0038] Regarding S1: Construct a full-duplex single-base system, which includes a base station equipped with a transmitting movable antenna and a receiving movable antenna, a reconfigurable smart surface, communication users, and sensing targets; acquire channel state information from the base station to the reconfigurable smart surface, from the base station to the user, from the reconfigurable smart surface to the user, and from the base station to the target, and construct a near-field self-interference channel model based on the position coordinate parameters of the transmitting movable antenna and the receiving movable antenna.

[0039] Specifically, base station equipment is configured A movable transmitting antenna and A movable receiving antenna, with the transmitting and receiving antennas located on two parallel line segments. and Inside, such as Figure 3 As shown. The position vector of the transmitting antenna is represented as... The position vector of the receiving antenna is represented as The base station is A single-antenna user provides communication services while simultaneously sensing a single target and utilizing [the technology / method / equipment]. The RIS of a single reflector unit is used to enhance downlink communication performance. A system diagram is shown below. Figure 2 As shown.

[0040] For the communication channel, the channel matrix from the base station to the RIS is modeled as follows: ,in and These are the array response matrices of the transmitting antenna and the RIS unit, respectively. The location of the RIS unit. This is the path response matrix from the base station to the RIS. (Base station to RIS) The equivalent communication channel for each user is the superposition of a direct link and a RIS-assisted link, that is: ,in, and RIS and base station respectively to the first Channels for individual users. Represents the diagonal phase shift matrix of RIS. For the first Phase shift of each element.

[0041] For the sensing channel, considering the severe path loss of multi-hop reflected signals, a single-path line-of-sight model is adopted. This includes the target RCS coefficient. The round-trip sensing channel matrix is ​​modeled as follows: ,in and These represent the guidance vectors from the base station to the target and from the target to the base station, respectively. These are the path response parameters.

[0042] For near-field self-interference channels, based on the antenna near-field conditions, the first... One transmitting antenna With the One receiving antenna The distance between them is , in, The reference distance between the sending and receiving areas. The offset angle. Self-interference channel matrix. Element modeling as ,in , representing the near-field path loss parameter. For antenna gain, λ is the wavelength.

[0043] Then, transmit signals The receiving end The signal-to-interference-plus-noise ratio (SIR) for an individual user is expressed as: , The signal-to-noise ratio of radar-sensed echoes at the base station is expressed as: . Regarding S2: Taking the total reachable rate of all communication users and the weighted sum of the perceived mutual information of the sensing target as the objective function, under the conditions of satisfying the maximum transmit power constraint of the base station, the constant envelope constraint of the phase shift of the reconfigurable smart surface, the moving area constraint of the movable antenna, and the minimum safe distance constraint to avoid antenna coupling, a non-convex optimization problem is established to jointly optimize transmit and receive beamforming, the phase shift matrix of the reconfigurable smart surface, and the position of the transmit and receive movable antenna.

[0044] Specifically, the utility of the joint optimization objective function is defined as follows: .in To balance the weighting factors of communication and sensing, For user communication rate, To perceive mutual information, we establish the following optimization problem for a nonconvex system:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] in, That is the maximum transmission power. It is the minimum antenna spacing to avoid coupling effects.

[0052] Regarding S3: Using fractional programming (FP), the non-convex optimization problem containing a logarithmic fractional objective function is transformed into an equivalent non-fractional, easily tractable subproblem by introducing auxiliary variables.

[0053] To decouple the logarithmic and fractional forms, an auxiliary variable set is introduced. To address logarithmic coupling, an auxiliary vector is introduced. and Handling the non-convex coupling of SINR. The objective function is transformed into an equivalent form:

[0054] As an optional implementation, S4 includes: S41: obtaining the current optimal beamforming of each transmit movable antenna and each receive movable antenna by solving the KKT conditions; S42: substituting the current optimal beamforming of the transmit movable antenna and each receive movable antenna into multiple easily tractable subproblems, and then solving them using a semidefinite relaxation method to obtain the current optimal phase shift matrix of the reconfigurable smart surface; S43: solving for the current optimal position of each transmit movable antenna and each receive movable antenna based on the current phase shift matrix; wherein, the current solution results include: the current optimal beamforming of each transmit movable antenna and each receive movable antenna, their respective current optimal positions, and the current optimal phase shift matrix of the reconfigurable smart surface.

[0055] Regarding S4: Based on the alternating optimization (AO) architecture, the transformed non-convex subproblem is solved jointly. In each iteration, the following steps are performed sequentially: the optimal transmit and receive beamforming is obtained by solving the KKT conditions; the closed-form optimal auxiliary variables are obtained; the phase shift matrix of the reconfigurable smart surface is optimized using a semidefinite relaxation method; and the positions of the transmit and receive movable antennas are optimized using a two-stage method combining coarse-grained search and projected gradient ascent.

[0056] 1) Solving for transmit / receive beamforming using KKT conditions: Solve for the optimal transmit beam generation matrix given other variables. By using the Lagrange duality method, its 6th solution is obtained. The optimal closed-form solution for the column is: , in, The Lagrange multipliers corresponding to the transmit power constraint are searched in one dimension using the bisection method to satisfy the constraint. Furthermore,

[0057]

[0058] Similarly, the optimal closed-form solution for obtaining the received beamforming vector is:

[0059] in,

[0060]

[0061] 2) Solve for the auxiliary variables that yield the closed-form optimality: By letting the function When the partial derivative with respect to the corresponding variable is zero, update the auxiliary variable in a closed loop: , , And by solving it using a quadratic equation. , in, and .

[0062] 3) Optimize the RIS phase shift matrix using the semidefinite relaxation method (SDR): The phase shift vector of RIS is expressed as a quadratic optimized form. An augmented vector is defined. , Let be the RIS phase shift vector, and let the rank-one matrix be... The objective is transformed into a more manageable problem. Then, by abandoning the non-convex rank-one constraint, a standard semidefinite programming convex optimization problem is obtained, which can be efficiently solved using the CVX solver to find the optimal solution. Then, the optimal phase shift is approximately extracted through eigenvalue decomposition and Gaussian randomization. .

[0063] 4) Optimize antenna position using a two-stage method combining coarse-grained search and projected gradient ascent: Phase 1 (Coarse-grained search): Constructing a system containing multiple intervals... The set of discrete starting points and The function convergence effect of the antenna under various legal initial value combinations is evaluated in order to select a globally better initial point and thus avoid getting trapped in local optima.

[0064] Second stage (projected gradient ascent): Dynamically iteratively update using the gradient ascent method:

[0065]

[0066] in This refers to the learning step size. After the update, a projection function is established to ensure physical constraints. If the boundary is exceeded or the safety distance is violated, the projection is applied to the effective boundary. The projection formulas for the transmitting movable antenna and the receiving movable antenna are shown below:

[0067]

[0068] Regarding S5: Determine whether the objective function has converged. If it has not converged, return to S4 for the next iteration. If the convergence condition is met, output the optimal transmit / receive beamforming, reconfigurable smart surface phase shift matrix, and transmit / receive movable antenna position configuration parameters to complete the coordinated allocation of system communication and sensing resources.

[0069] Specifically, monitor the objective function In the increment of the current iteration step, since the update of each subproblem guarantees the non-decreasing objective function and the system power has an upper bound, the FP-AO algorithm will inevitably converge. After the system converges, the output parameters configure the physical antenna position controller and the RIS phase controller, and then initiate the actual RF signal transmission and reception.

[0070] The invention will be described in detail below with reference to simulation: The simulation parameters are set as follows: Number of reconfigurable smart surface elements Number of users Number of transmitting antennas Number of receiving antennas The movable range of the transmitting antenna is arrive The movable range of the receiving antenna is arrive , and The distance between them is , and The angle between the line connecting the two antennas and the transmitting antenna is... Antenna gain Minimum spacing between adjacent antennas ,wavelength m, maximum transmit power dBm, received noise power dBm, the complex radar cross section coefficient of the target Weighting factor The number of transmit and receive paths for the base station-reconfigurable smart surface link. Base station and the Number of transmit and receive paths for direct links between users Path response matrix satisfy The ratio of average power of line-of-sight path to average power of non-line-of-sight path Path loss , dB represents the reference distance. The path loss factor at point m, the path loss index of the base station-user link, the base station-reconfigurable smart reflector link, and the reconfigurable smart reflector-user link. They are 3.5, 2.5, and 2.5 respectively.

[0071] like Figure 4 As shown, the convergence performance of the proposed FP-AO algorithm for solving the integrated communication and sensing optimization problem based on movable antennas and reconfigurable smart surfaces is compared with other methods. Specifically, compared with the Genetic Algorithm (GA), the proposed FP-AO algorithm converges faster and approaches the performance ceiling achievable by GA. Specifically, after convergence, the ISAC performance, i.e., utility, of the proposed FP-AO method is only 0.97% lower than that of GA, a difference that is within an acceptable range. Furthermore, compared with other benchmark schemes, including Random Phase Shift (RPS), Direct Gradient Ascent (DGA), and Fixed-Position Antenna (FPA), the proposed FP-AO algorithm... The AO algorithm achieves better ISAC performance, thus verifying its superiority.

[0072] Figure 5The relationship between the ISAC performance of different methods and different maximum transmit powers at the base station is described. The results show that the ISAC performance of different methods improves with increasing maximum transmit power at the base station, because higher transmit power at the base station enhances the strength of the transmitted signal. Specifically, when the maximum transmit power is 40 dBm, the proposed FP... Compared to RPS, DGA, and FPA methods, the AO algorithm achieved performance gains of 4.2%, 7.4%, and 11.3%, respectively.

[0073] Figure 6 The relationship between the ISAC performance of different methods and the number of transmit antennas is described. It can be seen that the utility of all methods increases with the increase of the number of transmit antennas. This is because increasing the number of transmit antennas can achieve higher beamforming gain and spatial diversity gain. For example, when the number of transmit antennas is 10, the proposed FP... The AO algorithm improves the utility of RPS, DGA and FPA methods by 4.5%, 8.1% and 12.5% ​​respectively.

[0074] Figure 7 The relationship between the ISAC performance of different methods and the maximum movable range of the transmit antenna is described. The utility of the proposed FP-AO algorithm increases with the increase of the maximum movable range of the transmit antenna. This is because movable antennas can fully utilize the degrees of freedom provided by multiple antennas, thereby achieving higher spatial diversity and multiplexing performance. Furthermore, optimizing the position of the movable antenna can effectively suppress self-interference.

[0075] Figure 8 This paper describes the trade-off between the total communication rate and perceived mutual information for different methods. For all methods, adjusting the weighting factor from 0.1 to 0.9 increases the total communication rate while decreasing the perceived mutual information. This is because, as the weighting factor increases, the total communication rate has a greater weight in the overall utility, while the weight of perceived mutual information decreases.

[0076] The simulations effectively demonstrate that movable antennas can fully utilize the spatial degrees of freedom provided by multiple antennas. By optimizing the position of the movable antennas, better spatial diversity and multiplexing performance can be achieved, and self-interference caused by the transmitting antenna to the receiving antenna can be effectively suppressed. The simulations also effectively demonstrate that reconfigurable smart surfaces can achieve advantageous reconfiguration of the wireless environment. By adjusting the phase of the incident signal and establishing an artificial line-of-sight link, higher communication quality can be obtained. The effectiveness and superiority of the proposed FP-AO algorithm are also proven; it can quickly approach the upper performance bound and achieve better ISAC performance than other benchmark methods. Furthermore, the simulations effectively illustrate the inherent trade-off between communication and sensing performance. In practical implementation, the weighting factor values ​​need to be adjusted according to the priority of communication or sensing.

[0077] Example 2 This embodiment provides an integrated communication and sensing device for a full-duplex single-base system, including: A construction module is used to construct a full-duplex single-base system including: a base station, a reconfigurable smart surface, a communication user terminal, and a target to be sensed; the base station is equipped with multiple movable transmitting antennas and receiving antennas; A module is established to maximize the total reachable rate of all communication user terminals in the full-duplex single-base system and the weighted sum of the mutual information obtained from sensing the sensing target as the objective function; and to establish a non-convex optimization problem for jointly optimizing the phase shift matrix of the reconfigurable smart surface, the beamforming of each of the transmitting movable antennas and the receiving movable antennas, and the positions of each antenna. An introduction module is used to transform the non-convex optimization problem, which contains a logarithmic fractional objective function, into multiple easily tractable subproblems in non-fractional form. The solution module is used to solve multiple easily tractable subproblems based on an alternating optimization architecture to obtain the current solution result; The judgment module is used to determine whether the objective function corresponding to the current solution result has converged. If it has not converged, it returns to the solution module for the next round of solution. If it has converged, it uses the current solution result to configure the full-duplex single-base system so that it can realize the integration of communication and sensing.

[0078] Example 3 This embodiment provides an integrated communication and sensing system for a full-duplex single-base system, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the integrated communication and sensing method for the full-duplex single-base system.

[0079] This system can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor implements various system functions by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0080] Example 4 This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the communication and sensing integration method for the full-duplex single-base system.

[0081] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0082] Example 5 This application provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this application.

[0083] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "as in another example" in this application are intended to illustrate the application and are not intended to limit the application.

[0084] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for integrating communication and sensing in a full-duplex single-base system, characterized in that, include: S1: Construct a full-duplex single-base system comprising: a base station, a reconfigurable smart surface, a communication user terminal, and a target to be sensed; the base station is equipped with multiple movable transmitting antennas and multiple movable receiving antennas; S2: The objective function is to maximize the total reachable rate of all communication user terminals in the full-duplex single-base system and the weighted sum of the mutual information obtained by sensing the sensing target; establish a non-convex optimization problem for jointly optimizing the phase shift matrix of the reconfigurable smart surface, the beamforming of each of the transmitting movable antennas and the receiving movable antennas, and the position of each antenna. S3: Transform the non-convex optimization problem containing a logarithmic fractional objective function into multiple easily tractable subproblems of non-fractional form; S4: Solve multiple easily tractable subproblems based on the alternating optimization architecture to obtain the current solution result; S5: Determine whether the objective function corresponding to the current solution result has converged. If it has not converged, return to S4 for the next round of solution. If it has converged, use the current solution result to configure the full-duplex single-base system so that it can realize the integration of communication and sensing.

2. The integrated communication and sensing method for a full-duplex single-base system as described in claim 1, characterized in that, S4 includes: S41: Use the KKT conditions to obtain the current optimal beamforming for each transmit movable antenna and each receive movable antenna; S42: Substitute the current optimal beamforming of the transmitting movable antenna and each receiving movable antenna into multiple easily processed subproblems, and then use the semidefinite relaxation method to solve them to obtain the current optimal phase shift matrix of the reconfigurable smart surface. S43: Based on the current phase shift matrix, solve for the current optimal position of each transmitting movable antenna and each receiving movable antenna; The current solution results include: the current optimal beamforming and current optimal position of each transmitting movable antenna and each receiving movable antenna, as well as the current optimal phase shift matrix of the reconfigurable smart surface.

3. The communication and sensing integration method for a full-duplex single-base system as described in claim 2, characterized in that, S43 includes: Phase 1: Within a given movable one-dimensional or two-dimensional closed area of ​​each transmitting and receiving movable antenna, a candidate point set is formed by discretizing the grid according to a preset step size; the legal position combinations of each transmitting and receiving movable antenna within the candidate point set are traversed, and the objective function utility value is calculated. The legal combination that maximizes the utility is selected as the initial update position. The second stage involves using the initial updated position as the starting point of the iteration. The gradient ascent method is used to iteratively calculate the current updated positions of the transmitting and receiving movable antennas along the gradient direction of the objective function with respect to the antenna position coordinate variables. Then, position projection is performed. After each update of the position coordinates based on gradient ascent, it is checked whether the current updated position violates the boundary constraints of the moving area or the minimum safe distance constraint. If it violates the constraint, a pre-constructed projection function is used to forcibly project the out-of-bounds or non-compliant antenna position coordinates onto the boundary of the valid feasible region closest to the current illegal coordinates. If it does not violate the constraint, the corresponding current updated position is taken as the current optimal position.

4. The communication and sensing integration method for a full-duplex single-base system as described in claim 3, characterized in that, The current updated positions of the transmitting movable antenna and the receiving movable antenna are: in, and This refers to the current updated position and the initial antenna position of the transmitting antenna. and This refers to the current updated position and the initial antenna position of the receiving antenna. Let be the objective function. To learn step length, and The objective functions are respectively For the One transmitting antenna location The real gradient and objective function For the Each receiving antenna position The real gradient.

5. The communication and sensing integration method for a full-duplex single-base system as described in claim 1, characterized in that, The objective function in S2 is expressed as: ; in, The position vectors of the transmitting antennas are: , The position vector of each receiving antenna is ; The beamforming matrix of the transmitting antenna. The beamforming vector of the receiving antenna. This is the RIS phase shift matrix. To balance the weighting factors of communication and sensing, For the first The communication rate per user, where K is the total number of user terminals. The mutual information of the target radar reflected echo.

6. The integrated communication and sensing method for a full-duplex single-base system as described in claim 5, characterized in that, The amount of mutual information sensed is: =log2(1+ ); where the perceived mutual information term is The superscript H indicates the conjugate transpose. For the target RCS coefficient, and These represent the guidance vectors from the base station to the target and from the target to the base station, respectively. For path response parameters, To receive noise power; The self-interference channel matrix is ​​obtained by using the following formula: Calculate the first The transmitting antenna and the first The straight-line distance between the receiving antennas Establish a self-interference channel matrix Self-interference channel matrix element in row j and column i All are modeled as including free space path loss parameters and the straight-line distance. The function, , λ is the wavelength.

7. The integrated communication and sensing method for a full-duplex single-base system as described in claim 5, characterized in that, S3 includes: Introducing the first auxiliary variable Used to convert the coupling of communication rate with the external variable of the logarithmic function of perceived mutual information; Introducing a second auxiliary vector and the third auxiliary vector The fractional projection within the signal-to-interference-plus-noise ratio (SIR) is used to obtain the transformed equivalent objective function. .

8. A communication and sensing integrated device for a full-duplex single-base system, characterized in that, include: A construction module is used to construct a full-duplex single-base system including: a base station, a reconfigurable smart surface, a communication user terminal, and a target to be sensed; the base station is equipped with multiple movable transmitting antennas and receiving antennas; A module is established to maximize the total reachable rate of all communication user terminals in the full-duplex single-base system and the weighted sum of the mutual information obtained from sensing the sensing target as the objective function; and to establish a non-convex optimization problem for jointly optimizing the phase shift matrix of the reconfigurable smart surface, the beamforming of each of the transmitting movable antennas and the receiving movable antennas, and the positions of each antenna. An introduction module is used to transform the non-convex optimization problem, which contains a logarithmic fractional objective function, into multiple easily tractable subproblems in non-fractional form. The solution module is used to solve multiple easily tractable subproblems based on an alternating optimization architecture to obtain the current solution result; The judgment module is used to determine whether the objective function corresponding to the current solution result has converged. If it has not converged, it returns to the solution module for the next round of solution. If it has converged, it uses the current solution result to configure the full-duplex single-base system so that it can realize the integration of communication and sensing.

9. A communication and sensing integrated system for a full-duplex single-base system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the communication and sensing integration method for the full-duplex single-base system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the communication and sensing integration method for the full-duplex single-base system as described in any one of claims 1 to 7.

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