A method, device and equipment for determining a transmission signal integrated with communication perception

Abstract

The present invention discloses a method, device and equipment for determining a communication-perception integrated transmission signal, and relates to the field of communication-perception integrated technology. The present invention first obtains channel state information from a synaesthesia integrated base station to different users and a steering vector to a detection target, and then constructs a communication model and a radar model based on this information. Thus, the communication-perception integrated transmission signal is used as an optimization variable, the lighting power requirement and the preset total power requirement of the detection target are used as constraints, and the maximization of the minimum communication scaling factor between users is used as the optimization goal. The synaesthesia integrated transmission signal optimization model is constructed and solved. When solving the problem, the optimization model is converted into multiple convex subproblems and solved separately. The optimal solution of each convex subproblem is used as the local optimal solution of the synaesthesia integrated transmission signal optimization model. The global optimal solution is determined from the multiple local optimal solutions to obtain the preferred communication-perception integrated transmission signal. The structure and solution process of the optimization model are simplified, and the computational complexity is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of communication perception integration, and in particular to a communication perception integration transmission signal determination method, device and equipment. Background Art

[0002] With the rapid development of wireless services, spectrum resources are becoming increasingly scarce. Radar bands contain a significant portion of available spectrum, potentially shared with various communication systems. Spectrum sharing between radar and communication systems aligns with the ongoing convergence of Integrated Awareness and Communication (ISAC), sparking extensive research on the coexistence, collaboration, and joint design of these two capabilities.

[0003] In recent years, many researchers have focused on precoding design for Multiple-Input Multiple-Output (MIMO)-ISAC systems, optimizing the transmit precoding matrix based on different radar and communication metrics. MIMO architectures are widely used in ISAC systems, providing waveform diversity for radar target detection and precoding gain and spatial multiplexing for multi-user communications. In MIMO-ISAC systems, there are inherent conflicts between the requirements of radar and communication components in terms of antenna placement, power amplifier operating area, and signal format. Typical radar metrics include the radar receiver's signal-to-noise ratio (SNR), beam direction mean squared error (MSE), Cramer-Rao bound, and the similarity between the designed beamformer and that of a reference radar system. Meanwhile, widely used communication metrics include achievable rate, signal-to-interference plus noise ratio (SINR) of the communicating users, and multi-user interference (MUI). Given system resource constraints, waveform design for ISAC systems must balance radar and communication performance. Therefore, the transmit waveform should be carefully designed to balance the requirements of these two functions to achieve optimal system performance.

[0004] The above-mentioned transmit waveform designs are all traditional block-level precoding (BLP), that is, the same precoding matrix is ​​used within a block, and the precoding matrix is ​​independent of the data symbols. However, since traditional BLP designs usually use second-order statistics (such as SINR and MSE) as performance indicators to optimize the average transmit beam pattern, radar sensing performance can only be guaranteed when the number of transmitted symbols is large enough, rather than on an instantaneous basis. Therefore, if the number of samples collected is limited, the instantaneous transmit beam patterns in different time slots may be significantly distorted, resulting in a serious degradation in the performance of target detection and parameter estimation. In view of the shortcomings of BLP, the ISAC system further adopts symbol-level precoding (SLP) technology.

[0005] Unlike traditional BLP, SLP is a nonlinear and symbol-dependent approach that optimizes each instantaneous transmission vector based on the specific symbol being transmitted, rather than simply eliminating multi-user interference (MUI) at the symbol level. From a communications perspective, SLP can leverage symbol information to convert detrimental MUI into constructive interference (CI), thereby reducing the symbol error rate (SER) and achieving more reliable multi-user communications. From a radar perspective, the instantaneous transmit beam pattern for each time slot can be carefully designed, ensuring a well-formed beam pattern with a limited number of waveform samples.

[0006] In the prior art, there is a CI-SLP scheme based on Penalty Dual Decomposition (PDD), Majorization-Minimization (MM), and Block Coordinate Descent (BCD), known as the PDD-MM-BCD scheme. The transmit vector in the PDD-MM-BCD scheme is optimized to minimize the squared error between the obtained beam pattern and the desired beam pattern, subject to both communication CI constraints and power constraints. This work demonstrates that SLP offers significant advantages over traditional BLP in improving communication performance. Furthermore, there is a scheme for implementing MIMO-ISAC using Space-Time Adaptive Processing (STAP) and CI-SLP. This scheme demonstrates good performance in the presence of strong signal-correlated clutter. There is also a low-frequency sidelobe waveform design for a MIMO-OFDM-ISAC system based on SLP, and simulation results show improved radar ranging performance.

[0007] However, all of the above studies use radar performance as the objective function of the optimization problem. The obtained objective function is non-convex, making it difficult to solve the optimization problem directly. Using iterative methods to provide a feasible solution requires a large number of iterative calculations, which has high computational complexity. Summary of the Invention

[0008] Based on this, it is necessary to provide a method, device and equipment for determining the transmission signal of integrated communication perception to address the above technical problems.

[0009] The present invention adopts the following technical solutions:

[0010] The present invention provides a method for determining a communication-sensing integrated transmission signal, wherein a communication-sensing integrated base station performs the following steps, including:

[0011] Obtain channel state information from the integrated telepathy base station to different users and the guidance vector to the detection target;

[0012] A communication model is constructed based on the channel state information from the synergy-integrated base station to different users, and a radar model is constructed based on the guidance vector from the synergy-integrated base station to the detection target.

[0013] Based on the communication model and radar model, a communication-sensing integrated transmission signal optimization model is constructed, with the communication-sensing integrated transmission signal as the optimization variable, the illumination power requirement of the detection target and the preset total power requirement as the constraints, and maximizing the minimum communication scaling factor between users as the optimization goal.

[0014] The synaesthesia integrated transmission signal optimization model is converted into multiple convex subproblems and solved separately. The optimal solution of each convex subproblem is used as the local optimal solution of the synaesthesia integrated transmission signal optimization model. The global optimal solution is determined from the multiple local optimal solutions, and the global optimal solution is used as the preferred communication perception integrated transmission signal.

[0015] Optionally, the constructing of a communication model according to channel state information from the telepathic integrated base station to different users specifically includes:

[0016] The following formula is used to determine the signals received by each user in different time slots when the telepathic integrated base station transmits and receives signals based on the time division protocol:

[0017]

[0018] Based on the symbol scaling metric, the noise-free signal received by the user is decomposed along the two decision boundaries of QPSK modulation by the following formula:

[0019]

[0020] The set vector of each user's scaling factor and the communication-sensing integrated transmission signal are constructed by the following formula:

[0021]

[0022]

[0023] α E =Mx E ,

[0024]

[0025]

[0026] Among them, y k [l] is the signal received by the kth user in the lth time slot, is the channel between ISAC-BS and the kth user, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, is the additive noise of the kth user in the lth time slot, is the noise power, is the noise-free signal received by the k-th user in the l-th time slot, and is the scaling factor of the kth user, indicating the influence of constructive components between users, α E is the set vector of scaling factors for each user, x E is the communication and perception integrated transmission signal, M is the coefficient matrix, p k and q k is the coefficient.

[0027] Optionally, the step of constructing a radar model based on a steering vector from the telepathic integrated base station to the detection target specifically includes:

[0028] The event signal at which the communication-sensing integrated signal reaches the detection target is determined by the following formula:

[0029] r[l]=βa(θ) H x[l],a(θ)=[1,e jsinθ ,…,e j(N-1)sinθ ] T ;

[0030] The illumination power for detecting the target is determined by the following formula:

[0031]

[0032] Where a(θ) is the guidance vector of the communication-sensing integrated base station toward the detection target, θ is the corresponding azimuth, β is the effective channel propagation coefficient, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, and xE Transmitting signals for integrated communication and perception, a E and b E is the expansion vector of a(θ).

[0033] Optionally, the constraints of the illumination power requirement and the preset total power requirement of the detection target specifically include:

[0034] The lighting power requirement of the detection target is expressed as follows:

[0035] The preset total power requirement is expressed as:

[0036] Where β is the effective channel propagation coefficient, x E Transmitting signals for integrated communication and perception, a E and b E is the extended vector of the guidance vector of the synaesthesia integrated base station toward the detection target, p r is the preset minimum requirement for the detection target lighting power, and p0 represents the communication perception integrated transmission signal power budget for each time slot.

[0037] Optionally, the constructing of a synaesthesia integrated transmission signal optimization model with maximizing the minimum communication scaling factor between users as the optimization goal specifically includes:

[0038] The following formula is used to construct the synaesthesia integrated transmission signal optimization model with maximizing the minimum communication scaling factor between users as the optimization goal:

[0039]

[0040] Among them, x E is the communication perception integrated transmission signal as the optimization variable, α E is the set vector of scaling factors for each user, α i is α E The i-th element of , β is the effective channel propagation coefficient, a E and b E is the extended vector of the guidance vector of the synaesthesia integrated base station toward the detection target, p r is the preset minimum requirement for the detection target lighting power, and p0 represents the communication perception integrated transmission signal power budget for each time slot.

[0041] Optionally, converting the synaesthesia integrated transmission signal optimization model into multiple convex subproblems and solving them separately specifically includes:

[0042] The lighting power requirement constraint for detecting targets is expressed as follows: According to the lighting power requirements of the detection target, the corresponding feasible domain is constrained. The tangent of the feasible domain boundary circle is used as an approximation of the feasible domain boundary circle. The non-convex feasible domain is approximated as a set of multiple convex feasible domains, so as to transform the synaesthesia integrated transmission signal optimization model into multiple convex subproblems.

[0043] Among them, p r is the preset minimum requirement for the detection target illumination power, β is the effective channel propagation coefficient, (r1, r2) correspond to the real part and imaginary part of the event signal respectively, x E is the communication perception integrated transmission signal as the optimization variable, α E is the set vector of scaling factors for each user, a E and b E It is the extended vector of the guidance vector of the synesthesia integrated base station toward the detection target.

[0044] The present invention provides a communication-sensing integrated transmission signal determination device, comprising:

[0045] An acquisition module is used to obtain channel state information from the synesthesia integrated base station to different users and a steering vector to the detection target;

[0046] The communication perception construction module is used to build a communication model based on the channel state information from the integrated synergy base station to different users, and to build a radar model based on the guidance vector from the integrated synergy base station to the detection target;

[0047] An optimization model construction module is used to construct an optimization model for the communication-sensing integrated transmission signal based on the communication model and radar model, with the communication-sensing integrated transmission signal as the optimization variable, the illumination power requirement of the detection target and the preset total power requirement as constraints, and maximizing the minimum communication scaling factor between users as the optimization goal;

[0048] The solution module is used to convert the synaesthesia integrated transmission signal optimization model into multiple convex subproblems and solve them separately, using the optimal solution of each convex subproblem as the local optimal solution of the synaesthesia integrated transmission signal optimization model, determining the global optimal solution from the multiple local optimal solutions, and using the global optimal solution as the preferred communication perception integrated transmission signal.

[0049] The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned communication perception integrated transmission signal determination method is implemented.

[0050] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned communication perception integrated transmission signal determination method when executing the program.

[0051] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0052] First, channel state information from the integrated communication and perception base station to different users and the steering vector to the detection target are obtained. Based on this information, a communication model and a radar model are then constructed. The integrated communication and perception transmission signal is used as the optimization variable, the illumination power requirement and the preset total power requirement of the detection target are used as constraints, and the optimization goal is to maximize the minimum communication scaling factor between users. The integrated communication and perception transmission signal optimization model is constructed and solved. During the solution, the optimization model can be converted into multiple convex subproblems and solved separately. The optimal solution of each convex subproblem is used as the local optimal solution of the integrated communication and perception transmission signal optimization model. The global optimal solution is determined from the multiple local optimal solutions to obtain the preferred integrated communication and perception transmission signal. Based on the communication and perception processes of the integrated communication and perception base station, the present invention reconstructs the structure of the optimization model and simplifies the solution process, thereby achieving a higher SER performance gain, reducing computational complexity, and shortening the execution time of the integrated communication and perception transmission signal determination process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0054] Figure 1 A flow chart of a method for determining a transmission signal with integrated communication and perception provided by the present invention;

[0055] Figure 2 A schematic diagram of an ISAC system scenario provided by the present invention;

[0056] Figure 3 A geometric diagram of a CI measurement method based on symbol scaling for a communication model provided by the present invention;

[0057] Figure 4 A geometric diagram of the transformation and approximate results of a non-convex constraint provided by the present invention;

[0058] Figure 5 The SER performance and transmit beam pattern simulation results of different solutions provided by the present invention are shown in the figure;

[0059] Figure 6 Execution time simulation results of different solutions provided by the present invention;

[0060] Figure 7 A schematic diagram of a communication-sensing integrated transmission signal determination device provided by the present invention;

[0061] Figure 8 A schematic diagram of a computer device for implementing a method for determining a transmission signal integrating communication and perception provided by the present invention. DETAILED DESCRIPTION

[0062] The transmit vector of the current PDD-MM-BCD scheme is optimized to minimize the squared error between the obtained beam pattern and the desired beam pattern, subject to both the communication CI constraint and the power constraint. This work confirms that SLP has significant advantages over traditional BLP in improving communication performance, but the complexity of the optimization problem is unacceptable due to the non-convexity of the objective function.

[0063] Alternatively, there are also schemes for implementing MIMO-ISAC using spatiotemporal adaptive processing (STAP) and CI-SLP. This scheme demonstrates good performance in the presence of strong signal-correlated clutter. Simulation results show improved radar ranging performance from existing SLP-based low-frequency sidelobe waveform designs for MIMO-OFDM-ISAC systems. While these studies all use radar performance as the objective function for the optimization problem, the non-convex nature of the objective function makes it difficult to solve directly. Furthermore, the numerous iterations required to achieve a solution using iterative methods make these schemes computationally complex and unacceptable.

[0064] Furthermore, under stringent communication requirements, the probability of these solutions becoming unfeasible increases. In other words, the communication performance of these CI-SLPISAC solutions is limited. To address these shortcomings, the present invention employs a symbol scaling metric in the CI-SLPISAC system, using communication performance as the objective function of the optimization problem and radar performance as a necessary constraint to construct and solve the optimization problem.

[0065] Typically, in a MIMO-ISAC system, a multi-antenna ISAC-BS can simultaneously transmit radar detection waveforms to a target and communication symbols to downlink users. In one or more embodiments of the present invention, the ISAC-BS employs a uniform linear array to serve multiple single-antenna users while simultaneously detecting targets of interest. Using a time-division protocol, the same antenna array is used for signal transmission and reception in different time slots.

[0066] Based on this, the present invention designs a transmitted signal vector to maximize the minimum communication scaling factor within the target lighting power requirement and the total power budget constraints, thereby improving overall communication performance within the considered time period. This constructs the CI-SLP optimization problem in the ISAC system. Two sets of tangents are used to approximate the non-convex feasible region into two approximate feasible regions, thereby transforming the non-convex CI-SLP optimization problem into a convex problem for solution.

[0067] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0068] Figure 1 This is a flow chart of a method for determining a transmission signal of integrated communication and perception in the present invention. The integrated communication and perception base station performs the following steps, specifically including the following steps:

[0069] S101: Acquire channel state information from the intersensory integration base station to different users and a steering vector to a detection target.

[0070] Generally, for a pre-built MIMO-ISAC system, the multi-antenna ISAC-BS can simultaneously send radar detection waveforms to the detection target and send communication symbols to the downlink user. Figure 2 As shown, Figure 2 This is a schematic diagram of an ISAC system scenario in the present invention.

[0071] Assume that ISAC-BS uses a uniform linear array composed of N antennas, K u Single antenna users provide services and detect targets of interest at the same time. Generally, K u ≤ N. Through the time division protocol, the same antenna array is used for signal transmission and reception in different time slots.

[0072] Further, let the transmit signal X=[x[1],x[2],…,x[L]] matrix be used as the ISAC waveform for radar and communication, where x[l]=[x1[l],x2[l],…,x N [l]] T is the ISAC waveform corresponding to the lth time slot. In addition, K of the lth time slot u The data symbol vector of a user is expressed as The values ​​are taken from the normalized PSK constellation points.

[0073] Based on this, the server of the integrated telepathy base station can obtain the channel state information from the integrated telepathy base station to different users and the steering vector to the detection target to build a communication model and a radar model to optimize the communication and perception integrated transmission signal. Among them, the steering vector from the integrated telepathy base station to the detection target refers to the steering vector of the radar when the integrated telepathy base station is used as a detection radar. The radar steering vector is a very important concept in radar signal processing. It describes the response characteristics of the radar beam in a specific direction. The steering vector is used to represent the direction in array signal processing and is the basis of spatial filtering and beamforming. The elements of the steering vector represent the phase difference caused by the direction from each radar array element to the detection target. When beamforming is performed, the steering vector matches the desired beam direction. By weighted summing the signals of each array element, the radar beam is enhanced in the desired direction and suppressed in other directions.

[0074] The server mentioned in the present invention can be a server installed in the synaesthesia integrated base station, or a device such as a desktop computer or a notebook computer that can execute the solution of the present invention. For the sake of convenience, the following description only takes the server as the execution subject.

[0075] S102: Building a communication model based on the channel state information from the integrated synergy base station to different users, and building a radar model based on the steering vector from the integrated synergy base station to the detection target.

[0076] By obtaining the channel state information from the synaesthesia base station to different users, the server can determine the signals received by each user in different time slots when the synaesthesia base station transmits and receives signals based on the time division protocol using the following formula:

[0077]

[0078] Where y k [l] is the signal received by the kth user in the lth time slot, is the channel between ISAC-BS and the kth user, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, is the additive noise of the kth user in the lth time slot, is the noise power.

[0079] Figure 3 The figure is a geometric diagram of a CI measurement method based on symbol scaling for a communication model in the present invention. Figure 3 It can be seen that the CI measurement method based on symbol scaling is described for the quarter case of the QPSK constellation. For the convenience of description, Figure 3 The time slot subscript [l] is removed in the example. In one or more embodiments of the present invention, it is assumed that is the nominal constellation point of user k in the lth time slot:

[0080]

[0081] The noise-free received signal with interference can be expressed as:

[0082]

[0083] Can be considered as the sum of CI from other user flows.

[0084] Based on the symbol scaling metric, two decision boundaries along the QPSK modulation Decompose it and get and

[0085]

[0086] Following a similar process, the noise-free signal received It can also be decomposed along two decision boundaries as:

[0087]

[0088] in, and is the non-negative scaling factor of the kth user. It can be seen that and represents the influence of constructive components among users, or Larger values ​​of means that the symbol can be pushed away from one of its decision boundaries.

[0089] Define the set vector α of all user scaling factors E , and communication perception integrated transmission signal x E :

[0090]

[0091] The set vector α of each user's scaling factor E Can be obtained by x E Expressed as:

[0092] α E =Mx E

[0093] M is a coefficient matrix, which is calculated as follows:

[0094]

[0095] Where p k and q k is the coefficient.

[0096] For the radar model, the event signal where the communication-perception integrated signal reaches the detection target can be determined by the following formula:

[0097] r[l]=βa(θ) H x[l],a(θ)=[1,e jsinθ ,…,e j(N-1)sinθ ] T

[0098] Where a(θ) is the guidance vector of the telepathic base station toward the detection target, θ is the corresponding azimuth angle, and β is the effective channel propagation coefficient.

[0099] Therefore, the illumination power of the detection target can be determined by the following formula:

[0100]

[0101] Among them, a E and b E is defined as:

[0102]

[0103] S103: Based on the communication model and the radar model, a communication-perception integrated transmission signal optimization model is constructed with the communication-perception integrated transmission signal as the optimization variable, the illumination power requirement of the detection target and the preset total power requirement as the constraints, and maximizing the minimum communication scaling factor between users as the optimization goal.

[0104] Based on the communication model and radar model constructed above, it can be seen from radar detection that the radar illumination power should not be less than the preset minimum value to ensure the detection probability. Therefore, the illumination power requirement of the detection target is expressed as:

[0105]

[0106] Among them, p r >0 is the preset minimum target illumination power requirement.

[0107] The preset total power requirement is expressed as: p0 represents the transmit signal power budget for each time slot.

[0108] Then the SLP optimization problem of ISAC can be expressed as:

[0109]

[0110] Among them, x E The signal is transmitted for the communication-aware integration optimization variable. Maximizing the minimum scaling factor ensures that all users' noise-free received signals fall within the CI region, thereby reducing the system's communication SER. The second constraint in the optimization problem is the radar constraint, which ensures that the illumination power in the target direction is greater than a given threshold. The final constraint is the total power constraint.

[0111] S104: Convert the synaesthesia integrated transmission signal optimization model into multiple convex subproblems and solve them separately, use the optimal solution of each convex subproblem as the local optimal solution of the synaesthesia integrated transmission signal optimization model, determine the global optimal solution from the multiple local optimal solutions, and use the global optimal solution as the preferred communication perception integrated transmission signal.

[0112] For the synaesthesia integrated emission signal optimization model P0 constructed above, P0 is non-convex because the target lighting power constraint is non-convex. Therefore, when solving the problem, the target lighting power constraint can be processed first.

[0113] In one or more embodiments of the present invention, the definition Then the target lighting power constraint can be expressed as:

[0114]

[0115] Among them, (r1, r2) correspond to the real part and imaginary part of the event signal respectively.

[0116] Then, the server can constrain the corresponding feasible domain according to the lighting power requirements of the detection target, use the tangent of the feasible domain boundary circle as an approximation of the feasible domain boundary circle, and approximate the non-convex feasible domain as a set of multiple convex feasible domains, so as to transform the synaesthesia integrated transmission signal optimization model into multiple convex sub-problems.

[0117] Figure 4 This is a geometric diagram of the transformation and approximation results of non-convex constraints in the present invention. Figure 4 As can be seen from the left side, the above constraints indicate that the feasible solutions of r1 and r2 fall outside the circle shown in the figure, which is a non-convex constraint. Figure 4 As can be seen in the middle and right parts, several tangents of a circle can be used as approximations of the circle, so that the non-convex feasible domain can be approximated as a set of several convex feasible domains. In one or more embodiments of the present invention, two groups of approximate feasible domains can be selected, namely S1 and S2. The first group has four tangents, and its approximate feasible domain includes four convex sets, as shown below:

[0118] S1=T1∪T2,

[0119]

[0120] The second group has eight tangent lines, and its approximate feasible region consists of eight convex sets, as shown below:

[0121] S2=S1∪T3∪T4,

[0122]

[0123] Both feasible regions are unions of simple convex sets. Therefore, solving the original optimization problem P0 can be easily transformed into solving several convex optimization problems. By choosing different groups, different trade-offs between ISAC performance and computational complexity can be achieved. Of course, the number of tangent lines can be further increased to achieve a more accurate approximation, but at the cost of increased computational complexity.

[0124] Specifically, the convex optimization problems corresponding to these different convex sets can be written in a unified form:

[0125]

[0126] Where c and d take different values ​​according to different convex sets, and the values ​​are shown in Table 1:

[0127] Table 1 Value correspondence table

[0128]

[0129] Furthermore, the problem P1 can be equivalently transformed into the following minimization problem P2 by introducing the auxiliary variable t:

[0130]

[0131] P2 is a standard convex optimization problem that can be solved directly using optimization tools such as CVX. In addition, the Hooke-Jeeves pattern search algorithm can also be used to efficiently solve P2.

[0132] Because the convex optimization problem of P3 satisfies the Slater condition, P3 can be optimally solved by solving the dual problem of P3.

[0133] The Lagrangian dual function of P3 can be expressed as:

[0134]

[0135] Where λ, μ, and ν are the dual variables related to the inequality and equality constraints in P3, respectively. Therefore, the KKT condition for the optimality of problem P3 can be expressed as:

[0136]

[0137] Assuming μ = 0, λ must satisfy the first and second conditions above, namely:

[0138]

[0139] Because K u ≤N, we can get:

[0140]

[0141] That is to say, λ has no solution, so we can get μ>0. This means that when P3 optimality is achieved, the total power constraint is equal, that is:

[0142]

[0143] Considering μ>0, based on the second condition in the KKT condition, the optimal transmitted signal vector x can be obtained E The closed form of the Lagrange multiplier is:

[0144]

[0145] Substituting the above equation into the radar equality constraint and the total power equality constraint, we can get the expression of μ and ν with respect to λ after sorting:

[0146]

[0147] Next, the first KKT condition, radar equality constraint, total power equality constraint, and Substitute L(x E ,t,λ,μ,ν), and then substituting the above expressions of μ and ν with respect to λ, the objective function of the dual problem can be transformed into a function with respect to λ, expressed as F(λ). Therefore, the dual problem of P3 can be expressed as problem P4 as follows:

[0148]

[0149] Although the objective function of this dual problem is complex, its constraints are simple. The Hooke-Jeeves pattern search method can be used to efficiently solve P3 in an iterative manner to obtain the optimal solution. The Hooke-Jeeves pattern search method consists of two steps: the first step is to detect the most favorable descent direction by moving the base point along all dimensions in sequence with a certain step size; the second step is to update the variables along this direction with a certain step size until the performance cannot be improved, and then reduce the step size to half of the value in the next iteration, and repeat this process until the convergence criterion is met. Due to the additional constraint 1 in the dual problem T When λ=1, the traditional Hooke-Jeeves pattern search algorithm cannot be used directly. Therefore, the present invention proposes an improved Hooke-Jeeves pattern search algorithm, which adds a variable normalization step. The algorithm includes the following steps:

[0150] Set the initial feasible point d = 1; set the outer loop iteration index iter = 1. When iter ≤ the maximum number of iterations and d ≥ the tolerance accuracy, execute the inner loop, including the following steps:

[0151] Set the inner loop iteration index i = 1, when i ≤ 2K u Repeat the following steps:

[0152] calculate (u i is a unit vector);

[0153] calculate

[0154] if Then execute

[0155] if Then execute

[0156] i=i+1 until the inner loop ends.

[0157] if Then if d>d th , then execute

[0158] otherwise

[0159] iter=iter+1, until the outer loop ends;

[0160] Finally obtained

[0161] After obtaining the dual problem λ, we have

[0162]

[0163] Calculate μ and ν, and then use

[0164]

[0165] The optimal x can be calculated E The corresponding optimal objective function value t is:

[0166] t=min(Mx E )

[0167] For each feasible region, we only need to solve multiple optimization problems P2 under the convex set of specific c and d to obtain the local optimal solution set of the original optimization problem P0, and then select the solution with the best result from these local optimal solutions as the global optimal solution. Assume that the number of P2 to be solved is N p , then we can get N p Local optimal solutions. For example, N of the first feasible domain and the second feasible domain p are 4 and 8 respectively. These local optimal solutions are denoted as The global optimal solution It can be determined according to the following rules:

[0168]

[0169] Finally, the optimal transmitted signal vector for the lth time slot can be obtained:

[0170]

[0171] based on Figure 1The illustrated method for determining an integrated communication and perception transmission signal first obtains channel state information from an integrated synaesthesia base station to different users, as well as a steering vector to the detection target. Based on this information, a communication model and a radar model are then constructed. The integrated communication and perception transmission signal is then used as the optimization variable, the illumination power requirement and the preset total power requirement of the detection target are constrained, and maximizing the minimum communication scaling factor between users is the optimization objective. An optimization model for the integrated communication and perception transmission signal is constructed and solved. During the solution, the optimization model is converted into multiple convex subproblems and solved separately. The optimal solution to each convex subproblem is used as the local optimal solution of the integrated communication and perception transmission signal optimization model. A global optimal solution is then determined from the multiple local optimal solutions, resulting in a preferred integrated communication and perception transmission signal. This method simplifies the optimization model structure and solution process, achieving higher SER performance gains, reducing computational complexity, and shortening the execution time of the integrated communication and perception transmission signal determination process.

[0172] Figure 5 2 is a diagram showing the simulation results of SER performance and transmit beam pattern of different schemes in the present invention. Figure 5 The scheme of the present invention is referred to as ISAC-SS-SLP. ISAC-SS-SLP(1) and ISAC-SS-SLP(2) represent the first and second groups of approximate feasible domains, respectively. PDD-MM-BCD refers to an existing CI-SLP scheme as a comparative scheme. The transmit power budget of each time slot is set to p0 = 30 dBm, QPSK modulation is used, the BS is equipped with N = 10 antennas, the number of users Ku = 3, the target is at θ = 0°, β = 1, and the target's illumination power requirement is p r =σ r Γ r , where Γ r =18.4dB, σ r =20dBm, in the PDD-MM-BCD scheme, the QoS required by the communication is set to β c =σ c Γ c , where Γ c =2.2dB,σ c = 22dBm. Because the second feasible region is closer to the original feasible region, the ISAC-SS-SLP scheme achieves better SER performance under the constraints of the second feasible region. ISAC-SS-SLP achieves better SER performance than PDD-MM-BCD under both the first and second feasible region constraints, demonstrating the communication performance advantages of the CI-SLP scheme proposed in this invention.

[0173] Figure 6 Graph showing the execution time simulation results of different schemes in the present invention. Figure 6The scheme of the present invention is referred to as ISAC-SS-SLP, and PDD-MM-BCD refers to an existing CI-SLP scheme. The transmit power budget of each time slot is set to p0 = 30dBm, QPSK modulation is used, the number of users Ku = 3, the target is at θ = 0°, β = 1, and the target's illumination power requirement is p r =σ r Γ r , where Γ r =18.4dB, σ r =20dBm, in the PDD-MM-BCD scheme, the QoS required by the communication is set to β c =σ c Γ c , where Γ c =2.2dB,σ c = 22dBm. For comparison purposes, the Hooke-Jeeves pattern search algorithm was used to solve the optimization problems in PDD-MM-BCD and ISAC-SS-SLP. As the number of transmit antennas increases, the computational complexity of each scheme increases accordingly, as the number of optimization variables in the optimization problem also increases. It can be seen that the CI-SLPISAC scheme based on symbol scaling executes in significantly less time than the PDD-MM-BCD scheme.

[0174] When applying the communication perception integrated transmission signal determination method provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0175] The above is a method for determining a communication perception integrated transmission signal provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding communication perception integrated transmission signal determination device, such as Figure 7 shown.

[0176] Figure 7 A schematic diagram of a communication-sensing integrated transmission signal determination device provided by the present invention includes:

[0177] An acquisition module 201 is used to obtain channel state information from the telepathy integrated base station to different users and a steering vector to a detection target;

[0178] Communication perception construction module 202, configured to construct a communication model based on channel state information from the synesthesia base station to different users, and to construct a radar model based on the steering vector from the synesthesia base station to the detection target;

[0179] An optimization model construction module 203 is configured to construct an optimization model for the communication-sensing integrated transmission signal based on the communication model and the radar model, using the communication-sensing integrated transmission signal as the optimization variable, the illumination power requirement of the detection target and the preset total power requirement as constraints, and maximizing the minimum communication scaling factor between users as the optimization goal;

[0180] The solution module 204 is used to convert the synaesthesia integrated transmission signal optimization model into multiple convex subproblems and solve them separately, using the optimal solution of each convex subproblem as the local optimal solution of the synaesthesia integrated transmission signal optimization model, determining the global optimal solution from the multiple local optimal solutions, and using the global optimal solution as the preferred communication perception integrated transmission signal.

[0181] Optionally, the communication perception construction module 202 determines the signals received by each user in different time slots when the synaesthesia integrated base station transmits and receives signals based on the time division protocol by the following formula:

[0182]

[0183] Based on the symbol scaling metric, the noise-free signal received by the user is decomposed along the two decision boundaries of QPSK modulation by the following formula:

[0184]

[0185] The set vector of each user's scaling factor and the communication-sensing integrated transmission signal are constructed by the following formula:

[0186]

[0187] α E =Mx E ,

[0188]

[0189]

[0190] Among them, y k [l] is the signal received by the kth user in the lth time slot, is the channel between ISAC-BS and the kth user, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, is the additive noise of the kth user in the lth time slot, is the noise power, is the noise-free signal received by the k-th user in the l-th time slot, and is the scaling factor of the kth user, indicating the influence of constructive components between users, α E is the set vector of scaling factors for each user, xE is the communication and perception integrated transmission signal, M is the coefficient matrix, p k and q k is the coefficient.

[0191] Optionally, the communication perception construction module 202 determines the event signal of the communication perception integrated signal arriving at the detection target by the following formula:

[0192] r[l]=βa(θ) H x[l],a(θ)=[1,e jsinθ ,…,e j(N-1)sinθ ] T ;

[0193] The illumination power for detecting the target is determined by the following formula:

[0194]

[0195] Where a(θ) is the guidance vector of the communication-sensing integrated base station toward the detection target, θ is the corresponding azimuth, β is the effective channel propagation coefficient, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, and x E Transmitting signals for integrated communication and perception, a E and b E is the expansion vector of a(θ).

[0196] Optionally, the optimization model construction module 203 is expressed as follows, taking the illumination power requirement of the detection target as a constraint:

[0197] The preset total power requirement is expressed as:

[0198] Where β is the effective channel propagation coefficient, x E Transmitting signals for integrated communication and perception, a E and b E is the extended vector of the guidance vector of the synaesthesia integrated base station toward the detection target, p r is the preset minimum requirement for the detection target lighting power, and p0 represents the communication perception integrated transmission signal power budget for each time slot.

[0199] Optionally, the optimization model construction module 203 constructs a synaesthesia integrated transmission signal optimization model by taking maximizing the minimum communication scaling factor between users as the optimization goal through the following formula:

[0200]

[0201] Among them, x E is the communication perception integrated transmission signal as the optimization variable, α Eis the set vector of scaling factors for each user, α i is α E The i-th element of , β is the effective channel propagation coefficient, a E and b E is the extended vector of the guidance vector of the synaesthesia integrated base station toward the detection target, p r is the preset minimum requirement for the detection target lighting power, and p0 represents the communication perception integrated transmission signal power budget for each time slot.

[0202] Optionally, the solving module 204 expresses the lighting power requirement constraint of the detection target by the following formula:

[0203]

[0204] According to the lighting power requirements of the detection target, the corresponding feasible domain is constrained. The tangent of the feasible domain boundary circle is used as an approximation of the feasible domain boundary circle. The non-convex feasible domain is approximated as a set of multiple convex feasible domains, so as to transform the synaesthesia integrated transmission signal optimization model into multiple convex subproblems.

[0205] Among them, p r is the preset minimum requirement for the detection target illumination power, β is the effective channel propagation coefficient, (r1, r2) correspond to the real part and imaginary part of the event signal respectively, x E is the communication perception integrated transmission signal as the optimization variable, α E is the set vector of scaling factors for each user, a E and b E It is the extended vector of the guidance vector of the synesthesia integrated base station toward the detection target.

[0206] For the specific limitations of the communication perception integrated transmission signal determination device, please refer to the limitations of the communication perception integrated transmission signal determination method above, and will not be repeated here. The various modules in the above-mentioned communication perception integrated transmission signal determination device can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0207] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A communication-aware integrated transmission signal determination method is provided.

[0208] The present invention also provides Figure 8 The structural diagram of the computer equipment shown in FIG. Figure 8As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A communication-aware integrated transmission signal determination method is provided.

[0209] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0210] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for determining a transmission signal by integrating communication and perception, characterized in that: The synaesthesia integrated base station performs the following steps, including: Obtain channel state information from the integrated telepathy base station to different users and the guidance vector to the detection target; A communication model is constructed based on the channel state information from the synergy-integrated base station to different users, and a radar model is constructed based on the guidance vector from the synergy-integrated base station to the detection target. Based on the communication model and radar model, a communication-sensing integrated transmission signal optimization model is constructed, with the communication-sensing integrated transmission signal as the optimization variable, the illumination power requirement of the detection target and the preset total power requirement as the constraints, and maximizing the minimum communication scaling factor between users as the optimization goal. According to the lighting power requirement constraints of the detection target, the corresponding feasible domain is constrained, and the tangent of the feasible domain boundary circle is used as an approximation of the feasible domain boundary circle. The non-convex feasible domain is approximated as a set of multiple convex feasible domains, so as to convert the synaesthesia integrated transmission signal optimization model into multiple convex subproblems and solve them separately. The optimal solution of each convex subproblem is used as the local optimal solution of the synaesthesia integrated transmission signal optimization model, and the global optimal solution is determined from multiple local optimal solutions. The global optimal solution is used as the communication perception integrated transmission signal.

2. The communication-aware integrated transmission signal determination method according to claim 1, wherein: The communication model is constructed based on the channel state information from the telepathic integrated base station to different users, specifically including: The following formula is used to determine the signals received by each user in different time slots when the telepathic integrated base station transmits and receives signals based on the time division protocol: Based on the symbol scaling metric, the noise-free signal received by the user is decomposed along the two decision boundaries of QPSK modulation by the following formula: The set vector of each user's scaling factor and the communication-sensing integrated transmission signal are constructed by the following formula: a E =Mx E , Among them, y k [l] is the signal received by the kth user in the lth time slot, is the channel between ISAC-BS and the kth user, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, is the additive noise of the kth user in the lth time slot, is the noise power, is the noise-free signal received by the k-th user in the l-th time slot, and is the scaling factor of the kth user, indicating the influence of constructive components between users, α E is the set vector of scaling factors for each user, x E is the communication and perception integrated transmission signal, M is the coefficient matrix, p k and q k is the coefficient.

3. The communication-aware integrated transmission signal determination method according to claim 1, wherein: The step of constructing a radar model based on a guidance vector from the intersensory integration base station to the detection target specifically includes: The event signal at which the communication-sensing integrated signal reaches the detection target is determined by the following formula: r[l]=βa(θ) H x[l],a(θ)=[1,e jsinθ ,…,And j(N-1)sinθ ] T ; The illumination power for detecting the target is determined by the following formula: Where a(θ) is the guidance vector of the communication-sensing integrated base station toward the detection target, θ is the corresponding azimuth, β is the effective channel propagation coefficient, x[l] is the communication-sensing integrated waveform corresponding to the lth time slot, and x E Transmitting signals for integrated communication and perception, a E and b E is the expansion vector of a(θ).

4. The communication-aware integrated transmission signal determination method according to claim 1, wherein: The constraints of the illumination power requirement of the detection target and the preset total power requirement specifically include: The lighting power requirement of the detection target is expressed as follows: The preset total power requirement is expressed as: Where β is the effective channel propagation coefficient, x E Transmitting signals for integrated communication and perception, a E and b E is the extended vector of the guidance vector of the synaesthesia integrated base station toward the detection target, p r is the preset minimum requirement for the detection target lighting power, and p0 represents the communication perception integrated transmission signal power budget for each time slot.

5. The communication-aware integrated transmission signal determination method according to claim 1, wherein: The optimization goal is to maximize the minimum communication scaling factor between users and construct a synaesthesia integrated transmission signal optimization model, which specifically includes: The following formula is used to construct the synaesthesia integrated transmission signal optimization model with maximizing the minimum communication scaling factor between users as the optimization goal: s.t.α E =Mx E Among them, x E is the communication perception integrated transmission signal as the optimization variable, α E is the set vector of scaling factors for each user, α i is α E The i-th element of , β is the effective channel propagation coefficient, a E and b E is the extended vector of the guidance vector of the synaesthesia integrated base station toward the detection target, p r is the preset minimum requirement for the detection target lighting power, and p0 represents the communication perception integrated transmission signal power budget for each time slot.

6. The communication-aware integrated transmission signal determination method according to claim 1, wherein: The following formula expresses the lighting power requirement constraint for detecting the target: Among them, p r is the preset minimum requirement for the detection target illumination power, β is the effective channel propagation coefficient, (r1, r2) correspond to the real part and imaginary part of the event signal respectively, x E is the communication perception integrated transmission signal as the optimization variable, α E is the set vector of scaling factors for each user, a E and b E It is the extended vector of the guidance vector of the synesthesia integrated base station toward the detection target.

7. A communication perception integrated transmission signal determination device, characterized in that: include: An acquisition module is used to obtain channel state information from the synesthesia integrated base station to different users and a steering vector to the detection target; The communication perception construction module is used to build a communication model based on the channel state information from the integrated synergy base station to different users, and to build a radar model based on the guidance vector from the integrated synergy base station to the detection target; An optimization model construction module is used to construct an optimization model for the communication-sensing integrated transmission signal based on the communication model and radar model, with the communication-sensing integrated transmission signal as the optimization variable, the illumination power requirement of the detection target and the preset total power requirement as constraints, and maximizing the minimum communication scaling factor between users as the optimization goal; A solution module is used to constrain the corresponding feasible domain according to the lighting power requirements of the detection target, use the tangent of the feasible domain boundary circle as an approximation of the feasible domain boundary circle, approximate the non-convex feasible domain as a set of multiple convex feasible domains, and convert the synaesthesia integrated transmission signal optimization model into multiple convex subproblems and solve them separately, use the optimal solution of each convex subproblem as the local optimal solution of the synaesthesia integrated transmission signal optimization model, determine the global optimal solution from the multiple local optimal solutions, and use the global optimal solution as the communication perception integrated transmission signal.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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