A full-duplex sensing and communication integrated system transceiving beam and power joint optimization method

Abstract

This invention discloses a joint optimization method for transmit and receive beams and power in a full-duplex inductive integrated system. The method includes: constructing an initial optimization problem, where the optimization objective is to minimize the sum of the power of the base station's transmitted signal and the uplink user's transmitted signal, constrained by minimum signal-to-interference-plus-noise ratio (SNR) requirements for both uplink and downlink communication and minimum SNR requirements for sensing target detection; obtaining the optimal solution in the closed-form of the base station's received filter vector, substituting it into the original problem to obtain an equivalent optimization problem concerning the base station's transmit beam and the uplink user's transmit power; introducing auxiliary variables to equivalently transform the optimization problem into a new expression; based on the transformed new problem, using semidefinite relaxation to discard the rank constraint, and employing a sequential convex approximation method for iterative solution; and constructing a set of optimal solutions satisfying the rank constraint after iterative convergence. This improves the spectral efficiency of the inductive integrated system while reducing the total system power consumption.

Description

Technical Field

[0001] This invention belongs to the field of joint optimization technology of transmit and receive beam and power control in integrated inductive and hyper-inductive systems, and specifically relates to a joint optimization method for transmit and receive beam and power in a full-duplex integrated inductive and hyper-inductive system. Background Technology

[0002] 6G will serve as the nerve center connecting the physical and digital worlds. With ubiquitous sensing and the Internet of Things as a development vision of 6G, future communication systems will inevitably incorporate sensing capabilities, giving rise to integrated communication and sensing systems. Integrated communication and sensing refers to the fusion of communication and sensing functions, with sensing operations performed synchronously during information transmission. Integrated communication and sensing systems offer two significant advantages: First, unlike existing single communication or sensing systems, integrated systems allow communication and sensing functions to share hardware, signal processing algorithms, and system time-frequency resources, significantly reducing system overhead. Second, while transmitting information, integrated systems actively recognize and analyze channel characteristics to perceive the surrounding environment and utilize the sensing results to assist and enhance the performance of the communication system. For example, by actively sensing and employing specific signal processing algorithms, base stations can achieve real-time measurement and even prediction of the azimuth angle of mobile users, thereby improving the directivity accuracy of the transmitted beam, establishing high-quality communication links, and enhancing communication reliability. In short, integrated communication and sensing has become a key technology for next-generation communication systems.

[0003] Beam design optimization in integrated sensing systems is an important research topic. The introduction of sensing functionality brings new types of requirements and constraints not previously considered in communication systems. Furthermore, to increase the degree of freedom of the sensing signal, the transmitter often introduces a dedicated sensing signal in addition to the communication symbols to be transmitted. Moreover, the receiver needs to process the sensing and communication signals separately. These factors increase the difficulty of solving the beam optimization design problem in integrated sensing systems.

[0004] Recently, with the continuous advancement of radio frequency technology, especially self-interference cancellation technology, the feasibility of full-duplex technology has been significantly improved. Benefiting from simultaneous transmission and reception at the same frequency, full-duplex mode can greatly improve the spectral efficiency of the system. Therefore, it is necessary to explore the integrated design of sensing in full-duplex mode. Summary of the Invention

[0005] The purpose of this invention is to provide a method for jointly optimizing the transmit and receive beams and power of a full-duplex integrated sensing system, which can meet sensing requirements, achieve efficient full-duplex communication, and significantly reduce the total power consumption of the system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for joint optimization of transmit and receive beams and power in a full-duplex inductive integrated system, comprising:

[0007] Step S1: Construct an initial optimization problem. In this initial optimization problem, the optimization objective is to minimize the total power consumption of the base station and uplink users in the full-duplex sensing integrated system. The constraints are the minimum signal-to-interference-plus-noise ratio (SIR) requirements for both uplink and downlink users in full-duplex communication and the minimum SIR requirements for target detection. Optimize the base station transmit beam. The covariance matrix V0 of the dedicated sensing signal s0 transmitted by the base station, and the received filter vector The base station's receive filter vector u for radar-detected targets, and the uplink user transmit power. Where L and K are the number of downlink and uplink communication users accessing the system, respectively, and v l It is the beamforming vector of the base station for the l-th downlink user, w k It is the base station's receive filter vector for the k-th uplink user;

[0008] Step S2: Optimize the received filter vector obtained in step S1, denoted as u. * and will u * and Substituting into the initial optimization problem, we obtain information about V0 and The equivalent optimization problem;

[0009] Step S3: Introduce auxiliary variables The result obtained in step S2 regarding V0 and The equivalent optimization problem is equivalently transformed into, where (·) H Represents the conjugate transpose of a matrix;

[0010] Step S4: Use semidefinite relaxation to discard information about the matrix The rank constraint is applied, and the sequential convex approximation method is used to iteratively solve the problem obtained by the equivalent transformation in step S3;

[0011] Step S5: Denote the optimal solution obtained after iterative convergence as... based on Construct a set of optimal solutions in, Satisfy the rank constraint discarded by the semidefinite relaxation in step S4;

[0012] Step S6: The solution constructed based on step S5 The optimal sensing signal covariance matrix of the full-duplex inductive integrated system is obtained. and optimal transmit power for uplink users right The optimal transmission beam is obtained by performing Cholesky decomposition.

[0013] Further, in step S1, the initial optimization problem is constructed as follows:

[0014] The optimization objective is to minimize

[0015] The constraints are:

[0016]

[0017]

[0018] Where |·| represents the modulus, ‖·‖ represents the L2 norm of the vector, and Tr(·) represents the trace of the matrix. Let X be a positive semi-definite matrix, and g l h is the channel vector from the base station to the l-th downlink user. k and h k′ Let τ represent the channel vectors from the k-th and k′-th uplink users to the base station receiving antenna, respectively. rad It is the minimum SINR required to complete radar target detection. It is the minimum SINR required by the k-th uplink user. It is the minimum SINR required for the l-th downlink user, σ 2 I represents the noise power of the base station and user receiving antennas. N Let N be an N×N dimensional identity matrix. t and N r These represent the number of transmitting and receiving antennas at the base station, respectively. H SI This represents the self-interference channel of a full-duplex base station, where β0 and θ0 represent the channel gain coefficient and direction of the target to be detected, respectively. i and θ i Let I represent the channel gain coefficient and direction of the i-th interfering object, respectively, and let I be the total number of interfering objects. and These are the direction vectors of the base station's transmitting and receiving antenna arrays in the θ direction, respectively. T This represents the matrix transpose, where e is the natural base and j is the imaginary unit.

[0019] Further, in step S2, the base station receives the filter vector u and The optimal forms are as follows:

[0020]

[0021] in,(·) -1 Represents the inverse of the matrix. Let u... * and Substituting into the initial optimization problem described in step S1, we obtain the following about V0 and The equivalent optimization problem is expressed as:

[0022] The optimization objective is to minimize

[0023] The constraints are:

[0024]

[0025]

[0026] Furthermore, based on the equivalent optimization problem obtained in step S2, in step S3, auxiliary variables are introduced to further transform the obtained problem into the following equivalent problem:

[0027] The optimization objective is to minimize

[0028] The constraints are:

[0029]

[0030] in, V is an auxiliary variable introduced. l and V l′ These are the auxiliary vectors for the l-th and l′-th users, respectively. rank(·) represents finding the rank of a matrix.

[0031] Furthermore, based on the problem obtained in step S3, in step S4, the constraint rank(V) with respect to rank is discarded using semidefinite relaxation. l The optimization problem is solved iteratively using the sequential convex approximation method, where ) = 1, l = 1, ..., L. In the t-th iteration, the optimization problem is expressed as follows:

[0032] The optimization objective is to minimize

[0033] The constraints are:

[0034]

[0035]

[0036] in, This represents the optimal solution obtained in the (t-1)th iteration.

[0037] Furthermore, in each iteration of step S4, the problem to be solved is a convex optimization problem, which is solved using the interior point method.

[0038] Further, in step S5, after the iteration converges, the obtained optimal solution is denoted as... based on Construct an optimal solution as follows:

[0039]

[0040] in Constructed solution Satisfying the semidefinite relaxation rejection constraint, i.e. This is the optimal solution to the problem described in step S3.

[0041] Beneficial effects:

[0042] 1. The sequential convex approximation algorithm used in this invention is a general algorithm for indirectly solving non-convex optimization problems. It monotonically approximates the original problem by iteratively solving a series of approximate convex optimization problems, and obtains the local optimal solution of the original non-convex problem within an acceptable time complexity.

[0043] 2. This invention provides a method for joint optimization design of transmit and receive beams and power in a full-duplex sensing integrated system. Step S1 constructs a power minimization joint optimization design problem, and steps S2-S6 solve the problem. The obtained optimal solution can realize the integration of full-duplex communication and sensing, and significantly reduce the total power consumption of the system. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a method for joint optimization of transmit and receive beams and power in a full-duplex inductive integrated system, as provided in an embodiment of the present invention.

[0045] Figure 2 The figure shows the simulation results of a method for joint optimization of transmit and receive beams and power in a full-duplex inductive integrated system provided in an embodiment of the present invention. Detailed Implementation

[0046] The invention will now be further explained with reference to the accompanying drawings.

[0047] This embodiment provides a method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system. This method is applicable to full-duplex inductive integrated scenarios, where the base station transmits a signal as follows: The received signal is represented as By optimizing the base station transmit beam The covariance matrix V0 of the dedicated sensing signal s0 transmitted by the base station, and the received filter vector The base station's receive filter vector u for radar-detected targets, and the uplink user transmit power. Minimize the total power consumption of the full-duplex sensing integrated system shown, while satisfying the minimum SINR requirements for uplink and downlink communication and the minimum SINR requirements for target detection.

[0048] Specifically, in this implementation, the process of the power minimization beam and power joint optimization method is as follows: Figure 1 As shown, the specific steps include:

[0049] Step S1: Construct an initial optimization problem. In this initial optimization problem, the optimization objective is to minimize the total power consumption of the base station and uplink users in the full-duplex sensing integrated system. The constraints are the minimum signal-to-interference-plus-noise ratio (SIR) requirements for both uplink and downlink users in full-duplex communication and the minimum SIR requirements for target detection. Optimize the base station transmit beam. The covariance matrix V0 of the dedicated sensing signal s0 transmitted by the base station, and the received filter vector The base station's receive filter vector u for radar-detected targets, and the uplink user transmit power. Where L and K are the number of downlink and uplink communication users accessing the system, respectively, and v l It is the beamforming vector of the base station for the l-th downlink user, w k It is the base station's receive filter vector for the k-th uplink user;

[0050] Step S2: Optimize the received filter vector obtained in step S1, denoted as u. * and will u * and Substituting into the initial optimization problem, we obtain information about V0 and The equivalent optimization problem.

[0051] Step S3: Introduce auxiliary variables The result obtained in step S2 regarding V0 and The equivalent optimization problem is equivalently transformed into, where (·) H This represents the conjugate transpose of a matrix.

[0052] Step S4: Use semidefinite relaxation to discard information about the matrix The rank constraint is applied, and the sequential convex approximation method is used to iteratively solve the problem obtained from the equivalent transformation in step S3.

[0053] Step S5: Denote the optimal solution obtained after iterative convergence as... based on Construct a set of optimal solutions in, The rank constraint that is discarded by the semidefinite relaxation in step S4 is satisfied.

[0054] Step S6: The solution constructed based on step S5 The optimal sensing signal covariance matrix of the full-duplex inductive integrated system is obtained. and optimal transmit power for uplink users right The optimal transmission beam is obtained by performing Cholesky decomposition.

[0055] In step S1, the initial optimization problem is constructed as follows:

[0056] The optimization objective is to minimize

[0057] The constraints are:

[0058]

[0059] Where |·| represents the modulus, ‖·‖ represents the L2 norm of the vector, and Tr(·) represents the trace of the matrix. Let X be a positive semi-definite matrix, and g l h is the channel vector from the base station to the l-th downlink user. k and h k′ Let τ represent the channel vectors from the k-th and k′-th uplink users to the base station receiving antenna, respectively. rad It is the minimum SINR required to complete radar target detection. It is the minimum SINR required by the k-th uplink user. It is the minimum SINR required for the l-th downlink user, σ 2 I represents the noise power of the base station and user receiving antennas. N Let N be an N×N dimensional identity matrix. t and N r These represent the number of transmitting and receiving antennas at the base station, respectively. H SI This represents the self-interference channel of a full-duplex base station, where β0 and θ0 represent the channel gain coefficient and direction of the target to be detected, respectively. i and θ i Let I represent the channel gain coefficient and direction of the i-th interfering object, respectively, and let I be the total number of interfering objects. and These are the direction vectors of the base station's transmitting and receiving antenna arrays in the θ direction, respectively. T This represents the matrix transpose, where e is the natural base and j is the imaginary unit.

[0060] The specific optimization solution steps for this initial optimization problem are as follows:

[0061] In step S2, the base station receives the filter vector u and The optimal forms are as follows:

[0062]

[0063] in,(·) -1 Represents the inverse of the matrix. Let u... * and Substituting into the initial optimization problem described in step S1, we obtain the following about V0 and The equivalent optimization problem is expressed as:

[0064] The optimization objective is to minimize

[0065] The constraints are:

[0066]

[0067] Based on the problem obtained in step S2, in step S3, auxiliary variables are introduced to further transform the problem into the following equivalent form: The optimization objective is: minimize

[0068] The constraints are:

[0069]

[0070]

[0071] in, V is an auxiliary variable introduced. l and V l′ These are the auxiliary vectors for the l-th and l′-th users, respectively. rank(·) represents finding the rank of a matrix.

[0072] In step S4, the constraint rank(V) with respect to rank is discarded using semidefinite relaxation. l The optimization problem is solved iteratively using the sequential convex approximation method, where ) = 1, l = 1, ..., L. In the t-th iteration, the optimization problem is expressed as follows:

[0073] The optimization objective is to minimize

[0074] The constraints are:

[0075]

[0076] in, This represents the optimal solution obtained in the (t-1)th iteration.

[0077] The sequential convex approximation algorithm is a general algorithm for indirectly solving non-convex optimization problems. It monotonically approximates the original problem by iteratively solving a series of approximate convex optimization problems, obtaining a local optimum of the original non-convex problem within an acceptable time complexity. In this implementation, in each iteration of step S4, the problem is a convex optimization problem, solved using the interior-point method.

[0078] In step S5, after the iteration converges, the obtained optimal solution is denoted as... based on Construct an optimal solution as follows:

[0079]

[0080] in Constructed solution Satisfying the semidefinite relaxation rejection constraint, i.e. This is the optimal solution to the problem described in step S3.

[0081] in The solution obtained by the interior point method usually does not satisfy the rank-one constraint discarded by semidefinite relaxation, i.e.,

[0082]

[0083] Therefore, this step, through an additional calculation, is based on Construct a new set of solutions that satisfy the rank constraint. at this time, In particular, and The optimal objective function values ​​obtained by substituting the two sets of solutions into the optimization problem described in step S3 are the same; the difference is... Satisfies the rank-1 constraint. Not satisfied.

[0084] In step S6, based on the solution constructed in step S5 The optimal sensing signal covariance matrix of the full-duplex inductive integrated system is obtained. and optimal transmit power for uplink users right The optimal transmission beam is obtained by performing Cholesky decomposition. Meanwhile, substituting the above results into the expression in step S2 yields the optimal receiving beam vector.

[0085] To verify the effectiveness of the full-duplex inductive integrated power minimization and transmit / receive beam and power joint optimization method provided in this embodiment, a simulation experiment was conducted. The parameters involved in the simulation experiment are shown in the table below:

[0086] Table 1. Simulation Experiment Parameter Table

[0087]

[0088] Figure 2 The simulation results show that the proposed full-duplex transmit / receive beam and power allocation optimization method can significantly reduce the total power consumption of the system compared with the half-duplex inductive integrated scheme.

[0089] This invention provides a method for joint optimization design of transmit and receive beams and power in a full-duplex sensing integrated system. Step S1 constructs a power minimization joint optimization design problem, and steps S2-S6 solve this problem. The obtained optimal solution can realize the integration of full-duplex communication and sensing, and can further improve the spectrum utilization of the sensing integrated system while reducing the total power consumption of the system.

[0090] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system, characterized in that, include: Step S1: Construct an initial optimization problem. In this initial optimization problem, the optimization objective is to minimize the total power consumption of the base station and uplink users in the full-duplex sensing integrated system. The constraints are the minimum signal-to-interference-plus-noise ratio (SIR) requirements for both uplink and downlink users in full-duplex communication and the minimum SIR requirements for target detection. Optimize the base station transmit beam. The covariance matrix V0 of the dedicated sensing signal s0 transmitted by the base station, and the received filter vector The base station's receive filter vector u for radar-detected targets, and the uplink user transmit power. Where L and K are the number of downlink and uplink communication users accessing the system, respectively, and v l It is the beamforming vector of the base station for the l-th downlink user, w k It is the base station's receive filter vector for the k-th uplink user; Step S2: Optimize the received filter vector obtained in step S1, denoted as u. * and will u * and Substituting into the initial optimization problem, we obtain information about and The equivalent optimization problem; Step S3: Introduce auxiliary variables The result obtained in step S2 regarding V0 and The equivalent optimization problem is equivalently transformed into, where (·) H Represents the conjugate transpose of a matrix; Step S4: Use semidefinite relaxation to discard information about the matrix The rank constraint is applied, and the sequential convex approximation method is used to iteratively solve the problem obtained by the equivalent transformation in step S3; Step S5: Denote the optimal solution obtained after iterative convergence as... based on Construct a set of optimal solutions in, Satisfy the rank constraint discarded by the semidefinite relaxation in step S4; Step S6: The solution constructed based on step S5 The optimal sensing signal covariance matrix of the full-duplex inductive integrated system is obtained. and optimal transmit power for uplink users right The optimal transmission beam is obtained by performing Cholesky decomposition.

2. The method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system according to claim 1, characterized in that, In step S1, the initial optimization problem is constructed as follows: The optimization objective is to minimize The constraints are: Where |·| represents the modulus, ‖·‖ represents the L2 norm of the vector, and Tr(·) represents the trace of the matrix. Let X be a positive semi-definite matrix, and g l h is the channel vector from the base station to the l-th downlink user. k and h k′ Let τ represent the channel vectors from the k-th and k′-th uplink users to the base station receiving antenna, respectively. rad It is the minimum SINR required to complete radar target detection. It is the minimum SINR required by the k-th uplink user. It is the minimum SINR required for the l-th downlink user, σ 2 I represents the noise power of the base station and user receiving antennas. N Let N represent an N×N dimensional identity matrix. t and N r These represent the number of transmitting and receiving antennas at the base station, respectively. H SI This represents the self-interference channel of a full-duplex base station, where β0 and θ0 represent the channel gain coefficient and direction of the target to be detected, respectively. i and θ i Let I represent the channel gain coefficient and direction of the i-th interfering object, respectively, and let I be the total number of interfering objects. and These are the direction vectors of the base station's transmitting and receiving antenna arrays in the θ direction, respectively. T This represents the matrix transpose, where e is the natural base and j is the imaginary unit.

3. The method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system according to claim 2, characterized in that, In step S2, the base station receives the filter vector u and The optimal forms are as follows: in,(·) -1 Represents the inverse of the matrix. Let u... * and Substituting into the initial optimization problem described in step S1, we obtain the following about V0 and The equivalent optimization problem is expressed as: The optimization objective is to minimize The constraints are:

4. The method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system according to claim 3, characterized in that, Based on the equivalent optimization problem obtained in step S2, in step S3, auxiliary variables are introduced to further transform the obtained problem into the following equivalent problem: The optimization objective is to minimize The constraints are: p k ≥0,k=1,…,K rank(V l )=1,l=1,…,L in, V is an auxiliary variable introduced. l and V l′ These are the auxiliary vectors for the l-th and l′-th users, respectively. rank(·) represents finding the rank of a matrix.

5. The method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system according to claim 4, characterized in that, Based on the problem obtained in step S3, in step S4, the constraint rank(V) with respect to rank is discarded using semidefinite relaxation. l The optimization problem is solved iteratively using the sequential convex approximation method, where ) = 1, l = 1, ..., L. In the t-th iteration, the optimization problem is expressed as follows: The optimization objective is to minimize The constraints are: p k ≥0,k=1,…,K in, This represents the optimal solution obtained in the (t-1)th iteration.

6. The method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system according to claim 5, characterized in that, In each iteration of step S4, the problem to be solved is a convex optimization problem, which is solved using the interior point method.

7. The method for joint optimization of transmit and receive beam and power in a full-duplex inductive integrated system according to claim 1, characterized in that, In step S5, after the iteration converges, the obtained optimal solution is denoted as... based on Construct an optimal solution as follows: in Constructed solution Satisfying the semidefinite relaxation rejection constraint, i.e. This is the optimal solution to the problem described in step S3.

Images