Near field communication transmission-oriented communication and inductance integrated design method and device

Abstract

The invention relates to a near-field communication transmission-oriented communication and inductance integrated design method and device, and the method comprises the steps: firstly, building a near-field hidden communication and inductance integrated system model; secondly, under the constraints of the maximum transmitting power of a base station, the minimum communication rate of a user and concealment, constructing a CRB matrix minimization optimization problem for evaluating the sensing performance of the system by jointly optimizing communication beam forming and sensing beam forming; and finally, converting an original non-convex optimization problem into a convex optimization problem by adopting a positive semidefinite relaxation algorithm, and obtaining a globally optimal solution of the original problem. According to the method, the near-field hidden sensing integrated system is researched, the CRB matrix for evaluating the sensing performance of the system is minimized, the effectiveness and feasibility of the designed system are verified by simulation results, and it is indicated that the sensing performance of the designed system is equivalent to that of a near-field ISAC system.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a synaesthesia integrated design method and device for near field communication transmission. Background Art

[0002] ISAC is considered to be one of the key technologies of the sixth generation of mobile communications (6G), which can realize the sharing of spectrum resources and hardware platforms.

[0003] Due to the growing demand for sixth-generation mobile communications and sensing, more and more researchers are studying the performance of near-field interaceptive integrated systems in the near-field area, with the main purpose of maximizing near-field perception performance while maintaining the minimum near-field communication rate. However, due to the open and broadcast nature of the ISAC system, it is vulnerable to eavesdropping and interception by illegal users, so the security issues of the ISAC system cannot be ignored. The purpose of covert communication is to hide legitimate transmissions while maintaining the information rate. Although many scholars have studied the optimization issues related to the integrated covert communication and perception system and near-field covert communication, existing research has not yet explored the near-field covert interaceptive integrated system.

[0004] Inspired by the existing technical solutions, the present invention studies a synaesthesia integrated design method and device for near-field communication transmission. Under the constraints of base station maximum transmission power, user minimum communication rate and concealment, a CRB matrix minimization optimization problem for evaluating system perception performance is constructed and a semi-positive definite relaxation algorithm is used to solve the original optimization problem. A design scheme of a synaesthesia integrated design method and device for near-field communication transmission is given. Summary of the invention

[0005] The purpose of the present invention is to propose a synaesthesia integration design method for near-field communication transmission in order to minimize the CRB matrix for evaluating system perception performance in a near-field covert synaesthesia integration system.

[0006] In a first aspect, a synaesthesia integrated design method for near field communication transmission includes:

[0007] S1: Establish a near-field covert synaesthesia integrated system model;

[0008] S2: Under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user and the concealment, the CRB matrix minimization optimization problem for evaluating the system perception performance is constructed by jointly optimizing the communication beamforming and the sensor beamforming;

[0009] S3: Use the semi-positive definite relaxation algorithm to transform the original non-convex optimization problem into a convex optimization problem and obtain the global optimal solution of the original problem.

[0010] The step S1 specifically includes:

[0011] A near-field covert synaesthesia integrated system is established, which includes a dual-function base station (Alice) equipped with N antennas, a sensing target, K single-antenna legitimate users (Bobs) and a single-antenna eavesdropping user (Willie).

[0012] The step S2 specifically includes:

[0013] The objective function is planned. Under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user, and concealment, the CRB matrix minimization optimization problem for evaluating the system perception performance is constructed by jointly optimizing the communication beamforming and the sensor beamforming:

[0014]

[0015] Where P is the transmit power budget of the base station, R min,k is the minimum required communication rate of the Kth legal user; (1a) is a non-convex optimization problem, (1b) represents the maximum transmission power constraint of the base station, (1c) represents the minimum communication rate requirement of each legal user, and (1d) represents the concealment constraint.

[0016] The step S3 specifically includes:

[0017] Since the objective function and constraints in the optimization problem are non-convex, the semi-positive definite relaxation algorithm can be used to effectively solve problem (1);

[0018] First, based on the covariance matrix of the transmitted signal, the optimization problem can be restated as:

[0019]

[0020] sttr(R x )≤P(2b)

[0021]

[0022] Among them, constraint (2d) can be obtained by Due to the complexity of the CRB matrix, an auxiliary matrix is ​​introduced Constraints (2c) to (2e) are non-convex, and a semi-positive definite relaxation algorithm is used, defining Satisfy F c,k ≥0, rank(F c,k )=1, problem (2) can be further transformed into:

[0023]

[0024] tr(R x )≤P (3c)

[0025]

[0026]

[0027] rank(F c,k )=1(3g)

[0028] Due to the rank 1 constraint, formula (3) is still non-convex; when the rank 1 constraint is omitted, the interior point method can be used to solve it and obtain the optimal solution (F c,k ,R x ), construct the following rank-one solution to obtain the communication beamforming f c,k , the sensing beamforming F is obtained by eigenvalue decomposition r ,Right now:

[0029]

[0030] That is, the global optimal solution to the original problem is obtained.

[0031] A synaesthesia integrated design device for near field communication transmission, comprising:

[0032] A model building module is used to build a near-field covert synaesthesia integrated system model;

[0033] The equation construction module constructs the CRB matrix minimization optimization problem for evaluating the system perception performance by jointly optimizing the communication beamforming and the sensor beamforming under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user, and the concealment.

[0034] The optimization processing module uses a semi-positive definite relaxation algorithm to transform the original non-convex optimization problem into a convex optimization problem and obtain the global optimal solution of the original problem.

[0035] The model building module specifically includes:

[0036] A near-field covert synaesthesia integrated system is established, which includes a dual-function base station (Alice) equipped with N antennas, a sensing target, K single-antenna legitimate users (Bobs) and a single-antenna eavesdropping user (Willie).

[0037] The equation construction module specifically includes:

[0038] The optimization equation of the equation construction module, which jointly optimizes the communication beamforming and the sensor beamforming to calculate the minimum CRB matrix for evaluating the system perception performance, is:

[0039]

[0040] The optimization processing module specifically includes:

[0041] Optimization processing module, in view of the non-convexity of the original problem, the present invention firstly restates the original problem according to the covariance matrix of the transmitted signal, and then introduces the auxiliary matrix A semi-positive definite relaxation algorithm is used to transform the original non-convex optimization problem into a convex optimization problem, and finally the global optimal solution of the original problem is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a schematic flow chart of a synaesthesia integrated design method for near field communication transmission provided by an embodiment of the present invention;

[0044] Figure 2 This is a model diagram of the system for implementing the present invention;

[0045] Figure 3 is a curve diagram showing the relationship between RCRB and base station transmission power provided by the implementation of the present invention;

[0046] Figure 4 is a graph showing the relationship between the RCRB and the target distance provided by the implementation of the present invention;

[0047] Figure 5 It is a curve diagram showing the relationship between RCRB and the minimum communication rate of the user provided by the implementation of the present invention;

[0048] Figure 6 It is a module structure diagram provided by the implementation of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] S1: Establish a near-field covert synaesthesia integrated system model;

[0051] S2: Under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user and the concealment, the CRB matrix minimization optimization problem for evaluating the system perception performance is constructed by jointly optimizing the communication beamforming and the sensor beamforming;

[0052] S3: Use the semi-positive definite relaxation algorithm to transform the original non-convex optimization problem into a convex optimization problem and obtain the global optimal solution of the original problem.

[0053] Specifically, step S1 includes:

[0054] The method described in this embodiment is applied to Figure 2 The system model shown in the figure includes: a dual-function base station (Alice) equipped with N = 61 antennas, a sensing target, K = 4 single-antenna legitimate users (Bobs) and a single-antenna eavesdropping user (Willie). The base station's transmission power is 20dBm, the noise power is -60dBm, the eavesdropping user's noise uncertainty is 3dB, and the concealment threshold is 0.1.

[0055] Further, step S2 includes:

[0056] The sending signal can be expressed as:

[0057]

[0058] in Beamforming for base station communications, Beamforming for base station sensing, For communication signals, is the sensing signal, satisfying And there is zero correlation between the communication signal and the sensor signal components, that is,

[0059] The received signal of the Kth legal user Bob is:

[0060]

[0061] in, represents the additive white Gaussian noise (AWGN) received by the Kth legitimate user Bob.

[0062] Then the communication rate of the Kth legal user Bob can be expressed as:

[0063]

[0064] For target perception, the echo signal received at the base station is:

[0065] y s (t) = gx(t) + z s(t) (9)

[0066] in, represents additive white Gaussian noise (AWGN); the received echo signal samples are used to estimate the target parameters in the perception space of the entire coherent time block, namely: Y s =[y s [1],...,y s [T]].

[0067] The CRB matrix used to estimate distance and angle is:

[0068]

[0069] In this system, the eavesdropping user Willie wants to know whether the legitimate user Bob is communicating with the base station Alice, so the eavesdropping user Willie needs to receive the signal y w (t) Perform hypothesis testing to distinguish between two hypotheses. Specifically, H 0 Indicates that the base station Alice only sends the sensing signal; H 1 It means that the base station Alice sends both sensing signals and communication signals, so the received signal y w (t) can be expressed as:

[0070]

[0071] in, Represents the noise received by the eavesdropping user Willie.

[0072] Considering the uncertainty of the noise received by the eavesdropping user Willie, a bounded uncertainty model is adopted. Assume that the noise power at the eavesdropping user Willie is Evenly distributed in in represents the Willie noise power of the eavesdropping user without noise uncertainty, ρ db =10log 10 (ρ) represents the noise uncertainty. Then the probability density function of the noise power is:

[0073]

[0074] For the detection of whether there is communication, assuming that the eavesdropping user Willie adopts the energy detection method and uses the average received signal power at the eavesdropping user Willie as the test statistic for detection, then:

[0075]

[0076] Let D 1 and D 0Pr(D represents the binary decision made by the eavesdropping user Willie to infer whether there is communication between the base station Alice and the legitimate user Bob. 1 |H 0 ) and Pr(D 0 |H 1 ) represent the false alarm probability and missed detection probability respectively. Therefore, assuming H 0 and H 1 The prior probability of is equal, then the total detection error probability can be expressed as:

[0077] η=Pr(D 1 |H 0 )+Pr(D 0 |H 1 ) (14)

[0078] Let φ be the detection threshold of eavesdropping user Willie, and define Then the false alarm probability and missed detection probability can be expressed as:

[0079]

[0080] The total detection error probability can be further expressed as:

[0081]

[0082] Differentiate to get:

[0083]

[0084] when hour, when hour,

[0085] Then the optimal detection threshold φ * for:

[0086]

[0087] Substituting it into formula (17) we get:

[0088]

[0089] Given a known threshold 0≤ε≤1, there is a hidden constraint η * ≥1-ε, so the maximum P can be derived from formula (20): w,c for:

[0090]

[0091] There is a hidden constraint:

[0092]

[0093] Therefore, the CRB matrix minimization optimization problem for evaluating the system perception performance is:

[0094]

[0095] Since the objective function and constraints in the optimization problem are non-convex, the semi-positive definite relaxation algorithm can be used to effectively solve problem (23);

[0096] The covariance matrix of the transmitted signal is:

[0097]

[0098] Then formula (8) can be transformed into:

[0099]

[0100] Based on the covariance matrix of the transmitted signal, the optimization problem can be restated as:

[0101]

[0102] sttr(R x )≤P(26b)

[0103]

[0104]

[0105] Among them, constraint (26d) can be obtained by Due to the complexity of the CRB matrix, an auxiliary matrix is ​​introduced Constraints (26c) to (26e) are non-convex, and a semi-positive definite relaxation algorithm is used, defining Satisfy F c,k ≥0, rank(F c,k )=1, problem (26) can be further transformed into:

[0106]

[0107] tr(R x )≤P (27c)

[0108]

[0109] rank(F c,k )=1 (27g)

[0110] Due to the rank 1 constraint, formula (27) is still non-convex; when the rank 1 constraint is omitted, the interior point method can be used to solve it and obtain the optimal solution (Fc,k ,R x ), construct the following rank-one solution to obtain the communication beamforming f c,k , the sensing beamforming F is obtained by eigenvalue decomposition r ,Right now:

[0111]

[0112] That is, the global optimal solution to the original problem is obtained.

[0113] It can be seen from the above technical scheme that the present invention provides a synaesthesia integrated design method for near-field communication transmission. Under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user and the concealment, the CRB matrix for evaluating the perception performance of the system is minimized by jointly optimizing the communication beamforming and the sensor beamforming.

[0114] The system configuration parameters for this implementation are as follows:

[0115] parameter value Number of transmitter antennas N 61 The number of legal users K 4 Base station transmission power P 20dBm Base station noise power -60dBm Eavesdropping user noise uncertainty ρ 3dB Antenna aperture D 0.5m Hidden threshold ε 0.1 Sense target location (25m,Π / 4rad)

[0116] Figure 3 The relationship between RCRB used to estimate angle and distance and base station transmission power is given. The results show that as the base station transmission power increases, RCRB gradually decreases, indicating that the system's perception performance is gradually improving. In addition, under the same base station transmission power, the perception performance of the near-field ISCC system is worse than that of the near-field ISAC system, but the difference in RCRB between the two is very small, indicating that the perception performance of the near-field ISCC system is comparable to that of the near-field ISAC system, verifying the effectiveness of the near-field covert synaesthesia integration system.

[0117] Figure 4 The relationship between the RCRB used to estimate the angle and distance and the target distance is given. Since the path loss of the signal changes when the distance changes, resulting in a change in the signal-to-noise ratio (SNR), path loss is not considered in this implementation for fair comparison. The results show that when the target distance increases, the RCRB of the estimated distance gradually increases, indicating that the accuracy of the system in calculating the distance decreases; while the RCRB of the estimated angle gradually decreases, indicating that as the target distance increases, the echo signal at each antenna is transmitted from almost the same direction, making the angle estimation more favorable.

[0118] Figure 5 The relationship between RCRB used to estimate angle and distance and the user's minimum communication rate under different concealment thresholds ε is given. The results show that when the user's minimum communication rate increases or the concealment constraint becomes stricter, RCRB gradually increases, indicating that when higher requirements are placed on both communication rate and concealment, the power allocated by the base station for sensing the target is smaller, thus resulting in a decrease in the system perception performance.

[0119] Figure 6 It is a structural schematic diagram of a synaesthesia integrated design device for near field communication transmission provided by an embodiment of the present invention.

[0120] In an embodiment, the model establishment specifically includes:

[0121] The model building module establishes a near-field covert synaesthesia integrated system, which includes a dual-function base station (Alice) equipped with N antennas, a sensing target, K single-antenna legitimate users (Bobs) and a single-antenna eavesdropping user (Willie).

[0122] In this embodiment, the equation construction module specifically includes:

[0123] The optimization equation of the equation construction module, which jointly optimizes the communication beamforming and the sensor beamforming to calculate the minimum CRB matrix for evaluating the system perception performance, is:

[0124]

[0125] In this embodiment, the optimization processing module specifically includes:

[0126] Optimization processing module, in view of the non-convexity of the original problem, the present invention firstly restates the original problem according to the covariance matrix of the transmitted signal, and then introduces the auxiliary matrix A semi-positive definite relaxation algorithm is used to transform the original non-convex optimization problem into a convex optimization problem, and finally the global optimal solution of the original problem is obtained.

Claims

1. A synaesthesia integrated design method for near field communication transmission, characterized in that: include: S1: Establish a near-field covert synaesthesia integrated system model; S2: Under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user and the concealment, the CRB matrix minimization optimization problem for evaluating the system perception performance is constructed by jointly optimizing the communication beamforming and the sensor beamforming; S3: Use the semi-positive definite relaxation algorithm to transform the original non-convex optimization problem into a convex optimization problem and obtain the global optimal solution of the original problem.

2. The synaesthesia integrated design method for near field communication transmission according to claim 1, characterized in that: The step S1 specifically includes: A near-field covert synaesthesia integrated system is established, which includes a dual-function base station (Alice) equipped with N antennas, a sensing target, K single-antenna legitimate users (Bobs) and a single-antenna eavesdropping user (Willie).

3. The synaesthesia integrated design method for near field communication transmission according to claim 1, characterized in that: The step S2 specifically includes: The objective function is planned. Under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user, and concealment, the CRB matrix minimization optimization problem for evaluating the system perception performance is constructed by jointly optimizing the communication beamforming and the sensor beamforming: Where P is the transmit power budget of the base station, R min,k is the minimum required communication rate of the Kth legal user; (1a) is a non-convex optimization problem, (1b) represents the maximum transmission power constraint of the base station, (1c) represents the minimum communication rate requirement of each legal user, and (1d) represents the concealment constraint.

4. The synaesthesia integrated design method for near field communication transmission according to claim 1, characterized in that: The step S3 specifically includes: Since the objective function and constraints in the optimization problem are non-convex, the semi-positive definite relaxation algorithm can be used to effectively solve problem (1); First, based on the covariance matrix of the transmitted signal, the optimization problem can be restated as: s.t.tr(R x )≤P(2b) Among them, constraint (2d) can be obtained by Due to the complexity of the CRB matrix, an auxiliary matrix is ​​introduced Constraints (2c) to (2e) are non-convex, and a semi-positive definite relaxation algorithm is used, defining Satisfy F c,k ≥0, rank(F c,k )=1, problem (2) can be further transformed into: tr(R x )≤P(3c) rank(F c,k )=1(3g) Due to the rank 1 constraint, formula (3) is still non-convex; when the rank 1 constraint is omitted, the interior point method can be used to solve it and obtain the optimal solution (F c,k ,R x ), construct the following rank-one solution to obtain the communication beamforming f c,k , the sensing beamforming F is obtained by eigenvalue decomposition r ,Right now: That is, the global optimal solution to the original problem is obtained.

5. A synaesthesia integrated design device for near field communication transmission, characterized in that: include: A model building module is used to build a near-field covert synaesthesia integrated system model; The equation construction module constructs the CRB matrix minimization optimization problem for evaluating the system perception performance by jointly optimizing the communication beamforming and the sensor beamforming under the constraints of the maximum transmission power of the base station, the minimum communication rate of the user, and the concealment. The optimization processing module uses a semi-positive definite relaxation algorithm to transform the original non-convex optimization problem into a convex optimization problem and obtain the global optimal solution of the original problem.

6. The synaesthesia integrated design device for near field communication transmission according to claim 5, characterized in that: The model building module specifically includes: A near-field covert synaesthesia integrated system is established, which includes a dual-function base station (Alice) equipped with N antennas, a sensing target, K single-antenna legitimate users (Bobs) and a single-antenna eavesdropping user (Willie).

7. The synaesthesia integrated design device for near field communication transmission according to claim 5, characterized in that: The equation construction module specifically includes: The optimization equation of the equation construction module, which jointly optimizes the communication beamforming and the sensor beamforming to calculate the minimum CRB matrix for evaluating the system perception performance, is:

8. The synaesthesia integrated design device for near field communication transmission according to claim 5, characterized in that: The optimization processing module specifically includes: Optimization processing module, in view of the non-convexity of the original problem, the present invention firstly restates the original problem according to the covariance matrix of the transmitted signal, and then introduces the auxiliary matrix A semi-positive definite relaxation algorithm is used to transform the original non-convex optimization problem into a convex optimization problem, and finally the global optimal solution of the original problem is obtained.

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