A full-duplex relay-assisted interoception integrated signal-to-interference-to-noise ratio optimization method and system

Abstract

The present invention relates to the field of wireless communication technology, and discloses a full-duplex relay-assisted interaceptive integration signal-to-noise ratio optimization method and system, including: obtaining channel fading coefficients from a source node to a relay node, from a relay node to a destination node, and from a relay node to a perceived target node, calculating the communication signal-to-noise ratio, the perceived signal-to-noise ratio, and the received signal-to-noise ratio of the destination node; based on the calculated signal-to-noise ratio, taking the total transmission power of the source node and the relay node as a constraint, constructing an original optimization problem; introducing new variables, using a successive convex approximation algorithm to find the upper bound of a concave function, and converting the original optimization problem into a convex approximation optimization problem; directly solving the convex approximation optimization problem through a solver, obtaining the optimal transmission power configuration of the source node and the relay node, and realizing the maximization of the minimum signal-to-noise ratio of communication and perception. The present invention effectively improves the communication performance and perception performance of the full-duplex relay-assisted interaceptive integration system.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method and system. Background Art

[0002] With the increasing popularity of various IoT applications, sensory interconnection and interaction of all things have become important scenario requirements for 6G, which also requires wireless communication network nodes to have perception functions. Earlier studies proposed "perception-centric" and "communication-centric" design schemes, but no systematic solution has been proposed for synaesthesia integration. Synaesthesia integration usually refers to the integrated design of radio communication and perception systems to more effectively utilize wireless spectrum resources, share hardware equipment, and even achieve synergy between the two functions. Therefore, it is crucial to jointly consider communication and perception performance in the design of wireless networks.

[0003] A relay is a device used to forward signals during communication, helping signals to cross long distances or overcome signal attenuation, while a full-duplex relay is a relay that can receive and send data at the same time, allowing two-way communication. Considering that a full-duplex relay node can receive and forward signals at the same frequency at the same time, it can avoid spectrum inefficiency, reduce signal delays, and increase data rates.

[0004] The equal power allocation scheme in the existing synaesthesia integrated system may cause the signal-to-interference-noise ratio of some links to be low due to unreasonable power allocation, which directly affects the communication quality and perception accuracy. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method and system to solve the problem of limited signal-to-interference-noise ratio in existing equal power allocation schemes.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method, comprising:

[0009] Obtain the channel fading coefficients from the source node to the relay node, from the relay node to the destination node, and from the relay node to the perceived target node, and calculate the communication signal-to-interference-to-noise ratio, the perceived signal-to-interference-to-noise ratio, and the received signal-to-interference-to-noise ratio of the destination node;

[0010] Based on the calculated signal-to-interference-noise ratio, the original optimization problem is constructed with the total transmit power of the source node and the relay node as the constraint;

[0011] New variables are introduced, and the upper bound of the concave function is found by using the successive convex approximation algorithm, so as to transform the original optimization problem into a convex approximate optimization problem.

[0012] The convex approximate optimization problem is directly solved by the solver to obtain the optimal transmission power configuration of the source node and the relay node, thereby maximizing the minimum signal-to-interference-noise ratio for communication and perception.

[0013] As a preferred solution of the full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method of the present invention, wherein:

[0014] The calculating of the communication signal to interference and noise ratio and the perception signal to interference and noise ratio comprises the following steps:

[0015] The calculating of the communication signal to interference and noise ratio and the perception signal to interference and noise ratio comprises the following steps:

[0016] The full-duplex decoding relay node receives and obtains a composite signal; the composite signal includes a communication signal, a sensing signal, a loop self-interference and noise;

[0017] According to the composite signal, when the sensed signal is regarded as interference, the communication signal-to-interference-to-noise ratio of the relay node is calculated;

[0018] Based on the composite signal, the perceived signal-to-interference-and-noise ratio of the relay node is calculated when the communication signal is regarded as interference.

[0019] As a preferred solution of the full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method of the present invention, wherein:

[0020] The composite signal received by the relay node is expressed as:

[0021] ,

[0022] in, represents the channel fading coefficient from the source node to the relay node, represents the transmission power of the source node, Represents the communication signal sent by the source node, represents the channel fading coefficient for sending and returning the joint sensing signal, b ∈ [0,1] represents the reflection factor of the target, represents the transmit power of the relay node, Represents the perception signal sent by the relay node, represents the self-interference channel fading coefficient at the relay node, a ∈ [0,1] represents the loop self-interference coefficient, and They represent the loop self-interference signal generated when the relay node forwards the decoding result of the source node's communication signal and the loop self-interference signal generated when sending the perception signal. The mean at the relay node is 0 and the variance is Gaussian white noise;

[0023] The communication signal to interference and noise ratio of the relay node is expressed as:

[0024] ,

[0025] The perceived signal to interference and noise ratio of the relay node is expressed as:

[0026] .

[0027] As a preferred solution of the full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method of the present invention, wherein:

[0028] The step of calculating the received signal to interference and noise ratio of the destination node comprises the following steps:

[0029] After receiving the signal from the source node, the relay node decodes the composite signal , if the communication signal sent by the source node If the relay node decodes successfully, the communication signal sent by the source node will be Send to the destination node;

[0030] Based on the communication signal sent by the source node , get the signal received by the destination node , calculation

[0031] The received signal-to-interference-noise ratio of the node .

[0032] As a preferred solution of the full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method of the present invention, wherein:

[0033] The signal received by the destination node is expressed as:

[0034] ,

[0035] in, represents the channel fading coefficient from the relay node to the destination node, and RD represents the link from the relay node to the destination node; The mean at the destination node is 0 and the variance is Gaussian white noise;

[0036] The received signal to interference and noise ratio of the destination node is expressed as:

[0037] .

[0038] As a preferred solution of the full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method of the present invention, wherein:

[0039] The original optimization problem is constructed, including an objective function and constraints, wherein the objective function and constraints are respectively expressed as:

[0040] ,

[0041] ,

[0042] in, It represents the minimum value of the signal-to-noise ratio in the whole communication process, that is, when the full-duplex relay node forwards the communication signal, the signal-to-noise ratio of the relay node and the signal-to-noise ratio of the destination node take the minimum value. Indicates the maximum limit of the total transmission power of the source node and the relay node.

[0043] As a preferred solution of the full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method of the present invention, wherein:

[0044] The method of introducing new variables and using a successive convex approximation algorithm to find the upper bound of the concave function and transforming the original optimization problem into a convex approximation optimization problem includes the following steps:

[0045] Introducing new variables into the original optimization problem P0 , transforming the minimum signal-to-interference-noise ratio maximization problem into maximization Problems;

[0046] The constraint of minimum signal-to-interference-noise ratio in the original problem is transformed into three independent constraints;

[0047] Will , and Substitute the expression formula into the maximum The constraints are converted into new constraints, and the denominator of the new constraints is moved to the left side of the inequality formula to form a new constraint formula, that is, the original optimization problem P0 is converted into a new optimization problem P1;

[0048] Identify the non-convex constraints in the new optimization problem P1, use an iterative algorithm based on successive convex approximation to find the upper bound of the concave function, and transform the new optimization problem P1 into a convex approximate optimization problem P2.

[0049] In a second aspect, the present invention provides a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization system, comprising:

[0050] An acquisition module is used to obtain the channel fading coefficients from the source node to the relay node, from the relay node to the destination node, and from the relay node to the perception target node, and calculate the communication signal-to-interference-to-noise ratio, the perception signal-to-interference-to-noise ratio, and the receiving signal-to-interference-to-noise ratio of the destination node;

[0051] A construction module for constructing the original optimization problem based on the calculated signal-to-interference-and-noise ratio and taking the total transmit power of the source node and the relay node as a constraint;

[0052] The introduction module is used to introduce new variables, use the successive convex approximation algorithm to find the upper bound of the concave function, and transform the original optimization problem into a convex approximate optimization problem;

[0053] The solution module is used to directly solve the convex approximate optimization problem through the solver to obtain the optimal transmission power configuration of the source node and the relay node, thereby maximizing the minimum signal-to-interference-noise ratio of communication and perception.

[0054] In a third aspect, the present invention provides a computing device, comprising:

[0055] Memory, used to store programs;

[0056] The processor is used to execute the computer executable instructions, and when the computer executable instructions are executed by the processor, the steps of the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method are implemented.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method are implemented.

[0058] The beneficial effects of the present invention are as follows: by optimizing the power allocation strategy, when the total transmission power is the same, compared with the traditional equal power allocation scheme, the power joint optimization scheme proposed in the present invention significantly improves the minimum signal-to-interference-noise ratio of the system, effectively improves the communication performance and perception performance of the full-duplex relay-assisted interawareness integrated system, realizes the effective utilization of resources, and improves the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0060] Figure 1 A basic flow chart of a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method provided by an embodiment of the present invention;

[0061] Figure 2 A model diagram of embodiment 2 of a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method provided by an embodiment of the present invention;

[0062] Figure 3 A comparison diagram of a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method provided by an embodiment of the present invention and a traditional equal power allocation scheme. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation formula of the present invention is described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented using other methods different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0066] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method, such as Figure 1 As shown, the following steps are included:

[0067] S1: Obtain the channel fading coefficients from the source node to the relay node, from the relay node to the destination node, and from the relay node to the perceived target node, and calculate the communication signal-to-interference-and-noise ratio, the perceived signal-to-interference-and-noise ratio, and the received signal-to-interference-and-noise ratio of the destination node;

[0068] In the embodiment of the present application, the full-duplex decoding relay node senses the target and assists the source node to communicate with the destination node. The received signal of the full-duplex relay node can be expressed as:

[0069] (1)

[0070] in, represents the transmission power of the source node, Represents the transmit power of the relay node; represents the channel fading coefficient from the source node to the relay node, and SR represents the link from the source node to the relay node; is the self-interference channel fading coefficient at the relay node, LI represents the self-interference link of the relay node; is the channel fading coefficient for the joint sensing signal transmission and return, and ,in represents the channel fading coefficient from the sensing target node to the relay node, TR represents the backhaul link from the sensing target node to the relay node; represents the channel fading coefficient from the relay node to the sensing target node, and RT represents the link from the relay node to the sensing target node; represents the communication signal sent by the source node, Represents the perception signal sent by the relay node, and They represent the loop self-interference signal generated when the relay node forwards the decoding result of the source node communication signal and the loop self-interference signal generated when sending the perception signal, and satisfy

[0071] ,in represents the modulo operation on complex numbers, Represents the mathematical expectation operation; a ∈ [0,1] represents the loop self-interference coefficient; b ∈ [0,1] represents the reflectivity factor of the target; The mean at the relay node is 0 and the variance is Gaussian white noise.

[0072] According to formula (1), when the sensed signal is regarded as interference, the communication signal-to-noise ratio of the relay node can be expressed as:

[0073] (2)

[0074] According to formula (1), when the communication signal is regarded as interference, the perceived signal-to-interference-to-noise ratio of the relay node can be expressed as:

[0075] (3)

[0076] In the embodiment of the present application, after receiving the signal from the source node, the relay node decodes the received signal and forwards it to the destination node. It can be expressed as:

[0077] (4)

[0078] in, represents the channel fading coefficient from the relay node to the destination node, and RD represents the link from the relay node to the destination node; The mean at the destination node is 0 and the variance is Gaussian white noise.

[0079] if If the decoding is successful at the relay node, the signal will be sent to the destination node. According to formula (4), the received signal-to-interference-noise ratio of the destination node can be expressed as:

[0080] (5)

[0081] S2: Based on the calculated signal-to-interference-noise ratio, the original optimization problem is constructed with the total transmit power of the source node and the relay node as the constraint;

[0082] In the embodiment of the present application, the minimum signal to interference and noise ratio maximization problem of communication and perception is constructed as follows:

[0083] ,

[0084] (6a)

[0085] The optimization problem is constrained by the total transmission power of the source node and the relay node. It represents the minimum value of the signal-to-noise ratio in the whole communication process, that is, when the full-duplex relay node forwards the communication signal, the signal-to-noise ratio of the relay node and the signal-to-noise ratio of the destination node take the minimum value. The optimization goal is the minimum value of the communication signal-to-noise ratio and the perception signal-to-noise ratio.

[0086] S3: Introduce new variables and use the successive convex approximation algorithm to find the upper bound of the concave function, transforming the original optimization problem into a convex approximate optimization problem;

[0087] In the embodiment of the present application, the problem-solving method is as follows:

[0088] Since the original optimization problem The optimization objective is more complex. By introducing a new variable , transforming the original problem into:

[0089]

[0090] (7a)

[0091] (7b)

[0092] (7c)

[0093] (7d)

[0094] Substituting formulas (2), (3) and (5) into (7c), (7d) and (7b) respectively, we obtain:

[0095]

[0096] (8a)

[0097] (8b)

[0098] (8c)

[0099] (8d)

[0100] Since constraints (8c) and (8d) are fractional formula constraints, it is not easy to see their concavity and convexity. At the same time, the denominators in constraints (8c) and (8d) are both greater than zero. Multiplying the denominators to the left, we get:

[0101] (9a)

[0102] (9b)

[0103] According to the Hessian matrix, it can be proved that the left side of the inequality formula in formula (9a) and (9b) is a concave function. Therefore, the Sequential Convex Approximation (SCA) method can be used to transform the original non-convex objective function into an approximate convex function and obtain the optimal solution for power allocation. ; ;

[0104] According to the properties of concave function and Taylor expansion formula, we can get:

[0105] (10a)

[0106]

[0107] (10b)

[0108] in, is the current time, is the initial time, is the initial value of the source node’s transmit power, is the initial value of the relay node transmit power. is the first function assumed, is the assumed second function, and is the upper bound of the assumed function.

[0109] Therefore, formulas (9a) and (9b) can be transformed into:

[0110] (11a) (11b)

[0111] Therefore, for the optimization problem after conversion can be transformed into:

[0112] (12)

[0113] S4: The convex approximate optimization problem is directly solved by the solver to obtain the optimal transmission power configuration of the source node and the relay node, thereby maximizing the minimum signal-to-interference-noise ratio for communication and perception.

[0114] In the embodiment of the present application, the constraints of the above optimization problems are all convex structures. Therefore, the convex approximation optimization problem The problem is a standard convex optimization problem, which can be solved by the CVX solver to obtain the optimal solution for the transmission power of the source node and the relay node.

[0115] This embodiment also provides a full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization system, including:

[0116] An acquisition module is used to obtain the channel fading coefficients from the source node to the relay node, from the relay node to the destination node, and from the relay node to the perception target node, and calculate the communication signal-to-interference-to-noise ratio, the perception signal-to-interference-to-noise ratio, and the receiving signal-to-interference-to-noise ratio of the destination node;

[0117] A construction module for constructing the original optimization problem based on the calculated signal-to-interference-and-noise ratio and taking the total transmit power of the source node and the relay node as a constraint;

[0118] The introduction module is used to introduce new variables, use the successive convex approximation algorithm to find the upper bound of the concave function, and transform the original optimization problem into a convex approximate optimization problem;

[0119] The solution module is used to directly solve the convex approximate optimization problem through the solver to obtain the optimal transmission power configuration of the source node and the relay node, thereby maximizing the minimum signal-to-interference-noise ratio of communication and perception.

[0120] Furthermore, it also includes:

[0121] Memory, used to store programs;

[0122] A processor is used to load the program to execute the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method.

[0123] In an embodiment of the present application, the method is applicable to a synaesthesia integrated system consisting of a source node, a relay node, a perception target node and a destination node.

[0124] In an embodiment of the present application, the source node sends a communication signal, and the full-duplex relay node is equipped with two antennas, which are responsible for receiving and sending signals respectively. While the relay node senses the target, it assists the source node in communicating with the destination node, and each of the other nodes is equipped with an antenna.

[0125] This embodiment also provides a computer-readable storage medium storing a program, and when the program is executed by a processor, the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method is implemented.

[0126] The storage medium proposed in this embodiment and the full-duplex relay assisted synaesthesia integrated signal-to-interference-noise ratio optimization method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0127] Through the above description of the implementation formula, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation formula. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0128] Example 2, reference Figure 2-Figure 3 , which is an embodiment of the present invention, provides a full-duplex relay assisted synaesthesia integrated signal-to-interference-noise ratio optimization method. In order to verify its beneficial effects, this embodiment gives an example of implementing the invention using MATLAB on a computer.

[0129] Model diagram Figure 2As shown in Figure 1, it is assumed that the channels are independent of each other and the channel state information remains constant in the relevant time block. The distance between the source node and the relay node is 200 meters, the distance between the relay node and the destination node is 200 meters, the distance between the relay node and the sensing target is 75 meters, and the distance between the two antennas in the full-duplex relay is set to 0.5 meters. The path loss fading is modeled as ,in, represents the path loss fading coefficient between nodes, represents the distance between nodes, , reference distance Assuming 1 meter, the path loss When the value is 2, each channel follows Rayleigh distribution. Additive Gaussian white noise The value is -80 dB, setting the maximum total transmit power It is 10W.

[0130] Figure 3 Graph showing the relationship between the minimum signal to interference and noise ratio and the maximum transmission power for communication and perception in an example of the present invention.

[0131] The traditional equal power allocation comparison scheme is to optimize by fixing the transmission power of the source node and the transmission power of the relay node to half of the maximum total transmission power.

[0132] from Figure 3 It can be seen that the communication and perception minimum signal to noise ratio of the scheme proposed in the present invention is much higher than that of the traditional equal power allocation comparison scheme. The minimum signal to noise ratio of communication and perception obtained by the two schemes increases with the maximum total transmit power. At the same time, the power joint optimization scheme proposed in the present invention further improves the minimum signal-to-interference-noise ratio of communication and perception without increasing the total transmit power, thereby improving the performance of the system. Therefore, the scheme of the present invention can significantly achieve effective power joint optimization, which is superior to the traditional equal power allocation scheme.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A full-duplex relay-assisted interaceptive integrated signal-to-interference-noise ratio optimization method, characterized in that: include: Obtain the channel fading coefficients from the source node to the relay node, from the relay node to the destination node, and from the relay node to the perceived target node, and calculate the communication signal-to-interference-to-noise ratio, the perceived signal-to-interference-to-noise ratio, and the received signal-to-interference-to-noise ratio of the destination node; Based on the calculated signal-to-interference-noise ratio, the original optimization problem is constructed with the total transmit power of the source node and the relay node as the constraint; New variables are introduced, and the upper bound of the concave function is found by using the successive convex approximation algorithm, so as to transform the original optimization problem into a convex approximate optimization problem. The convex approximate optimization problem is directly solved by the solver to obtain the optimal transmission power configuration of the source node and the relay node, thereby maximizing the minimum signal-to-interference-to-noise ratio for communication and perception. The calculating of the communication signal to interference and noise ratio and the perception signal to interference and noise ratio comprises the following steps: The full-duplex decoding relay node receives and obtains a composite signal; the composite signal includes a communication signal, a sensing signal, a loop self-interference and noise; According to the composite signal, when the sensed signal is regarded as interference, the communication signal-to-interference-to-noise ratio of the relay node is calculated; Based on the composite signal, the perceived signal-to-interference-and-noise ratio of the relay node is calculated when the communication signal is regarded as interference; The original optimization problem is constructed, including an objective function and constraints, wherein the objective function and constraints are respectively expressed as: s.t. 0≤P R +P S ≤P max Among them, min(γ SR ,γ RD ) represents the minimum value of the signal-to-noise ratio in the whole communication process, that is, when the full-duplex relay node forwards the communication signal, the signal-to-noise ratio of the relay node and the signal-to-noise ratio of the destination node take the minimum value, P R Represents the transmission power of the relay node, P S represents the transmission power of the source node, γ SR represents the communication signal-to-interference-to-noise ratio of the relay node, γ RD represents the received signal-to-interference-noise ratio of the destination node, γ RR represents the perceived signal-to-interference-noise ratio of the relay node, P max Indicates the maximum limit of the total transmission power of the source node and the relay node; The method of introducing new variables and using a successive convex approximation algorithm to find the upper bound of the concave function and transforming the original optimization problem into a convex approximation optimization problem includes the following steps: Introducing a new variable t into the original optimization problem, the problem of maximizing the minimum signal-to-interference-noise ratio is transformed into the problem of maximizing t; The minimum signal to noise ratio in the original optimization problem is transformed into t≤γ RD , t≤γ SR , t≤γ RR Three independent constraints; Will γ SR , γ RR and γ RD Substitute the expression formula into the constraint condition of maximizing t to obtain a new constraint condition. Move the denominator of the new constraint condition to the left side of the inequality formula to form a new constraint form formula, that is, the original optimization problem is transformed into a new optimization problem. Identify the non-convex constraints in the new optimization problem, use an iterative algorithm based on successive convex approximation to find the upper bound of the concave function, and transform the new optimization problem into a convex approximate optimization problem.

2. The full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method according to claim 1, characterized in that: The composite signal received by the relay node is expressed as: Among them, h SR represents the channel fading coefficient from the source node to the relay node, P S represents the transmission power of the source node, x S，C represents the communication signal sent by the source node, h RR represents the channel fading coefficient of the joint sensing signal transmission and return, b∈[0,1] represents the reflection factor of the target, P R represents the transmission power of the relay node, x R，S represents the sensing signal sent by the relay node, h LI represents the self-interference channel fading coefficient at the relay node, a∈[0,1] represents the loop self-interference coefficient, and They represent the loop self-interference signal generated when the relay node forwards the decoding result of the source node communication signal and the loop self-interference signal generated when sending the perception signal, respectively. R Represents Gaussian white noise with a mean of 0 and a variance of N0 at the relay node; The communication signal to interference and noise ratio of the relay node is expressed as: The perceived signal to interference and noise ratio of the relay node is expressed as:

3. The full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method according to claim 2, characterized in that: The step of calculating the received signal to interference and noise ratio of the destination node comprises the following steps: After receiving the signal from the source node, the relay node decodes the composite signal y RR , if the communication signal x sent by the source node S，C If the relay node decodes successfully, the communication signal x sent by the source node will be S，C Send to the destination node; Based on the communication signal x sent by the source node S，C , get the signal y received by the destination node D , calculate the received signal-to-interference-to-noise ratio γ of the destination node RD .

4. The full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method according to claim 3, characterized in that: The signal received by the destination node is expressed as: Among them, h RD represents the channel fading coefficient from the relay node to the destination node, RD represents the link from the relay node to the destination node; n D Represents Gaussian white noise with a mean of 0 and a variance of N0 at the destination node; The received signal to interference and noise ratio of the destination node is expressed as:

5. A system based on the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method according to claim 1, characterized in that: include: An acquisition module is used to obtain the channel fading coefficients from the source node to the relay node, from the relay node to the destination node, and from the relay node to the perception target node, and calculate the communication signal-to-interference-to-noise ratio, the perception signal-to-interference-to-noise ratio, and the receiving signal-to-interference-to-noise ratio of the destination node; A construction module for constructing the original optimization problem based on the calculated signal-to-interference-and-noise ratio and taking the total transmit power of the source node and the relay node as a constraint; The introduction module is used to introduce new variables, use the successive convex approximation algorithm to find the upper bound of the concave function, and transform the original optimization problem into a convex approximate optimization problem; The solution module is used to directly solve the convex approximate optimization problem through the solver to obtain the optimal transmission power configuration of the source node and the relay node, thereby maximizing the minimum signal-to-interference-noise ratio of communication and perception.

6. An electronic device, characterized in that: include: Memory, used to store programs; A processor is used to load the program to execute the steps of the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the steps of the full-duplex relay-assisted synaesthesia integrated signal-to-interference-noise ratio optimization method as described in any one of claims 1 to 4 are implemented.

Images