A resource allocation method and system for perception communication integration

Abstract

The application discloses a kind of resource allocation methods and systems for integrated sensing communication, method includes: the networking system of being made of integrated sensing communication base station is positioned to a target and the scene of being communicated with a user;Establish the resource allocation model of element division and subcarrier selection and transmission for integrated sensing communication with system total power consumption minimization as objective function, with sensing performance, communication rate, spectrum and element resource as constraint condition;High coupling and non-convexity existing in resource allocation model are solved.The application can minimize the power consumption of integrated sensing communication system by optimizing spectrum, element and power under the constraint of sensing positioning accuracy and communication rate.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of communication integration resource allocation, and particularly relates to a resource allocation method and system for perception communication integration. BACKGROUND

[0002] With the continuous development of intelligent driving and Internet of Things technology, the integration of communication and perception systems has become an important research field. Traditionally, communication systems and perception systems are designed and operated independently, but with the shortage of spectrum resources and the progress of technology, the integration of perception communication has become an effective way to improve spectrum utilization efficiency, improve system performance and save cost. The integration of communication and perception systems not only optimizes the use of spectrum, but also improves the reliability and efficiency of signal transmission through joint design.

[0003] At present, some researches have proposed resource allocation methods for the integration of communication and perception. Under the constraints of ensuring the accuracy of target parameter estimation and the data rate of communication, the system power consumption is optimized by optimizing the allocation of integrated orthogonal frequency division multiplexing subcarriers and the transmission power on each subcarrier. However, due to the lack of effective algorithm support, the existing research results have not considered the coordinated optimization of spectrum, array elements and power in multiple dimensions, resulting in limited performance improvement of the system under multiple resource constraints. Therefore, a method for dividing array elements, selecting subcarriers and allocating transmission resources for the integration of perception communication is proposed, which has important theoretical value and application prospect.

[0004] In the design process of the integrated perception communication system, how to reasonably allocate spectrum, array elements and power resources has become a key problem to realize the efficient performance of the system. The sharing of spectrum resources enables communication and perception systems to work in the same frequency band, but different detection targets and communication users have different demands and usage methods for spectrum, resulting in resource conflict and interference problems. The reasonable allocation of array elements and power directly affects the perception performance and communication quality of the system. Therefore, how to meet the service needs of the communication system while ensuring the perception ability is a core problem that must be solved in the design of the integrated perception communication system. SUMMARY

[0005] The technical problem to be solved by the application is to provide a resource allocation method that can minimize the power consumption of the integrated perception communication system by optimizing spectrum, array elements and power under the constraints of perception positioning accuracy and communication rate.

[0006] To solve the above technical problems, the application provides the following technical solutions.

[0007] A resource allocation method for the integration of perception communication, comprising:

[0008] constructing a model by MA network system composed of integrated sensing and communication base stations is positioned L One goal and with Q Scenarios involving communication between individual users;

[0009] Establish a resource allocation model for array element partitioning, subcarrier selection, and transmission of integrated sensing and communication, with the objective function of minimizing total system power consumption and constraints of sensing performance, communication rate, spectrum, and array element resources.

[0010] Solve for the high coupling and non-convexity in the resource allocation model.

[0011] In one embodiment of the present invention, the resource allocation model is expressed by the following formula:

[0012] ,(1);

[0013] In the formula, To represent as through optimization , , , , , Minimize the objective function. The power allocation vector for the sensing function, where the first... The elements are , indicating the first The base station for the first Power allocated to each target location. It is the power allocation matrix for communication functions. express A real matrix, The total number of communication carriers for each base station, Indicates the first The communication spectrum portion of the base station The subcarrier pair Power of individual user communication Represented as the array element allocation vector for sensing functions, where the first... The elements are , is the The base station for the first The number of array elements allocated to each target location; It is the array element allocation matrix for communication functions, where Indicates the first The first base station generates The subcarrier beam and the first The number of array elements required for each user to communicate; Represents the target's position and state vector. This is the matrix transpose. It is the bandwidth allocation vector for the sensing function, where the first... The elements are , indicating the first The base station for the first Bandwidth allocated to each target location; It is a communication carrier allocation matrix, where , A value of 1 indicates the first The communication spectrum portion of the base station The subcarrier is assigned to the first One user is used for communication. A value of 0 indicates no allocation; Indicates the first The base station for the first q Total bandwidth allocated to each user, of which It is the width of each subcarrier; and This represents the predefined positioning accuracy threshold and communication rate threshold; and This represents the total number of array elements and total bandwidth for each base station; , and These represent the sensing bandwidth, sensing power, and communication power, respectively, and their values ​​are non-negative. The number of array elements used for sensing and communication must be from 1 to... Integers in These are represented by constraint symbols, with C1 to C9 indicating the constraint condition numbers. The trace of the matrix, Let Cramer-Rao be the lower bound matrix. For the first q Communication rate per user is any symbol in mathematics.

[0014] In one embodiment of the present invention, solving for the high coupling and non-convexity in the resource allocation model includes:

[0015] For now, we will temporarily ignore the coupling constraints of shared spectrum and array elements between sensing and communication in a single base station, and decompose the overall constraint optimization problem into two independent sub-problems: the power, bandwidth and array element resource allocation sub-problem of the sensing system, and the power, carrier and array element resource allocation sub-problem of the communication system.

[0016] The problem of the sensing system is solved by introducing semidefinite programming, taking advantage of the collinear relationship between the transmission power of the sensing system and its allocated bandwidth and the number of array elements; the problem of the communication system is solved by the alternating direction multiplier method.

[0017] To address the issue of shared array elements in sensing and communication systems, the system transmit power is minimized when the ratio of the power of sensing and communication to the number of their corresponding array elements is equal. This principle is used to divide the array elements.

[0018] For the overall spectrum allocation, based on the above resource allocation situation, a greedy algorithm is used to dynamically adjust the spectrum allocation of the sensing and communication systems within the same base station to meet the physical constraints of their shared spectrum and achieve reasonable coordination and allocation of spectrum resources.

[0019] In one embodiment of the present invention, the subproblem of the sensing system is solved by introducing semidefinite programming, taking advantage of the collinear relationship between the transmission power of the sensing system and its allocated bandwidth and the number of array elements. The subproblem includes:

[0020] Introducing Lemma 1: For each base station with a given bandwidth, the solutions to the power and bandwidth allocation of the sensing function in the sensing system subproblem are collinear;

[0021] According to Lemma 1, the perception system subproblem is transformed for the first time to reduce the fitness vector in the perception system subproblem; based on the first transformation, Lemma 2 is introduced to optimize the perception system subproblem; where Lemma 2 states that the array elements and bandwidth in the perception system subproblem are collinear;

[0022] ① Power allocation: Based on Lemma 1 and Lemma 2, and taking the first... The base station for the first The bandwidth allocated for each target location is taken as a fixed value, and the subproblem of the sensing system is transformed a second time.

[0023] For the problem of minimizing the total power in the problem, it is equivalent to finding the sum of the power required to locate each target, thus decomposing the subproblem of the sensing system after the second transformation into... L Solving each subproblem is represented as follows:

[0024] ,(twenty two);

[0025] Introduce an auxiliary matrix Rewrite equation (22):

[0026] ,(twenty three);

[0027] The optimal solution for power allocation is obtained by using a semi-positive definite rule algorithm.

[0028] ②Bandwidth Allocation: Based on the obtained sensing function power allocation results, and using Lemma 1, the corresponding bandwidth allocation results are obtained through iterative optimization. The iterative expression is as follows:

[0029] ;

[0030] ③ Repeat steps ① and ② until the power converges; obtain the solution to the constrained optimization sensing system subproblem. and Then, according to Lemma 2, we get ,in, , , ;

[0031] In the formula, It is the identity matrix. Fisher matrix for target position parameter sensing, , All are constants greater than 0. and They represent the first time. In the nth iteration The bandwidth and power vector allocated to the sensing function on each base station. For the first In the nth iteration The total power allocated to the sensing function on each base station.

[0032] In one embodiment of the present invention, the communication system subproblem is solved using the alternating direction multiplier method, including:

[0033] Introducing Lemma 3: For each base station with a given number of array elements, the power allocated to the communication function in the communication system subproblem and the solution of the array elements are collinear;

[0034] ① Fixed element values Then the binary variable Continuous transformation Ignore the same carrier The constraint that resources cannot be allocated to multiple users simultaneously decouples resource allocation between different users, making minimizing the total transmit power equivalent to adding the minimum power of each user's communication, thus decomposing the communication system subproblem into... Solve the sub-problems one by one, and perform the first formal transformation of the communication system sub-problem;

[0035] ②According to Compared to It is convex, and the Lagrange multipliers are used to optimize the convex optimization problem in the communication system subproblem; where the Lagrange function of the communication system subproblem is expressed as follows:

[0036] (27);

[0037] The Lagrangian function representation of the communication system subproblem. It is a Lagrange multiplier. For communication rate threshold, For the first m The first base station n The subcarrier pair q Channel parameters for individual user communication. For from the first m The base station to the q Signal attenuation for individual users; This represents the power of zero-mean Gaussian white noise;

[0038] ③ The alternating direction multiplier method is used to iteratively update the power, carrier, and corresponding multipliers allocated for communication functions, as shown in the following formula:

[0039] (28);

[0040] , , , , and They represent the first time. and The power, subcarriers, and Lagrange multipliers used for communication in the next iteration. , and All are update step sizes. , They are respectively and The gradient;

[0041] ④ Update the array element allocation results for communication according to the following formula:

[0042] (29);

[0043] Indicates the first In the nth iteration The array element vectors allocated to the communication functions on each base station. and Indicates the first In the nth iteration The power vector and total power allocated to the communication functions on each base station; and All are constants greater than 0. The total number of array elements allocated for communication in each base station;

[0044] ⑤ Processing and The boundary, if ,but ;if ,but , or, if ,but ;

[0045] For the first The next iteration , For the first The next iteration ;

[0046] ⑥ Repeat steps ③, ④, and ⑤ until the power converges, thus obtaining the solution to the constrained optimization communication system subproblem. , and ,in, , , .

[0047] In one embodiment of the present invention, the system transmit power is minimized when the ratio of the power of sensing and communication to the number of its corresponding array elements is equal. Array element partitioning is performed based on this ratio, including:

[0048] The array element partitioning should satisfy the following relationship:

[0049] ,(34;

[0050] , , and These represent the power and array element allocation results of the sensing function in the sensing system subproblem and the power and array element allocation results of the communication function in the communication system subproblem, respectively.

[0051] Then, based on the relationships that the array elements should satisfy, the constraints are obtained. Power and element allocation results for sensing and communication functions:

[0052] (35);

[0053] In the formula, , representing the power and array element ratio coefficients required for sensing and communication functions in each base station, where, , ; The total number of array elements for each base station.

[0054] In one embodiment of the present invention, achieving reasonable coordination and allocation of spectrum resources includes:

[0055] ① Divide the spectrum of each base station into equal parts. One, of which Represented as:

[0056] ,(36;

[0057] ② Sequentially divide each spectrum Substituting into step ① above, calculate the changes in sensing intensity and communication power before and after the increase in spectrum; if ,but ;on the contrary ;

[0058] , These represent the bandwidth allocated to sensing and communication in each base station, and the total bandwidth, respectively. , To increase the spectrum The change in power consumption of the sensing and communication systems before and after. Total bandwidth for each base station.

[0059] In one embodiment of the present invention, the lower bound matrix of Craméro is taken. Methods include: obtaining it by inverting the Fisher matrix; and obtaining it by... The Fisher matrix for sensing the location parameters of each target is:

[0060] (2);

[0061] formula, and They are represented as follows:

[0062] (3);

[0063] (4);

[0064] (5);

[0065] In the formula, , They represent the first m The first base station and the first The location coordinates of the target , For the first The first objective is relative to the second objective. Radar cross-section of each base station, For the first The base station and the first The distance between the targets; The power of zero-mean Gaussian white noise;

[0066] The lower bound matrix of Cramer-Rao is obtained by inverting the Fisher matrix:

[0067] , (7).

[0068] In one embodiment of the present invention, the first q Communication rate per user It can be obtained through the following formula:

[0069] (8);

[0070] In the formula, Indicates the first The first base station The subcarrier pair Channel parameters for individual user communication; This represents the power of zero-mean Gaussian white noise; Indicates from the first The base station to the Signal attenuation for individual users ,in It is the first The base station and the first The distance between users.

[0071] This invention also provides a resource allocation system for integrated sensing and communication, which applies the above-described resource allocation method for integrated sensing and communication, including:

[0072] The scene building module is used to build scenes from... A network system composed of integrated sensing and communication base stations is positioned One goal and with Scenarios involving communication between individual users;

[0073] The resource allocation module is used to establish a resource allocation model for array element partitioning, subcarrier selection, and transmission in the context of integrated sensing and communication, with the objective function of minimizing total system power consumption and the constraints of sensing performance, communication rate, spectrum, and array element resources.

[0074] The optimization module is used to solve for the high coupling and non-convexity in the resource allocation model.

[0075] Compared with the prior art, the beneficial effects of the present invention are:

[0076] This invention proposes a method for array element partitioning, subcarrier selection, and transmission resource allocation for integrated sensing and communication. The main technical problem to be solved is: firstly, constructing a method consisting of… A network system composed of integrated sensing and communication base stations is positioned One goal and with This invention addresses the scenario of individual user communication. Secondly, with the objective function of minimizing total system power consumption and constraints such as sensing performance, communication rate, spectrum, and array element resources, a model for array element partitioning, subcarrier selection, and transmission resource allocation for integrated sensing and communication is established. Finally, to address the high coupling and non-convexity issues in the proposed model, this invention designs an efficient solution strategy, specifically including the following steps:

[0077] Problem Decomposition and Preliminary Optimization: First, temporarily ignoring the coupling constraints of shared spectrum and array elements between sensing and communication in a single base station, the overall constraint optimization problem is decomposed into two independent sub-problems: the power, bandwidth, and array element resource allocation sub-problem of the sensing system, and the power, carrier, and array element resource allocation sub-problem of the communication system. The sensing system sub-problem is solved using semidefinite programming (SDP) by leveraging the collinear relationship between the sensing system's transmit power and its allocated bandwidth and number of array elements. The communication sub-problem is solved using the Alternating Direction Method of Multipliers (ADMM).

[0078] Overall array element partitioning: Further considering the issue of shared array elements between the sensing and communication systems, firstly, it is proven that the total system transmit power is minimized when the ratio of the power required for sensing and communication functions to the number of their corresponding array elements is equal. Then, array element partitioning is performed based on this.

[0079] Global spectrum allocation: Based on the above resource allocation, a greedy algorithm is used to dynamically adjust the spectrum allocation of the sensing and communication systems within the same base station to meet the physical constraints of their shared spectrum and achieve reasonable coordination and allocation of spectrum resources.

[0080] Ultimately, this strategy achieves an integrated resource allocation scheme for sensing and communication that satisfies all performance constraints and minimizes total power consumption, effectively improving the overall performance and resource utilization efficiency of the system.

[0081] This invention minimizes the power consumption of an integrated sensing and communication system by optimizing the spectrum, array elements, and power, under the constraints of sensing and positioning accuracy and communication rate. This advantage arises because the invention employs an array element partitioning, subcarrier selection, and transmission resource allocation method for integrated sensing and communication. This method uses minimizing total transmission power as the objective function and constrains sensing performance, communication rate, spectrum, and array element resources to establish an integrated sensing and communication array element partitioning, subcarrier selection, and transmission resource allocation model. Attached Figure Description

[0082] Figure 1This is a flowchart of a resource allocation method for integrated sensing and communication according to an embodiment of the present invention.

[0083] Figure 2 This is a block diagram of a resource allocation system for integrated sensing and communication according to an embodiment of the present invention. Detailed Implementation

[0084] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0085] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0086] Please see Figure 1 As shown, the present invention provides a resource allocation method for integrated sensing and communication, comprising:

[0087] S10, constructed by A network system composed of integrated sensing and communication base stations is positioned One goal and with A scenario involving communication between individual users.

[0088] In this embodiment, on a two-dimensional plane by A network system composed of integrated sensing and communication base stations is positioned L One goal and with Individual user communication. Assume that the base station can obtain the location information of each target and user, as well as the radar cross section (RCS) of each target and the channel parameters of each user from previous historical information.

[0089] S20 establishes a resource allocation model for array element partitioning, subcarrier selection, and transmission of integrated sensing and communication, with the objective function of minimizing total system power consumption and the constraints of sensing performance, communication rate, spectrum, and array element resources.

[0090] In this embodiment, the expression for the resource allocation model is as follows:

[0091] ,(1);

[0092] In the formula, To represent as through optimization , , , , , Minimize the objective function. The power allocation vector for the sensing function, where the first... The elements are , indicating the first The base station for the first Power allocated to each target location. It is the power allocation matrix for communication functions. express A real matrix, The total number of communication carriers for each base station, Indicates the first The communication spectrum portion of the base station The subcarrier pair Power of individual user communication Represented as the array element allocation vector for sensing functions, where the first... The elements are , is the The base station for the first The number of array elements allocated to each target location; It is the array element allocation matrix for communication functions, where Indicates the first The first base station generates The subcarrier beam and the first The number of array elements required for each user to communicate; Represents the target's position and state vector. This is the matrix transpose. It is the bandwidth allocation vector for the sensing function, where the first... The elements are , indicating the first The base station for the first Bandwidth allocated to each target location; It is a communication carrier allocation matrix, where , A value of 1 indicates the first The communication spectrum portion of the base station The subcarrier is assigned to the first One user is used for communication. A value of 0 indicates no allocation; Indicates the first The base station for the first q Total bandwidth allocated to each user, of which It is the width of each subcarrier; and This represents the predefined positioning accuracy threshold and communication rate threshold; and This represents the total number of array elements and total bandwidth for each base station; , and These represent the sensing bandwidth, sensing power, and communication power, respectively, and their values ​​are non-negative. The number of array elements used for sensing and communication must be from 1 to... Integers in These are represented by constraint symbols, with C1 to C9 indicating the constraint condition numbers. The trace of the matrix, Let Cramer-Rao be the lower bound matrix. For the first q Communication rate per user is any symbol in mathematics.

[0093] In one embodiment of the present invention, This is the Cramer-Rao Lower Bound (CRLB), used to measure the intensity of target location perception. It can be obtained by inverting the Fisher Information Matrix (FIM). The FIM for sensing the location parameters of each target is:

[0094] (2);

[0095] In the formula, and They are represented as follows:

[0096] (3);

[0097] (4);

[0098] (5);

[0099] In the formula, , They represent the first m The first base station and the first l The location coordinates of the target , For the first The first objective is relative to the second objective. m Radar cross-section of each base station, The power of zero-mean Gaussian white noise, For the first m The base station and the first The distance between the targets is represented as:

[0100] (6);

[0101] Then, by inverting the FIM matrix, the CRLB matrix is ​​obtained:

[0102] (7);

[0103] In this embodiment, It is the first q The communication rate of an individual user is used to characterize the performance of communication, and its expression is:

[0104] (8);

[0105] In the formula, Indicates the first The first base station The subcarrier pair Channel parameters for individual user communication; This represents the power of zero-mean Gaussian white noise; Indicates from the first The base station to the Signal attenuation for individual users ,in It is the first The base station and the first The distance between users is represented as:

[0106] (9);

[0107] In the formula, For the first The location coordinates of each user.

[0108] S30 solves for the high coupling and non-convexity in the resource allocation model.

[0109] In one embodiment of the present invention, solving for the high coupling and non-convexity in the resource allocation model includes:

[0110] S31, temporarily ignore the coupling constraints of shared spectrum and array elements in a single base station for sensing and communication, and decompose the overall constraint optimization problem into two independent sub-problems: the power, bandwidth and array element resource allocation sub-problem of the sensing system, and the power, carrier and array element resource allocation sub-problem of the communication system.

[0111] In this embodiment, since the constrained optimization problem in equation (1) is a mixed-integer nonlinear programming problem (MINLP), which is an NP-hard problem, it is difficult to find its optimal solution. Secondly, due to the constraints... and The existence of this factor creates a coupling relationship between the sensing and communication functions in the allocation of array elements and spectrum, further increasing the difficulty of resource allocation. Therefore, we first relax the integer variables. and Then temporarily ignore the constraints. and The spectrum and number of array elements of each base station are divided into two parts, one for sensing and the other for communication functions. The problem of array element partitioning, subcarrier selection, and transmission resource allocation for integrated sensing and communication is decomposed into two sub-problems: sensing and communication systems.

[0112] (10);

[0113] ,(11)

[0114] In the formula, , , and The total number of array elements and total bandwidth allocated to sensing and communication in each base station are given respectively; next, the resource allocation for sensing and communication will be solved separately.

[0115] S32 utilizes the characteristic that the transmission power of the sensing system is collinear with its allocated bandwidth and the number of array elements to introduce semidefinite programming for solving the sensing system subproblem; the alternating direction multiplier method is used to solve the communication system subproblem.

[0116] In this embodiment, I. The resource allocation sub-problem in the sensing system:

[0117] Introducing Lemma 1: For each base station with a given bandwidth, the solutions to the power and bandwidth allocation of the sensing function in the sensing system subproblem are collinear.

[0118] In this embodiment, for the perception resource allocation problem in (10), the following lemma is first introduced:

[0119] Lemma 1: For each base station with a given bandwidth, the solutions for the power and bandwidth of the sensing function allocation in (10) are collinear, i.e.:

[0120] (12);

[0121] In the formula, and The first The optimal solution for the power and bandwidth allocated to the sensing function in each base station. It is a constant greater than 0.

[0122] Proof: First, define two... L A vector of components and ,satisfy:

[0123] (13);

[0124] ,(14)

[0125] In the formula, for The first in l One portion, for The first in l One portion, For the first m The total bandwidth allocated to the sensing function in each base station. for The first in l One portion, for The first in l Each component.

[0126] From (13) and (14) we obtain:

[0127] (15);

[0128] According to Hölder's inequality (16), the numerator satisfies:

[0129] (17);

[0130] Then, substituting equation (17) into equation (16), we obtain the following result:

[0131] (18);

[0132] It can be seen from equations (15) and (18) that there exists a pair of collinear vectors. and This can reduce or keep the system power consumption constant. Therefore, the optimal solution must satisfy the collinearity condition, thus proving Lemma 1.

[0133] According to Lemma 1, the perceptual system subproblem undergoes a first formal transformation, reducing the fitness vector in the perceptual system subproblem. The expression for the transformed problem (10) is:

[0134] ,(19)

[0135] Although one adaptation vector is reduced, (19) is still a multivariate optimization problem and is difficult to solve. This is because the first constraint is for the same target, but the second constraint is for the same base station, which leads to coupling between different targets, making it impossible to decompose (19) into subproblems of the same target or the same base station. Therefore, in order to solve this problem, we designed an efficient algorithm and proposed the following Lemma 2.

[0136] Based on the first formal transformation, Lemma 2 is introduced to optimize the perceptual system subproblem; where Lemma 2 states that the array elements and bandwidth are collinear in the perceptual system subproblem.

[0137] In this embodiment, Lemma 2: In the optimization problem (10), the solutions for the array elements and the bandwidth are also collinear, that is:

[0138] (20);

[0139] in, It is the first m The solution for the allocated array elements and bandwidth in each base station. The proof is similar to Lemma 1 and will not be repeated here.

[0140] Based on Lemma 1 and Lemma 2, we introduce an iterative algorithm to solve the optimization problem (10):

[0141] ① Power allocation: Based on Lemma 1 and Lemma 2, and taking the first... The base station for the first Taking the bandwidth allocated for target localization as a fixed value, the perception system subproblem is transformed a second time, with the specific expression as follows:

[0142] ,(twenty two);

[0143] in, Regarding the power allocation problem for sensing functions (21), since there is no coupling between different objectives, there is no impact between the objectives during resource allocation. Therefore, the problem of minimizing the total power in (21) can be equivalently transformed into finding the sum of the power required to locate each objective, thus decomposing (21) into... L Solve the sub-problems.

[0144] For the problem of minimizing the total power in the problem, it is equivalent to finding the sum of the power required to locate each target, thus decomposing the subproblem of the sensing system after the second transformation into... L Solving each subproblem is represented as follows:

[0145] ,(twenty two);

[0146] Introduce an auxiliary matrix Rewrite equation (22):

[0147] ,(twenty three);

[0148] The optimal solution for power allocation is obtained using a semi-positive definite rule algorithm. In the formula, It is The identity matrix, Fisher matrix for target position parameter sensing, This indicates that the matrix is ​​positive semi-definite. Problem (23) can be solved by finding the optimal solution for power allocation using the CVX toolbox.

[0149] ②Bandwidth Allocation: Based on the obtained sensing function power allocation results, and using Lemma 1, the corresponding bandwidth allocation results are obtained through iterative optimization. The iterative expression is as follows:

[0150] ;

[0151] In the formula, , All are constants greater than 0, and , and They represent the first time. In the nth iteration The bandwidth and power vector allocated to the sensing function on each base station. For the first In the nth iteration The total power allocated to the sensing function on each base station.

[0152] ③ Repeat steps ① and ② until the power converges; obtain the solution to the constrained optimization sensing system subproblem. and Then, according to Lemma 2, we get ,in, , , .

[0153] II. The communication system sub-problem, namely the communication carrier selection and resource allocation sub-problem:

[0154] In this embodiment, the communication system subproblem is solved using the alternating direction multiplier method, including:

[0155] Introducing Lemma 3: For each base station with a given number of array elements, the power allocated to the communication function in the communication system subproblem is collinear with the solution of the array elements.

[0156] In this embodiment, the constrained optimization problem (11) involves binary variables. and continuous variables after relaxation The resulting MINLP problem is non-convex and difficult to find the optimal solution. Therefore, based on the characteristics of the problem, the variables are handled according to the following lemma. .

[0157] In Lemma 3: For each base station with a given number of array elements, the power allocated to the communication function and the solution of the array elements in problem (11) are also collinear, that is:

[0158] (25);

[0159] middle, and The first The optimal solution for the power allocated to the communication functions and the array elements in each base station. It is a constant greater than 0. The proof is similar to that of Lemma 1, and will not be repeated here.

[0160] The following algorithm is designed to solve the communication carrier selection and resource allocation problem:

[0161] ① Fixed element values Then the binary variable Continuous transformation Ignore the same carrier Constraints that cannot be assigned to multiple users simultaneously, i.e. This constraint will be considered during the rounding operation. At this point, resource allocation between different users is decoupled, making minimizing the total transmit power equivalent to adding the minimum power of each user's communication, thus decomposing the communication system subproblem into... Solve the subproblems and perform the first formal transformation of the communication system subproblem.

[0162] ,(26;

[0163] In the formula, .

[0164] ②In formula (8), Compared to It is convex, and the Lagrange multipliers are used to optimize the convex optimization problem in the communication system subproblem; where the Lagrange function of the communication system subproblem is expressed as follows:

[0165] (27);

[0166] The Lagrangian function representation of the communication system subproblem. It is a Lagrange multiplier. For communication rate threshold, For the first mThe first base station The subcarrier pair Channel parameters for individual user communication. For from the first The base station to the Signal attenuation for individual users; This represents the power of zero-mean Gaussian white noise. The Lagrangian function of equation (26) is expressed as in equation (27), but for simplicity, the power of the noise is omitted. and The boundary terms and corresponding multipliers.

[0167] ③ The alternating direction multiplier method is used to iteratively update the power, carrier, and corresponding multipliers allocated for communication functions, as shown in the following formula:

[0168] (28);

[0169] , , , , and They represent the first time. and The power, subcarriers, and Lagrange multipliers used for communication in the next iteration. , and All are update step sizes. , They are respectively and The gradient;

[0170] ④ Update the array element allocation results for communication according to the following formula:

[0171] (29);

[0172] Indicates the first In the nth iteration The array element vectors allocated to the communication functions on each base station. and Indicates the first In the nth iteration The power vector and total power allocated to the communication functions on each base station; and All are constants greater than 0, and , The total number of array elements allocated for communication in each base station.

[0173] ⑤ Processing and The boundary, if ,but ;if ,but , or, if ,but ;

[0174] For the first The next iteration , For the first The next iteration ;

[0175] ⑥ Repeat steps ③, ④, and ⑤ until the power converges, thus obtaining the solution to the constrained optimization communication system subproblem. , and ,in, , , .

[0176] S33 addresses the issue of shared array elements in sensing and communication systems by dividing array elements based on the principle that the system transmit power is minimized when the ratio of the power of sensing and communication to the number of their corresponding array elements is equal.

[0177] III. Overall Array Element Partitioning: In this embodiment, after obtaining the power, bandwidth, and array elements allocated to the sensing function, as well as the power, subcarriers, and array elements allocated to the communication function, the constraints are now considered. According to Lemmas 1 and 2, the transmit power allocated to the sensing function is also collinear with the number of its corresponding array elements, expressed as:

[0178] (30);

[0179] In the formula, A constant greater than 0 and The first two lemmas given in Lemma 1 and Lemma 2 are respectively... The solution for the allocated power and array elements in each base station.

[0180] Lemma 4: When the power allocated to the sensing and communication functions is equal to the ratio of their corresponding array elements... and When the values ​​are equal, the sum of the power allocated to the sensing and communication functions is minimized.

[0181] Proof: According to the differential theorem, when and When changes occur, the corresponding and It becomes:

[0182] (31);

[0183] (32);

[0184] Then, by adding equations (31) and (32), we get:

[0185] (33);

[0186] Due to constraints The total number of array elements in the integrated sensing and communication base station is a fixed value. Therefore, the numerical changes of the array elements allocated for sensing and communication functions should satisfy... So when and When the values ​​are not equal, there are two cases:

[0187] a. At this point, you can reduce To reduce the total power, i.e. ,at this time .

[0188] b. At this point, you can increase To reduce the total power, i.e. ,at this time .

[0189] In summary, in both cases, the power change Since all values ​​are less than 0, another optimal array element value can be found to reduce the total power. This applies if and only if... At any time, under any circumstances The total power cannot be reduced further. Therefore, only when... When the total power is minimized, Lemma 4 is proved.

[0190] Combining Lemmas 1, 2, 3, and 4, the result of array element partitioning should satisfy the following relationship:

[0191] (34);

[0192] , , and These represent the power and array element allocation results of the sensing function in the sensing system subproblem and the power and array element allocation results of the communication function in the communication system subproblem, respectively.

[0193] Then, based on the relationship (34) that the array elements should satisfy, the constraint conditions are obtained. Power and element allocation results for sensing and communication functions:

[0194] (35);

[0195] In the formula, , representing the power and array element ratio coefficients required for sensing and communication functions in each base station, where, , ; The total number of array elements for each base station.

[0196] S34. For the overall spectrum allocation, based on the above resource allocation situation, a greedy algorithm is used to dynamically adjust the spectrum allocation of the sensing and communication systems within the same base station to meet the physical constraints of their shared spectrum and achieve reasonable coordination and allocation of spectrum resources.

[0197] IV. Solution to the original problem:

[0198] For the obtained carrier Harmony Formation , Perform a rounding operation on the subcarrier selection vector. The processing involves allocating subcarrier values ​​(from...) to different users on the same carrier at the same base station. arrive The carrier is compared. Then the carrier with the highest value is assigned to the user. Assign a value of 1, and set everything else to 0, so the binary variable... Satisfied This satisfies the constraint that a subcarrier can only be allocated to one user. This is achieved after obtaining the spectrum and array element allocation results. , , and Then, the power allocation is recalculated, and the process is similar to that of equations (21) and (26), which will not be repeated here.

[0199] V. Global Spectrum Allocation:

[0200] After completing the bandwidth allocation for the sensing function and the carrier allocation for the communication function, considering constraint C4, this invention uses a greedy algorithm to solve the spectrum allocation problem for both the sensing and communication parts. The specific steps are as follows:

[0201] ① Divide the spectrum of each base station into equal parts. One, of which Represented as:

[0202] ,(36;

[0203] ② Sequentially divide each spectrum Substituting into step ① above, calculate the changes in sensing intensity and communication power before and after the increase in spectrum; if ,but ;on the contrary ;

[0204] , These represent the bandwidth allocated to sensing and communication in each base station, and the total bandwidth, respectively. , To increase the spectrum The change in power consumption of the sensing and communication systems before and after. Total bandwidth for each base station.

[0205] In this embodiment, the overall algorithm framework for step S30 is as shown in Algorithm 1. Specifically, the solution scheme for the integrated sensing and communication array element partitioning, subcarrier selection, and transmission resource allocation model sequentially solves the sensing resource allocation subproblem, the communication resource allocation subproblem, the overall array element partitioning, and the overall spectrum partitioning. Subsequently, the optimization process is executed cyclically until the total bandwidth is exhausted, as detailed in Table 1.

[0206] Table 1 Resource Allocation Algorithm

[0207]

[0208] Please see Figure 2 As shown, the present invention also provides a resource allocation system for integrated sensing and communication, which applies the above-described resource allocation method for integrated sensing and communication, including:

[0209] The scene building module is used to build scenes from... A network system composed of integrated sensing and communication base stations is positioned L One goal and with A scenario involving communication between individual users.

[0210] The resource allocation module is used to establish a resource allocation model for array element partitioning, subcarrier selection, and transmission in the context of integrated sensing and communication, with the objective function of minimizing total system power consumption and constraints such as sensing performance, communication rate, spectrum, and array element resources.

[0211] The optimization module is used to solve for the high coupling and non-convexity in the resource allocation model.

[0212] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0213] The above embodiments are merely examples of implementation methods of the invention. The scope of protection of the present invention is not limited to the above embodiments. For those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A resource allocation method for integrated sensing and communication, characterized in that, include: Build by A network system composed of integrated sensing and communication base stations is positioned One goal and with Scenarios involving communication between individual users; A resource allocation model for array element partitioning, subcarrier selection, and transmission in integrated sensing and communication is established, with the objective function of minimizing total system power consumption and constraints including sensing performance, communication rate, spectrum, and array element resources. ，（1）； In the formula, To represent as through optimization , , , , , Minimize the objective function. The power allocation vector for the sensing function, where the first... The elements are , indicating the first The base station for the first Power allocated to each target location. It is the power allocation matrix for communication functions. express A real matrix, The total number of communication carriers for each base station, Indicates the first The communication spectrum portion of the base station The subcarrier pair Power of individual user communication Represented as the array element allocation vector for sensing functions, where the first... The elements are , is the The base station for the first The number of array elements allocated to each target location; It is the array element allocation matrix for communication functions, where Indicates the first The first base station generates The subcarrier beam and the first The number of array elements required for each user to communicate; Represents the target's position and state vector. This is the matrix transpose. It is the bandwidth allocation vector for the sensing function, where the first... The elements are , indicating the first The base station for the first Bandwidth allocated to each target location; It is a communication carrier allocation matrix, where , A value of 1 indicates the first The communication spectrum portion of the base station The subcarrier is assigned to the first One user is used for communication. A value of 0 indicates no allocation; Indicates the first The base station for the first q Total bandwidth allocated to each user, of which It is the width of each subcarrier; and This represents the predefined positioning accuracy threshold and communication rate threshold; and This represents the total number of array elements and total bandwidth for each base station; , and These represent the sensing bandwidth, sensing power, and communication power, respectively, and their values ​​are non-negative. The number of array elements used for sensing and communication must be from 1 to... Integers in These are represented by constraint symbols, with C1 to C9 indicating the constraint condition numbers. The trace of the matrix, Let Cramer-Rao be the lower bound matrix. For the first q Communication rate per user Any symbol in mathematics Solving for the high coupling and non-convexity in the resource allocation model includes: For now, we will temporarily ignore the coupling constraints of shared spectrum and array elements between sensing and communication in a single base station, and decompose the overall constraint optimization problem into two independent sub-problems: the power, bandwidth and array element resource allocation sub-problem of the sensing system, and the power, carrier and array element resource allocation sub-problem of the communication system. The problem of the sensing system is solved by introducing semidefinite programming, taking advantage of the collinear relationship between the transmission power of the sensing system and its allocated bandwidth and the number of array elements; the problem of the communication system is solved by the alternating direction multiplier method. To address the issue of shared array elements in sensing and communication systems, the system transmit power is minimized when the ratio of the power of sensing and communication to the number of their corresponding array elements is equal. This principle is used to divide the array elements. For the overall spectrum allocation, based on the above resource allocation situation, a greedy algorithm is used to dynamically adjust the spectrum allocation of the sensing and communication systems within the same base station to meet the physical constraints of their shared spectrum and achieve reasonable coordination and allocation of spectrum resources.

2. The resource allocation method for integrated sensing and communication according to claim 1, characterized in that, The subproblem of the sensing system is solved by introducing semidefinite programming, taking advantage of the collinear relationship between the transmission power of the sensing system and its allocated bandwidth and the number of array elements. This includes: Introducing Lemma 1: For each base station with a given bandwidth, the solutions to the power and bandwidth allocation of the sensing function in the sensing system subproblem are collinear; According to Lemma 1, the perception system subproblem is transformed for the first time to reduce the fitness vector in the perception system subproblem; based on the first transformation, Lemma 2 is introduced to optimize the perception system subproblem; where Lemma 2 states that the array elements and bandwidth in the perception system subproblem are collinear; ① Power allocation: Based on Lemma 1 and Lemma 2, and taking the first... The base station for the first The bandwidth allocated for each target location is taken as a fixed value, and the sub-problem of the sensing system is transformed a second time. For the problem of minimizing the total power, it is equivalent to finding the sum of the power required to locate each target, thus decomposing the subproblem of the sensing system after the second transformation into... L Solving each subproblem is represented as follows: ，（22）； Introduce an auxiliary matrix Rewrite equation (22): ，（23）； The optimal solution for power allocation is obtained by using a semi-positive definite rule algorithm. ②Bandwidth Allocation: Based on the obtained sensing function power allocation results, and using Lemma 1, the corresponding bandwidth allocation results are obtained through iterative optimization. The iterative expression is as follows: ； ③ Repeat steps ① and ② until the power converges; obtain the solution to the constrained optimization sensing system subproblem. and Then, according to Lemma 2, we get ,in, , , ; In the formula, It is the identity matrix. Fisher matrix for target position parameter sensing, , All are constants greater than 0. and They represent the first time. In the nth iteration The bandwidth and power vector allocated to the sensing function on each base station. For the first In the nth iteration The total power allocated to the sensing function on each base station.

3. The resource allocation method for integrated sensing and communication according to claim 1, characterized in that, The communication system subproblems are solved using the alternating direction multiplier method, including: Introducing Lemma 3: For each base station with a given number of array elements, the power allocated to the communication function in the communication system subproblem and the solution of the array elements are collinear; ① Fixed element value Then the binary variable Continuous transformation Ignore the same carrier The constraint that resources cannot be allocated to multiple users simultaneously decouples resource allocation between different users, making minimizing the total transmit power equivalent to adding the minimum power of each user's communication, thus decomposing the communication system subproblem into... Solve the sub-problems one by one, and perform the first formal transformation of the communication system sub-problem; ②According to Compared to It is convex, and the Lagrange multipliers are used to optimize the convex optimization problem in the communication system subproblem; where the Lagrange function of the communication system subproblem is expressed as follows: ，（27）； The Lagrangian function representation of the communication system subproblem. It is a Lagrange multiplier. For communication rate threshold, For the first The first base station The subcarrier pair Channel parameters for individual user communication. For from the first The base station to the Signal attenuation for individual users; This represents the power of zero-mean Gaussian white noise; ③ The alternating direction multiplier method is used to iteratively update the power, carrier, and corresponding multipliers allocated for communication functions, as shown in the following formula: ，（28）； , , , , and They represent the first time. and The power, subcarriers, and Lagrange multipliers used for communication in each iteration. , and All are update step sizes. , They are respectively and The gradient; ④ Update the array element allocation results for communication according to the following formula: ，（29）； Indicates the first In the nth iteration The array element vectors allocated to the communication functions on each base station. and Indicates the first In the nth iteration The power vector and total power allocated to the communication functions on each base station; and All are constants greater than 0. The total number of array elements allocated for communication in each base station; ⑤ Processing and The boundary, if ,but ;if ,but , or, if ,but ; For the first The next iteration , For the first The next iteration ; ⑥ Repeat steps ③, ④, and ⑤ until the power converges, thus obtaining the solution to the constrained optimization communication system subproblem. , and ,in, , , .

4. The resource allocation method for integrated sensing and communication according to claim 1, characterized in that, The system transmit power is minimized when the ratio of sensing and communication power to the number of corresponding array elements is equal. Array element division is based on this ratio, including: The array element partitioning should satisfy the following relationship: ，（34）； , , and These represent the power and array element allocation results of the sensing function in the sensing system subproblem and the power and array element allocation results of the communication function in the communication system subproblem, respectively. Then, based on the relationships that the array elements should satisfy, the constraints are obtained. Power and element allocation results for sensing and communication functions: ，（35）； In the formula, , representing the power and array element ratio coefficients required for sensing and communication functions in each base station, where, , ; The total number of array elements for each base station.

5. The resource allocation method for integrated sensing and communication according to claim 1, characterized in that, Achieving rational coordination and allocation of spectrum resources includes: ① Divide the spectrum of each base station into equal parts. One, of which Represented as: ，（36）； ② Sequentially divide each spectrum Substituting into step ① above, calculate the changes in sensing intensity and communication power before and after the increase in spectrum; if ,but ;on the contrary ; , These represent the total bandwidth allocated to sensing and communication in each base station. , To increase the spectrum The change in power consumption of the sensing and communication systems before and after. Total bandwidth for each base station.

6. The resource allocation method for integrated sensing and communication according to claim 1, characterized in that, Obtain the lower bound matrix of Clamelloe Methods include: obtaining it by inverting the Fisher matrix; and obtaining it by... The Fisher matrix for sensing the location parameters of each target is: ，（2）； formula, and They are represented as follows: ，（3）； ，（4）； ，（5）； In the formula, , They represent the first m The first base station and the first l The location coordinates of the target , For the first The first objective is relative to the second objective. m Radar cross-section of each base station, For the first m The base station and the first The distance between the targets; The power of zero-mean Gaussian white noise; The lower bound matrix of Cramer-Rao is obtained by inverting the Fisher matrix: ，（7）。 7. The resource allocation method for integrated sensing and communication according to claim 1, characterized in that, No. q Communication rate per user It can be obtained through the following formula: ，（8）； In the formula, Indicates the first The first base station The subcarrier pair Channel parameters for individual user communication; This represents the power of zero-mean Gaussian white noise; Indicates from the first The base station to the Signal attenuation for individual users ,in It is the first The base station and the first The distance between users.

8. A resource allocation system for integrated sensing and communication, characterized in that, The resource allocation method for integrated sensing and communication as described in any one of claims 1-7 includes: The scene building module is used to build scenes from... A network system composed of integrated sensing and communication base stations is positioned One goal and with Scenarios involving communication between individual users; The resource allocation module is used to establish a resource allocation model for array element partitioning, subcarrier selection, and transmission in the context of integrated sensing and communication, with the objective function of minimizing total system power consumption and the constraints of sensing performance, communication rate, spectrum, and array element resources. The optimization module is used to solve for the high coupling and non-convexity in the resource allocation model.

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