Emergency communication service quality guarantee method based on perception coupling

Abstract

The invention discloses an emergency communication service quality assurance method based on perception coupling, which mainly solves the problem that the existing emergency communication technology lacks an integrated scheme design for perceiving trapped users and assurance of communication time delay service quality, and the implementation scheme of the method comprises the following steps: determining a communication time delay index theta; taking theta as a communication service quality index and calculating a user communication rate to obtain a user effective capacity C; the detection probability Pd and the false alarm rate Pf of the user are calculated according to the radar echo signal energy detection result; fusing Pd and Pf into an effective capacity formula to obtain an expanded effective capacity; and constructing an optimization problem by taking the total expansion effective capacity of the system as a target function and taking the power, the time delay index and the detection probability as constraint conditions, and distributing the user power according to a solving result of the optimization problem. The method can meet the requirements of communication time delay and detection probability of users at the same time, improves the aggregation effective capacity of the system, guarantees the quality of communication service of the users, and can be used for multi-user communication and sensing service in post-disaster rescue scenes.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and specifically is an emergency communication service quality assurance method, which can be used to provide assurance for multi-user communication and perception service quality in post-disaster rescue scenarios. Background Art

[0002] After a major natural disaster, the "three disconnections" of "circuit, network and power outage" are often faced. On-site resources are severely limited and the electromagnetic environment is complex. Under such conditions, emergency rescue equipment must meet the needs of high-speed data transmission and high-precision perception and detection at the same time. The limited carrying capacity caused by the extremely harsh environment puts forward the requirements of portability and miniaturization of the equipment. However, there are many traditional emergency equipment, and large equipment is difficult to carry, which poses a challenge to the carrying capacity of emergency rescue personnel; multi-standard rescue equipment is difficult to interconnect, and the interference of clutter causes low communication efficiency, which is easy to miss the golden rescue time; in emergency scenarios, resources are scarce, and the limited available frequency bands and power will lead to difficulties in disaster information perception and transmission. Based on the above problems in traditional emergency communications, new technologies that can achieve lower hardware complexity and higher resource utilization efficiency are currently needed to improve rescue efficiency. ISAC technology, which supports the sharing of wireless communication and radar perception on hardware platforms and spectrum resources, can improve spectrum utilization and reduce the complexity and overhead of hardware design. It will become a key technology to support emergency synaesthesia service quality assurance.

[0003] At present, the research on the integration of interawareness in emergency scenarios at home and abroad mainly focuses on the optimization of drone networking and resource scheduling. In the optimization of drone networking, the patent document with application number CN202110631951.9 discloses a method and system for integrated interaction and multi-target emergency networking of interawareness and guidance. According to the target mission role of the drone to be networked in the target area, the task energy consumption of the target drone in the target mission role is determined; according to the task energy consumption of the target drone, the flight energy consumption of the target drone and the target optimization problem followed by the target optimization problem, the target optimization problem to be solved in the target area is determined; by solving the target optimization problem, the drone position, drone resource allocation scheme and drone scheduling strategy in the target area are optimized; according to the optimized drone position, optimized resource allocation scheme and optimized drone scheduling strategy, the drones in the target area are networked. Although this method takes into account the problem of emergency networking deployment under limited power constraints and solves the current problems of insufficient network elasticity and distributed construction, it ignores the requirements for high-speed and low-latency data transmission in emergency scenarios, which will result in a large transmission delay of data packets with high latency requirements when transmitting between drones, reducing the efficiency of rescue missions.

[0004] The patent document with application number CN202311443778.5 discloses a scheduling method and system for a communication drone group. It uses the integrated synaesthesia signal to obtain the current actual position of the user, and uses a clustering algorithm to determine the first distribution position of the drone group according to the current actual position of the user; according to the user's historical position and the corresponding historical communication service request, the predicted user position at the next moment and the predicted value of the corresponding communication service request are predicted as prior information, and the reinforcement learning model is used to determine the transmission power allocation scheme with the maximum network utility; according to the first distribution position, the user's predicted position at the next moment, and the transmission power allocation scheme, the drone group is adjusted to the second distribution position, so as to achieve the effect of ensuring the user's communication rate and quality in a scene with more emergencies, and efficiently utilize network resources. However, due to the lack of an integrated framework for communication perception coupling, this method does not jointly consider the comprehensive impact of perception and communication performance on the drone networking position, which will lead to insufficient measurement accuracy of the drone for the user's position, moving speed and other perception information, affecting the efficiency of the drone in performing rescue missions.

[0005] In terms of resource scheduling optimization, the patent document with application number CN202111206099.7 discloses a three-dimensional heterogeneous power Internet of Things cloud-edge-end collaborative resource allocation method, which builds a system model, establishes a three-dimensional heterogeneous power Internet of Things scenario consisting of satellites, drones, and terminals, and refines the model, proposes queuing delay constraints and joint optimization problems, and solves the optimization problem based on the Lyapunov optimization principle. Finally, a cloud-edge-end collaborative task offloading decision algorithm based on deep reinforcement learning is designed. This method of solving high-level task offloading problems based on deep reinforcement learning effectively solves the problem of dimensionality disaster under information uncertainty, but because the method focuses on the design of resource allocation schemes in cloud, edge, and end scenarios, base stations are often damaged in emergency post-disaster scenarios, so it is not suitable for emergency rescue communication networks. Summary of the invention

[0006] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and propose an emergency communication service quality assurance method based on perception coupling to improve the measurement accuracy and the efficiency of drones in performing rescue missions, while meeting the delay and perception requirements of multi-user heterogeneous communications and ensuring the quality of user interaceptive services.

[0007] The technical ideas to achieve the above objectives are: by matching filtering the echo signal, and modeling the user existence as a binary hypothesis problem to meet the requirements of high-precision user perception measurement; on the basis of effective capacity theory, by coupling the perception index and the communication index into a target expression to simultaneously meet the requirements of the user synaesthesia index; by using the coupled expression as the objective function, the power allocation of multiple users is optimized under the constraints of power and perception performance, so as to ensure the quality of user synaesthesia service.

[0008] According to the above ideas, the implementation steps of the present invention include the following:

[0009] (1) Determine the communication delay index θ and obtain the effective capacity C of the corresponding user:

[0010] 1a) Taking the communication delay index θ as the communication service quality Qos indicator, the received signal of each user and the corresponding user's signal-to-noise ratio γ are obtained according to the general expression of MIMO signal m , according to γ m The communication rate R of the corresponding user is obtained by Shannon's theorem;

[0011] 1b) According to the communication rate R m , calculate the effective capacity expression C of the corresponding user using the effective capacity theory;

[0012] (2) Determine the detection probability P d and false alarm rate P f :

[0013] 2a) Separating the total echo signal received by the mobile ISAC base station into the echo signals of each user through matched filtering;

[0014] 2b) performing signal energy detection on the echo signal of each separated user, and modeling the user existence problem as a binary hypothesis model according to the different energy amplitudes of the detected signals;

[0015] 2c) Obtain the detection probability P of the corresponding user through the binary hypothesis model d and false alarm rate P f ;

[0016] (3) Building a synaesthesia indicator coupling framework

[0017] 3a) Assume that the empirical prediction values ​​of the probability of the user's presence and absence in the detection area are H1 and H0 respectively;

[0018] 3b) The above two empirical prediction values ​​are combined with the above effective capacity C and detection probability P d , false alarm rate P f Fusion to obtain expanded effective capacity That is, the fusion framework:

[0019]

[0020] (4) Ensuring the quality of multi-user heterogeneous synaesthesia services:

[0021] (4a) Let the extended effective capacity of the mth user be:

[0022] The total extended effective capacity of M users is

[0023] (4b) As the objective function, the power of M users and their respective detection probability P d and their respective delay index θ as constraints, construct the multi-user optimization problem P1:

[0024]

[0025]

[0026] P dm ≥η m ,m=1,2,...,M

[0027]

[0028] in, and are the power used for communication and sensing by the mth user, η m is the lower limit of the detection probability of the mth user, is the total power of M users;

[0029] (4c) Performing an equivalent transformation on the multi-user optimization problem, transforming it from a non-convex optimization problem into a convex optimization problem;

[0030] (4d) Using convex optimization theory to solve the communication power allocated to each user in a multi-user system The system maximizes the total effective capacity of the system under the premise of meeting the synaesthesia needs of each user, ensures the synaesthesia service quality of multiple users, and improves the overall performance of the system. represents the baseband signal transmitted to the mth user.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] 1. Since the present invention determines the communication delay index θ as an indicator for measuring the quality of communication services, it can reflect the delay sensitivity of user services in the ISAC system and provide protection for low-delay communication services.

[0033] 2. The present invention determines the delay index θ and detection probability P of each user in a multi-user system. d and false alarm probability P f , which can ensure the diversity and differentiation of synaesthesia services in post-disaster emergency rescue.

[0034] 3. The present invention is due to the P d , P fCombined with the effective capacity theory, an ISAC integrated framework integrating synaesthesia indicators is constructed, which can simultaneously guarantee the service quality of multi-user heterogeneous perception services and delay-sensitive communication services. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the implementation of the present invention;

[0036] Figure 2 is an implementation scenario diagram of the present invention;

[0037] Figure 3 It is a comparison diagram of the multi-user heterogeneous power allocation method and the homogeneous power allocation method obtained according to the delay index θ under multi-user conditions of the present invention;

[0038] Figure 4 The present invention is based on the delay index θ and the detection probability P under multi-user conditions. d Comparison diagram of the obtained multi-user heterogeneous power allocation and homogeneous power allocation methods. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without creative work should all fall within the protection scope of the present invention.

[0040] It should be noted that the step numbers in the specification and claims of the present invention are only for a clear description of the implementation scheme of the present invention to facilitate understanding, and the order of the step numbers is not limited.

[0041] Reference Figure 2 ,The implementation scenario of this example includes an emergency vehicle and M drone users. The emergency vehicle acts as a mobile ISAC base station to transmit radar-communication dual-function signals to M users for user detection and downlink communication, where each communication link has the property of Nakagami-m fading. The emergency vehicle obtains the detection probability of each user through the radar echo signal, and allocates the transmission power in combination with the pre-set user delay index θ, thus achieving interoception service quality assurance for M users.

[0042] Reference Figure 1 In this example, an emergency communication service quality assurance method based on perception coupling is implemented in the above scenario, and its implementation steps include the following:

[0043] Step 1: Determine the communication service quality Qos indicator and calculate the effective capacity C.

[0044] 1.1) Use the communication delay index θ as the communication QoS indicator:

[0045] 1.2) Calculate the effective capacity C:

[0046] The effective capacity C refers to the maximum source rate that the time-varying channel capacity can support when meeting the QoS index. In order to obtain the effective capacity C of the user, it is necessary to express the communication rate R of the corresponding user according to the calculation process of the MIMO signal. m The specific implementation process is as follows:

[0047] 1.2.1) Let Δ = {1, 2, ..., M} represent the user index. The baseband signal s(l) sent by the mobile ISAC base station to M users in the lth time slot is:

[0048]

[0049] Where m = 1, 2, ..., M, s m (l) represents the baseband signal transmitted to the mth user;

[0050] 1.2.2) Let W be the precoding matrix of the base station:

[0051]

[0052] Where N t is the number of base station transmitting antennas;

[0053] 1.2.3) Get the base station's transmitted signal x(l):

[0054] x(l)=Ws(l)

[0055] Where l is the time index, m = 1, 2, ..., M, s m (l) represents the baseband signal transmitted to the mth user;

[0056] 1.2.4) Let H be the channel gain matrix in the signal propagation space, let the noise matrix to which the signal is subjected be n(l), and calculate the total signal matrix y(l) received at all user terminals according to the expression of MIMO signal:

[0057] y(l)=Hx(l)+n(l)

[0058] 1.2.5) Take the mth vector of the total signal matrix y(l) as the received signal of the mth user, and extract the mth vector of the channel matrix H, the transmitted signal matrix x(l) and the noise matrix n(l) in turn:

[0059] The mth vector h of the channel matrix H m as the channel state between the base station and the mth user;

[0060] The mth vector x of the transmitted signal matrix x(l) m (l) as a signal transmitted by the base station to the mth user;

[0061] The mth vector n of the noise matrix n(l) m (l) as the noise vector;

[0062] 1.2.6) Set is the power of the base station communicating with the mth user, The power of communication to other users is calculated to obtain the received signal y of the mth user. m (l):

[0063]

[0064] where x i (l) is the signal transmitted by the base station to other users, which is regarded as the interference received by the mth user, [] T represents transpose;

[0065] 1.2.7) According to the received signal y of the mth user m (l), and obtain the received signal-to-interference-noise ratio γ of the mth user m :

[0067]

[0068] in represents the 2-norm of the transposed communication channel between the mobile base station and the mth user, σ c 2 is the noise power;

[0069] 1.2.8) According to Shannon's theorem, we get the rate R of the mth user m :

[0070] R m =log(1+γ m )

[0071] 1.3) R calculated based on θ and m , we get the expression of effective capacity C:

[0072]

[0073] Step 2: Establish a signal detection model.

[0074] 2.1) Assume that the emergency vehicle is equipped with a uniform linear array ULA with half-wavelength spacing, and obtain the transmission steering vector a t (φ) and the receiving steering vector a r (φ):

[0075]

[0076] Where φ is the signal angle, N r is the number of receiving antennas on the base station, N t is the number of transmitting antennas on the base station, λ is the carrier wavelength, d is the distance between the base station antennas, [] T is the transpose operation;

[0077] 2.2) In the perception scene, the target detection task is completed by determining the energy of the echo signal:

[0078] 2.2.1) Separate is the receiving beamforming matrix of the base station, according to the power used by the mth user for sensing The combination of radar cross section and path loss of the mth user ξ m and the base station's receiving noise z r (l), calculate the echo signal r(l) received by the mobile ISAC base station:

[0079]

[0080] for[] H is the conjugate transpose;

[0081] 2.2.2) Let the base station’s transmit beamforming vector be w j and the receive beamforming vector is f k , respectively expressed as:

[0082]

[0083] in represents the estimated value of the signal departure angle AoD, represents the estimated value of the signal arrival angle AoA);

[0084] 2.2.3) Base station uses s m (l) Perform matched filtering on the received echo signal r(l) to obtain the detection signal y from the mth user rm , to get better detection performance:

[0085]

[0086] where r m (l) is the mth vector of r(l), s m (l) is the mth vector of the baseband original signal s(l), [] * is complex conjugate;

[0087] 2.2.4) According to the transmit beamforming vector w applied by the base station to the mth user m Calculate receive beamforming gain

[0088]

[0089] 2.2.5) According to the receive beamforming vector f applied by the base station to the mth user m Calculating transmit beamforming gain

[0090] 2.2.6) The base station's receiving noise z r The mth vector of (l) is represented by z rm (l), calculate the perceived noise received by the mth user after matched filtering

[0091] 2.2.7) According to the echo signal r(l), the receiving beam forming gain A r , transmit beamforming gain A t and the perceived noise z of the mth user m , y rm Further expanded to:

[0092]

[0093] 2.2.8) The target detection task is described as the following binary hypothesis problem:

[0094] No target exists

[0095] Existence goal

[0096] 2.2.9) Based on the binary hypothesis and the expanded detection signal y rm The initial description of the target detection problem is as follows:

[0097]

[0098] 2.2.10) According to radar signal detection theory and binary hypothesis problem, calculate the energy of the echo signal received by the base station |y rm | 2 Specific distributions under two different target states:

[0099] When the target does not exist in the detection area, since the signal energy only contains noise power, according to the target detection model, the echo signal energy at this time |y rm | 2 The distribution followed is calculated as in is the perceived noise z m The power, is a chi-square distribution with 2 degrees of freedom;

[0100] When there is a target in the detection area, since the signal energy includes both the transmission power and the noise power, according to the target detection model, the echo signal energy at this time |y rm | 2 The distribution followed is calculated as

[0101] 2.2.11) According to the detection signal energy |y in two cases rm | 2 Different distribution expressions of , the signal detection model is described as:

[0102]

[0103] Among them, ~ indicates that the signal energy obeys a certain distribution.

[0104] Step 3: Calculate the user's detection probability P according to the signal energy detection model d and false alarm probability P f , establish the extended effective capacity of the mth user

[0105] 3.1) Based on the signal detection model The echo signal energy distribution expression in this case is used to calculate the detection threshold ε:

[0106]

[0107] in Represents the inverse of the cumulative distribution function of the chi-square distribution with 2 degrees of freedom;

[0108] 3.2) Calculate the user false alarm probability P according to the detection threshold ε f :

[0109]

[0110] Where Pr() represents probability calculation;

[0111] 3.3) According to the signal detection model The echo signal energy distribution expression in this case is used to calculate the detection probability P of the user. d :

[0112]

[0113] in Represents the cumulative distribution function of the chi-squared distribution with 2 degrees of freedom.

[0114] 3.4) Based on the effective capacity C, the detection probability P of the mth userdm , the false alarm rate P of the mth user fm Calculate the extended effective capacity of the mth user

[0115]

[0116] Among them, θ m is the delay index of the mth user, H 1m is the prior existence probability of the mth user, H 0m is the prior probability of non-existence of the mth user.

[0117] Step 4: Establish a multi-user optimization problem to ensure the user communication perceived service quality.

[0118] 4.1) Extended effective capacity according to the mth user Calculate the aggregate effective capacity C of the system M users Pd (θ1,...,θ M ):

[0119]

[0120] 4.2) As the objective function, the power of M users and their respective detection probability P dm and their respective delay index θ m As constraints, construct the multi-user optimization problem P1:

[0121]

[0122] P dm ≥η m ,m=1,2,…,M

[0123]

[0124] in, and are the power used by the mth user for communication perception, η m is the lower limit of the detection probability of the mth user, is the total power of M users, M is the number of system users;

[0125] 4.3) Solve the multi-user optimization problem P1:

[0126] 4.3.1) The objective function is transformed equivalently to simplify the solution process of the optimization problem:

[0127] 4.3.1a) Based on the aggregate effective capacity of M users Theoretical properties of effective capacity and The boundedness of , we get the following relation:

[0128]

[0129] in, for The lower bound of

[0130] for The upper bound of A m =P dm H 1m +P fm H 0m is the total probability of the base station sending information, γ m is the signal-to-noise ratio of the mth user, θ max =max(θ1,θ2,θ m ,...,θ M ) is to take (θ1,θ2,θ m ,...,θ M ),

[0131] θ min =min(θ1,θ2,θ m ,...,θ M ) is to take (θ1,θ2,θ m ,...,θ M ),

[0132] θ m is the delay service index of the mth user, For the sake of expectation;

[0133] 4.3.1b) According to the total probability A of the base station sending information m , the delay service index θ of the mth user m , the signal drying ratio γ of the mth user m , define an equivalent extended effective capacity function A(α):

[0134]

[0135] 4.3.1c) According to A(α) in α∈[θ min ,θ max ], we get the following inequality:

[0136]

[0137] where θ0 is in [θ min ,θ max ] is the only real value obtained on is a positive real number tending to 0;

[0138] 4.3.1d) Order According to the continuous property of A(α) function, we get the following inequality:

[0139]

[0140] 4.3.1e) According to the inequality in step 4.3.1d), the extended effective capacity Perform the following equivalent conversions:

[0141]

[0142] 4.3.2) According to the equivalent transformation, the optimization problem P1 is rewritten into the form of P2 as shown below:

[0143]

[0144] P dm ≥η m ,m=1,2,…,M;

[0145]

[0146] 4.3.3) According to the application scenario of this example, the communication channels of all users conform to the properties of Nakagami-m fading. Let |h| be the channel response amplitude, m° be the fading parameter of Nakagami-m distribution, Ω be the shape parameter of the channel, and calculate the probability density f of the channel response amplitude |h| h (x):

[0147]

[0148] Where x = |h|, Γ is the chi-square distribution function;

[0149] 4.3.4) According to the monotonicity of the log function and the probability density f |h| (x), and further transform the optimization problem P2 into the following form P3:

[0150]

[0151] P dm ≥η m ,m=1,2,…,M

[0152]

[0153] in f h (x) is the probability density distribution followed by the channel amplitude response between the base station and the user;

[0154] 4.3.5) According to the optimization problem P3 Convexity on the spanned space, constructing the Lagrangian equation

[0155]

[0156] where λ is the Lagrange multiplier associated with the inequality constraint;

[0157] 4.3.6) Let the Lagrange equation right The partial derivative of is equal to 0, and the following equation is obtained:

[0158]

[0159] 4.3.7) According to the partial derivatives of Lagrange equations Solve the communication power allocated by the system to the mth user

[0160]

[0161] in, is the 2-norm of the Nakagami-m channel gain of the m-th user.

[0162] The optimal communication power is obtained by solving the optimization problem Allocating power to each user enables the system to adaptively adjust communication power allocation while meeting the user's communication delay and perception accuracy requirements, thereby ensuring the quality of communication perception service for the user.

[0163] The effect of the present invention can be further illustrated by the following simulation results.

[0164] 1. Simulation conditions

[0165] Condition 1: The simulation platform is MATLAB, the total number of users in the system is initialized as M = 2, the total system power is P = 25W, and according to P f =10 -3 and P f =10 -6 Two groups of comparative simulations are carried out for the two situations.

[0166] Condition 2: Assume that the detection probability of user 1 and user 2 is P d1 =P d2 =0.7, the delay index of user 1 θ1 = 10 -2 , the delay index θ2 of user 2 is 10 -6 to 10 -1 Varies within range.

[0167] Condition 3, let the detection probability of user 1 be P d1=0.6, the detection probability of user 2 is P d2 =0.8, the delay index of user 1 is θ1=10 -2 , the delay index θ2 of user 2 is 10 -6 to 10 -1 Varies within range.

[0168] 2. Simulation content

[0169] Simulation 1: Under the above-mentioned simulation conditions 1 and 2, the aggregate effective capacity of the system is calculated using the present invention and the existing isomorphic power allocation method respectively. The results are as follows Figure 3 .

[0170] from Figure 3 It can be seen that when the detection probability P of all users in the system is the same, the perception index no longer constrains the system, and the power allocation scheme at this time degenerates into a power allocation scheme based on the heterogeneous communication QoS index in the communication network. When the θ index of two users is equal, the aggregate effective capacity achieved by the present invention is the same as that of the homogeneous power allocation method, but in other cases, the aggregate effective capacity of the present invention is larger than that of the homogeneous power allocation method.

[0171] Simulation 2: Under the above conditions 1 and 3, the aggregate effective capacity of the system is calculated using the present invention and the existing isomorphic power allocation method respectively. The results are as follows Figure 4 :

[0172] Depend on Figure 4 It can be seen that when the detection probability P and the θ index of all users in the system are different, the perception and communication indicators constrain the system at the same time. At this time, compared with the isomorphic power allocation method, the present invention can achieve a larger aggregate effective capacity under any circumstances.

[0173] The above simulation results show that the present invention can improve the aggregate effective capacity of the system while meeting the user's requirements for communication delay and perception accuracy, thereby ensuring the user's synaesthesia service quality.

Claims

1. A method for ensuring the quality of emergency communication services based on sensory coupling, characterized in that: The steps include: (1) Determine the communication delay index θ and obtain the effective capacity C of the corresponding user: 1a) Taking the communication delay index θ as the communication QoS indicator, the received signal of each user and the corresponding user's signal-to-noise ratio γ are obtained according to the general expression of MIMO signal. m , according to γ m The communication rate R of the corresponding user is obtained from Shannon's theorem m ; 1b) According to the communication rate R m , calculate the effective capacity expression C of the corresponding user using the effective capacity theory; (2) Determine the detection probability P d and false alarm rate P f : 2a) Separating the total echo signal received by the mobile ISAC base station into the echo signals of each user through matched filtering; 2b) performing signal energy detection on the echo signal of each separated user, and modeling the user existence problem as a binary hypothesis model according to the different energy amplitudes of the detected signals; 2c) Obtain the detection probability P of the corresponding user through the binary hypothesis model d and false alarm rate P f ; (3) Building a synaesthesia indicator coupling framework 3a) Assume that the empirical prediction values ​​of the probability of the user's presence and absence in the detection area are H1 and H0 respectively; 3b) The above two empirical prediction values ​​are combined with the above effective capacity C and detection probability P d , false alarm rate P f Fusion to obtain expanded effective capacity That is, the fusion framework: (4) Ensuring the quality of multi-user heterogeneous synaesthesia services: (4a) Let the extended effective capacity of the mth user be: The total extended effective capacity of M users is (4b) As the objective function, the power of M users and their respective detection probability P d and their respective delay index θ as constraints, construct the multi-user optimization problem P1: in, and is the power used by the mth user for communication perception, η m is the lower limit of the detection probability of the mth user, is the total power of M users. (4c) Performing an equivalent transformation on the multi-user optimization problem, transforming it from a non-convex optimization problem into a convex optimization problem; (4d) Using convex optimization theory to solve the communication power allocated to each user in a multi-user system The system maximizes the total effective capacity of the system under the premise of meeting the synaesthesia needs of each user, ensures the synaesthesia service quality of multiple users, and improves the overall performance of the system. represents the baseband signal transmitted to the i-th user.

2. The method according to claim 1, characterized in that The received signal of each user obtained in step 1a) and the signal-to-noise ratio γ of the corresponding user m and the corresponding user's communication rate R m , the implementation steps include the following: 1a1) Let Δ = {1, 2, ..., M} represent the user index. The baseband signal s(l) sent by the mobile ISAC base station to M users in the lth time slot is: Where m = 1, 2, ..., M, s m (l) represents the baseband signal transmitted to the mth user; 1a2) Assume is the precoding matrix. The mobile ISAC base station performs precoding matrix processing on the baseband signal s(l) to obtain the final transmitted signal x(l) after processing: x(l) = Ws(l); 1a3) Each user receives the transmitted signal from the mobile ISAC base station, where the signal y received by the mth user in the lth time slot is m (l) is: in represents the communication power of the mth user, represents the communication power of other users, n m (l) is the variance σ of the mth user c 2 Gaussian white noise, x m (l) is the signal transmitted by the base station to the mth user, x i (l) is the signal transmitted by the base station to other users; is the channel between the mth user and the mobile ISAC base station, Indicates h m The transpose of this channel amplitude response is assumed to follow Nakagami-m fading, and its probability density f |h| (x) follows the following distribution: In the formula is the shape parameter, represents the expected operation, |h| represents the channel amplitude gain, x = |h|, m° is the fading parameter of the Nakagami-m distribution, and Γ is the chi-square distribution function; 1a4) According to the user received signal in step 1a3), the signal-to-noise ratio γ of the mth user is obtained m : in represents the 2-norm of the transposed communication channel between the mobile base station and the mth user; 1a5) According to the dryness ratio γ m The communication rate R of the mth user is obtained m for: R m =log(1+γ m )。 3. The method according to claim 1, characterized in that In step 1b), the effective capacity theory is used to calculate the effective capacity expression C of the corresponding user, and the formula is as follows: Among them, θ is the delay service index of each user, R m is the communication rate of the mth user, To achieve expectations.

4. The method according to claim 1, characterized in that: In step 2a), the total echo signal received by the mobile ISAC base station is separated into the echo signals of each user by matched filtering, and the implementation steps include the following: 2a1) Assume that the mobile ISAC base station is equipped with a uniform linear array ULA with half-wavelength spacing, and obtain the receiving steering vector a generated by the base station r (φ) and the guidance vector a in the launch direction t (φ) are expressed as follows: Where φ is the signal angle, N r is the number of receiving antennas on the base station, N t is the number of transmitting antennas on the base station, λ is the carrier wavelength, d is the distance between the base station antennas, [] T is the transpose operation; 2a2) After the sensing signal transmitted by the base station is reflected by M users, the received echo signal r(l) is: in, represents the receive beamforming matrix of the base station, (·) H is the conjugate transpose operation, represents the energy used by the mth user for sensing, ξ m is the combination of the radar cross section and path loss of the mth user, φ m is the signal angle of the mth user, Indicates a t (φ m ), the conjugate transpose of r (l) is the base station receiving noise; 2a3) Assume that the transmit and receive beamforming vectors of the base station are w j and f k : in represents the estimated value of the signal departure angle AoD, represents the estimated value of the signal arrival angle AoA); 2a4) The base station performs matched filtering on the echo signal r(l) to obtain the separated user signal y r : where φ m is the signal angle of the mth user, w m is the transmit beamforming vector applied by the base station to the mth user, f m is the receive beamforming vector applied by the base station to the mth user, z m is the perceived noise of the mth user, represents the receive beamforming gain, Represents the transmit beamforming gain.

5. The method according to claim 1, characterized in that In step 2b), the user existence problem is modeled as a binary hypothesis model, and the implementation steps include the following: 2b1) The target detection problem is formulated as a binary hypothesis problem as follows: No target exists Existence goal 2b2) Based on the above assumptions, the target detection model is established as: where x m represents the echo signal of the mth user separated by the base station, represents the energy used by the mth user for sensing, ξ m is the combination of the radar cross section and path loss of the mth user, A r represents the receive beamforming gain, A t represents the transmit beamforming gain, z m is the perceived noise of the mth user; 2b3) The detection model is represented as the following energy detector: where |x| 2 represents the signal energy received by the base station, ~ represents that the signal energy follows a certain distribution, and N r Indicates the number of receiving antennas on the base station. represents the power of the noise perceived by the mth user, represents the energy used by the mth user for sensing, represents a chi-square distribution with 2 degrees of freedom.

6. The method according to claim 1, characterized in that In step 2c), the detection probability P of the corresponding user is obtained through the binary hypothesis model d and false alarm rate P f , the implementation steps include the following: 2c1) According to the number of base station receiving antennas N r , the mth user perceived noise power Get the false alarm probability P of the mth user f : Among them, Pr() represents probability calculation; represents a chi-square distribution with 2 degrees of freedom, Indicated by P f The detection threshold is obtained, Represents the inverse of the cumulative distribution function of the chi-square distribution with 2 degrees of freedom; 2c2) According to the signal energy |x| 2 , the number of base station receiving antennas N r , the mth user perceived noise power The power used by the mth user for sensing Detection threshold ε, receive beamforming gain A r , transmit beamforming gain A t , we get the detection probability P of the mobile base station for the mth user d : in Represents the cumulative distribution function of the chi-squared distribution with 2 degrees of freedom.

7. The method according to claim 1, characterized in that In step (4c), the multi-user optimization problem is equivalently transformed, and the implementation steps include the following: 4c1) Based on the total extended effective capacity of M users The detection probability P of the mth user dm , the prior existence probability H of the mth user 1m , the false alarm rate P of the mth user fm , the a priori probability H that the mth user does not exist 0m , the theoretical properties of effective capacity are determined The boundedness of , we get the following relation: in, for The lower bound of for The upper bound of A m =P dm H 1m +P fm H 0m is the total probability of the base station sending information, γ m is the signal-to-noise ratio of the mth user, θ max =max(θ1,θ2,θ m ,...,θ M ) is to take (θ1,θ2,θ m ,...,θ M ), θ min =min(θ1,θ2,θ m ,...,θ M ) is to take (θ1,θ2,θ m ,...,θ M ), θ m is the delay service index of the mth user, For the sake of expectation; 4c3) According to the total probability A of the base station sending information m , the delay service index θ of the mth user m , the signal drying ratio γ of the mth user m , define an equivalent extended effective capacity function A(α): According to A(α) in α∈[θ min ,θ max ], we get the following inequality: where θ0 is in [θ min ,θ max ] is the only real value obtained on is a positive real number tending to 0; 4c4) Order According to the continuous property of A(α) function, we get the following inequality: 4c5) According to the inequality of 4c4), the following equivalent conversion of the extended effective capacity is obtained:

8. The method according to claim 1, characterized in that In step (4d), convex optimization theory is used to solve the communication power allocated to each user in the multi-user system. The implementation steps include the following: 4d1) According to the base-changing property of logarithmic function, the optimization problem P1 is rewritten into the following form P2: where θ min ≤θ0≤θ max ,θ max represents (θ1,θ2,θ m ,...,θ M ), θ min represents (θ1,θ2,θ m ,...,θ M ), To find the expectation, θ m is the delay service index of the mth user, γ m is the signal-to-drying ratio of the mth user, is the power used by the mth user for communication, is the power used by the mth user for sensing, P dm is the detection probability of the mth user, η m is the lower limit of the detection probability of the mth user, is the total power of M users; 4d2) According to the monotonicity of the log function, the optimization problem P2 is further transformed into the following form P3: in f h (x) is the probability density distribution followed by the channel amplitude response between the base station and the user; 4d3) According to the optimization problem P3, Convexity on the spanned space, constructing the Lagrangian equation where λ is the Lagrange multiplier associated with the inequality constraint; 4d4) Let the Lagrange equation right The partial derivative of is equal to 0, and the following equation is obtained: 4d5) Solve the equation obtained in step 4d4) to calculate the communication power allocated to the mth user by the system in, is the 2-norm of the Nakagami-m channel gain of the m-th user, and M is the total number of system users.

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