Method for quality of service guarantee of emergency communication based on inductive coupling

Abstract

The application discloses an emergency communication quality of service guarantee method based on perception coupling, and mainly solves the problem that the existing emergency communication technology lacks integrated scheme design for the perception and communication time delay quality of service guarantee of trapped users. d And the false alarm rate P f ; the P d and P f are fused into the effective capacity formula to obtain the expanded effective capacity; the total expanded effective capacity of the system is taken as a target function, power, a time delay index and a detection probability are taken as constraint conditions to construct an optimization problem, and user power is distributed according to the solving result of the optimization problem. The application can simultaneously meet the communication time delay and detection probability requirements of users, improve the aggregate effective capacity of the system, guarantee the quality of service of user communication and perception, and be used for multi-user communication and perception service in post-disaster rescue scenes.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of communication, and specifically relates to an emergency communication service quality guarantee method which can be used for guaranteeing the quality of multi-user communication and sensing services in a post-disaster rescue scene. BACKGROUND

[0002] After a heavy natural disaster occurs, a "three-break" scene of "break, break network, and break power" is often faced, and the on-site resources are severely limited, and the electromagnetic environment is complex. Under such conditions, the emergency rescue equipment needs to meet the requirements of high-speed data transmission and high-precision sensing detection at the same time, and the problem of limited carrying capacity caused by extremely harsh environment puts forward the requirements of portability and miniaturization for the equipment. However, the traditional emergency equipment is too much, and it is difficult to carry large equipment, which challenges the carrying capacity of the emergency rescue personnel; the multi-standard rescue equipment is difficult to interconnect, and the communication efficiency is low due to the interference of the clutter, which is easy to miss the golden rescue time; in the emergency scene, resources are scarce, and due to the limited available frequency band and power, it will lead to difficulties in disaster information sensing and transmission. Based on the above problems existing in the traditional emergency communication, the current needs to apply new technologies with lower hardware complexity and higher resource utilization efficiency to improve the rescue efficiency. The ISAC technology supports the sharing of wireless communication and radar sensing in hardware platform and spectrum resources, can improve the spectrum utilization rate and reduce the complexity and overhead of hardware design, and will become a key technology to support the quality of emergency communication and sensing services.

[0003] At present, the research on the integration of communication and sensing in emergency scenes at home and abroad mainly focuses on the optimization of unmanned aerial vehicle networking and resource scheduling. In the optimization of unmanned aerial vehicle networking, the patent file with the application number CN202110631951.9 discloses a sensing and communication guide integrated interaction and multi-target emergency networking method and system. According to the target task role of the unmanned aerial vehicle to be networked in the target area, the task energy consumption of the target unmanned aerial vehicle in the target task role is determined; according to the task energy consumption of the target unmanned aerial vehicle, the flight energy consumption of the target unmanned aerial vehicle, and the target optimization problem followed by the target optimization problem, the target optimization problem to be solved in the target area is determined; the unmanned aerial vehicle position, unmanned aerial vehicle resource allocation scheme, and unmanned aerial vehicle scheduling strategy of the target area are optimized by solving the target optimization problem; according to the optimized unmanned aerial vehicle position, the optimized resource allocation scheme, and the optimized unmanned aerial vehicle scheduling strategy, the unmanned aerial vehicles in the target area are networked. Although this method considers the emergency networking deployment problem under the limited power constraint, solves the problem of insufficient network flexibility and distributed construction, it ignores the requirement of high-speed data transmission with low latency in the emergency scene, which will cause the transmission time delay of data packets with high time delay requirement between unmanned aerial vehicles to be large, and reduce the rescue task efficiency.

[0004] Patent application number CN202311443778.5 discloses a method and system for dispatching a swarm of communication drones. This method uses integrated synaesthesia signals to obtain the user's current actual location, and then uses a clustering algorithm to determine a first distribution position for the drone swarm based on the user's current actual location. Based on the user's historical location and corresponding historical communication service requests, the predicted user location at the next moment and the predicted value of the corresponding communication service request are predicted as prior information, and a reinforcement learning model is used to determine the transmit power allocation scheme that maximizes network utility. Based on the first distribution position, the predicted user location at the next moment, and the transmit power allocation scheme, the drone swarm is adjusted to a second distribution position, ensuring user communication speed and quality in scenarios with frequent emergencies and efficiently utilizing network resources. However, this method lacks an integrated framework for communication-perception coupling and fails to jointly consider the combined impact of perception and communication performance on drone network location. This results in insufficient accuracy in drone measurements of perceived information such as user location and movement speed, impacting the efficiency of drones 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. It builds a system model, establishes a three-dimensional heterogeneous power Internet of Things scenario consisting of satellites, drones, and terminals, and refines the model. It proposes queuing delay constraints and joint optimization problems, and solves the optimization problem based on the Lyapunov optimization principle. Finally, it designs a cloud-edge-end collaborative task offloading decision algorithm based on deep reinforcement learning. This method of solving high-level task offloading problems based on deep reinforcement learning effectively solves the problem of dimensionality disaster under information uncertainty. However, since this 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 existing technologies and propose an emergency communication service quality assurance method based on perception coupling to improve measurement accuracy and the efficiency of drones in performing rescue missions, while meeting the multi-user heterogeneous communication delay requirements and perception requirements, and ensuring the user's interawareness service quality.

[0007] The technical approach to achieve the above objectives is as follows: by performing matched filtering on the echo signal and modeling user presence as a binary hypothesis problem to achieve high-precision user perception measurement; based on the 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, thereby ensuring 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) Using 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 from 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) Perform signal energy detection on the separated echo signals of each user, and model the user existence problem as a binary hypothesis model based on 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) Let the empirical predicted values ​​of the probability of the user's presence and absence in the detection area be H1 and H0 respectively;

[0018] 3b) Combine the above two empirical prediction values ​​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 This allows the system to maximize its total effective capacity while meeting the synaesthesia needs of each user, ensuring the synaesthesia service quality for multiple users and improving the overall system performance. 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-latency 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 was 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 an implementation flow chart of the present invention;

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

[0037] Figure 3 3. This 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 chart 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 solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described 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 fall within the scope of protection 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 the purpose of clearly describing the embodiments of the present invention and facilitating understanding, and the order of the step numbers is not limited.

[0041] Reference Figure 2 This example implementation scenario includes an emergency vehicle and M drone users. The emergency vehicle, acting as a mobile ISAC base station, transmits dual-function radar and communication signals to these M users for user detection and downlink communication. Each communication link exhibits Nakagami-m fading. The emergency vehicle uses radar echo signals to determine the detection probability of each user. This, combined with a pre-set user delay exponent θ, allocates transmit power to ensure QoS for these 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. The implementation steps include the following:

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

[0044] 1.1) Using 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} denote 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) Obtain 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 n(l) be the noise matrix to which the signal is subjected. Based on the expression for the MIMO signal, calculate the total signal matrix y(l) received at all user terminals:

[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 sequence:

[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), get the received signal-to-interference-and-noise ratio γ of the mth user m :

[0066]

[0067] 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;

[0068] 1.2.8) According to Shannon's theorem, we can get the rate R of the mth user: m :

[0069] R m =log(1+γ m )

[0070] 1.3) R calculated based on θ and m , we get the expression of effective capacity C:

[0071]

[0072] Step 2: Establish a signal detection model.

[0073] 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):

[0074]

[0075] 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 base station antennas, [] T is the transpose operation;

[0076] 2.2) In the perception scenario, the target detection task is completed by determining the energy of the echo signal:

[0077] 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:

[0078]

[0079] for[] H is the conjugate transpose;

[0080] 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:

[0081]

[0082] in represents the estimated value of the signal departure angle AoD, represents the estimated value of the signal arrival angle (AoA);

[0083] 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:

[0084]

[0085] 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;

[0086] 2.2.4) According to the transmit beamforming vector w applied by the base station to the mth user m Calculating receive beamforming gain

[0087]

[0088] 2.2.5) Based on the receive beamforming vector f applied by the base station to the mth user m Calculating transmit beamforming gain

[0089] 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 of the mth user after matching filtering

[0090] 2.2.7) Based on the echo signal r(l) and the receive beamforming gain A r , transmit beamforming gain A t and the perceived noise z of the mth user m , y rm Further expanded to:

[0091]

[0092] 2.2.8) The object detection task is described as the following binary hypothesis problem:

[0093] No target exists

[0094] Existence goal

[0095] 2.2.9) Based on the binary hypothesis and the expanded detection signal y rm The initial description of the target detection problem is:

[0096]

[0097] 2.2.10) Based on radar signal detection theory and the 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:

[0098] 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 Power, is a chi-square distribution with 2 degrees of freedom;

[0099] 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

[0100] 2.2.11) According to the two cases, the detection signal energy |y rm | 2 Different distribution expressions of , the signal detection model is described as:

[0101]

[0102] Here, ~ indicates that the signal energy obeys a certain distribution.

[0103] 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

[0104] 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:

[0105]

[0106] in Represents the inverse of the cumulative distribution function of the chi-square distribution with 2 degrees of freedom;

[0107] 3.2) Calculate the user false alarm probability P based on the detection threshold ε f :

[0108]

[0109] Where Pr() represents probability calculation;

[0110] 3.3) According to the signal detection model The echo signal energy distribution expression under the condition is used to calculate the detection probability P of the user. d :

[0111]

[0112] in Represents the cumulative distribution function of the chi-squared distribution with 2 degrees of freedom.

[0113] 3.4) Based on the effective capacity C and 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

[0114]

[0115] 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.

[0116] Step 4: Establish a multi-user optimization problem to ensure the user communication perceived service quality.

[0117] 4.1) Extended effective capacity based on the mth user Calculate the aggregate effective capacity C of the system M users Pd (θ1,...,θ M ):

[0118]

[0119] 4.2) As the objective function, the power of M users and their respective detection probability P dm and their respective delay exponents θ m As constraints, construct the multi-user optimization problem P1:

[0120]

[0121] P dm ≥η m ,m=1,2,…,M

[0122]

[0123] 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;

[0124] 4.3) Solve the multi-user optimization problem P1:

[0125] 4.3.1) The objective function is transformed into an equivalent function to simplify the optimization problem solving process:

[0126] 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 relationship:

[0127]

[0128] in, for The lower bound,

[0129] 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 ),

[0130] θ min =min(θ1,θ2,θ m ,...,θ M ) is to take (θ1,θ2,θ m ,...,θ M ),

[0131] θ m is the delay service index of the mth user, For the sake of expectation;

[0132] 4.3.1b) Based on 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):

[0133]

[0134] 4.3.1c) According to A(α) in α∈[θ min ,θ max ], we get the following inequality:

[0135]

[0136] where θ0 is in [θ min ,θ max ] is the only real value obtained on is a positive real number approaching 0;

[0137] 4.3.1d) Order According to the continuous property of the A(α) function, we get the following inequality:

[0138]

[0139] 4.3.1e) According to the inequality in step 4.3.1d) the effective capacity of the expansion Perform the following equivalent conversions:

[0140]

[0141] 4.3.2) According to the equivalent transformation, the optimization problem P1 is rewritten into the form of P2 as shown below:

[0142]

[0143] P dm ≥η m ,m=1,2,…,M;

[0144]

[0145] 4.3.3) Assuming that the communication channels of all users in this example scenario conform to the properties of Nakagami-m fading, let |h| be the channel response amplitude, m° be the fading parameter of the Nakagami-m distribution, and Ω be the shape parameter of the channel. Calculate the probability density f of the channel response amplitude |h| h (x):

[0146]

[0147] Where x = |h|, Γ is the chi-square distribution function;

[0148] 4.3.4) According to the monotonicity of the log function and the probability density f |h| (x), further transform the optimization problem P2 into the following form P3:

[0149]

[0150] P dm ≥η m ,m=1,2,…,M

[0151]

[0152] in f h (x) is the probability density distribution followed by the channel amplitude response between the base station and the user;

[0153] 4.3.5) According to the optimization problem P3 Convexity on spanned space, constructing Lagrange equations

[0154]

[0155] where λ is the Lagrange multiplier associated with the inequality constraint;

[0156] 4.3.6) Let the Lagrange equation right The partial derivative is equal to 0, and the following equation is obtained:

[0157]

[0158] 4.3.7) According to the partial derivatives of the Lagrange equation Calculate the communication power allocated to the mth user by the system

[0159]

[0160] in, is the 2-norm of the Nakagami-m channel gain for the m-th user.

[0161] The optimal communication power obtained by solving the optimization problem Assigning 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 user's communication perception service quality.

[0162] The effects of the present invention can be further illustrated by the following simulation results.

[0163] 1. Simulation conditions

[0164] Condition 1: The simulation platform is MATLAB, the total number of users in the initialization system is M = 2, the total system power is P = 25W, according to P f =10 -3 and P f =10 -6 Two groups of comparative simulations are performed under the two conditions.

[0165] Condition 2: Assume that the detection probability of user 1 and user 2 is P d1 =P d2 =0.7, user 1's delay index θ1 = 10 -2 , the delay index θ2 of user 2 is 10 -6 to 10 -1 Varies within the range.

[0166] Condition 3, let the detection probability of user 1 be P d1=0.6, detection probability P of user 2 d2 =0.8, user 1's delay index θ1=10 -2 , the delay index θ2 of user 2 is 10 -6 to 10 -1 Varies within the range.

[0167] 2. Simulation content

[0168] Simulation 1: Under the above conditions 1 and 2, the aggregate effective capacity of the system is calculated using the present invention and the existing isomorphic power allocation method. The results are as follows Figure 3 .

[0169] from Figure 3 As can be seen, when the detection probability P of all users in the system is the same, the perception metric no longer constrains the system, and the power allocation scheme at this point degenerates into a power allocation scheme based on heterogeneous communication QoS metrics 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 achieved by the homogeneous power allocation method. However, in all other cases, the aggregate effective capacity achieved by the present invention is greater than that achieved by the homogeneous power allocation method.

[0170] 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. The results are as follows Figure 4 :

[0171] 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 simultaneously constrain the system. In this case, compared with the homogeneous power allocation method, the present invention can achieve a larger aggregate effective capacity in any case.

[0172] The above simulation results show that the present invention can improve the system's aggregate effective capacity 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 perceptual 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) Using 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) Based on the total echo signal received by the mobile ISAC base station, the total echo signal is separated into the echo signals of each user through matched filtering; 2b) Perform signal energy detection on the separated echo signals of each user, and model the user existence problem as a binary hypothesis model based on 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) Let the empirical predicted values ​​of the probability of the user's presence and absence in the detection area be H1 and H0 respectively; 3b) Combine the two empirical prediction values ​​H1 and H0 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 to 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 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; (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 expansion capacity of the system while meeting the synaesthesia needs of each 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 ∆ = {1, 2, …, M} denote 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): x(l)=W·s(l); 1a3) Each user receives the signal transmitted by the mobile ISAC base station, where the signal received by the mth user in the lth time slot is y 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 the 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 m-th user; 1a5) According to the drying ratio γ m Get the communication rate R of the mth user m for: R m =log(1+γ m )。 3. The method according to claim 2, characterized in that In step 1b), the effective capacity expression C of the corresponding user is calculated using the effective capacity theory, 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 3, 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 using matched filtering. 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. The receive steering vector a generated by the base station is obtained as 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 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 base station’s receive beamforming matrix, (·) H is the conjugate transpose operation, represents the energy used by the mth user for sensing, ξ m is the combination of 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 z r (l) is the base station receiving noise; 2a3) Assume that the base station’s transmit and receive beamforming vectors 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, Indicates the transmit beamforming gain.

5. The method according to claim 1, wherein In step 2b), the user presence problem is modeled as a binary hypothesis model. The implementation steps include the following: 2b1) Formulate the object detection problem as the following binary hypothesis problem: 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) Express the detection model 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, N r Indicates the number of receiving antennas on the base station. represents the power of 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 noise power perceived by the mth user 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 obtained is 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 noise power perceived by the mth user 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 6, 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 prior 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 relationship: in, for The lower bound, 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 approaching 0; 4c4) order According to the continuous property of the A(α) function, we get the following inequality: 4c5) According to the inequality in 4c4), the following equivalent conversion of the extended effective capacity is obtained:

8. The method according to claim 7, 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) Based on the base-changing property of logarithmic functions, the optimization problem P1 can be rewritten as P2: where θ min ≤θ0≤θ max ,θ max represents (θ1,θ2,θ m ,…,θ M ), the maximum value in 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) Based on 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 spanned space, constructing Lagrange equations 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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