Wireless communication resource allocation method for IRS-assisted USV data unloading

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a wireless communication resource allocation method for IRS-assisted USV data unloading. The method comprises the following steps: constructing a USV-UAV network data unloading model; based on a USV-UAV network data unloading model, constructing a minimum USV maximum information age objective function; according to the minimum USV maximum information age objective function, an IRS phase angle constraint, an IRS deployment position constraint, a bandwidth constraint, an unloading decision variable constraint and a CUAV calculation time delay constraint are used as constraint conditions to construct a minimum USV maximum information age optimization problem; solving the problem of minimizing the maximum information age of the USV to obtain a wireless communication resource allocation scheme; according to the method, the integrity of the overall data and the richness of field information can be greatly improved while the real-time performance of unloading the data is guaranteed, and the method has relatively high practicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a wireless communication resource allocation method for IRS-assisted USV data unloading. Background Art

[0002] With the rapid development of the Internet of Things (IoT), the marine economy is booming. Traditional maritime operations rely on manual labor, which is inefficient and risky. Unmanned surface vehicles (USVs) can carry sensors to monitor their surroundings and are widely used in surveying, search and rescue, patrol, and other fields, suitable for small-scale operations on the water. Unmanned aerial vehicles (UAVs), due to their high maneuverability and low cost, are widely used in agricultural plant protection, emergency communications, and rescue, and are suitable for large-scale aerial operations. Therefore, the simultaneous operation of USVs and UAVs can combine data from both the air and the surface, helping to improve the system's data comprehensiveness and application in fields such as maritime rescue, pollution monitoring, and maritime operations monitoring. However, with the rapid increase in maritime data volume, USV-UAV networks struggle to cope with tasks requiring high computational demands and low latency. Therefore, low-cost intelligent reflecting surfaces (IRS) can be introduced to assist in data offloading to edge servers to improve computational efficiency.

[0003] Although there have been studies on USV-UAV networks, in the field of maritime data offloading, most of them only consider UAVs carrying IRS-assisted USVs for data offloading, ignoring the advantages of UAVs in aerial data collection. In addition, current research on IRS-assisted USV data offloading focuses more on indicators such as latency and energy consumption, and lacks consideration of data timeliness. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a wireless communication resource allocation method for IRS-assisted USV data offloading, which includes:

[0005] S1: Construct USV-UAV network data offloading model;

[0006] S2: Based on the USV-UAV network data offloading model, an objective function for minimizing the maximum information age of USV is constructed;

[0007] S3: Based on the objective function of minimizing the maximum information age of USV, the optimization problem of minimizing the maximum information age of USV is constructed with IRS phase angle constraint, IRS deployment position constraint, bandwidth constraint, offloading decision variable constraint and CUAV computing delay constraint as constraints;

[0008] S4: Solve the problem of minimizing the maximum information age of USV and obtain the wireless communication resource allocation scheme.

[0009] Preferably, the USV-UAV network data offloading model includes: a base station equipped with a single antenna, connected to the MEC via an optical fiber; a UAV CUAV that collects aerial image data; a UAV UIRS equipped with an IRS to provide a reflection link; the IRS has N reflective elements, and the reflection mode is total reflection; M USVs working around the monitoring point; the UIRS has a fixed height and the CUAV coordinates are fixed; the USV and CUAV can choose to perform calculations locally or offload data to the edge server for calculation.

[0010] Preferably, the objective function of minimizing the maximum information age of USV is expressed as:

[0011] min max A m

[0012]

[0013] Among them, A m represents the information age of the m-th USV, represents the calculation delay of the mth USV, τ represents the length of a time slot, α m represents the unloading decision variable of the mth USV, D m represents the amount of tasks generated by the mth USV, C m represents the number of CPU cycles required for the mth USV to complete 1-bit calculation, f m represents the computing resources of the mth USV, R m represents the transmission rate of the mth USV, C es represents the number of CPU cycles required for edge server to calculate 1 bit, f es Represents the computing resources of the edge server.

[0014] Furthermore, the calculation formula for the transmission rate of the mth USV is:

[0015]

[0016] Among them, l m represents the bandwidth allocation coefficient of the mth USV, B represents the system bandwidth, represents the transmission power of the mth USV, h m,b represents the channel gain from the mth USV to the base station, h r,b represents the channel gain from UIRS to the base station, h m,r represents the channel gain from UIRS to the mth USV, σ 2 represents the noise variance.

[0017] Preferably, the CUAV computation delay constraint is expressed as:

[0018]

[0019] in, represents the computational latency of CUAV, T max Indicates the maximum computational delay allowed by CUAV, α c represents the unloading decision variable of the mth USV, D c represents the amount of tasks generated by CUAV, C c Indicates the number of CPU cycles required for CUAV to complete 1-bit calculation, f c represents the computing resources of CUAV, R c Indicates the transmission rate of CUAV, C es represents the number of CPU cycles required for edge server to calculate 1 bit, f es represents the computing resources of the edge server, l c represents the bandwidth allocation coefficient of CUAV, B represents the system bandwidth, represents the transmission power of CUAV, h c,b represents the channel gain between CUAV and base station, σ 2 represents the noise variance.

[0020] Preferably, the optimization problem of minimizing the maximum information age of USV is expressed as:

[0021]

[0022] Among them, α represents the terminal offloading decision variable matrix, l represents the terminal bandwidth allocation coefficient matrix, θ represents the reflection coefficient vector matrix, L r Indicates IRS coordinates, A m represents the information age of the m-th USV, represents the phase angle of the nth reflective element, N represents the number of reflective elements in the IRS, and x min Indicates the minimum value of the horizontal axis, x r Indicates the horizontal coordinate of USV, x max Indicates the maximum value of the horizontal axis, y min Indicates the minimum value of the vertical coordinate, y r Indicates the vertical coordinate of USV, y max Indicates the maximum value of the vertical coordinate, z min Indicates the minimum value of the vertical coordinate, z r Indicates the vertical coordinate of USV, h c Indicates the vertical coordinate of CUAV, l c represents the bandwidth allocation coefficient of CUAV, l mrepresents the bandwidth allocation coefficient of the mth USV, M represents the number of USVs, α i represents the uninstallation decision variable of terminal i, Indicates the computational latency of CUAV, c represents CUAV, T max Indicates the maximum computational latency allowed by CUAV.

[0023] Preferably, the process of solving the problem of minimizing the maximum information age of USVs includes:

[0024] Convert the problem of minimizing the maximum information age of USV into a minimization problem;

[0025] Fix the IRS phase angle and IRS deployment location and decompose the minimization problem into the first subproblem. Solve the first subproblem to obtain the offloading decision and bandwidth allocation solution.

[0026] Fix the offloading decision, bandwidth allocation, and IRS deployment location, and decompose the minimization problem into the second sub-problem. Solve the second sub-problem to obtain the IRS phase angle.

[0027] Fix the offloading decision, bandwidth allocation, and RS phase angle, and decompose the minimization problem into a third sub-problem. Solve the third sub-problem to obtain the IRS deployment location plan.

[0028] The three sub-problem solving processes are repeated until the objective function of the minimization problem converges, and the optimal offloading decision, optimal bandwidth allocation plan, optimal IRS phase angle and optimal IRS deployment location plan, i.e., the wireless communication resource allocation plan, are obtained.

[0029] Preferably, the minimization problem is expressed as:

[0030]

[0031] Where s represents the slack variable, α represents the terminal offloading decision variable matrix, l represents the terminal bandwidth allocation coefficient matrix, θ represents the reflection coefficient vector matrix, N represents the number of reflective elements of the IRS, and x min Indicates the minimum value of the horizontal axis, x r Indicates the horizontal coordinate of USV, x max Indicates the maximum value of the horizontal axis, y min Indicates the minimum value of the vertical coordinate, y r Indicates the vertical coordinate of USV, y max Indicates the maximum value of the vertical coordinate, z min Indicates the minimum value of the vertical coordinate, z r Indicates the vertical coordinate of USV, h c Indicates the vertical coordinate of CUAV, l c represents the bandwidth allocation coefficient of CUAV, l mrepresents the bandwidth allocation coefficient of the mth USV, M represents the number of USVs, α i represents the uninstallation decision variable of terminal i, Indicates the computational latency of CUAV, c represents CUAV, T max Indicates the maximum computational delay allowed by CUAV, z m represents auxiliary variables, represents the computational delay of the mth USV, and τ represents the length of a time slot.

[0032] The beneficial effects of the present invention are:

[0033] This paper provides a wireless communication resource allocation method for IRS-assisted USV data offloading. This method leverages the aerial data acquisition capabilities of CUAVs and the offshore inspection capabilities of USVs to establish a three-dimensional, efficient data collection and transmission system. By constructing an optimization model aimed at minimizing the maximum information age of USVs, this method jointly considers the offloading methods of USVs and CUAVs, the rational allocation of system bandwidth, the location and phase shift matrix of the IRS, and ultimately develops an optimal communication and computation offloading strategy under multiple resource constraints.

[0034] The present invention can fully mobilize the complementary advantages of air and surface platforms while ensuring the real-time performance of unloaded data, achieve comprehensive data coverage and efficient collection, and significantly improve the integrity of the overall data and the richness of on-site information. Through reasonable resource allocation, the task completion time is effectively saved, and the efficiency of data processing and emergency response capabilities are improved. In addition, the present invention also helps to reduce the pressure on edge computing nodes, improve the overall energy efficiency of the system and the stability of the communication network, and is suitable for a variety of complex maritime application scenarios such as maritime rescue, maritime patrol, marine environmental monitoring, maritime accident emergency response, offshore resource exploration, etc., and has strong practicality and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the USV-UAV network data offloading model in the present invention;

[0036] Figure 2 Flowchart of the wireless communication resource allocation method for IRS-assisted USV data offloading in the present invention;

[0037] Figure 3 This is a graph showing the change of USV information age with the number of iterations under different N values ​​in the present invention;

[0038] Figure 4 This is a graph showing the change in USV information age with data volume for the present invention and the comparative method. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] The present invention proposes a wireless communication resource allocation method for IRS-assisted USV data unloading, such as Figure 1 As shown, the method includes the following contents:

[0041] S1: Construct a USV-UAV network data offloading model.

[0042] like Figure 2 As shown in the figure, the USV-UAV network data offloading model includes a base station equipped with a single antenna, which is connected to the MEC via an optical fiber; a UAV CUAV that collects aerial image data; a UAV equipped with an IRS that provides a reflection link. The IRS has N reflective elements and the reflection mode is total reflection; and M USVs working around the monitoring point. In the three-dimensional coordinate system, the coordinates of the CUAV are fixed at L c =(x c ,y c ,h c ), the position of the mth unmanned boat is L m =(x m ,y m ,0), UIRS height is fixed, coordinate is L r =(x r ,y r ,h r ), the coordinates of the base station are L b =(x b ,y b ,h b By optimizing the IRS's location coordinates, the terminal can obtain a better channel experience. The base station and server are connected via optical fiber, so the transmission delay between them can be ignored. It is assumed that the server knows the drone's location and channel status.

[0043] In the present invention, USV and UAV can choose to perform calculations locally or offload data to edge servers for calculations, and define the offloading decision variable α i ,i∈{c,m} represents the task offloading mode, where i=c represents the CUAV offloading mode and i=m represents the USV offloading mode. i =0 means the terminal device chooses local calculation, α i =1 means the terminal device chooses to offload all data to the edge server for calculation.i The time to complete the calculation is the calculation delay, which is expressed as Among them, D i Indicates the amount of tasks generated by the terminal device, C i Indicates the number of CPU cycles required for the terminal device to complete 1-bit calculation, f i represents the computing resources of the terminal device, C es represents the number of CPU cycles required for edge server to calculate 1 bit, f es Represents the computing resources of the edge server. Indicates the time when the data is calculated locally. Indicates the time when data is uploaded, R i represents the transmission rate of terminal i; Indicates the time required for data calculation at the edge server; since the amount of calculation result data is often much smaller than the amount of calculation data, the time it takes for the calculation result to be transmitted back to the terminal is ignored.

[0044] S2: Based on the USV-UAV network data offloading model, an objective function for minimizing the maximum information age of USV is constructed.

[0045] In order to characterize the freshness of the transmitted information, the performance indicator of information age is used. Since the channel conditions between the CUAV and the base station are better than those between the unmanned boat and the base station, the present invention focuses on the optimization of the information age of the USV, and the UAV only needs to meet the calculation delay requirements. In the process of unloading the unmanned boat data from the beginning to the completion of the calculation, the control center did not receive valid data during this period, so the information age will continue to increase over time. When the data calculation is completed, the information age will suddenly drop, and then begin to grow continuously until the next time valid data is received. Discrete time into several time slots, each time slot length is τ, then in the t+1 time slot, the information age can be expressed as follows:

[0046]

[0047] Here, τ represents the length of a time slot.

[0048] Therefore, the maximum information age of each USV can be uniformly expressed as in, Indicates rounding down.

[0049] The objective function of minimizing the maximum information age of USV is expressed as:

[0050] min max A m

[0051]

[0052]

[0053] Among them, A m represents the information age of the m-th USV, represents the calculation delay of the mth USV, τ represents the length of a time slot, α m represents the unloading decision variable of the mth USV, D m represents the amount of tasks generated by the mth USV, C m represents the number of CPU cycles required for the mth USV to complete 1-bit calculation, f m represents the computing resources of the mth USV, R m represents the transmission rate of the mth USV, C es represents the number of CPU cycles required for edge server to calculate 1 bit, f es represents the computing resources of the edge server, l m represents the bandwidth allocation coefficient of the mth USV, B represents the system bandwidth, represents the transmission power of the mth USV, h m,b represents the channel gain from the mth USV to the base station, h r,b represents the channel gain from UIRS to the base station, h m,r represents the channel gain from UIRS to the mth USV, σ 2 represents the noise variance.

[0054] S3: Based on the objective function of minimizing the maximum information age of USV, the problem of minimizing the maximum information age of USV is constructed with IRS phase angle constraint, IRS deployment position constraint, bandwidth constraint, offloading decision variable constraint and CUAV computing delay constraint as constraints.

[0055] The signal received by the base station is expressed as in, Indicates the transmission power of the UAV, h c,b represents the channel between CUAV and base station, x c is the transmission signal of CUAV, and E(|x c | 2 )=1, n represents noise, and its expectation is 0 and its variance is σ 2 The transmission rate of CUAV is where l c is the bandwidth allocation coefficient of CUAV, satisfying 0≤l c ≤1 indicates the ratio of CUAV to the total bandwidth, and B indicates the system bandwidth.

[0056] The signal received by the base station from USV m can be expressed as in, represents the transmission power of the unmanned boat m, xm is the transmission signal of the unmanned boat m, and E(|x m | 2 )=1. The channel from USV to base station follows the Rice channel model, h m,b It represents the channel from USV m to the base station, expressed as Where ρ0 represents the channel power gain when the reference distance is 1m, β represents the path loss exponent, and I is a unit vector representing the non-line-of-sight link component, which obeys a complex Gaussian distribution with a mean of 0 and a unit variance of 1. r,b and h m,r They represent the channels from UIRS to the base station and from USV m to UIRS, respectively, and are expressed as in, represents the line-of-sight link component, λ represents the wavelength, d represents the distance between the IRS reflective elements, represents the cosine of the angle of arrival and the angle of departure between the signal and the UIRS. The transmission rate of USV m is Among them, l m is the bandwidth allocation coefficient of USV m, satisfying Indicates the proportion of USV m to the total bandwidth. The bandwidth allocation of all terminal devices needs to meet

[0057] The IRS phase angle constraint, IRS deployment position constraint, bandwidth constraint, offloading decision variable constraint and CUAV computing delay constraint are used to establish the system to minimize the maximum information age of USV, which is expressed as:

[0058]

[0059] Among them, α represents the terminal uninstallation decision variable, which is expressed as α=[α1,α2,…,α M ,α c ] T ∈C (M+1)×1 , l represents the terminal bandwidth allocation coefficient, which is expressed as l=[l1,l2,…,l M ,l c ] T ∈C (M+1)×1 , θ represents the reflection coefficient vector, which is expressed as represents the phase angle of the nth reflective element, L r Indicates IRS coordinates, A m Indicates the information age of the mth USV, maxA m Indicates the maximum information age among all USVs; N represents the number of IRS reflection units, and the IRS coordinate is L r =(x r ,yr ,h r ), N represents the number of reflective elements of IRS, x min Indicates the minimum value of the horizontal axis, x r Indicates the horizontal coordinate of USV, x max Indicates the maximum value of the horizontal axis, y min Indicates the minimum value of the vertical coordinate, y r Indicates the vertical coordinate of USV, y max Indicates the maximum value of the vertical coordinate, z min Indicates the minimum value of the vertical coordinate, z r Indicates the vertical coordinate of USV, h c Indicates the vertical coordinate of CUAV, l c represents the bandwidth allocation coefficient of CUAV, l m represents the bandwidth allocation coefficient of the mth USV, M represents the number of USVs, α i represents the uninstallation decision variable of terminal i, Indicates the computational latency of CUAV, c represents CUAV, T max Indicates the maximum computational delay allowed by CUAV, C1 represents the IRS phase angle constraint, C2 represents the IRS location deployment constraint, C3, C4, and C5 represent bandwidth constraints, C6 represents the value constraint of the offloading decision variable, and C7 represents the CUAV computing delay constraint.

[0060] S4: Solve the problem of minimizing the maximum information age of USV and obtain the wireless communication resource allocation scheme.

[0061] In this embodiment, the information age optimization problem is transformed into three sub-problems for solution, and an infinite resource allocation scheme that minimizes the maximum information age is obtained. The solution process for minimizing the maximum information age problem includes:

[0062] The problem of minimizing the maximum information age of USV is transformed into a minimization problem. Specifically:

[0063] Introducing slack variables The original optimization problem is transformed into

[0064]

[0065] Since there is a rounding operation in constraint C8, in order to avoid the problem being unsolvable or complicated to solve due to the rounding operation, this paper adopts the method of introducing auxiliary variables and applying constraints to approximate the rounding relationship, thereby achieving continuous relaxation of the problem for the convenience of optimization solution. Therefore, the variable z is introduced m , constraint C8 is transformed into C8′: s ≥ z m +1, where z m satisfy Constraints C8′-C10 are equivalent to constraint C8. No integer constraints are imposed on the variables here because after the optimization is completed, appropriate discretization operations can be performed on the auxiliary variables to recover integer solutions that meet the actual constraints.

[0066] At this point, the optimization problem is converted to:

[0067]

[0068] This problem is still a multivariable, non-convex one. Due to the non-convex nature of the overall optimization problem and the complex coupling relationships between variables, an alternating optimization approach is employed to decompose the original problem into three subproblems for iterative solution. The offloading decision variable and the bandwidth allocation coefficient jointly influence the system's task completion latency and resource allocation. Therefore, the IRS phase and deployment location are first fixed to optimize the offloading decision and bandwidth allocation. Subsequently, the IRS phase is optimized based on the fixed offloading decision, bandwidth allocation, and deployment location. Finally, the IRS deployment location is optimized while also fixing the offloading decision, bandwidth allocation, and phase settings.

[0069] Fix the IRS phase angle and IRS deployment location and decompose the minimization problem into the first subproblem. Solve the first subproblem to obtain the offloading decision and bandwidth allocation solution. Specifically:

[0070] The optimization variables of the first sub-problem are the offloading decision variables and the bandwidth allocation coefficients. Given the IRS phase shift matrix and the position of the IRS, the problem becomes:

[0071]

[0072] At this point, the first subproblem is converted into a convex optimization problem, which can be solved using convex optimization tools to obtain the optimal unloading decision variable a in this round. * and bandwidth allocation coefficient l * .

[0073] Fix the offloading decision, bandwidth allocation, and IRS deployment location, and decompose the minimization problem into the second sub-problem. Solve the second sub-problem to obtain the IRS phase angle. Specifically:

[0074] The optimization variable of the second sub-problem is the IRS phase shift matrix. Given the unloading decision variables, bandwidth allocation coefficients, and IRS location, the optimal IRS phase shift matrix is ​​solved. At this point, the problem becomes:

[0075]

[0076] Constraints C9 and C10 are non-convex constraints, and the constraints are related to time If relevant, If is a convex function of the constraint variable, then the constraint is a convex constraint, otherwise it is a convex constraint. Constraint C9 can be converted to, in Similarly, constraint C10 can be converted to, in definition but make Available Define v and v=[θ H 1], The rate expression is then converted to

[0077] in V=vv H . Then the optimization problem can be written as:

[0078]

[0079] At this point, constraints C9′ and C10′ are still non-convex constraints. Convert the constraints to Since constraint C13 is a non-convex constraint, we can use semi-positive relaxation to ignore this constraint. At this time, the problem is converted to a convex problem and can be solved using the CVX toolbox. However, the optimal solution does not satisfy the rank-one constraint. The Gaussian random optimization method is used to construct a unique solution for V. First, the obtained V * Perform eigenvalue decomposition and get V * =U∑U H , where U=[e1,e2,…,e N+1 ],∑=diag{λ1,λ2,…,λ N+1} represents the unitary matrix and the diagonal matrix. The suboptimal solution can be expressed as v = U∑ 1 / 2 x, where x is a random variable generated from a circularly symmetric Gaussian distribution with x~CN(0;I), then the solution to the problem is V=vv H ,in Thus, the optimal phase shift (θ H ) * =[v] (1:N) , which represents a vector containing the first N elements of v.

[0080] Fix the offloading decision, bandwidth allocation, and RS phase angle, and decompose the minimization problem into the third sub-problem. Solve the third sub-problem to obtain the IRS deployment location solution. Specifically:

[0081] The optimization variable of the third sub-problem is the IRS position. Given the unloading decision variables, bandwidth allocation coefficients and IRS phase shift matrix, the IRS position is optimized. The problem becomes

[0082]

[0083] Constraints C9 and C10 are non-convex constraints. Slack variables r1 and r2 are introduced to represent the upper bounds of the distance between IRS and USV m and the upper bound of the distance between IRS and the base station, respectively. That is: d r,b ≤r1,d m,r ≤r2, so the constraint C9 can be written as in The original problem now becomes:

[0084]

[0085] The los component in the expressions of B1 and B2 is related to the distance. To simplify the calculation, the iterative value of the previous round is used each time. The problem is still non-convex. The product term is expanded by first-order Taylor to obtain an upper bound on the distance:

[0086]

[0087] Similarly:

[0088]

[0089] The optimization problem becomes:

[0090]

[0091] At this point, the problem is a convex problem, which can be solved using the CVX toolbox to obtain the optimal IRS position L for this round. * Through continuous iterative optimization, when the change in the information age obtained from several consecutive iterations is less than 0.01, the algorithm is considered to have converged, and the iteration is stopped to obtain the optimal a * 、l * ,θ * 、L * That is, the wireless communication resource allocation scheme.

[0092] Executing a wireless communication resource allocation scheme can minimize the maximum information age of the system and improve system performance.

[0093] The resource allocation scheme of the present invention is evaluated by effect simulation:

[0094] 1) Simulation conditions

[0095] Matlab is used for experimental simulation, and the main simulation parameter settings are given in Table 1:

[0096] Table 1 Simulation parameters

[0097]

[0098] 2) Simulation results

[0099] Figure 3 The maximum information age of the USV converges with the number of iterations under different IRS reflector numbers. It can be seen that the maximum information age decreases with the increase in the number of IRS reflector units, indicating that increasing the number of reflectors can enhance the quality of the communication link and thus reduce the maximum information age. Figure 4 The information age performance of different algorithms under varying task data volumes is demonstrated. As task data volume increases, the local computational burden increases. Edge offloading significantly reduces computational latency, reducing information age by up to approximately 60% compared to local computation strategies. Compared to optimizing bandwidth allocation, the fixed offloading strategy exhibits a higher information age, indicating that optimizing offloading decisions is more critical for reducing information age when local computational capacity is limited.

[0100] In summary, the present invention adopts IRS to assist USV to realize horizontal and underwater data acquisition and offloading, while giving full play to the aerial data acquisition capability of camera unmanned aerial vehicle (CUAV) to build a more comprehensive and efficient data acquisition and transmission model; according to the data offloading mode of USV, an optimization objective function of minimizing the maximum information age of USV is constructed; according to the CUAV and USV offloading modes, bandwidth allocation scheme, and the position and phase shift matrix size of IRS, relevant offloading decision variable constraints, bandwidth resource constraints, IRS position constraints, and IRS phase angle constraints are respectively constructed, and the optimization problem composed of the minimization of maximum information age objective function and various constraints is solved to obtain the wireless communication resource allocation scheme; the present invention fills the gap in the application of USV-UAV network in the field of data offloading, while ensuring the timeliness of collected data, and has high practicality.

[0101] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A wireless communication resource allocation method for IRS-assisted USV data offloading, characterized in that: include: S1: Construct USV-UAV network data offloading model; S2: Based on the USV-UAV network data offloading model, an objective function for minimizing the maximum information age of USV is constructed; S3: Based on the objective function of minimizing the maximum information age of USV, the optimization problem of minimizing the maximum information age of USV is constructed with IRS phase angle constraint, IRS deployment position constraint, bandwidth constraint, offloading decision variable constraint and CUAV computing delay constraint as constraints; S4: Solve the problem of minimizing the maximum information age of USV and obtain the wireless communication resource allocation scheme.

2. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 1, characterized in that: The USV-UAV network data offloading model includes: a base station equipped with a single antenna, connected to the MEC via optical fiber; a UAV (UAV) that collects aerial image data; a UAV (UIRS) equipped with an IRS to provide a reflection link; the IRS has N reflective elements, and the reflection mode is total reflection; M USVs working around the monitoring point; the UIRS has a fixed height and the CUAV has fixed coordinates; the USV and CUAV can choose to perform calculations locally or offload data to the edge server for calculation.

3. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 1, characterized in that: The objective function of minimizing the maximum information age of USV is expressed as: Among them, A m represents the information age of the m-th USV, represents the calculation delay of the mth USV, τ represents the length of a time slot, α m represents the unloading decision variable of the mth USV, D m represents the amount of tasks generated by the mth USV, C m represents the number of CPU cycles required for the mth USV to complete 1-bit calculation, f m represents the computing resources of the mth USV, R m represents the transmission rate of the mth USV, C es represents the number of CPU cycles required for edge server to calculate 1 bit, f es Represents the computing resources of the edge server.

4. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 3, characterized in that: The calculation formula for the transmission rate of the mth USV is: Among them, l m represents the bandwidth allocation coefficient of the mth USV, B represents the system bandwidth, represents the transmission power of the mth USV, h m,b represents the channel gain from the mth USV to the base station, h r,b represents the channel gain from UIRS to the base station, h m,r represents the channel gain from UIRS to the mth USV, σ 2 represents the noise variance.

5. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 1, characterized in that: The CUAV computation delay constraint is expressed as: in, Indicates the computational latency of CUAV, T max Indicates the maximum computational delay allowed by CUAV, α c represents the unloading decision variable of the mth USV, D c represents the amount of tasks generated by CUAV, C c Indicates the number of CPU cycles required for CUAV to complete 1-bit calculation, f c represents the computing resources of CUAV, R c Indicates the transmission rate of CUAV, C es represents the number of CPU cycles required for edge server to calculate 1 bit, f es represents the computing resources of the edge server, l c represents the bandwidth allocation coefficient of CUAV, B represents the system bandwidth, represents the transmission power of CUAV, h c,b represents the channel gain between CUAV and base station, σ 2 represents the noise variance.

6. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 1, characterized in that: The optimization problem of minimizing the maximum information age of USV is expressed as: Among them, α represents the terminal offloading decision variable matrix, l represents the terminal bandwidth allocation coefficient matrix, θ represents the reflection coefficient vector matrix, L r Indicates IRS coordinates, A m represents the information age of the m-th USV, represents the phase angle of the nth reflective element, N represents the number of reflective elements in the IRS, and x min Indicates the minimum value of the horizontal axis, x r Indicates the horizontal coordinate of USV, x max Indicates the maximum value of the horizontal axis, y min Indicates the minimum value of the vertical coordinate, y r Indicates the vertical coordinate of USV, y max Indicates the maximum value of the vertical coordinate, z min Indicates the minimum value of the vertical coordinate, z r Indicates the vertical coordinate of USV, h c Indicates the vertical coordinate of CUAV, l c represents the bandwidth allocation coefficient of CUAV, l m represents the bandwidth allocation coefficient of the mth USV, M represents the number of USVs, α i represents the uninstallation decision variable of terminal i, Indicates the computational latency of CUAV, c represents CUAV, T max Indicates the maximum computation latency allowed by CUAV.

7. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 1, characterized in that: The process of solving the problem of minimizing the maximum information age of USVs includes: Convert the problem of minimizing the maximum information age of USV into a minimization problem; Fix the IRS phase angle and IRS deployment location and decompose the minimization problem into the first subproblem. Solve the first subproblem to obtain the offloading decision and bandwidth allocation solution. Fix the offloading decision, bandwidth allocation, and IRS deployment location, and decompose the minimization problem into the second sub-problem. Solve the second sub-problem to obtain the IRS phase angle. Fix the offloading decision, bandwidth allocation, and RS phase angle, and decompose the minimization problem into a third sub-problem. Solve the third sub-problem to obtain the IRS deployment location plan. The three sub-problem solving processes are repeated until the objective function of the minimization problem converges, and the optimal offloading decision, optimal bandwidth allocation plan, optimal IRS phase angle and optimal IRS deployment location plan, i.e., the wireless communication resource allocation plan, are obtained.

8. The wireless communication resource allocation method for IRS-assisted USV data offloading according to claim 1, characterized in that: The minimization problem is expressed as: Where s represents the slack variable, α represents the terminal offloading decision variable matrix, l represents the terminal bandwidth allocation coefficient matrix, θ represents the reflection coefficient vector matrix, N represents the number of reflective elements of the IRS, and x min Indicates the minimum value of the horizontal axis, x r Indicates the horizontal coordinate of USV, x max Indicates the maximum value of the horizontal axis, y min Indicates the minimum value of the vertical coordinate, y r Indicates the vertical coordinate of USV, y max Indicates the maximum value of the vertical coordinate, z min Indicates the minimum value of the vertical coordinate, z r Indicates the vertical coordinate of USV, h c Indicates the vertical coordinate of CUAV, l c represents the bandwidth allocation coefficient of CUAV, l m represents the bandwidth allocation coefficient of the mth USV, M represents the number of USVs, α i represents the uninstallation decision variable of terminal i, Indicates the computational latency of CUAV, c represents CUAV, T max Indicates the maximum computational delay allowed by CUAV, z m represents auxiliary variables, represents the computational delay of the mth USV, and τ represents the length of a time slot.

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