Network data compression and transmission optimization method for unmanned aerial vehicle loaded with intelligent reflecting surface

Abstract

The invention discloses a network data compression transmission optimization method for unmanned aerial vehicles loaded with intelligent reflecting surfaces, and the method comprises the steps: building an intelligent reflecting surface and unmanned aerial vehicle assisted mobile edge computing system which comprises N terminal devices, L unmanned aerial vehicles loaded with the intelligent reflecting surfaces, and M base stations for deploying edge servers; establishing a communication model, a data compression model, a time delay model and an energy consumption model, and then according to the models, establishing an optimization problem of minimizing the energy consumption of the terminal equipment and the unmanned aerial vehicle, which satisfies a terminal equipment compression ratio constraint, a compression distortion rate constraint and a total time delay constraint of uploading data to a base station by the terminal equipment; and jointly optimizing the compression ratio of the terminal equipment and the phase shift coefficient matrix of the IRS based on an augmented Lagrangian multiplier assisted snow-melting optimization algorithm, and carrying out conversion solution on an optimization problem to obtain a final solution of the optimization problem. According to the invention, the communication quality between the blocked terminal equipment and the edge server can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more specifically, to a method for optimizing network data compression and transmission of an unmanned aerial vehicle equipped with an intelligent reflective surface. Background Art

[0002] Edge computing (EC) and intelligent reflective surfaces (IRS) are considered promising solutions for the Internet of Things (IoT). The computing resources of terminal devices are limited, which are usually insufficient to process a large amount of real-time sensitive data of terminal devices. Although cloud computing has expanded the computing power of terminal devices, it generally cannot meet the real-time and security requirements. Therefore, as an extension and supplement to cloud computing, edge computing is widely used in the Internet of Things to handle computationally intensive and time-sensitive tasks and ensure high security. In addition, due to its high flexibility and low energy consumption, IRS is suitable for edge computing in IoT scenarios. IRS can intelligently reconfigure the propagation environment while maintaining high-frequency spectrum and energy efficiency, thereby improving the performance of IoT systems. As an indispensable device in edge computing and IoT scenarios, drones provide a way to solve many problems due to their flexibility and designability.

[0003] Energy consumption is a key factor in edge computing and IRS-assisted IoT. Existing studies have shown that compressed data transmission can effectively reduce energy consumption during data transmission. In order to adapt to the data characteristics of IoT scenarios and further reduce the energy consumption of terminal devices, it is necessary to select appropriate data compression methods. In terms of data compression, lossy compression and lossless compression are two commonly used methods. Lossy compression is favored in edge computing and IRS-assisted IoT because it has a higher compression ratio and less data transmission energy consumption when slight data distortion is acceptable. Therefore, the distortion caused by data compression must be considered in the IoT system without significantly affecting the decision-making process.

[0004] In order to solve the problem of communication obstruction between terminal devices and edge servers in edge computing, the existing technical solution deploys IRS on the surface of obstacles (high-rise buildings) and realizes the communication between terminal devices and edge servers blocked by obstacles with the assistance of multi-hop IRS. However, the communication realized by the above design solution has to go through multiple hops, which may increase the latency. At the same time, the deployment location and number of IRS also need to be considered. Too many IRS may affect the latency, and too few IRS may affect the communication quality. Summary of the invention

[0005] In order to overcome the problem of increased delay caused by the need to communicate through multiple hops when the communication between the terminal device and the edge server is blocked in the existing edge computing, the present invention provides a data compression transmission optimization method for a drone network equipped with an intelligent reflective surface, which minimizes the energy consumption of the terminal device and the drone by considering the delay constraint and the terminal device compression constraint, and effectively improves the communication quality between the blocked terminal device and the edge server.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] The present invention proposes a method for optimizing network data compression transmission of an unmanned aerial vehicle equipped with an intelligent reflective surface, comprising:

[0008] Establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and M base stations deployed with edge servers;

[0009] A communication model is established according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed; a data compression model is established according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed; and a delay model and an energy consumption model are established according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed;

[0010] According to the communication model, data compression model, delay model and energy consumption model, an optimization problem of minimizing the energy consumption of the terminal device and the UAV is established to meet the constraints of the compression ratio of the terminal device, the compression distortion rate and the total delay of the terminal device uploading data to the base station;

[0011] The optimization problem is transformed and solved based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain the final solution of the optimization problem.

[0012] Preferably, each of the smart reflective surfaces deployed on the drone is a square, and the number of single-sided reflective elements of each of the smart reflective surfaces is S. Then, each of the smart reflective surfaces has S 2 reflective elements, and each terminal device communicates with only one corresponding smart reflective surface.

[0013] Preferably, the establishing of a communication model according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed includes:

[0014] The N terminal devices and the M base stations where edge servers are deployed are blocked by obstacles and cannot directly establish communication. The area blocked by obstacles between the N terminal devices and the M base stations where edge servers are deployed is divided into L sub-areas. Each of the sub-areas has a drone equipped with an intelligent reflective surface to assist the terminal devices in the area to communicate with the fixed nearby base stations. The N terminal devices are randomly and evenly distributed in the L sub-areas. Each of the terminal devices has a computationally intensive task with a data volume of D. i ,i∈{1,2,…,N}, the data needs to be compressed and uploaded to the edge server of the base station through the assistance of the drone equipped with the intelligent reflective surface corresponding to the area where the terminal device is located;

[0015] The terminal device offloads its tasks to the edge server of the base station f(l) through the orthogonal frequency division multiplexing channel. When the terminal device i in the lth sub-area transmits data to the nearest base station f(l) through the drone, the achieved data transmission rate is expressed as:

[0016]

[0017] Among them, r i,f(l) represents the data transmission rate of the terminal device i in the lth sub-area transmitting data to the nearest base station f(l) through the drone, B0 and N0 are the network bandwidth and the power spectrum density of the complex Gaussian white noise, respectively, P i,f(l) is the transmission power between terminal device i and base station f(l), represents the link channel from terminal device i in the lth sub-area to base station f(l) via the drone, represents the total channel gain of the UAV-BS link of terminal device i in the lth sub-area, represents the total channel gain of the UE-UAV link of terminal device i in the lth sub-area;

[0018] For the UAV in the lth sub-area, the calculation formula of the nearest base station f(l) is:

[0019] f(l)=argminF l (j)

[0020] Among them, F l (j) represents the distance from the UAV to the base station j in the lth sub-area, represents the coordinates of the drone in the lth sub-area, represents the coordinates of base station j.

[0021] Preferably, the method for determining the total channel gain of the UAV-BS link and the UE-UAV link of the terminal device i in the lth sub-area includes:

[0022]

[0023] Among them, PL l,f(l) represents the path loss from the UAV in the lth sub-area to the corresponding base station f(l), Φ l,f(l) and θ l,f(l) denote the departure azimuth and elevation angle of the link from the UAV in the lth sub-area to the base station f(l), a r (Φ l,f(l) ,θ l,f(l) )∈C N×1 is the array response of the smart reflective surface on the UAV in the link from the UAV to the base station f(l) in the lth sub-area, represents the nonlinear line-of-sight component of the link from the UAV to the base station f(l) in the lth sub-area, ∈ is the Rice factor;

[0024]

[0025] Among them, PL i,l is the path loss from terminal device i to UAV in the lth sub-area, ∈ is the Rice factor, a r (Φ i,l ,θ i,l )∈C N×1 is the array response of the IRS on the UAV in the lth sub-area, Φ i,i and θ i,l They represent the azimuth and elevation angles of the l-th sub-area from the terminal device i to the UAV link respectively. Represents the nonlinear line-of-sight component (NLOS component), whose elements are selected from the complex Gaussian variable distribution with mean 0 and covariance 1.

[0026] Preferably, establishing a data compression model according to the data transmission tasks of the N terminal devices to the M base stations where the edge servers are deployed includes:

[0027] Each terminal device performs lossy compression on the data before uploading it. The amount of data after compression on terminal device i is:

[0028]

[0029] Among them, μ i Indicates the terminal device i data D i Compression ratio;

[0030] Terminal device i data D i The distortion rate is:

[0031] U i = i μ i

[0032] Among them, i is the distortion factor, U i ≤u0, u0 represents the upper limit of the compression distortion rate.

[0033] Preferably, the establishing of a delay model and an energy consumption model according to the offloading task of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the m base stations deploying edge servers also includes:

[0034] When the UAV in the lth sub-area hovers to assist the terminal device i in offloading the task to the base station f(l), the transmission time of the offloaded task is expressed as:

[0035]

[0036] The number of CPU cycles required to compress 1 bit of data is modeled as:

[0037]

[0038] Where ε is a constant that depends on the specific compression method;

[0039] The computing power of the processor of terminal device i is (CPU cycles / s), then the delay of compressing data on terminal device i is:

[0040]

[0041] The distribution of terminal devices is known, and the set of terminal devices in the lth sub-area is I l , then the global set of terminal device distribution is I = {I l ,l=1,2,…,L}, the total delay of the terminal device uploading data to the base station satisfies the constraint:

[0042]

[0043] Among them, τ represents the upper limit of the total delay of the terminal device uploading data.

[0044] Preferably, the establishing of a delay model and an energy consumption model according to the offloading task of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations deploying edge servers includes:

[0045] Using lossy compression algorithm for data compression, the compression energy consumption of terminal device i is:

[0046]

[0047] in, represents the compression energy consumption of terminal device i, is the energy coefficient per data unit (bit);

[0048] When the terminal device i in the lth sub-area offloads its task to the base station f(l) through the drone, the transmission energy consumption of the terminal device i is:

[0049]

[0050] When the terminal device i in the lth sub-area offloads its task to the base station f(l) through the drone, the hovering energy consumption of the drone in the lth sub-area is:

[0051]

[0052] in, Represents the hovering power of the UAV in the lth sub-area.

[0053] Preferably, according to the communication model, data compression model, delay model and energy consumption model, establishing an optimization problem of minimizing the energy consumption of the terminal device and the drone that satisfies the terminal device compression ratio constraint, the compression distortion rate constraint and the total delay constraint of the terminal device uploading data to the base station includes:

[0054]

[0055]

[0056]

[0057]

[0058] Among them, μ and Θ are decision variables, μ={μ i ,i=1,2,…,N},Θ={Θ i,l,f(l) ; i=1,2,…,N;l=1,2,…,L} respectively represent the compression ratio of the terminal equipment and the phase shift coefficient matrix of the smart reflection surface, ω1 and ω2 are weight coefficients, and ω1+ω2=1.

[0059] Preferably, the optimization problem is transformed and solved based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers, and the final solution of the optimization problem is obtained, which includes:

[0060] First, transform the optimization problem P1 into the optimization problem P2:

[0061]

[0062]

[0063]

[0064] The constraints in the optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used to quickly solve it, specifically:

[0065] Let x=(μ,Θ), then:

[0066]

[0067]

[0068] By eliminating the constraint (a) in the optimization problem P2, the augmented Lagrangian function of the kth iteration is obtained:

[0069]

[0070] in, represents the augmented Lagrangian function of the kth iteration, is the Lagrange multiplier, σ k is the positive penalty parameter of the augmentation term;

[0071] In the kth iteration, we establish The optimization problem is:

[0072]

[0073]

[0074] The snowmelt optimization algorithm is used to iteratively solve P3 to obtain the final solution.

[0075] The present invention also proposes a UAV network data compression transmission optimization system equipped with an intelligent reflective surface, which is used to implement the above-mentioned optimization method, including:

[0076] A system building module for establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and M base stations deploying edge servers;

[0077] A model building module is used to establish a communication model according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed, establish a data compression model according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed, and establish a delay model and an energy consumption model according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed;

[0078] An optimization problem establishment module is used to establish an optimization problem of minimizing the energy consumption of the terminal device and the drone, which satisfies the terminal device compression ratio constraint, the compression distortion rate constraint and the total delay constraint of the terminal device uploading data to the base station, according to the communication model, the data compression model, the delay model and the energy consumption model;

[0079] The solution module is used to transform and solve the optimization problem based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain a final solution to the optimization problem.

[0080] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0081] The present invention proposes a method for optimizing data compression transmission of a drone network equipped with an intelligent reflective surface, including establishing a mobile edge computing system assisted by an intelligent reflective surface and a drone, and then establishing a communication model, a data compression model, a delay model, and an energy consumption model; according to the communication model, the data compression model, the delay model, and the energy consumption model, an optimization problem of minimizing the energy consumption of a terminal device and a drone that satisfies the compression ratio constraint of the terminal device, the compression distortion rate constraint, and the total delay constraint of the terminal device uploading data to the base station is constructed; based on the snow melting optimization algorithm assisted by the augmented Lagrange multiplier, the compression ratio of the terminal device and the phase shift coefficient matrix of the IRS are jointly optimized, and the optimization problem is transformed and solved to obtain the final solution of the optimization problem. The present invention minimizes the energy consumption of the terminal device and the drone by considering the delay constraint and the terminal device compression constraint, and can effectively improve the communication quality between the blocked terminal device and the edge server. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a flow chart of the method for optimizing network data compression and transmission of a drone equipped with a smart reflective surface as described in Example 1;

[0083] Figure 2 This is a block diagram of the mobile edge computing system assisted by the intelligent reflective surface and drone described in Example 2;

[0084] Figure 3 This is a flow chart of the snowmelt optimization algorithm based on augmented Lagrange multipliers as described in Example 2;

[0085] Figure 4 This is the structural intent of the method for optimizing network data compression and transmission of a drone equipped with an intelligent reflective surface as described in Example 3. DETAILED DESCRIPTION

[0086] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0087] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0088] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0089] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0090] Example 1

[0091] This embodiment provides a method for optimizing network data compression transmission of a drone equipped with a smart reflective surface. Figure 1 As shown, including:

[0092] Establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and M base stations deployed with edge servers;

[0093] A communication model is established according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed; a data compression model is established according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed; and a delay model and an energy consumption model are established according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed;

[0094] According to the communication model, data compression model, delay model and energy consumption model, an optimization problem of minimizing the energy consumption of the terminal device and the UAV is established to meet the constraints of the compression ratio of the terminal device, the compression distortion rate and the total delay of the terminal device uploading data to the base station;

[0095] The optimization problem is transformed and solved based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain the final solution of the optimization problem.

[0096] In the specific implementation process, firstly, a mobile edge computing system assisted by intelligent reflective surfaces and drones is established, and then a communication model, a data compression model, a delay model and an energy consumption model are established. Then, based on the communication model, the data compression model, the delay model and the energy consumption model, an optimization problem of minimizing the energy consumption of terminal devices and drones that satisfies the terminal device compression ratio constraints, the compression distortion rate constraints and the total delay constraints for the terminal device to upload data to the base station is established. Finally, based on the snow melting optimization algorithm assisted by the augmented Lagrangian multiplier, the optimization problem is transformed and solved to obtain the final solution of the optimization problem.

[0097] Example 2

[0098] This embodiment provides a method for optimizing network data compression transmission of a drone equipped with a smart reflective surface, including:

[0099] Establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and M base stations deployed with edge servers;

[0100] Each of the intelligent reflective surfaces deployed on the drone is a square, and the number of single-sided reflective elements of each of the intelligent reflective surfaces is S. Then each of the intelligent reflective surfaces has S2 reflective elements, and each terminal device communicates with only one corresponding smart reflective surface.

[0101] like Figure 2 As shown in the figure, the area blocked by high-rise buildings is divided into L small sub-areas. Each sub-area has a dedicated drone equipped with an intelligent reflective surface (IRS) to assist the terminal devices in the area to communicate with the fixed nearby base station. N terminal devices are randomly and evenly distributed in the L sub-areas. Each of the terminal devices has a computationally intensive task with a data volume of D. i ,i∈{1,2,…,N}, the data needs to be compressed and uploaded to the edge server of the base station with the assistance of the drone equipped with IRS in the area where the terminal device is located.

[0102] A communication model is established according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed; a data compression model is established according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed; and a delay model and an energy consumption model are established according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed;

[0103] (1) Communication model

[0104] Since there are tall buildings blocking the communication between the terminal device and the base station, the flexibility of the drone and IRS is utilized to deploy the IRS on the drone to assist the communication between the terminal device and the base station. Assuming that each IRS deployed on the drone is a square, and the number of single-sided reflection elements is S, then each IRS has S 2 reflective elements, and each terminal device communicates with only one corresponding smart reflective surface.

[0105] The coordinates of terminal device i are expressed as The coordinates of the UAV in the lth sub-area are expressed as The coordinates of base station j are expressed as Therefore, the distance between terminal device i and the drone in the lth sub-area is as follows:

[0106]

[0107] Similarly, the distance between the UAV in the lth sub-area and the base station j is expressed as:

[0108]

[0109] The distance from the UAV to the base station j in the lth sub-area is:

[0110]

[0111] Among them, j∈{1,2,…,M}.

[0112] For the UAV in the lth sub-area, the data should be sent to the nearest base station f(l), which can be expressed as:

[0113] f(l)=argmin F l (j) (4)

[0114] Each terminal device will not leave the divided sub-area during data transmission, and the location distribution of each terminal device is known. If terminal device i is located in the l-th sub-area, which uploads data to the base station f(l), then the phase shift coefficient matrix of the IRS on the drone in the l-th sub-area is where ρ represents an imaginary unit, represents the phase shift coefficient of the IRS on the UAV in the lth sub-area, and θ i,l,f(l),s ∈[0,2π],s=1,2,…,S 2 .

[0115] The communication path between terminal device i and base station f(l) in the lth sub-area is divided into two sections: UE-UAV link and UAV-BS link. The total channel gain of the UE-UAV link of terminal device i is as follows:

[0116]

[0117] Among them, PL i,l is the path loss from terminal device i to UAV in the lth sub-area, ∈ is the Rice factor, a r (Φ i,l ,θ i,l )∈C N×1 is the array response of the IRS on the UAV in the lth sub-area, Φ i,l and θ i,l They represent the azimuth and elevation angles of the l-th sub-area from the terminal device i to the UAV link respectively. represents the nonlinear line-of-sight component (NLOS component), whose elements are selected from a complex Gaussian variable distribution with a mean of 0 and a covariance of 1. Similarly, the total channel gain of the UAV-BS link of the terminal device i in the lth sub-area is expressed as:

[0118]

[0119] Among them, PL l,f(l) represents the path loss from the l-th sub-area UAV to the corresponding base station f(l), Φ l,f(l) and θ l,f(l)They represent the departure azimuth and elevation angle of the link from the UAV in the lth sub-area to the base station f(l), respectively. The logarithmic distance path loss model in dB can be expressed as:

[0120]

[0121] Among them, PL0 is the path loss at the reference distance d0, δ is the path loss index mainly determined by the propagation environment, and d is the distance from the transmitter to the receiver.

[0122] According to existing literature, there are:

[0123]

[0124] in, is the Kronecker product, u = 2πdcos(θ r ) / λ,v=2πdsin(θ r )cos(Φ r ) / λ, d is the antenna distance, λ is the signal wavelength. x (u) and a y (v) are:

[0125] a x (u)=[1,e ρu ,…,e ρ(S - 1)u ] T (9)

[0126] a y (v)=[1,e ρv ,…,e ρ(S - 1)v ] T (10)

[0127] Therefore, the link channel from terminal device i in the lth sub-area to base station f(l) through UAV is:

[0128]

[0129] in,(·) H Represents the conjugate transpose operation.

[0130] The terminal device offloads its tasks to the edge server through the orthogonal frequency division multiplexing (OFDM) channel, thus ensuring that there is no interference between the terminals. Therefore, when the terminal device i in the lth sub-area transmits data to the nearest base station f(l) through the UAV, the achieved data transmission rate is expressed as:

[0131]

[0132] Among them, r i,f(l)represents the data transmission rate of the terminal device i in the lth sub-area transmitting data to the nearest base station f(l) through the drone, B0 and N0 represent the network bandwidth and the power spectrum density of the complex Gaussian white noise respectively, P i,f(l) is the transmission power between terminal device i and base station f(l).

[0133] (2) Compression model

[0134] Each terminal device performs lossy compression on the data before uploading it, so the amount of data after compression of terminal device i is:

[0135]

[0136] Among them, μ i It is the terminal device i data D i Compression ratio.

[0137] Any lossy compression scheme will usually produce data distortion. Considering that the compression distortion rate increases linearly with the data compression ratio, the terminal device i data D i The distortion rate is:

[0138] U i = i μ i (14)

[0139] Among them, i is the distortion factor determined by the data set. It should be ensured that the compression distortion rate of the terminal device data is not higher than a certain upper limit u0, which is usually acceptable within 1%, so there are constraints:

[0140] U i ≤u0 (15)

[0141] (3) Delay Model

[0142] When the UAV in the lth sub-area hovers to assist the terminal device i in offloading the task to the base station f(l), the transmission time of the offloaded task is expressed as:

[0143]

[0144] The number of CPU cycles required to compress 1 bit of data is modeled as:

[0145]

[0146] Where ε is a constant that depends on the specific compression method, and the computing power of the processor of terminal device i is (CPU cycles / s), then the delay of compressing data on terminal device i is:

[0147]

[0148] The distribution of terminal devices is known, and the set of terminal devices in the lth sub-area is I l , then the global set of terminal device distribution is I = {I l ,l=1,2,…,L}, the total delay of the terminal device uploading data to the base station satisfies the constraint:

[0149]

[0150] Among them, τ represents the upper limit of the total delay of the terminal device uploading data.

[0151] (4) Energy consumption model

[0152] In this embodiment, the present invention adopts a lossy compression algorithm, such as the Lightweight Time Compression (LTC) algorithm, to reduce the time redundancy in the data input. The energy consumed in the data compression process usually depends on two parameters, namely the compression ratio and the data input. For the former, it has been experimentally proved that the compression energy of the LTC algorithm does not change with the compression ratio. Then the compression energy consumption of terminal device i is

[0153]

[0154] in, is the energy coefficient per data unit (bit). The value of is determined by the processing unit and the algorithm used for data compression. It is usually modeled as a random variable with a Gamma distribution. To simplify our analysis, in this study, we use The average value of is taken as the representative value.

[0155] When the terminal device i in the lth sub-area offloads its task to the base station f(l) through the UAV, the transmission energy consumption of the terminal device i is

[0156]

[0157] When the terminal device i in the lth sub-area offloads its task to the base station f(l) through the UAV, the hovering energy consumption of the UAV in the lth sub-area is

[0158]

[0159] in, is the hovering power of the UAV in the lth sub-area.

[0160] According to the communication model, data compression model, delay model and energy consumption model, an optimization problem of minimizing the energy consumption of terminal devices and drones is established to meet the constraints of terminal device compression ratio, compression distortion rate and total delay of terminal device uploading data to base station. Specifically,

[0161]

[0162]

[0163]

[0164]

[0165] Among them, μ and μ are decision variables, μ={μ i ,i=1,2,…,N},Θ={Θ i,l,f(l) ; i=1,2,…,N;l=1,2,…,L}respectively represent the compression ratio of the terminal equipment and the phase shift coefficient matrix of the smart reflection surface, ω1 and ω2 are weight coefficients, and ω1+ω2=1.

[0166] According to equations (14), (15) and (23), the optimization problem P1 is first transformed into the optimization problem P2:

[0167]

[0168]

[0169]

[0170] The snow ablation optimizer (SAO) achieves a balance between utilization and exploration in the solution space and prevents premature convergence by simulating the sublimation and melting of snow. This algorithm has better performance than other existing competitive methods, but is not suitable for optimization problems with complex constraints.

[0171] In this embodiment, since the optimization problem P2 has complex constraints, the present invention considers using augmented lagrangian multipliers (ALM) to eliminate the complex constraints, convert it into an optimization problem with simple constraints, and then use the SAO algorithm to quickly solve it. Therefore, a snow melting optimization algorithm (LSAO) assisted by augmented Lagrangian multipliers is proposed to transform and solve the optimization problem, jointly optimize the compression ratio of the terminal device and the phase shift coefficient matrix of the IRS, and obtain the final solution of the optimization problem, which is specifically:

[0172] The constraints in the optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used to quickly solve it, specifically:

[0173] Let x=(μ,Θ), then:

[0174]

[0175]

[0176] By eliminating the constraint (a) in the optimization problem P2, the augmented Lagrangian function of the kth iteration is obtained:

[0177]

[0178] in, represents the augmented Lagrangian function of the kth iteration, is the Lagrange multiplier, σ k is the positive penalty parameter of the augmentation term;

[0179] In the kth iteration, we establish The optimization problem is:

[0180]

[0181]

[0182] Then, the snowmelt optimization algorithm is used to iteratively solve P3 to obtain the final solution. For complex inequality constraints, we can define the constraint violation degree as:

[0183]

[0184] The algorithm flow chart of the snowmelt optimization algorithm based on augmented Lagrange multipliers is as follows: Figure 3 As shown, the penalty factor σ0, constraint violation constant ω, and precision constant η are all greater than 0; constants α, β, and ρ satisfy: 0<α≤β≤1,ρ>1.

[0185] Example 3

[0186] This embodiment provides a UAV network data compression transmission optimization system equipped with an intelligent reflective surface, which is used to implement the optimization method described in Embodiment 1 or 2, such as Figure 3 As shown, including:

[0187] A system building module for establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and M base stations deploying edge servers;

[0188] A model building module is used to establish a communication model according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed, establish a data compression model according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed, and establish a delay model and an energy consumption model according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed;

[0189] An optimization problem establishment module is used to establish an optimization problem of minimizing the energy consumption of the terminal device and the drone, which satisfies the terminal device compression ratio constraint, the compression distortion rate constraint and the total delay constraint of the terminal device uploading data to the base station, according to the communication model, the data compression model, the delay model and the energy consumption model;

[0190] The solution module is used to transform and solve the optimization problem based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain a final solution to the optimization problem.

[0191] The same or similar reference numerals correspond to the same or similar components;

[0192] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0193] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for optimizing network data compression and transmission of an unmanned aerial vehicle equipped with an intelligent reflective surface, characterized in that: include: Establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and M base stations deployed with edge servers; A communication model is established according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed; a data compression model is established according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed; and a delay model and an energy consumption model are established according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed; According to the communication model, data compression model, delay model and energy consumption model, an optimization problem of minimizing the energy consumption of the terminal device and the UAV is established to meet the constraints of the compression ratio of the terminal device, the compression distortion rate and the total delay of the terminal device uploading data to the base station; The optimization problem is transformed and solved based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain the final solution of the optimization problem.

2. The method for optimizing network data compression and transmission of a drone equipped with an intelligent reflective surface according to claim 1 is characterized in that: Each of the intelligent reflective surfaces deployed on the drone is a square, and the number of single-sided reflective elements of each of the intelligent reflective surfaces is S. Then each of the intelligent reflective surfaces has S 2 reflective elements, and each terminal device communicates with only one corresponding smart reflective surface.

3. The method for optimizing network data compression and transmission of a drone equipped with an intelligent reflective surface according to claim 2 is characterized in that: The communication model is established according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations deployed with edge servers, including: The N terminal devices and the M base stations where edge servers are deployed are blocked by obstacles and cannot directly establish communication. The area blocked by obstacles between the N terminal devices and the M base stations where edge servers are deployed is divided into L sub-areas. Each of the sub-areas has a drone equipped with an intelligent reflective surface to assist the terminal devices in the area to communicate with the fixed nearby base stations. The N terminal devices are randomly and evenly distributed in the L sub-areas. Each of the terminal devices has a computationally intensive task with a data volume of D. i ,i∈{1,2,…,N}, the data needs to be compressed and uploaded to the edge server of the base station through the assistance of the drone equipped with the intelligent reflective surface corresponding to the area where the terminal device is located; The terminal device offloads its tasks to the edge server of the base station f(l) through the orthogonal frequency division multiplexing channel. When the terminal device i in the lth sub-area transmits data to the nearest base station f(l) through the drone, the achieved data transmission rate is expressed as: Among them, r i,f(l) represents the data transmission rate of the terminal device i in the lth sub-area transmitting data to the nearest base station f(l) through the drone, B0 and N0 are the network bandwidth and the power spectrum density of the complex Gaussian white noise, respectively, P i,f(l) is the transmission power between terminal device i and base station f(l), represents the link channel from terminal device i in the lth sub-area to base station f(l) via the drone, represents the total channel gain of the UAV-BS link of terminal device i in the lth sub-area, represents the total channel gain of the UE-UAV link of terminal device i in the lth sub-area; For the UAV in the lth sub-area, the calculation formula of the nearest base station f(l) is: f(l)=argminF l (j) Among them, F l (j) represents the distance from the UAV to the base station j in the lth sub-area, represents the coordinates of the drone in the lth sub-area, represents the coordinates of base station k.

4. The method for optimizing network data compression and transmission of a drone equipped with an intelligent reflective surface according to claim 3 is characterized in that: The method for determining the total channel gain of the UAV-BS link and the UE-UAV link of the terminal device i in the lth sub-area includes: Among them, PL l,f(l) represents the path loss from the UAV in the lth sub-area to the corresponding base station f(l), Φ l,f(l) and θ l,f(l) denote the departure azimuth and elevation angle of the link from the UAV in the lth sub-area to the base station f(l), a r (φ l,f(l) ,θ l,f(l) )∈C N ×1 is the array response of the smart reflective surface on the UAV in the link from the UAV to the base station f(l) in the lth sub-area, represents the nonlinear line-of-sight component of the link from the UAV to the base station f(l) in the lth sub-area, ∈ is the Rice factor; Among them, PL i,l is the path loss from terminal device i to UAV in the lth sub-area, ∈ is the Rice factor, a r (φ i,l ,θ i,l )∈C N×1 is the array response of the IRS on the UAV in the lth sub-area, φ i,l and θ i,l They represent the azimuth and elevation angles of the l-th sub-area from the terminal device i to the UAV link respectively. Represents the nonlinear line-of-sight component (NLOS component), whose elements are selected from the complex Gaussian variable distribution with mean 0 and covariance 1.

5. The method for optimizing network data compression and transmission of a drone equipped with an intelligent reflective surface according to claim 3 is characterized in that: The establishing of a data compression model according to the data transmission tasks of the N terminal devices to the M base stations where the edge servers are deployed comprises: Each terminal device performs lossy compression on the data before uploading it. The amount of data after compression on terminal device i is: Among them, μ i Indicates the terminal device i data D i Compression ratio; Terminal device i data D i The distortion rate is: U i =a i m i Among them, i is the distortion factor, U i ≤u0, u0 represents the upper limit of compression distortion rate.

6. The method for optimizing network data compression and transmission of a drone equipped with an intelligent reflective surface according to claim 5 is characterized in that: The delay model and energy consumption model are established according to the offloading task of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed, and the following further includes: When the UAV in the lth sub-area hovers to assist the terminal device i in offloading the task to the base station f(l), the transmission time of the offloaded task is expressed as: The number of CPU cycles required to compress 1 bit of data is modeled as: Where ε is a constant that depends on the specific compression method; The computing power of the processor of terminal device i is Then the delay of compressed data of terminal device i is: The distribution of terminal devices is known, and the set of terminal devices in the lth sub-area is I l , then the global set of terminal device distribution is l = {I l ,l=1,2,…,L}, the total delay of the terminal device uploading data to the base station satisfies the constraint: Among them, τ represents the upper limit of the total delay of the terminal device uploading data.

7. The method for optimizing network data compression and transmission of a drone equipped with a smart reflective surface according to claim 6 is characterized in that: The delay model and energy consumption model are established according to the unloading task of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations deployed with edge servers, including: Using lossy compression algorithm for data compression, the compression energy consumption of terminal device i is: in, represents the compression energy consumption of terminal device i, θ is the energy coefficient per data unit (bit); When the terminal device i in the lth sub-area offloads its task to the base station f(l) through the drone, the transmission energy consumption of the terminal device i is: When the terminal device i in the lth sub-area offloads its task to the base station f(l) through the drone, the hovering energy consumption of the drone in the lth sub-area is: in, represents the hovering power of the UAV in the i-th sub-area.

8. The method for optimizing network data compression and transmission of a drone equipped with a smart reflective surface according to claim 7 is characterized in that: According to the communication model, data compression model, delay model and energy consumption model, the optimization problem of minimizing the energy consumption of the terminal device and the UAV is established to meet the constraints of the terminal device compression ratio, the compression distortion rate and the total delay of the terminal device uploading data to the base station, including: Among them, μ and Θ are decision variables, μ={μ i ,i=1,2,…,n},Θ={Θ i,l,f(l) ; i=1,2,…,N;l=1,2,…,L}respectively represent the compression ratio of the terminal equipment and the phase shift coefficient matrix of the smart reflection surface, ω1 and ω2 are weight coefficients, and ω1+ω2=1.

9. The method for optimizing network data compression and transmission of a drone equipped with a smart reflective surface according to claim 8 is characterized in that: The optimization problem is transformed and solved based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers, and the final solution of the optimization problem is obtained, including: First, transform the optimization problem P1 into the optimization problem P2: The constraints in the optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used to quickly solve it, specifically: Let x=(μ,Θ), then: By eliminating the constraint (s) in the optimization problem P2, the augmented Lagrangian function of the kth iteration is obtained: in, represents the augmented Lagrangian function of the kth iteration, is the Lagrange multiplier, σ k is the positive penalty parameter of the augmentation term; In the kth iteration, we establish The optimization problem is: The snowmelt optimization algorithm is used to iteratively solve P3 to obtain the final solution.

10. A UAV network data compression transmission optimization system equipped with an intelligent reflective surface, characterized in that: include: A system building module for establishing a mobile edge computing system assisted by a smart reflective surface and a drone, the system comprising N terminal devices, L drones equipped with smart reflective surfaces, and W base stations deploying edge servers; A model building module is used to establish a communication model according to the signal transmission tasks of the N terminal devices, the L drones equipped with intelligent reflective surfaces, and the M base stations where edge servers are deployed, establish a data compression model according to the data transmission tasks of the N terminal devices to the M base stations where edge servers are deployed, and establish a delay model and an energy consumption model according to the unloading tasks of the N terminal devices and the L drones equipped with intelligent reflective surfaces to the M base stations where edge servers are deployed; An optimization problem establishment module is used to establish an optimization problem of minimizing the energy consumption of the terminal device and the drone, which satisfies the terminal device compression ratio constraint, the compression distortion rate constraint and the total delay constraint of the terminal device uploading data to the base station, according to the communication model, the data compression model, the delay model and the energy consumption model; The solution module is used to transform and solve the optimization problem based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain a final solution to the optimization problem.

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