An Optimization Method for Network Data Compression and Transmission of Unmanned Aerial Vehicles Equipped with Smart Reflective Surfaces

Abstract

This invention discloses a method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflectors. The method includes establishing a mobile edge computing system assisted by intelligent reflectors and UAVs, comprising N terminal devices, L UAVs equipped with intelligent reflectors, and M base stations deploying edge servers. A communication model, a data compression model, a latency model, and an energy consumption model are established. Then, based on these models, an optimization problem is established to minimize the energy consumption of the terminal devices and UAVs, satisfying constraints on the compression ratio, compression distortion rate, and total latency of data upload from the terminal devices to the base stations. Using an augmented Lagrange multiplier-assisted snowmelt optimization algorithm, the compression ratio of the terminal devices and the phase shift coefficient matrix of the IRS are jointly optimized to transform and solve the optimization problem, obtaining the final solution. This invention can effectively improve the communication quality between obstructed terminal devices and edge servers.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflective surfaces. Background Technology

[0002] Edge computing (EC) and intelligent reflective surfaces (IRS) are considered promising solutions for the Internet of Things (IoT). Due to the limited computing resources of terminal devices, they are often insufficient to handle the large amounts of real-time sensitive data they generate. While cloud computing extends the computing power of terminal devices, it generally cannot meet the requirements for real-time performance and security. Therefore, as an extension and complement to cloud computing, edge computing is widely used in IoT to handle computationally intensive and time-sensitive tasks while ensuring high security. Furthermore, 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. Drones, as indispensable devices in edge computing and IoT scenarios, offer avenues for solving many problems due to their flexibility and designability.

[0003] Energy consumption is a key factor in edge computing and IRS-assisted IoT. Existing research shows that compressed data transmission can effectively reduce energy consumption during data transmission. 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. Lossy compression and lossless compression are two commonly used methods. Lossy compression is favored in edge computing and IRS-assisted IoT because it offers a higher compression ratio and lower data transmission energy consumption while allowing for slight data distortion. Therefore, the distortion caused by data compression must be considered in IoT systems without significantly impacting the decision-making process.

[0004] To address the communication bottleneck between terminal devices and edge servers in edge computing, existing solutions deploy IRSs (Internal Relationships) on the surfaces of obstacles (high-rise buildings), using multi-hop IRSs to facilitate communication between the blocked terminal devices and edge servers. However, this design requires multiple hops, which can increase latency. Furthermore, the deployment location and number of IRSs need to be considered; too many IRSs may affect latency, while too few may impact communication quality. Summary of the Invention

[0005] To overcome the problem of increased latency caused by multiple hops in communication between terminal devices and edge servers when communication is blocked in existing edge computing, this invention provides an optimized data compression and transmission method for UAVs equipped with intelligent reflective surfaces. This method minimizes the energy consumption of terminal devices and UAVs by considering latency constraints and terminal device compression constraints, effectively improving the communication quality between blocked terminal devices and edge servers.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] This invention proposes a method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflective surfaces, comprising:

[0008] Establish a mobile edge computing system assisted by intelligent reflective surfaces and drones, the system comprising N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers;

[0009] A communication model is established based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers. A data compression model is established based on the data transmission tasks of the N terminal devices to the M base stations deploying edge servers. A latency model and an energy consumption model are established based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deploying edge servers.

[0010] Based on the aforementioned communication model, data compression model, latency model, and energy consumption model, an optimization problem is established to minimize the energy consumption of the terminal device and the UAV, satisfying the constraints of terminal device compression ratio, compression distortion rate, and total latency of the terminal device uploading data to the base station.

[0011] The snowmelt optimization algorithm based on augmented Lagrange multipliers is used to transform and solve the optimization problem, thus obtaining the final solution to the optimization problem.

[0012] Preferably, each smart reflective surface deployed on the drone is a square, and the number of reflective elements on one side of each smart reflective surface is S, then each smart reflective surface has S... 2 Each terminal device has one reflective element, and each terminal device communicates with only one corresponding smart reflective surface.

[0013] Preferably, the step of establishing a communication model based on the signal transmission tasks of the N terminal devices, L UAVs equipped with intelligent reflective surfaces, and M base stations deploying edge servers includes:

[0014] The N terminal devices and M base stations deploying edge servers are blocked by obstacles, preventing direct communication. The area blocked by these obstacles between the N terminal devices and the M base stations is divided into L sub-regions. Each sub-region has a drone equipped with a smart reflector to assist the terminal devices within that region in communicating with the nearest fixed base station. The N terminal devices are randomly and evenly distributed across these L sub-regions, and each terminal device performs a computationally intensive task with a data size of D. i For i∈{1,2,…,N}, the data needs to be compressed and then uploaded to the edge server of the base station with the assistance of a drone equipped with a smart reflective surface in the area where the terminal device is located.

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

[0016]

[0017] Where, r i,f(l) Let P represent the data transmission rate of terminal device i in the l-th sub-region, transmitting data from a UAV to the nearest base station f(l). B0 and N0 represent the network bandwidth and the power spectral density of complex Gaussian white noise, respectively. i,f(l) Let f(l) be the transmit power between terminal device i and base station f(l). Let f(l) represent the link channel from terminal device i in the l-th sub-region to base station f(l) via the UAV. This represents the total channel gain of the UAV-BS link for terminal device i in the l-th sub-region. This represents the total channel gain of the UE-UAV link for terminal device i in the l-th sub-region;

[0018] For the UAV in the l-th sub-region, the formula for calculating 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 base station j within the l-th sub-region. This represents the coordinates of the UAV in the l-th sub-region. This represents the coordinates of base station j.

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

[0022]

[0023] Among them, PL l,f(l) Φ represents the path loss from the UAV in the l-th sub-region to the corresponding base station f(l). l,f(l) and θ l,f(l) Let a represent the starting azimuth and elevation angles of the link from the UAV in the l-th sub-region to the base station f(l), respectively. r (Φ l,f(l) ,θ l,f(l) )∈C N×1 It is the array response of the intelligent reflector on the UAV in the link from the UAV to the base station f(l) in the l-th sub-region. f(l) represents the nonlinear line-of-sight component of the link from the UAV to the base station in the l-th sub-region, where ∈ is the Rice factor;

[0024]

[0025] Among them, PL i,l Let be the path loss from terminal device i in the l-th sub-region to the UAV, ∈ be the Rice factor, and a r (Φ i,l ,θ i,l )∈C N×1 It is the array response of the IRS on the l-th sub-region UAV, Φ i,l and θ i,l These represent the azimuth and elevation angles of the UAV link from terminal device i to the l-th sub-region, respectively. This 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.

[0026] Preferably, establishing a data compression model based on the data transmission tasks of the N terminal devices to the N base stations deploying edge servers includes:

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

[0028]

[0029] Where, μ i Indicates terminal device i data D i The compression ratio;

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

[0031] Ui =ɑ i μ i

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

[0033] Preferably, the step of establishing latency and energy consumption models based on the offloading tasks of the N terminal devices and L UAVs equipped with intelligent reflective surfaces to the M base stations deploying edge servers further includes:

[0034] When the hovering assistance terminal device i in the l-th sub-region unloads the task to the base station f(l), the transmission time of the unloading task is expressed as:

[0035]

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

[0037]

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

[0039] The computing power of the terminal device's i processor is The delay of the compressed data from terminal device i is:

[0040]

[0041] The distribution of terminal devices is known, and the set of terminal devices in the l-th sub-region is I. l Then the global set of terminal device distribution is I = {I l The total latency for a terminal device to upload data to the base station, where I = 1, 2, ..., L, satisfies the following constraint:

[0042]

[0043] Where τ represents the upper limit of the total latency for data upload by the terminal device.

[0044] Preferably, the step of establishing a latency model and an energy consumption model based on the offloading tasks of the N terminal devices and L UAVs equipped with intelligent reflective surfaces to the M base stations deploying edge servers includes:

[0045] Data compression is performed using a lossy compression algorithm. The compression energy consumption of terminal device i is:

[0046]

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

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

[0049]

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

[0051]

[0052] in, This represents the hovering power of the drone within the l-th sub-region.

[0053] Preferably, based on the communication model, data compression model, latency model, and energy consumption model, the optimization problem of minimizing the energy consumption of the terminal device and the UAV, satisfying the terminal device compression ratio constraint, compression distortion rate constraint, and total latency constraint of the terminal device uploading data to the base station, includes:

[0054]

[0055] Where 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} represents the compression ratio of the terminal device and the phase shift coefficient matrix of the smart reflector, respectively. ω1 and ω2 are weight coefficients, and ω1+ω2=1.

[0056] Preferably, the optimization problem is transformed and solved using an augmented Lagrange multiplier-assisted snowmelt optimization algorithm to obtain the final solution to the optimization problem, including:

[0057] First, transform optimization problem P1 into optimization problem P2:

[0058]

[0059] The constraints in optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used for fast solution, specifically:

[0060] Let x = (μ, Θ), then we have:

[0061]

[0062] By eliminating constraint (a) in optimization problem P2, the augmented Lagrangian function for the k-th iteration is obtained:

[0063]

[0064] in, Let the augmented Lagrangian function be the function of the k-th iteration. For Lagrange multipliers, σ k The positive penalty parameter for the augmentation term;

[0065] In the k-th iteration, establish about Optimization issues:

[0066]

[0067] The snowmelt optimization algorithm is used to iteratively solve P3 to obtain the final solution.

[0068] This invention also proposes a UAV network data compression and transmission optimization system equipped with a smart reflective surface, used to implement the optimization method described above, including:

[0069] The system construction module is used to establish a mobile edge computing system assisted by intelligent reflective surfaces and drones. The system includes N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers.

[0070] The model building module is used to establish a communication model based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deployed with edge servers; to establish a data compression model based on the data transmission tasks of the N terminal devices to the M base stations deployed with edge servers; and to establish a latency model and an energy consumption model based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deployed with edge servers.

[0071] The optimization problem establishment module is used to establish an optimization problem that minimizes the energy consumption of terminal devices and drones, based on the communication model, data compression model, latency model, and energy consumption model, while satisfying the terminal device compression ratio constraint, compression distortion rate constraint, and total latency constraint of the terminal device uploading data to the base station.

[0072] The solution module is used to transform and solve the optimization problem based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers, so as to obtain the final solution of the optimization problem.

[0073] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0074] This invention proposes an optimization method for data compression and transmission in UAV networks equipped with intelligent reflectors. The method includes establishing a mobile edge computing system assisted by an intelligent reflector and a UAV, followed by establishing communication, data compression, latency, and energy consumption models. Based on these models, an optimization problem is constructed that minimizes the energy consumption of the terminal device and the UAV, satisfying constraints on terminal device compression ratio, compression distortion rate, and total latency of data upload from the terminal device to the base station. An augmented Lagrange multiplier-assisted snowmelt optimization algorithm is used to jointly optimize the compression ratio of the terminal device and the phase shift coefficient matrix of the IRS, transforming and solving the optimization problem to obtain the final solution. This invention minimizes the energy consumption of the terminal device and the UAV by considering latency and terminal device compression constraints, effectively improving the communication quality between obstructed terminal devices and edge servers. Attached Figure Description

[0075] Figure 1 This is a flowchart illustrating the method for optimizing network data compression and transmission of a UAV equipped with a smart reflective surface as described in Example 1.

[0076] Figure 2 This is a block diagram of the intelligent reflective surface and UAV-assisted mobile edge computing system described in Example 2;

[0077] Figure 3 This is a flowchart of the snowmelt optimization algorithm based on augmented Lagrange multipliers as described in Example 2;

[0078] Figure 4 This is a structural schematic of the UAV network data compression and transmission optimization method with an intelligent reflective surface described in Example 3. Detailed Implementation

[0079] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0080] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0081] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0082] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0083] Example 1

[0084] This embodiment provides a method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflective surfaces, such as... Figure 1 As shown, it includes:

[0085] Establish a mobile edge computing system assisted by intelligent reflective surfaces and drones, the system comprising N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers;

[0086] A communication model is established based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers. A data compression model is established based on the data transmission tasks of the N terminal devices to the M base stations deploying edge servers. A latency model and an energy consumption model are established based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deploying edge servers.

[0087] Based on the aforementioned communication model, data compression model, latency model, and energy consumption model, an optimization problem is established to minimize the energy consumption of the terminal device and the UAV, satisfying the constraints of terminal device compression ratio, compression distortion rate, and total latency of the terminal device uploading data to the base station.

[0088] The snowmelt optimization algorithm based on augmented Lagrange multipliers is used to transform and solve the optimization problem, thus obtaining the final solution to the optimization problem.

[0089] In the specific implementation process, firstly, a mobile edge computing system assisted by intelligent reflective surfaces and drones is established. Secondly, communication models, data compression models, latency models, and energy consumption models are established. Then, based on the communication model, data compression model, latency model, and energy consumption model, an optimization problem is established to minimize the energy consumption of terminal devices and drones, satisfying the constraints of terminal device compression ratio, compression distortion rate, and total latency of data upload from terminal devices to base stations. Finally, the optimization problem is transformed and solved using the snowmelt optimization algorithm assisted by augmented Lagrange multipliers to obtain the final solution to the optimization problem.

[0090] Example 2

[0091] This embodiment provides a method for optimizing network data compression and transmission of a drone equipped with a smart reflector, including:

[0092] Establish a mobile edge computing system assisted by intelligent reflective surfaces and drones, the system comprising N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers;

[0093] Each intelligent reflective surface deployed on the drone is a square, and the number of reflective elements on one side of each intelligent reflective surface is S. Therefore, each intelligent reflective surface has S... 2 Each terminal device has one reflective element, and each terminal device communicates with only one corresponding smart reflective surface.

[0094] like Figure 2As shown in the diagram, the area blocked by tall buildings is divided into L smaller sub-regions. Each sub-region is equipped with a drone carrying an Intelligent Reflector (IRS) to assist terminal devices within that region in communicating with a fixed, nearby base station. N terminal devices are randomly and evenly distributed across these L sub-regions. Each terminal device performs a computationally intensive task with a data size of D. i For each i∈{1,2,…,N}, the data needs to be compressed and then uploaded to the edge server of the base station with the assistance of a drone carrying an IRS in the area where the terminal device is located.

[0095] A communication model is established based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers. A data compression model is established based on the data transmission tasks of the N terminal devices to the M base stations deploying edge servers. A latency model and an energy consumption model are established based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deploying edge servers.

[0096] (1) Communication model

[0097] Because tall buildings obstruct communication between terminal devices and base stations, the flexibility of drones and IRSs is utilized. IRSs are deployed on drones to assist communication between terminal devices and base stations. Assuming each IRS deployed on a drone is square, with S reflective elements on one side, then each IRS has S... 2 Each terminal device has one reflective element, and each terminal device communicates with only one corresponding smart reflective surface.

[0098] The coordinates of terminal device i are represented as The coordinates of the UAV in the l-th sub-region are represented as follows: The coordinates of base station j are represented as Therefore, the distance between terminal device i in the l-th sub-region and the drone is as follows:

[0099]

[0100] Similarly, the distance between the UAV in the l-th sub-region and base station j is expressed as:

[0101]

[0102] The distance from the UAV to base station j within the l-th sub-region is:

[0103]

[0104] Where j∈{1,2,…,M}.

[0105] For the drone in the l-th sub-region, the data should be sent to the nearest base station f(l), expressed as:

[0106] f(l) = argmin F l (j) (4)

[0107] Each terminal device will not leave the defined sub-region during data transmission, and the location distribution of each terminal device is known. If terminal device i is located in the l-th sub-region, and data is uploaded from this region to base station f(l), then the phase shift coefficient matrix of the IRS on the UAV in the l-th sub-region is: Where ρ represents the imaginary unit. Let θ represent the phase shift coefficient of the IRS on the UAV in the l-th sub-region, and θ i,l,f(l),s ∈[0,2π],s=1,2,…,S 2 .

[0108] The communication path between terminal device i located in the l-th sub-region and base station f(l) is divided into two segments: a UE-UAV link and a UAV-BS link. The total channel gain of the UE-UAV link for terminal device i is as follows:

[0109]

[0110] Among them, PL i,l Let be the path loss from terminal device i in the l-th sub-region to the UAV, ∈ be the Rice factor, and a r (Φ i,l ,θ i,l )∈C N×1 It is the array response of the IRS on the l-th sub-region UAV, Φ i,l and θ i,l These represent the azimuth and elevation angles of the UAV link from terminal device i to the l-th sub-region, respectively. Let represent the nonlinear line-of-sight (NLOS) component, whose elements are selected from a complex Gaussian distribution with a mean of 0 and a covariance of 1. Similarly, the total channel gain of the UAV-BS link for terminal device i in the l-th sub-region is expressed as:

[0111]

[0112] Among them, PL l,f(l) Φ represents the path loss from the l-th sub-region UAV to the corresponding base station f(l). l,f(l) and θ l,f(l) Let f(l) represent the azimuth and elevation angles of the link from the UAV in the l-th sub-region to the base station f(l), respectively. The logarithmic distance path loss model in dB can be expressed as:

[0113]

[0114] Where 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.

[0115] Based on existing literature, we have:

[0116]

[0117] in, For the Kronecker product, u = 2πdcos(θ) r ) / λ, v=2πdsin(θ) r cos(Φ) r ) / λ, where d is the antenna spacing and λ is the signal wavelength. x (u) and a y (v) are respectively:

[0118] a x (u)=[1,e ρu ,…,e ρ(S-1)u ] T (9)

[0119] a y (v)=[1,e ρv ,…,e ρ(S-1)v ] T (10)

[0120] Therefore, the link channel from terminal device i in the l-th sub-region to base station f(l) via UAV is:

[0121]

[0122] in,(·) H This indicates the conjugate transpose operation.

[0123] Terminal devices offload their tasks to edge servers via Orthogonal Frequency Division Multiplexing (OFDM) channels, thus ensuring no interference between terminals. Therefore, when terminal device i in the l-th sub-region transmits data to the nearest base station f(l) via UAV, the achieved data transmission rate is expressed as:

[0124]

[0125] Where, r i,f(l) Let P represent the data transmission rate of terminal device i in the l-th sub-region, transmitting data from a UAV to the nearest base station f(l). B0 and N0 represent the network bandwidth and the power spectral density of complex Gaussian white noise, respectively. i,f(l) Let f(l) be the transmit power between terminal device i and base station f(l).

[0126] (2) Compression Model

[0127] Each terminal device performs lossy compression on the data before uploading. Therefore, the compressed data size for terminal device i is:

[0128]

[0129] Where, μ i It is the terminal device i data D i Compression ratio.

[0130] Any lossy compression scheme will typically produce data distortion. Considering that the compression distortion rate increases linearly with the data compression ratio, the data D of the terminal device i... i The distortion rate is:

[0131] U i =ɑ i μ i (14)

[0132] Among them, α i The distortion factor is determined by the dataset. The compression distortion rate of the data on the terminal device should not exceed a certain upper limit u0, which is generally acceptable within 1%. Therefore, there are constraints:

[0133] U i ≤u0 (15)

[0134] (3) Delay Model

[0135] When the hovering assistance terminal device i in the l-th sub-region unloads the task to the base station f(l), the transmission time of the unloading task is expressed as:

[0136]

[0137] The number of CPU cycles required to compress 1 bit of data can be modeled as:

[0138]

[0139] Where ε is a constant that depends on the specific compression method, and the computing power of the terminal device's processor is... The delay of the compressed data from terminal device i is:

[0140]

[0141] The distribution of terminal devices is known, and the set of terminal devices in the l-th sub-region is I. l Then the global set of terminal device distribution is I = {I l The total latency for a terminal device to upload data to the base station (l = 1, 2, ..., L) satisfies the following constraint:

[0142]

[0143] Where τ represents the upper limit of the total latency for data upload by the terminal device.

[0144] (4) Energy consumption model

[0145] In this embodiment, the present invention employs a lossy compression algorithm, such as Lightweight Time Compression (LTC), to reduce temporal redundancy in the data input. The energy consumed in the data compression process typically depends on two parameters: the compression ratio and the data input. For the former, experiments have shown that the compression energy of the LTC algorithm does not change with the compression ratio. Therefore, the compression energy consumption of terminal device i is...

[0146]

[0147] in, Energy coefficient per data unit. The value of is determined by the processing unit and the algorithm used for data compression. It is typically modeled as a random variable with a Gamma distribution. To simplify our analysis, in this study, we use The average value is used as the representative value. That is...

[0148] When terminal device i in the l-th sub-region offloads its task to base station f(l) via UAV, the transmission power consumption of terminal device i is:

[0149]

[0150] When terminal device i in the l-th sub-region offloads its tasks to base station f(l) via UAV, the hovering energy consumption of the UAV in the l-th sub-region is:

[0151]

[0152] in, This represents the hovering power of the drone within the l-th sub-region.

[0153] Based on the aforementioned communication model, data compression model, latency model, and energy consumption model, an optimization problem is established to minimize the energy consumption of the terminal device and the drone, satisfying the constraints of the terminal device's compression ratio, compression distortion rate, and the total latency of the terminal device uploading data to the base station. Specifically:

[0154]

[0155] Where 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} represent the compression ratio of the terminal device and the phase shift coefficient matrix of the smart reflector, respectively. ω1 and ω2 are weight coefficients, and ω1+ω2=1.

[0156] According to equations (14), (15) and (23), the optimization problem P1 is first transformed into the optimization problem P2:

[0157]

[0158] The Snow Ablation Optimizer (SAO) algorithm simulates the sublimation and melting of snow to achieve a balance between exploitation and exploration in the solution space, preventing premature convergence. This algorithm outperforms other existing competing methods, but it is not suitable for optimization problems with complex constraints.

[0159] In this embodiment, since the optimization problem P2 has complex constraints, this invention considers using augmented lagrangian multipliers (ALM) to eliminate the complex constraints, transforming it into an optimization problem with simple constraints, and then using the SAO algorithm to solve it quickly. Therefore, a snowmelt optimization algorithm (LSAO) based on augmented lagrangian multipliers is proposed to transform and solve the optimization problem, jointly optimizing the compression ratio of the terminal device and the phase shift coefficient matrix of the IRS to obtain the final solution of the optimization problem, specifically:

[0160] The constraints in optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used for fast solution, specifically:

[0161] Let x = (μ, Θ), then we have:

[0162]

[0163] By eliminating constraint (a) in optimization problem P2, the augmented Lagrangian function for the k-th iteration is obtained:

[0164]

[0165] in, Let the augmented Lagrangian function be the function of the k-th iteration. For Lagrange multipliers, σ k The positive penalty parameter for the augmentation term;

[0166] In the k-th iteration, establish about Optimization issues:

[0167]

[0168] 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:

[0169]

[0170] The flowchart of the snowmelt optimization algorithm based on augmented Lagrange multipliers is as follows: Figure 3 As shown, the penalty factor σ0, the constraint violation constant ω, and the precision constant η are all greater than 0; the constants α, β, and ρ satisfy: 0 < α ≤ β ≤ 1, ρ > 1.

[0171] Example 3

[0172] This embodiment provides a UAV network data compression and transmission optimization system equipped with a smart reflective surface, used to implement the optimization method described in Embodiment 1 or 2, such as... Figure 4 As shown, it includes:

[0173] The system construction module is used to establish a mobile edge computing system assisted by intelligent reflective surfaces and drones. The system includes N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers.

[0174] The model building module is used to establish a communication model based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deployed with edge servers; to establish a data compression model based on the data transmission tasks of the N terminal devices to the M base stations deployed with edge servers; and to establish a latency model and an energy consumption model based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deployed with edge servers.

[0175] The optimization problem establishment module is used to establish an optimization problem that minimizes the energy consumption of terminal devices and drones, based on the communication model, data compression model, latency model, and energy consumption model, while satisfying the terminal device compression ratio constraint, compression distortion rate constraint, and total latency constraint of the terminal device uploading data to the base station.

[0176] The solution module is used to transform and solve the optimization problem based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers, so as to obtain the final solution of the optimization problem.

[0177] The same or similar reference numerals correspond to the same or similar components;

[0178] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0179] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflective surfaces, characterized in that, include: Establish a mobile edge computing system assisted by intelligent reflective surfaces and drones, the system comprising N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers; A communication model is established based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers. A data compression model is established based on the data transmission tasks of the N terminal devices to the M base stations deploying edge servers. A latency model and an energy consumption model are established based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deploying edge servers. Based on the aforementioned communication model, data compression model, latency model, and energy consumption model, an optimization problem is established to minimize the energy consumption of the terminal device and the UAV, satisfying the constraints of the terminal device's compression ratio, compression distortion rate, and the total latency of the terminal device uploading data to the base station. Specifically, it includes: Where 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} represent the compression ratio of the terminal device and the phase shift coefficient matrix of the smart reflector, respectively. ω1 and ω2 are weight coefficients, and ω1+ω2=1; This represents the compression energy consumption of terminal device i; The transmission power consumption of terminal device i in the l-th sub-region is represented when terminal device i offloads its task to base station f(l) via a drone. The hovering energy consumption of the UAV in the l-th sub-region when the terminal device i in the l-th sub-region offloads its task to the base station f(l) via the UAV; This indicates the delay in compressing data on terminal device i; μ represents the transmission time for unloading the task when the hovering assistance terminal device i in the l-th sub-region unloads the task to the base station f(l); i Indicates terminal device i data D i The compression ratio; u0 represents the upper limit of compression distortion rate; U i Indicates terminal device i data D i The distortion rate; The snowmelt optimization algorithm based on augmented Lagrange multipliers is used to transform and solve the optimization problem, thereby obtaining the final solution to the optimization problem; Specifically: First, transform optimization problem P1 into optimization problem P2: The constraints in optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used for fast solution, specifically: Let x = (μ, Θ), then we have: By eliminating constraint (a) in optimization problem P2, the augmented Lagrangian function for the k-th iteration is obtained: in, Let the augmented Lagrangian function be the function of the k-th iteration. For Lagrange multipliers, σ k The positive penalty parameter for the augmentation term; In the k-th iteration, establish about Optimization issues: The snowmelt optimization algorithm is used to iteratively solve P3 to obtain the final solution.

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

3. The method for optimizing UAV network data compression and transmission with an intelligent reflective surface according to claim 2, characterized in that, The establishment of a communication model based on the signal transmission tasks of the N terminal devices, L UAVs equipped with intelligent reflective surfaces, and M base stations deploying edge servers includes: The N terminal devices and M base stations deploying edge servers are blocked by obstacles, preventing direct communication. The area blocked by these obstacles between the N terminal devices and the M base stations is divided into L sub-regions. Each sub-region has a drone equipped with a smart reflector to assist the terminal devices within that region in communicating with the nearest fixed base station. The N terminal devices are randomly and evenly distributed across these L sub-regions, and each terminal device performs a computationally intensive task with a data size of D. i For i∈{1,2,…,N}, the data needs to be compressed and then uploaded to the edge server of the base station with the assistance of a drone equipped with a smart reflective surface in the area where the terminal device is located. The terminal device offloads its task to the edge server of base station f(l) through an orthogonal frequency division multiplexing channel. When terminal device i in the l-th sub-area transmits data to the nearest base station f(l) via a drone, the achieved data transmission rate is expressed as: Where, r i,f(l) Let P represent the data transmission rate of terminal device i in the l-th sub-region, transmitting data from a UAV to the nearest base station f(l). B0 and N0 represent the network bandwidth and the power spectral density of complex Gaussian white noise, respectively. i,f(l) Let f(l) be the transmit power between terminal device i and base station f(l). Let f(l) represent the link channel from terminal device i in the l-th sub-region to base station f(l) via the UAV. This represents the total channel gain of the UAV-BS link for terminal device i in the l-th sub-region. This represents the total channel gain of the UE-UAV link for terminal device i in the l-th sub-region; For the UAV in the l-th sub-region, the formula for calculating the nearest base station f(l) is: f(l)=argminF l (j) Among them, F l (j) represents the distance from the UAV to base station j within the l-th sub-region. This represents the coordinates of the UAV in the l-th sub-region. This represents the coordinates of base station j.

4. The method for optimizing network data compression and transmission of UAVs equipped with intelligent reflective surfaces according to claim 3, characterized in that, The method for determining the total channel gain of the UAV-BS link and UE-UAV link of terminal device i in the l-th sub-region includes: Among them, PL l,f(l) Φ represents the path loss from the UAV in the l-th sub-region to the corresponding base station f(l). l,f(l) and θ l,f(l) Let a represent the starting azimuth and elevation angles of the link from the UAV in the l-th sub-region to the base station f(l), respectively. r (Φ l,f(l) ,θ l,f(l) )∈C N ×1 It is the array response of the intelligent reflector on the UAV in the link from the UAV to the base station f(l) in the l-th sub-region. f(l) represents the nonlinear line-of-sight component of the link from the UAV to the base station in the l-th sub-region, where ∈ is the Rice factor; Among them, PL i,l Let be the path loss from terminal device i in the l-th sub-region to the UAV, ∈ be the Rice factor, and a r (Φ i,l ,θ i,l )∈C N×1 It is the array response of the IRS on the l-th sub-region UAV, Φ i,l and θ i,l These represent the azimuth and elevation angles of the UAV link from terminal device i to the l-th sub-region, respectively. This 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.

5. The method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflective surfaces according to claim 3, characterized in that, The step of establishing a data compression model based on the data transmission tasks of the N terminal devices to the M base stations deploying edge servers includes: Each terminal device performs lossy compression on the data before uploading. The compressed data size of terminal device i is: Where, μ i Indicates terminal device i data D i The compression ratio; Terminal device i data D i The distortion rate is: The i =ɑ i μ i Among them, α i U is the distortion factor. i ≤u0, where u0 represents the upper limit of compression distortion rate.

6. The method for optimizing network data compression and transmission of unmanned aerial vehicles (UAVs) equipped with intelligent reflective surfaces according to claim 5, characterized in that, The process of establishing latency and energy consumption models based on the offloading tasks of the N terminal devices and L UAVs equipped with intelligent reflective surfaces to the M base stations deploying edge servers also includes: When the hovering assistance terminal device i in the l-th sub-region unloads the task to the base station f(l), the transmission time of the unloading task is expressed as: The number of CPU cycles required to compress 1 bit of data can be modeled as: Where ε is a constant that depends on the specific compression method; The computing power of the terminal device's i processor is The delay of the compressed data from terminal device i is: The distribution of terminal devices is known, and the set of terminal devices in the l-th sub-region is I. l Then the global set of terminal device distribution is I = {I l The total latency for a terminal device to upload data to the base station (l = 1, 2, ..., L) satisfies the following constraint: Where τ represents the upper limit of the total latency for data upload by the terminal device.

7. The method for optimizing network data compression and transmission of UAVs equipped with intelligent reflective surfaces according to claim 6, characterized in that, The establishment of latency and energy consumption models based on the offloading tasks of the N terminal devices and L UAVs equipped with intelligent reflective surfaces to the M base stations deploying edge servers includes: Data compression is performed using a lossy compression algorithm. The compression energy consumption of terminal device i is: in, θ represents the compression energy consumption of terminal device i, where θ is the energy coefficient per data unit; When terminal device i in the l-th sub-region offloads its task to base station f(l) via a drone, the transmission energy consumption of terminal device i is: When terminal device i in the l-th sub-region offloads its task to base station f(l) via a drone, the hovering energy consumption of the drone in the l-th sub-region is: in, This represents the hovering power of the drone within the l-th sub-region.

8. A UAV network data compression and transmission optimization system equipped with an intelligent reflective surface, characterized in that, include: The system construction module is used to establish a mobile edge computing system assisted by intelligent reflective surfaces and drones. The system includes N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deploying edge servers. The model building module is used to establish a communication model based on the signal transmission tasks of the N terminal devices, L drones equipped with intelligent reflective surfaces, and M base stations deployed with edge servers; to establish a data compression model based on the data transmission tasks of the N terminal devices to the M base stations deployed with edge servers; and to establish a latency model and an energy consumption model based on the offloading tasks of the N terminal devices and L drones equipped with intelligent reflective surfaces to the M base stations deployed with edge servers. The optimization problem establishment module is used to establish an optimization problem that minimizes the energy consumption of terminal devices and drones, based on the communication model, data compression model, latency model, and energy consumption model, while satisfying the terminal device compression ratio constraint, compression distortion rate constraint, and total latency constraint of the terminal device uploading data to the base station. Specifically, it includes: Where 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} represent the compression ratio of the terminal device and the phase shift coefficient matrix of the smart reflector, respectively. ω1 and ω2 are weight coefficients, and ω1+ω2=1; This represents the compression energy consumption of terminal device i; This represents the compression energy consumption of terminal device i; The transmission power consumption of terminal device i in the l-th sub-region is represented when terminal device i offloads its task to base station f(l) via a drone. The hovering energy consumption of the UAV in the l-th sub-region when the terminal device i in the l-th sub-region offloads its task to the base station f(l) via the UAV; This indicates the delay in compressing data on terminal device i; μ represents the transmission time for unloading the task when the hovering assistance terminal device i in the l-th sub-region unloads the task to the base station f(l); i Indicates terminal device i data D i The compression ratio; u0 represents the upper limit of compression distortion rate; U i Indicates terminal device i data D i The distortion rate; The solution module is used to transform and solve the optimization problem based on the snowmelt optimization algorithm assisted by augmented Lagrange multipliers, so as to obtain the final solution of the optimization problem. Specifically: First, transform optimization problem P1 into optimization problem P2: The constraints in optimization problem P2 are transformed using augmented Lagrange multipliers, and then the snowmelt optimization algorithm is used for fast solution, specifically: Let x = (μ, Θ), then we have: By eliminating constraint (a) in optimization problem P2, the augmented Lagrangian function for the k-th iteration is obtained: in, Let the augmented Lagrangian function be the function of the k-th iteration. For Lagrange multipliers, σ k The positive penalty parameter for the augmentation term; In the k-th iteration, establish about Optimization issues: The snowmelt optimization algorithm is used to iteratively solve P3 to obtain the final solution.

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