A method and system for joint optimization of UAV deployment location and sub-channel allocation

Abstract

The application discloses a method and system for jointly optimizing deployment positions of unmanned aerial vehicles (UAVs) and subchannel allocation, and relates to the technical field of UAV communication. The method comprises the following steps: based on initial horizontal coordinates of each UAV base station and horizontal coordinates of each ground user in an initial OFDMA multi-UAV communication network, each ground user is divided into M clusters by using a K-means clustering algorithm; horizontal coordinates of cluster centers of each cluster are determined; corresponding UAV base stations are deployed based on the horizontal coordinates of the cluster centers, so as to obtain an OFDMA multi-UAV communication network after position optimization; subchannel allocation of the optimized OFDMA multi-UAV communication network is initialized; a subchannel allocation objective function is constructed; subchannel allocation constraints are constructed; and based on the initialized subchannel allocation, the subchannel allocation objective function and the subchannel allocation constraints, the optimized subchannel allocation is determined. The application realizes joint optimization of subchannel allocation and deployment positions suitable for multi-UAV communication.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method and system for joint optimization of UAV deployment location and sub-channel allocation. Background Technology

[0002] Future mobile communication networks are expected to offer greater capacity, higher spectral efficiency, better reliability, and wider coverage. Generally, these benefits can be achieved through the deployment of dense, fixed ground infrastructure. However, in certain scenarios, such as forest fire prevention, natural disasters, base station offloading, and military surveillance, installing ground base stations (BS) presents a significant challenge. Due to their lower cost, rapid deployment, and high flexibility and mobility, unmanned aerial vehicles (UAVs) have been widely used as airborne base stations to provide wireless communication services. UAV-supported wireless communication is considered a key component in establishing a comprehensive wireless network and has received extensive research from academia and industry in recent years. Compared to ground infrastructure, using UAVs as an airborne wireless platform offers the following advantages: 1) UAVs can be deployed quickly, overcoming the installation barriers of ground nodes. 2) The flexible deployment of UAVs provides additional freedom to improve performance. 3) Due to lower signal attenuation and higher probabilistic line-of-sight (LoS) links, UAV-assisted networks can enhance coverage and data transmission.

[0003] Typically, an aerial base station will serve multiple users simultaneously with limited resources, including bandwidth and transmit power. Furthermore, to improve system performance, the appropriate selection of UAV network configurations is crucial, including factors such as trajectory, flight time, placement location, and time switching schemes. Therefore, in UAV communication scenarios, joint optimization of UAV location deployment and sub-channel allocation is essential for Orthogonal Frequency Division Multiplexing Access (OFDMA) UAV communication networks facing co-channel interference. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for joint optimization of UAV deployment location and sub-channel allocation, which realizes joint optimization of sub-channel allocation and deployment location applicable to multi-UAV communication.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A joint optimization method for UAV deployment location and sub-channel allocation includes:

[0007] Obtain the initial horizontal coordinates of M UAV base stations and K ground users in the initial OFDMA multi-UAV communication network; M≥1, K≥1, M≥K; the OFDMA multi-UAV communication network is a multi-UAV communication network based on OFDMA mode;

[0008] Based on the initial horizontal coordinates of each UAV base station and the horizontal coordinates of each ground user, the ground users are divided into M clusters using the K-means clustering algorithm, and the horizontal coordinates of the cluster center of each cluster are determined.

[0009] Based on the horizontal coordinates of each cluster center, corresponding UAV base stations are deployed to obtain a location-optimized OFDMA multi-UAV communication network.

[0010] The sub-channel allocation of the optimized OFDMA multi-UAV communication network is initialized; the sub-channel allocation includes: the allocation of the nth sub-channel to the mth UAV base station and the allocation of the nth sub-channel to the kth ground user; the allocation is either allocated or not allocated; 1≤m≤M, N is the number of sub-channels, 1≤k≤K, 1≤n≤N;

[0011] Construct a sub-channel allocation objective function; the sub-channel allocation objective function is a function of the allocation of each sub-channel to each UAV base station, the allocation of each sub-channel to each ground user, and the information rate that each ground user can obtain;

[0012] Construct sub-channel allocation constraints; the sub-channel allocation constraints include: auxiliary function constraints, UAV base station information rate constraints, and allocation status constraints;

[0013] Based on the initial sub-channel allocation, the sub-channel allocation objective function, and the sub-channel allocation constraints, the optimized sub-channel allocation is determined.

[0014] Optionally, based on the horizontal coordinates of each cluster center, corresponding UAV base stations are deployed to obtain a location-optimized OFDMA multi-UAV communication network, specifically including:

[0015] By deploying corresponding UAV base stations in the air above the horizontal coordinates of each cluster center to provide services to all ground users of the corresponding cluster, a location-optimized OFDMA multi-UAV communication network is obtained.

[0016] Optionally, the OFDMA multi-drone communication network includes: one high-altitude platform drone, M drone base stations, and K ground users.

[0017] Optionally, the sub-channel allocation objective function is:

[0018]

[0019] Where U represents the set of allocation cases for the first sub-channel. μ m,n Ω represents the allocation of the nth sub-channel to the mth UAV base station; Ω represents the set of UAV base station service information. ω k,m Let Q represent the service status of the m-th UAV base station to the k-th ground user, where service status is either provided or not provided; Q is the set of horizontal coordinates of the UAV base station. q m Let Θ be the horizontal coordinate of the m-th UAV base station; Θ is the set of second sub-channel allocation cases. θ k,n This describes the allocation of the nth sub-channel to the kth ground user.

[0020] η is an auxiliary function. K represents the set of ground users, R k Let k be the information rate that the k-th ground user can obtain. Δf is the bandwidth of each sub-channel; γ k,m,n The signal-to-interference-plus-noise ratio (SIR) of the k-th ground user when the m-th UAV base station uses the n-th sub-channel to serve the k-th ground user. p U The power of the carrier in each sub-channel for each UAV base station; g k,m δ represents the channel power gain from the m-th UAV base station to the k-th ground user; 2 denoted as , where is the additive thermal noise power of the ground user; j is the serial number of the UAV base station.

[0021] Optionally, the auxiliary function constraint specifically includes:

[0022]

[0023] in, The information rate that can be provided for the m-th UAV base station to serve the k-th ground user using the n-th sub-channel; For expression The lower bound function.

[0024] Optionally, the unmanned aerial vehicle (UAV) base station information rate constraint specifically includes:

[0025]

[0026] Among them, C m Let m be the information rate that the m-th UAV base station can acquire. λ m,n p represents the information rate at which the m-th UAV base station acquires information using the n-th sub-channel. H For the carrier power of the high-altitude platform UAV in each sub-channel, gH,m The channel power gain from the high-altitude platform UAV to the m-th UAV base station; For expression The upper bound function.

[0027] Optionally, the sub-channel allocation constraints specifically include:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] A joint optimization system for UAV deployment location and subchannel allocation includes:

[0035] The coordinate acquisition module is used to acquire the initial horizontal coordinates of M UAV base stations and K ground users in the initial OFDMA multi-UAV communication network; M≥1, K≥1, M≥K; the OFDMA multi-UAV communication network is a multi-UAV communication network based on OFDMA mode;

[0036] The clustering module is used to divide each of the ground users into M clusters based on the initial horizontal coordinates of each of the UAV base stations and the horizontal coordinates of each of the ground users, and to determine the horizontal coordinates of the cluster center of each cluster.

[0037] The drone base station deployment module is used to deploy corresponding drone base stations based on the horizontal coordinates of each cluster center to obtain a location-optimized OFDMA multi-drone communication network.

[0038] An initialization module is used to initialize the sub-channel allocation of the optimized OFDMA multi-UAV communication network. The sub-channel allocation includes the allocation of the nth sub-channel to the mth UAV base station and the allocation of the nth sub-channel to the kth ground user. The allocation is either allocated or not allocated; 1≤m≤M, N is the number of sub-channels, 1≤k≤K, 1≤n≤N.

[0039] The objective function construction module is used to construct the sub-channel allocation objective function; the sub-channel allocation objective function is a function concerning the allocation of each sub-channel to each UAV base station, the allocation of each sub-channel to each ground user, and the information rate that each ground user can obtain;

[0040] A constraint construction module is used to construct sub-channel allocation constraints; the sub-channel allocation constraints include: auxiliary function constraints, UAV base station information rate constraints, and allocation status constraints.

[0041] The sub-channel allocation optimization module is used to determine the optimized sub-channel allocation based on the initial sub-channel allocation status, the sub-channel allocation objective function, and the sub-channel allocation constraints.

[0042] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0043] This invention discloses a joint optimization method and system for UAV deployment location and subchannel allocation. First, based on the initial horizontal coordinates of each UAV base station and the horizontal coordinates of each ground user in the initial OFDMA multi-UAV communication network, the ground users are divided into M clusters using the K-means clustering algorithm, and the horizontal coordinates of the cluster centers of each cluster are determined. Based on the horizontal coordinates of each cluster center, the corresponding UAV base stations are deployed, resulting in a location-optimized OFDMA multi-UAV communication network. Second, the subchannel allocation of the optimized OFDMA multi-UAV communication network is initialized. Then, a subchannel allocation objective function and subchannel allocation constraints are constructed. Finally, based on the initialized subchannel allocation, the subchannel allocation objective function, and the subchannel allocation constraints, the optimized subchannel allocation is determined. This achieves joint optimization of subchannel allocation and deployment location suitable for multi-UAV communication. Furthermore, this invention fully considers the co-channel interference problem in multi-UAV communication and effectively reduces the impact of co-channel interference through a reasonable subchannel allocation method. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the joint optimization method for UAV deployment location and sub-channel allocation provided in Embodiment 1 of the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of a multi-UAV communication system model;

[0047] Figure 3 This is a schematic diagram illustrating the deployment of UAV communication network nodes based on K-means clustering.

[0048] Figure 4 A schematic diagram of the sub-channel allocation scheme for a drone base station;

[0049] Figure 5 A schematic diagram of sub-channel allocation for GU;

[0050] Figure 6 This is a comparison chart of the user information acquisition rates of the present invention's solution and the UA solution;

[0051] Figure 7 The diagram shows a comparison of the results obtained by the present invention and the UA solution under different conditions. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The purpose of this invention is to provide a method and system for joint optimization of UAV deployment location and sub-channel allocation, aiming to achieve joint optimization of sub-channel allocation and deployment location applicable to multi-UAV communication.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Example 1

[0056] Figure 1 This is a schematic diagram of the joint optimization method for UAV deployment location and sub-channel allocation provided in Embodiment 1 of the present invention. Figure 1 As shown, the joint optimization method for UAV deployment location and sub-channel allocation in this embodiment includes:

[0057] Step 101: Obtain the initial horizontal coordinates of M UAV base stations and K ground users in the initial OFDMA multi-UAV communication network; M≥1, K≥1, M≥K.

[0058] Among them, the OFDMA multi-UAV communication network is a multi-UAV communication network based on the OFDMA mode.

[0059] As an optional implementation, the OFDMA multi-UAV communication network includes: one high-altitude platform UAV, M UAV base stations, and K ground users.

[0060] Step 102: Based on the initial horizontal coordinates of each UAV base station and the horizontal coordinates of each ground user, use the K-means clustering algorithm to divide each ground user into M clusters and determine the horizontal coordinates of the cluster center of each cluster.

[0061] Step 103: Deploy corresponding UAV base stations based on the horizontal coordinates of each cluster center to obtain the location-optimized OFDMA multi-UAV communication network.

[0062] As an optional implementation, step 103 specifically includes:

[0063] By deploying corresponding UAV base stations in the air above the horizontal coordinates of each cluster center to provide services to all ground users of the corresponding cluster, a location-optimized OFDMA multi-UAV communication network is obtained.

[0064] Step 104: Initialize the sub-channel allocation of the optimized OFDMA multi-UAV communication network.

[0065] The sub-channel allocation includes: the allocation of the nth sub-channel to the mth UAV base station and the allocation of the nth sub-channel to the kth ground user; the allocation is either allocated or not allocated; 1≤m≤M, N is the number of sub-channels, 1≤k≤K, 1≤n≤N.

[0066] Step 105: Construct the sub-channel allocation objective function.

[0067] The sub-channel allocation objective function is a function of the allocation of each sub-channel to each UAV base station, the allocation of each sub-channel to each ground user, and the information rate that each ground user can obtain.

[0068] As an optional implementation, the sub-channel allocation objective function is:

[0069]

[0070] Where U represents the set of allocation cases for the first sub-channel. μ m,n Ω represents the allocation of the nth sub-channel to the mth UAV base station; Ω represents the set of UAV base station service information. ω k,m Let Q represent the service status of the m-th UAV base station to the k-th ground user, where service status is either provided or not provided; Q is the set of horizontal coordinates of the UAV base station. q m Let Θ be the horizontal coordinate of the m-th UAV base station; Θ is the set of second sub-channel allocation cases. θ k,n This describes the allocation of the nth sub-channel to the kth ground user.

[0071] η is an auxiliary function. K represents the set of ground users, R k Let k be the information rate that the k-th ground user can obtain. Δf is the bandwidth of each sub-channel; γ k,m,n The signal-to-interference-plus-noise ratio (SIR) of the k-th ground user when the m-th UAV base station uses the n-th sub-channel to serve the k-th ground user. p U The power of the carrier in each sub-channel for each UAV base station; g k,m δ represents the channel power gain from the m-th UAV base station to the k-th ground user; 2 denoted as , where is the additive thermal noise power of the ground user; j is the serial number of the UAV base station.

[0072] Step 106: Construct sub-channel allocation constraints.

[0073] The sub-channel allocation constraints include: auxiliary function constraints, UAV base station information rate constraints, and allocation status constraints.

[0074] As an optional implementation method, auxiliary function constraints specifically include:

[0075]

[0076] in, The information rate that can be provided for the m-th UAV base station to serve the k-th ground user using the n-th sub-channel; For expression The lower bound function.

[0077] As an optional implementation method, the information rate constraint of the UAV base station specifically includes:

[0078]

[0079] Among them, C m Let m be the information rate that the m-th UAV base station can acquire. λ m,n p represents the information rate at which the m-th UAV base station acquires information using the n-th sub-channel. H For the carrier power of the high-altitude platform UAV in each sub-channel, g H,m The channel power gain from the high-altitude platform UAV to the m-th UAV base station; For expression The upper bound function.

[0080] As an optional implementation method, the sub-channel allocation constraints specifically include:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Step 107: Based on the initial sub-channel allocation, sub-channel allocation objective function, and sub-channel allocation constraints, determine the optimized sub-channel allocation.

[0088] A specific embodiment provides a method for joint optimization of subchannel allocation and deployment location applicable to multi-UAV communication, as follows:

[0089] Step 1: Determine the system model.

[0090] like Figure 2 As shown, this specific embodiment considers an OFDMA multi-UAV communication network, where M Unmanned Aerial Vehicle Base Stations (UAV-BSs) provide wireless data links to K Ground Users (GUs), and one High Altitude Platform (HAP) UAV provides a wireless backhaul link to all UAV-BSs. This represents a collection of drone base stations. This represents the set of ground users.

[0091] To support data transmission, it is assumed that the OFDMA multi-UAV communication network operates in two modes: 1) The connection between HAP and UAV, and between UAV and GU, operates in Time Division Multiplexing (TDM) mode, with each stage occupying half of a frame. 2) The i-th UAV-BS communicates with multiple GUs simultaneously in the OFDMA protocol.

[0092] To ensure a stable wireless connection, this specific embodiment considers a real-world scenario where the HAP is at a fixed altitude Z. H and Z U Hovering, all UAV-BS are at a fixed altitude and Z-axis. U Hovering provides communication services to all static GUs. Accordingly, the horizontal coordinates of the HAP, the m-th UAV-BS, and the k-th GU can be represented as follows: and Furthermore, assuming that the available spectrum for the HAP and the UAV is the same, the total available bandwidth W is divided into N uniform sub-channels, i.e., the bandwidth Δf of each sub-channel is Δf = W / N. Additionally, it is assumed that the UAV and HAP use a uniform power allocation scheme, i.e., each sub-channel has equal transmit power, p H =P H / N and p U =P U / N, where P H P represents the total power of the HAP. U p is the total power of UAV-BS H p represents the carrier power of the HAP in the subchannel. U This represents the carrier power of the UAV-BS in the sub-channel.

[0093] Therefore, severe co-channel interference exists between GUs. Due to the relatively high altitude of the UAVs, the air-to-ground and air-to-air channels will be dominated by the LoS link. This specific embodiment uses the LoS channel model to describe the propagation channel, that is, only the direct path is considered, where the free space path loss model can be used to approximate the channel power gain, that is, only large-scale path fading is considered. The channel power gain from the HAP to the m-th UAV-BS (hereinafter referred to as UAV) and the channel power gain from the m-th UAV to the k-th GU are expressed by formulas (1) and (2), respectively.

[0094]

[0095]

[0096] Where, d H,m d is the Euclidean distance between the HAP and the m-th UAV; m,k ρ is the Euclidean distance between the m-th UAV and the k-th GU; ρ0 is the signal propagation power gain at 1m.

[0097] In setups where the drone base station and the GU share the same time-frequency resources, all GUs will suffer from co-channel interference due to spectrum sharing. For clarity, this specific embodiment defines a corresponding binary variable ω. k,m To describe the UAV-GU association, i.e., ω k,m =1 indicates that the k-th GU is provided with communication services by the m-th UAV; otherwise, it is represented as ω. k,m =0.

[0098] Considering that a GU can only be served by one UAV, the following constraints need to be met:

[0099]

[0100] Where, ωk,m This represents the service status of the m-th drone base station to the k-th ground user, where the service status is either providing service or not providing service.

[0101] In addition to the sub-channel allocation for drone base stations and GUs, the binary variable μ m,n and θ k,n The decision variables representing the corresponding sub-channels can be defined as follows:

[0102]

[0103]

[0104] Where, μ m,n This represents the allocation of the nth sub-channel to the mth UAV base station; θ k,n This describes the allocation of the nth sub-channel to the kth ground user.

[0105] Specifically, to maintain continuous communication capabilities between UAVs and GUs, the OFDMA multi-UAV communication network should ensure that each UAV or GU is allocated at least one sub-channel. This constraint can be expressed as follows:

[0106]

[0107]

[0108] On the other hand, to ensure the orthogonality between the sub-channels, another constraint should be added: a sub-channel can be assigned to at most one UAV base station or GU. Therefore, the corresponding constraint is written as:

[0109]

[0110]

[0111] Based on the above analysis, the signal-to-interference plus noise ratio (SINR) of the m-th UAV and the k-th GU in the n-th sub-channel are expressed as follows:

[0112]

[0113]

[0114] Where, λ m,n δ represents the signal-to-dryness ratio of the m-th UAV in the n-th sub-channel; 2 Additive thermal noise power for ground users; γ k,m,nThe signal-to-interference-plus-noise ratio (SIR) of the k-th ground user when the m-th UAV base station uses the n-th sub-channel to serve the k-th ground user; ω i,j Let θ represent the service provided by the j-th UAV base station to the i-th ground user; i,n This represents the allocation of the nth subchannel to the i-th GU; g j,k Let be the channel power gain from the j-th UAV to the k-th GU.

[0115] Furthermore, the information rates that the m-th UAV and the k-th GU can acquire are respectively:

[0116]

[0117]

[0118] Among them, C m R represents the information rate that the m-th UAV can acquire; k Let be the information rate that the k-th GU can acquire.

[0119] On the other hand, the total information rate provided by the m-th UAV to all corresponding GUs is the total information rate obtained by the m-th UAV from the HAP. Represented as:

[0120]

[0121] Step 2: Problem Transformation.

[0122] To improve network fairness, under the constraints of system bandwidth and backhaul links, this specific embodiment considers the joint optimization of UAV-GU association, sub-channel allocation, and UAV deployment location to maximize the minimum user information rate. That is, a max-min optimization problem is established.

[0123] For ease of description, let and This represents the set of decision variables. Of particular note is the achievable minimum rate, minR. k It is a nonlinear function of U, Ω, Θ, and Q. To ensure the tractability of the problem, this specific embodiment introduces an auxiliary function. Let's reformulate the max-min optimization problem. Therefore, to maximize the minimum average rate of all GUs, the optimization problem involving joint UAV-GU association, sub-channel allocation, and UAV deployment can be formulated as:

[0124]

[0125] Constraint C2 ensures that the total transmission rate from each UAV-BS cannot exceed the data rate it receives from the HAP. It is easy to observe that constraints C1, C2, and Equation (9) are nonlinear and involve multiple integer decision variables. Therefore, the optimization problem (P0) is a mixed-integer nonlinear optimization problem (MINLOP), which is implemented using the optimization method below.

[0126] Step 3: Algorithm design.

[0127] By using gradient descent and iterative optimization, the optimization problem (P0) can be decomposed into two sub-problems: (1) joint optimization of UAV-GU connection and UAV location; and (2) sub-channel allocation optimization. Then, a joint optimization design scheme is obtained through alternating iterative optimization.

[0128] 3.1 Joint optimization of UAV-GU connection and UAV location under given sub-channel allocation conditions.

[0129] Specifically, the goal of UAV-GU association and UAV deployment location design is to improve the maximum-minimum rate of all UAVs. This is considering that the achievable rate depends on the quality of the communication link. Furthermore, path loss is primarily determined by the propagation distance. On the other hand, it is noted that the received power of co-channel interference decreases monotonically with respect to transmission distance. The optimization design of UAV location deployment and user pairing is a typical integer nonlinear optimization problem. Simply put, this optimization design can be based on the idea that a UAV wants to communicate with a GU at a short distance. Therefore, this specific embodiment proposes an optimization algorithm based on K-means clustering (K-means clustering algorithm) to solve this problem.

[0130] First, this specific embodiment reasonably assumes that the network control center possesses the geographic coordinate data of all nodes in advance. In fact, obtaining location information using GPS technology, network registration information, or other methods is not difficult. Then, the Euclidean distance between different nodes can be easily obtained through simple mathematical operations. By employing the K-means clustering algorithm, all GUs can be divided into M clusters based on the location information. Accordingly, the horizontal coordinates of the UAV-BS can be set as the center of each cluster. Therefore, one UAV will provide services to GUs belonging to the same cluster. Accordingly, the detailed UAV-GU association and UAV layout design scheme can be as shown in Algorithm 1 below.

[0131] A. Input: Location coordinates of all GUs; Number of clusters (i.e., number of UAVs) M; Select M initial cluster center locations.

[0132] B. Initialization: Set r = 0 to form the initial set of GU indices in the cluster. In other words, GU should belong to the cluster containing the nearest cluster center.

[0133] C. Repeat r:=r+1.

[0134] D. Update cluster centers (Centr) (r) for in

[0135] E. Recalculate d j,k Simultaneously update the cluster membership set in

[0136] F. Until Centr (r) =Centr (r-1) .

[0137] G. Output: The final M cluster center locations serve as the deployment coordinates for M UAVs. A single UAV is specified to serve the GUs in its same cluster.

[0138] In Algorithm 1, Centr (0) This is the initial set of cluster center locations; This is the location of the first initial cluster center; This is the location of the second initial cluster center; Let r be the initial cluster center position; r is the iteration number; cluster (0) This is the initial set of GU indexes for clustering; This is the first initial GU cluster; This is the second initial GU cluster; For the Mth initial GU cluster; Centr (r) Let be the set of locations of the cluster centers in the r-th iteration; Let be the position of the cluster center in the i-th iteration and the r-th iteration. Let x be the x-coordinate of the cluster center in the i-th iteration and r-th iteration. Let y be the y-coordinate of the cluster center in the i-th iteration and the r-th iteration. Let x be the x-coordinate of the position of the j-th GU in the i-th cluster; mean{·} is used to calculate the average. for; d represents the y-coordinate of the position of the j-th GU in the i-th cluster; j,k The Euclidean distance between the j-th UAV and the k-th GU; cluster (r) Let GU be the set of clustering in the r-th iteration; This is the first GU cluster in the r-th iteration; This is the second GU cluster in the r-th iteration; For the M-th GU cluster in the r-th iteration; Centr (r-1) This is the set of locations of the cluster centers in the (r-1)th iteration.

[0139] 3.2 Subchannel allocation optimization method given UAV-GU connection and UAV location.

[0140] Given the UAV-GU association and UAV location, the subchannel allocation sub-optimization problem is a difficult integer nonlinear optimization problem to solve. We can consider relaxing the integer variables U and Θ into continuous decision variables, i.e., 0 ≤ μ. m,n ≤1 and 0≤θ k,n ≤1.

[0141] On the one hand, by introducing auxiliary variables The following inequalities can be established:

[0142]

[0143] Among them, R k,m,n The information rate obtained by the k-th ground user when the m-th UAV base station uses the n-th sub-channel to serve the k-th ground user; For R k,m,n The lower bound function.

[0144]

[0145]

[0146]

[0147] and These are all intermediate values ​​used to make the form of formula (17) more concise.

[0148] because and Relative to {θ k,n Since all} are concave, the right side of equation (17) is non-convex. A concave function at a given feasible point can be bounded by its corresponding first-order Taylor expansion, so equation (17) is approximately:

[0149]

[0150] in, Representation function exist The first-order Taylor expansion at [location]. This represents the allocation of the nth subchannel to the kth ground user in the rth iteration.

[0151] Furthermore, based on the first-order Taylor expansion, the variable θ can be decoupled using an upper bound function. k,n and It can be given by formula (21).

[0152]

[0153] in, and This represents a feasible point in the r-th iteration, specifically... This describes the allocation of the nth subchannel to the kth ground user in the rth iteration. When it is the r-th iteration The value of .

[0154] On the other hand, a similar inequality can be expressed as follows:

[0155]

[0156] in, The information rate that can be provided for the m-th UAV base station to serve the k-th ground user using the n-th sub-channel.

[0157]

[0158] Through approximation, constraint (23) can be relaxed to:

[0159]

[0160] Furthermore, by leveraging the feasible points The first-order Taylor expansion at that point, A lower bound function can be expressed as:

[0161]

[0162] in, For expression The lower bound function; When it is the r-th iteration The value of .

[0163] Using the above approximation method, an approximate optimization problem can be constructed as follows:

[0164]

[0165] By employing convex approximation techniques, it can be observed that the objective function and all constraints in Equation (26) are either linear or convex. Therefore, the approximate subproblem in Equation (26) is considered a standard convex optimization problem, which can be solved using mature solution tools such as CVX. Specifically, given a feasible point... Solving this subproblem only provides a suboptimal solution. Iterative updates can be used to update the obtained results. A series of feasible solutions are generated by performing an iterative optimization process. Then, an optimized subchannel allocation design is obtained until the number of iterations reaches a predefined maximum threshold or the achievable maximum-minimum rate reaches convergence accuracy. Algorithm 2 lists the process of the proposed subchannel allocation algorithm, as follows:

[0166] Initialization: Input a randomly generated initial feasible point Set r = 0, convergence threshold ε, and maximum number of iterations Iter max .

[0167] repeat:

[0168] For a given point This leads to a new approximate optimization problem, such as problem (P1).

[0169] Solve the problem (P1) using software tools such as CVX and calculate the objective function value.

[0170] Update sub-channel allocation scheme

[0171] Update: r:=r+1.

[0172] Until the maximum number of iterations is reached (r ≥ Iter) max Or the objective function value converges.

[0173] Output:

[0174] In Algorithm 2, This represents the initial allocation of the nth sub-channel to the mth UAV base station; This describes the initial allocation of the nth sub-channel to the kth ground user. This represents the allocation of the nth sub-channel to the mth UAV base station in the rth iteration. This represents the allocation of the nth subchannel to the kth ground user in the rth iteration. Let be the objective function value of the r-th iteration, which is the minimum user rate obtained in the r-th iteration; This represents the allocation of the nth sub-channel to the mth UAV base station in the (r+1)th iteration. This represents the allocation of the nth subchannel to the kth ground user in the (r+1)th iteration. This represents the objective function value of the (r-1)th iteration, which is the minimum user rate obtained in the (r-1)th iteration.

[0175] To verify the method in Example 1, a simulation experiment was also conducted: The simulation conditions were set as follows: Consider a circular area with a radius of 500 meters, where the coordinates of the HAP are [0,0,0], and the altitudes of the HAP and the UAV are set to Z. H =300m and Z U =100m. Furthermore, the horizontal positions of the UAV and GU follow a uniform distribution within the service area. Unless otherwise specified, some key simulation parameters are set as follows: M=4, K=20, W=10MHz, N=100, Δf=100kHz, P H =P U =20dBmW, δ 2 =-110dBmW, ρ0=-50dB.

[0176] like Figure 3 The diagram illustrates the drone deployment and ground user pairing results obtained based on K-means clustering. For clarity, Figure 3 Only the horizontal coordinates of the network nodes are shown. Specifically, different cluster members are distinguished by different regions divided by solid-line ellipses. Clearly, all GUs are assigned to four clusters UC1-UC4 based on Euclidean distance. Figure 3 In the diagram, UC1 is the solid-line ellipse containing U1, comprising 4 drones, each corresponding to the numbers 10, 12, 17, and 20; UC2 is the solid-line ellipse containing U2, comprising 4 drones, each corresponding to the numbers 1, 13, 14, and 15; UC3 is the solid-line ellipse containing U3, comprising 6 drones, each corresponding to the numbers 3, 5, 6, 7, 9, and 11; and UC4 is the solid-line ellipse containing U4, comprising 6 drones, each corresponding to the numbers 2, 4, 8, 16, 18, and 19. That is, after the clustering scheme is determined, the drones will be located at the cluster centers. Then, each GU will choose to communicate with the nearest drone. Specifically, Figure 3 The simulations show that when GU locations are relatively concentrated, a cluster will contain more members. In fact, this is reasonable for a practical communication environment. Furthermore, this phenomenon highlights the necessity of resource allocation and co-channel interference management.

[0177] In Figure 3 With the same parameter settings, Figures 4-5 The subchannel allocation scheme obtained by the proposed iterative optimization algorithm is described, with the corresponding parameters set to Iter. max=10 and ε=0.01, where light-colored bars represent allocated sub-channels and dark-colored bars represent unallocated resources. First, from Figure 4 This shows that the sub-channels are not evenly distributed among drones. This is because drone base stations only need a portion of the spectrum resources to meet their maximum rate requirements. On the other hand, Figure 5 This illustrates that to achieve the highest rate performance, the system allocates all sub-channels. Furthermore, it can be seen that more sub-channels are allocated to GUs with worse propagation conditions. This phenomenon can be explained by the system sacrificing some GUs with better transmission quality to improve the fairness of the entire network.

[0178] For comparison, the simulation experiment also implemented a uniform allocation scheme (UA), which uniformly allocated all sub-channels to all UAVs and GUs. Figure 6 The rate performance of the proposed sub-channel allocation algorithm and a uniform allocation scheme was compared. Figure 6 The results show that the method in this embodiment is significantly better than the uniform allocation scheme; that is, the proposed method can achieve a higher minimum user information rate. Furthermore, by combining... Figures 4-6 The results show that the method in this embodiment tends to allocate more sub-channels to GUs located further away from the serving UAV, thereby compensating for the rate difference. Furthermore, Figure 7 The minimum achievable rates for two schemes with different configurations are depicted, demonstrating that the method in this embodiment is always superior to the UA scheme. Furthermore, it is readily observed that the minimum user rate achieved by both schemes increases with increasing available bandwidth, consistent with general observations. In particular, the results show that, with fixed bandwidth, the system achieves better performance with more available sub-channels. This can be interpreted as more sub-channels providing greater degrees of freedom for the system to optimize resource allocation, thereby effectively improving system fairness.

[0179] This embodiment addresses the joint optimization design problem of UAV location deployment and sub-channel allocation in an OFDMA UAV communication network with co-channel interference. To improve network fairness, this embodiment models the joint sub-channel allocation, user association, and UAV deployment problem as a MINLOP. Using block coordinate descent, the problem is divided into two sub-optimization problems. These sub-problems are then solved by combining slack variables, successive convex approximation, and alternating optimization. Furthermore, numerical results are presented to validate the effectiveness of the proposed algorithm.

[0180] Example 2 provides a joint optimization system for UAV deployment location and sub-channel allocation, including:

[0181] The coordinate acquisition module is used to acquire the initial horizontal coordinates of M UAV base stations and K ground users in the initial OFDMA multi-UAV communication network; M≥1, K≥1, M≥K; the OFDMA multi-UAV communication network is a multi-UAV communication network based on OFDMA mode.

[0182] The clustering module is used to divide the ground users into M clusters based on the initial horizontal coordinates of each UAV base station and the horizontal coordinates of ground users in each region, and to determine the horizontal coordinates of the cluster center of each cluster.

[0183] The drone base station deployment module is used to deploy corresponding drone base stations based on the horizontal coordinates of each cluster center, thereby obtaining a location-optimized OFDMA multi-drone communication network.

[0184] The initialization module is used to initialize the sub-channel allocation of the optimized OFDMA multi-UAV communication network. The sub-channel allocation includes the allocation of the nth sub-channel to the mth UAV base station and the allocation of the nth sub-channel to the kth ground user. The allocation is either allocated or not allocated. 1≤m≤M, N is the number of sub-channels, 1≤k≤K, 1≤n≤N.

[0185] The objective function construction module is used to construct the sub-channel allocation objective function. The sub-channel allocation objective function is a function concerning the allocation of each sub-channel to each UAV base station, the allocation of each sub-channel to each ground user, and the information rate that each ground user can obtain.

[0186] The constraint construction module is used to construct sub-channel allocation constraints; sub-channel allocation constraints include: auxiliary function constraints, UAV base station information rate constraints, and allocation status constraints.

[0187] The sub-channel allocation optimization module is used to determine the optimized sub-channel allocation based on the initial sub-channel allocation status, the sub-channel allocation objective function, and the sub-channel allocation constraints.

[0188] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0189] This specific embodiment uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention; at the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the present invention. In summary, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A joint optimization method for UAV deployment location and sub-channel allocation, characterized in that, The method includes: Obtain the initial horizontal coordinates of M UAV base stations and K ground users in the initial OFDMA multi-UAV communication network; M≥1, K≥1, M≥K; the OFDMA multi-UAV communication network is a multi-UAV communication network based on OFDMA mode; Based on the initial horizontal coordinates of each UAV base station and the horizontal coordinates of each ground user, the ground users are divided into M clusters using the K-means clustering algorithm, and the horizontal coordinates of the cluster center of each cluster are determined. Based on the horizontal coordinates of each cluster center, corresponding UAV base stations are deployed to obtain a location-optimized OFDMA multi-UAV communication network. The sub-channel allocation of the optimized OFDMA multi-UAV communication network is initialized; the sub-channel allocation includes: the allocation of the nth sub-channel to the mth UAV base station and the allocation of the nth sub-channel to the kth ground user; the allocation is either allocated or not allocated; 1≤m≤M, N is the number of sub-channels, 1≤k≤K, 1≤n≤N; Construct a sub-channel allocation objective function; the sub-channel allocation objective function is a function of the allocation of each sub-channel to each UAV base station, the allocation of each sub-channel to each ground user, and the information rate that each ground user can obtain; Construct sub-channel allocation constraints; the sub-channel allocation constraints include: auxiliary function constraints, UAV base station information rate constraints, and allocation status constraints; Based on the initial sub-channel allocation, the sub-channel allocation objective function, and the sub-channel allocation constraints, the optimized sub-channel allocation is determined. The objective function for sub-channel allocation is: ； in, The set of allocation cases for the first sub-channel. , This describes the allocation of the nth sub-channel to the mth UAV base station; This is a collection of information on drone base station services. , This represents the service status of the m-th drone base station to the k-th ground user, where the service status is either providing service or not providing service. This is the set of horizontal coordinates of the drone base station. , Let m be the horizontal coordinate of the m-th UAV base station; The set of allocation cases for the second sub-channel. , This describes the allocation of the nth sub-channel to the kth ground user. For auxiliary functions, ; For ground users, Let k be the information rate that the k-th ground user can obtain. , This refers to the bandwidth of each sub-channel; The signal-to-interference-plus-noise ratio (SIR) of the k-th ground user when the m-th UAV base station uses the n-th sub-channel to serve the k-th ground user. ; The power of the carrier in each sub-channel for each UAV base station; Let m be the channel power gain from the m-th UAV base station to the k-th ground user; denoted as additive thermal noise power for ground users; j represents the serial number of the UAV base station. The auxiliary function constraints specifically include: ； in, The information rate that can be provided for the m-th UAV base station to serve the k-th ground user using the n-th sub-channel; For expression The lower bound function; The information rate constraint of the UAV base station specifically includes: ； in, Let m be the information rate that the m-th UAV base station can acquire. , The information rate at which the m-th UAV base station acquires information using the n-th sub-channel is given. The power of the carrier in each sub-channel for the high-altitude platform UAV. The channel power gain from the high-altitude platform UAV to the m-th UAV base station; For expression The upper bound function.

2. The method for joint optimization of UAV deployment location and sub-channel allocation according to claim 1, characterized in that, Based on the horizontal coordinates of each cluster center, corresponding UAV base stations are deployed to obtain a location-optimized OFDMA multi-UAV communication network, which specifically includes: By deploying corresponding UAV base stations in the air above the horizontal coordinates of each cluster center to provide services to all ground users of the corresponding cluster, a location-optimized OFDMA multi-UAV communication network is obtained.

3. The method for joint optimization of UAV deployment location and sub-channel allocation according to claim 1, characterized in that, OFDMA multi-drone communication network includes: one high-altitude platform drone, M drone base stations and K ground users.

4. The method for joint optimization of UAV deployment location and sub-channel allocation according to claim 3, characterized in that, The sub-channel allocation constraints specifically include: ； ； ； ； ； 。 5. A joint optimization system for UAV deployment location and sub-channel allocation, characterized in that, The system includes: The coordinate acquisition module is used to acquire the initial horizontal coordinates of M UAV base stations and K ground users in the initial OFDMA multi-UAV communication network; M≥1, K≥1, M≥K; the OFDMA multi-UAV communication network is a multi-UAV communication network based on OFDMA mode; The clustering module is used to divide each of the ground users into M clusters based on the initial horizontal coordinates of each of the UAV base stations and the horizontal coordinates of each of the ground users, and to determine the horizontal coordinates of the cluster center of each cluster. The drone base station deployment module is used to deploy corresponding drone base stations based on the horizontal coordinates of each cluster center to obtain a location-optimized OFDMA multi-drone communication network. An initialization module is used to initialize the sub-channel allocation of the optimized OFDMA multi-UAV communication network. The sub-channel allocation includes the allocation of the nth sub-channel to the mth UAV base station and the allocation of the nth sub-channel to the kth ground user. The allocation is either allocated or not allocated; 1≤m≤M, N is the number of sub-channels, 1≤k≤K, 1≤n≤N. The objective function construction module is used to construct the sub-channel allocation objective function; the sub-channel allocation objective function is a function concerning the allocation of each sub-channel to each UAV base station, the allocation of each sub-channel to each ground user, and the information rate that each ground user can obtain; A constraint construction module is used to construct sub-channel allocation constraints; the sub-channel allocation constraints include: auxiliary function constraints, UAV base station information rate constraints, and allocation status constraints. The sub-channel allocation optimization module is used to determine the optimized sub-channel allocation based on the initial sub-channel allocation status, the sub-channel allocation objective function, and the sub-channel allocation constraints. The objective function for sub-channel allocation is: ； in, The set of allocation cases for the first sub-channel. , This describes the allocation of the nth sub-channel to the mth UAV base station; This is a collection of information on drone base station services. , This represents the service status of the m-th drone base station to the k-th ground user, where the service status is either providing service or not providing service. This is the set of horizontal coordinates of the drone base station. , Let m be the horizontal coordinate of the m-th UAV base station; The set of allocation cases for the second sub-channel. , This describes the allocation of the nth sub-channel to the kth ground user. For auxiliary functions, ; For ground users, Let k be the information rate that the k-th ground user can obtain. , This refers to the bandwidth of each sub-channel; The signal-to-interference-plus-noise ratio (SIR) of the k-th ground user when the m-th UAV base station uses the n-th sub-channel to serve the k-th ground user. ; The power of the carrier in each sub-channel for each UAV base station; Let m be the channel power gain from the m-th UAV base station to the k-th ground user; denoted as additive thermal noise power for ground users; j represents the serial number of the UAV base station. The auxiliary function constraints specifically include: ； in, The information rate that can be provided for the m-th UAV base station to serve the k-th ground user using the n-th sub-channel; For expression The lower bound function; The information rate constraint of the UAV base station specifically includes: ； in, Let m be the information rate that the m-th UAV base station can acquire. , The information rate at which the m-th UAV base station acquires information using the n-th sub-channel is given. The power of the carrier in each sub-channel for the high-altitude platform UAV. The channel power gain from the high-altitude platform UAV to the m-th UAV base station; For expression The upper bound function.

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