Joint optimization method for multi-uav relay communication in cognitive wireless network and application

Abstract

The application discloses a joint optimization method and application of multi-unmanned aerial vehicle relay communication in a cognitive wireless network, relates to the technical field of wireless communication, and constructs a joint optimization model under a multi-unmanned aerial vehicle relay communication scenario in a cognitive wireless network. The joint optimization model takes maximizing the minimum throughput of a slave user as an optimization target, takes unmanned aerial vehicle distribution constraints, minimum bandwidth constraints, bandwidth equalization constraints, maximum throughput constraints and primary user interference constraints as constraint conditions, solves the joint optimization model, obtains a joint optimization scheme, and thus can jointly optimize unmanned aerial vehicle-slave user matching, unmanned aerial vehicle position deployment and bandwidth distribution under the multi-unmanned aerial vehicle relay communication scenario in the cognitive wireless network, improves the minimum throughput of the slave user under the premise of guaranteeing the communication quality of the primary network, and guarantees the frequency use fairness of the slave user.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a joint optimization method and application of drone-slave user matching, drone location deployment, and bandwidth allocation for multi-drone relay communication in a cognitive wireless network. Background Art

[0002] In cities, cellular mobile networks are typically implemented through the deployment of ground base stations to provide communication services to users. However, in areas with heavy obstructions, communication between ground base stations and users may be interfered with. As a new communication platform, drones are gradually being applied in various fields due to their high mobility and low manufacturing costs. Using drones as relays between ground base stations and users can effectively solve these problems. However, individual drones are prone to shortcomings such as small coverage and limited computing power during mission execution. Compared with single drones, multi-drone systems have better mission adaptability and application potential. However, scarce spectrum resources have become a limiting factor for multi-drone relay communications. At the same time, as the number of users increases, existing spectrum resource allocation strategies cannot guarantee the communication quality of each user, and user fairness in frequency usage is greatly challenged. Therefore, how to improve the spectrum resource utilization of multi-drone relay communications through cognitive radio technology while ensuring fair spectrum use is a difficult problem in drone spectrum management research.

[0003] Researchers have conducted extensive research on multi-UAV relay communication scenarios. For example, Wu Di et al. published a joint optimization method for user matching and spectrum resources in multi-UAV assisted communication in Telecommunications Technology, which studied the location deployment and resource optimization methods in multi-UAV relay communication. They used a genetic algorithm and convex optimization method to maximize the user's minimum transmission rate and ensure fairness in user frequency usage. Arribas E et al. published an Exact Resource Allocation for Fair Wireless Relay in IEEE Communications Letters, which studied the allocation of relay UAV bandwidth by continuously increasing the user's minimum throughput with the goal of maximizing the user's minimum throughput, ultimately achieving fairness in user communication. However, the above research mainly focused on the frequency allocation problem of multiple UAVs acting as relays in a single network, without considering the spectrum reuse problem between multiple networks.

[0004] Currently, some researchers have applied multi-UAV relay communication scenarios to cognitive wireless networks. For example, Ding R et al. published "From External Interaction to Internal Inference: An Intelligent Learning Framework for Spectrum Sharing and UAV Trajectory Optimization" in IEEE Transactions on Wireless Communications. This paper studied spectrum resource allocation and location optimization for UAVs in cognitive wireless networks. They employed a hybrid online-offline multi-agent neural network optimization method based on external interaction to achieve the maximum total throughput for system users. However, this study lacked consideration of user frequency fairness, resulting in unfair user transmission. Summary of the Invention

[0005] The purpose of this application is to provide a joint optimization method and application for multi-UAV relay communication in a cognitive wireless network, which can jointly optimize UAV-slave user matching, UAV location deployment and bandwidth allocation in a multi-UAV relay communication scenario in a cognitive wireless network, thereby improving the minimum throughput of slave users and ensuring the fairness of frequency usage for slave users while ensuring the communication quality of the main network.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a joint optimization method for multi-UAV relay communication in a cognitive wireless network, the joint optimization method for multi-UAV relay communication in a cognitive wireless network comprising:

[0008] A joint optimization model is constructed for a multi-UAV relay communication scenario in a cognitive wireless network. The joint optimization model takes maximizing the minimum throughput of slave users as the optimization objective and takes UAV allocation constraints, minimum bandwidth constraints, bandwidth equality constraints, maximum throughput constraints, and primary user interference constraints as constraints. A primary user is a ground user in a cognitive wireless network that is provided with communication services by the primary network, and a slave user is a ground user in a cognitive wireless network that is provided with communication services by a slave network. The slave network includes a temporary node, multiple UAVs, and multiple slave users. The temporary node provides communication services to the slave users through multiple UAV relays.

[0009] The joint optimization model is solved to obtain a joint optimization solution; the joint optimization solution includes a drone-slave user matching optimization solution, a drone location deployment optimization solution, and a bandwidth allocation optimization solution.

[0010] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned joint optimization method for multi-UAV relay communication in the cognitive wireless network.

[0011] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned joint optimization method for multi-UAV relay communication in a cognitive wireless network.

[0012] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned joint optimization method for multi-UAV relay communication in a cognitive wireless network.

[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0014] The present application provides a joint optimization method and application for multi-UAV relay communication in a cognitive wireless network, constructs a joint optimization model for a multi-UAV relay communication scenario in a cognitive wireless network, and the joint optimization model takes maximizing the minimum throughput of slave users as the optimization goal, and takes UAV allocation constraints, minimum bandwidth constraints, bandwidth equality constraints, maximum throughput constraints, and primary user interference constraints as constraints. The joint optimization model is solved to obtain a joint optimization scheme, which includes a UAV-slave user matching optimization scheme, a UAV position deployment optimization scheme, and a bandwidth allocation optimization scheme. Therefore, under the condition of simultaneously considering the throughput of slave users and the interference of primary users, UAV-slave user matching, UAV position deployment, and bandwidth allocation in a multi-UAV relay communication scenario in a cognitive wireless network can be jointly optimized, thereby improving the minimum throughput of slave users and ensuring the fairness of frequency use of slave users while ensuring the communication quality of the primary network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flow chart of a method for joint optimization of multi-UAV relay communications in a cognitive wireless network provided in Example 1 of the present application.

[0017] Figure 2Schematic diagram of the system model for a multi-UAV relay communication scenario in a cognitive wireless network provided in Example 1 of the present application.

[0018] Figure 3 Schematic diagram of the drone-user matching optimization solution and drone location deployment optimization solution provided in Example 1 of the present application.

[0019] Figure 4 A schematic diagram comparing user throughput before and after optimization of the linear precision algorithm provided in Example 1 of the present application; Figure 4 (a) in the figure is the throughput from the user before optimization; Figure 4 (b) in the figure is the throughput from the user after optimization.

[0020] Figure 5 Schematic diagram comparing the bandwidth and throughput of a drone before and after optimization using the linear precision algorithm provided in Example 1 of this application.

[0021] Figure 6 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] Example 1

[0024] like Figure 1 As shown, an embodiment of the present application provides a joint optimization method for multi-UAV relay communication in a cognitive wireless network, and the joint optimization method for multi-UAV relay communication in a cognitive wireless network includes:

[0025] S1: Construct a joint optimization model for a multi-UAV relay communication scenario in a cognitive wireless network; the joint optimization model takes maximizing the minimum throughput of slave users as the optimization goal, and takes UAV allocation constraints, minimum bandwidth constraints, bandwidth equality constraints, maximum throughput constraints, and primary user interference constraints as constraints; the primary user is a ground user provided with communication services by the primary network in the cognitive wireless network, and the slave user is a ground user provided with communication services by the slave network in the cognitive wireless network. The slave network includes a temporary node, multiple UAVs, and multiple slave users, and the temporary node provides communication services to the slave users through multiple UAV relays.

[0026] S2: Solve the joint optimization model to obtain a joint optimization solution; the joint optimization solution includes a drone-slave user matching optimization solution, a drone location deployment optimization solution, and a bandwidth allocation optimization solution.

[0027] In actual communication environments, uneven distribution of spectrum resources is an objective reality. By implementing steps S1 to S2 above, the embodiments of the present application can jointly optimize drone-slave user matching, drone location deployment, and bandwidth allocation in a multi-drone relay communication scenario in a cognitive wireless network. While ensuring the communication quality of the main network, the minimum throughput of the slave user can be improved, thereby ensuring the fairness of frequency usage for the slave user.

[0028] The system model of multi-UAV relay communication scenario in cognitive wireless network is as follows Figure 2 As shown in FIG, in order to alleviate the obstruction of buildings to communication links in urban communications, it is planned to deploy multiple rotary-wing drones as relays over the target area to transmit the transmission signals of temporary nodes (i.e., temporary base stations) to ground users. This embodiment explores the downlink scenario of multi-drone relay communication in a cognitive wireless network. In this scenario, the network consists of a temporary node g and multiple drones (i.e., Figure 2 There are multiple ground users distributed in the slave network, which are recorded as slave users (i.e. Figure 2 The temporary nodes are used for temporary communication. Specifically, the temporary nodes provide communication services to the slave users through multiple drone relays. The drones are represented as M∈{1,2…,M}, where M is the total number of drones. The slave users are represented as K∈{1,2…,K}, where K is the total number of slave users. There is a main network in working state near the slave network. The main network includes a cellular network base station G. There is a ground user distributed in the main network, which is recorded as the main user (i.e. Figure 2 Cellular base stations provide communication services for primary users. The primary and secondary networks share spectrum resources, employing an underlay spectrum reuse model. Mutual interference between the two networks is inevitable. To ensure smooth communication on the primary network, spectrum usage on the secondary network must be limited. Consequently, communication quality for secondary users on the secondary network is also affected by the primary network.

[0029] When faced with the problem of matching multiple drones with multiple slave users, the slave users will be divided into multiple clusters (i.e., groups) according to the number of drones. Each cluster is managed by a cluster head drone responsible for managing its communication services. The decision variables involved in this process are defined as: w r,u , when w r,u = 1, it means that drone r serves user u. On the contrary, when w r,u = 0, it means that drone r does not provide service to user u.

[0030] In order to ensure the sustainability of communication, the following constraints must be met: any slave user can only be provided with communication services by one drone, namely:

[0031]

[0032] In formula (1), M is the total number of drones; w r,u is a binary variable indicating whether drone r serves user u.

[0033] In the slave network, the temporary node g allocates all bandwidth to the drone, which then allocates its bandwidth to the slave users in its cluster (i.e., the slave users that match the drone). The bandwidth allocation for the slave users must meet the following constraints:

[0034] First, considering the fairness of frequency usage in the slave network, a minimum limit must be set for the bandwidth between drones and slave users, namely:

[0035]

[0036] In the above formula, w r is the bandwidth of drone r; is the minimum bandwidth of the UAV; w u is the bandwidth of user u; The minimum bandwidth for slave users.

[0037] Secondly, the sum of the bandwidths of all drones is equal to the maximum bandwidth that the temporary node can provide, and the sum of the bandwidths of the slave users in each cluster is equal to the bandwidth of the drones in that cluster, that is:

[0038]

[0039] In the above formula, is the maximum bandwidth that the temporary node g can provide; K is the total number of slave users.

[0040] Finally, the sum of the throughputs of the slave users in each cluster is less than or equal to the throughput of the UAVs in that cluster. In addition, the entire relay network must also consider the backhaul bottleneck. That is, the sum of the throughputs provided by the temporary node g to all UAVs cannot exceed its backhaul bottleneck, that is:

[0041]

[0042] In the above formula, T u is the throughput from user u; T r is the throughput of drone r; τ is the backhaul bottleneck of temporary node g.

[0043] UAVs may interfere with the primary users of the primary network. To limit this interference, the interference of each UAV on the primary user is set to a maximum threshold δ, that is:

[0044]

[0045] In formula (8), P r is the transmission power of UAV r; is the channel gain coefficient when UAV r communicates with the primary user u*; δ is the maximum tolerance threshold of the primary user to single-source interference.

[0046] In actual communication scenarios, air-to-ground communication links are often blocked by obstacles. This embodiment uses the A2G (Air-to-Ground) channel model at a 2GHz carrier frequency to describe the communication link from UAV m to ground user k. This communication link usually consists of two parts: Line of Sight (LoS) propagation link and Non-Line of Sight (NLoS) propagation link. The relevant channel gain coefficient g is m,k The calculation formula is:

[0047]

[0048] In formula (9), g m,k is the channel gain coefficient when UAV m communicates with ground user k; P LOS is the transmission probability of the line-of-sight link; d Um,k is the horizontal distance between UAV m and ground user k, which is calculated based on the horizontal coordinates of UAV m and ground user k; α u is the air-to-ground path loss index; P NLOS is the transmission probability of the non-line-of-sight transmission link, P NLOS =1-P LOS ; η is the attenuation factor of the non-line-of-sight propagation link, which reflects the additional loss in non-line-of-sight transmission.

[0049]

[0050] In formula (10), a and b are both constants, and they take different values ​​according to different propagation environments to adapt to specific communication scenarios; θ is the elevation angle.

[0051]

[0052] In formula (11), H is the flight altitude of the UAV.

[0053] This embodiment maximizes the minimum throughput of all slave users by optimizing the drone-slave user matching scheme, drone location deployment scheme, and bandwidth allocation scheme. The joint optimization model is:

[0054]

[0055] In formula (12), T u is the throughput from user u; M is the total number of drones; w r,u is a binary variable indicating whether drone r serves user u, all w r,u Composition of drones - matching solutions from users; w r is the bandwidth of drone r; is the minimum bandwidth of the UAV; w u is the bandwidth of user u; is the minimum bandwidth from the user; is the maximum bandwidth that the temporary node g can provide; K is the total number of slave users; T r is the throughput of drone r; τ is the backhaul bottleneck of temporary node g; P r is the transmission power of UAV r; g r,u* is the channel gain coefficient when UAV r communicates with the primary user u*; δ is the maximum tolerance threshold of the primary user to single-source interference.

[0056] In formula (12), C1 is the UAV allocation constraint, which means that any slave user can only have one UAV providing communication services for it; C2 and C3 are minimum bandwidth constraints, which means that the bandwidth of each UAV and each slave user has a minimum value; C4 means that the sum of the bandwidth of the UAVs is equal to the maximum bandwidth that the temporary node g can provide, and C5 means that the sum of the bandwidth of the slave users in the cluster is equal to the bandwidth of the UAVs in the cluster. C4 and C5 are bandwidth equality constraints; C6 means that the sum of the throughput of all slave users in the cluster is less than or equal to the throughput of the UAVs in the cluster, and C7 means that the sum of the throughput of all UAVs is less than or equal to the throughput of the temporary node g (i.e., the backhaul bottleneck). C6 and C7 are maximum throughput constraints; C8 is the master user interference constraint, which means that the interference of the slave network to the master network must be controlled within the maximum threshold range.

[0057] It can be concluded from formula (12) that the size of the slave user throughput is related to the distance between the slave user and the drone and the bandwidth allocation. The location deployment of the drone not only affects the throughput of the slave user, but also needs to take its interference to the master user into account. This embodiment uses the improved K-Means clustering algorithm to optimize the matching of drones and slave users and the location deployment of drones. For bandwidth allocation optimization, the linear precision algorithm is used to continuously increase the bandwidth of the user with the lowest throughput in each iteration without exceeding the backhaul bottleneck, thereby enhancing the fairness of user communication. That is, the joint optimization algorithm of this embodiment includes the improved K-Means clustering algorithm and the linear precision algorithm.

[0058] In actual communication scenarios, the distance between drones and slave users is a significant factor affecting communication quality. Multiple drones are matched and clustered with multiple slave users. One drone is deployed in each cluster, and the distance to the slave users in the cluster must be minimized. In addition, the different positions of the deployed drones will cause different degrees of interference to the primary user. If the magnitude of the interference reaches the maximum threshold that affects the communication quality of the primary network, the position of the drone needs to be re-corrected. Therefore, the location deployment of the drone must take into account both the distance to the cluster users and the distance to the primary user. This embodiment uses an improved K-Means clustering algorithm to achieve matching between drones and slave users and the location deployment of drones. The specific solution steps are as follows:

[0059] (1) Given the number of drones and the positions of slave users, randomly select slave users as initial center points for initialization, and obtain M initial center points.

[0060] (2) During the loop, traverse each slave user and calculate the distance from each slave user to each initial center point, as shown below:

[0061] d u,m =||(X u ,Y u )-(X Um ,Y Um )||2 (13)

[0062] In formula (13), d u,m is the distance from user u to the mth initial center point; || ||2 is the Euclidean distance; (X u ,Y u ) are the x-coordinate and y-coordinate of user u respectively; (X Um ,Y Um ) are the x-coordinate and y-coordinate of the m-th initial center point respectively.

[0063] Each slave user is assigned to the initial center point closest to the slave user. At this point, the process of clustering the slave users is completed, and M clusters are obtained. The center point of each cluster is calculated. The coordinates of the center point are the mean of the coordinates of all slave users in the cluster, and M new center points are obtained.

[0064] (3) If the distance between the new center point and the initial center point is less than the set value, as shown in the following formula (14), the loop stops. The coordinates of the M new center points at this time are the positions of the M drones, and the drone position deployment plan is obtained. The M clusters at this time are the matching results of the drones and the slave users, and the drone-slave user matching optimization plan is obtained; otherwise, the new center point is used as the initial center point of the next iteration and the loop continues.

[0065]

[0066] In formula (14), Δd is the distance between the new center point and the initial center point; are the x-coordinate and y-coordinate of the m-th new center point respectively; ε is the set value.

[0067] (4) After obtaining the UAV position deployment plan, for each UAV, if the interference of the UAV position to the primary user exceeds the maximum threshold, the interference is minimized by adjusting the position of the UAV. When adjusting the position of the UAV whose interference to the primary user exceeds the maximum threshold (i.e., the UAV to be adjusted), the maximum threshold of the interference of the UAV to be adjusted to the primary user is taken as the interference size. The maximum channel gain coefficient when the UAV to be adjusted communicates with the primary user is calculated according to formula (8). Then, the safe distance between the UAV to be adjusted and the primary user is inferred according to formula (9). The position of the UAV to be adjusted is adjusted based on the safe distance to obtain the UAV position deployment optimization plan.

[0068] The calculation formula for the maximum channel gain coefficient is:

[0069] g' r,u* =δ / P r (15)

[0070] In formula (15), g' r,u* is the maximum channel gain coefficient when the UAV r to be adjusted communicates with the primary user u*; δ is the maximum tolerance threshold of the primary user to single-source interference; P r is the transmission power of the UAV r to be adjusted.

[0071] The calculation formula for the safety distance is:

[0072]

[0073] In formula (16), d Ur,u* is the safe distance between the UAV r to be adjusted and the primary user u*; αu is the air-to-ground path loss index; P LOS is the transmission probability of the line-of-sight link; η is the attenuation factor of the non-line-of-sight link; P NLOS is the transmission probability of the non-line-of-sight link.

[0074] The specific process of the above improved K-Means clustering algorithm is shown in Table 1 below.

[0075] Table 1. UAV-user matching and UAV location deployment based on improved K-Means clustering algorithm

[0076]

[0077]

[0078] In the allocation of slave user bandwidth, an increase in the slave user bandwidth of each cluster will result in a corresponding increase in the bandwidth of the drones providing communication services to the slave users in the cluster. At the same time, the bandwidth of the drones cannot exceed the maximum bandwidth provided by the temporary node, and the total throughput of the system is also limited by the backhaul bottleneck. To optimize the minimum throughput and achieve fair allocation of slave user spectrum resources, this embodiment uses a linear precision algorithm to optimize the minimum throughput of the slave users to achieve bandwidth allocation optimization. The linear precision algorithm specifically includes:

[0079] (1) By analyzing the position of the drone and the interference of the main network, the signal-to-noise ratio γ of each slave user is calculated r,u The bandwidth of each drone is initially set to the minimum value The bandwidth of the drone is evenly distributed to each slave user within each cluster, resulting in the bandwidth of each slave user. The bandwidth of each slave user constitutes the bandwidth allocation scheme of the previous iteration. For each slave user, the signal-to-noise ratio and bandwidth of the slave user are used as inputs, and the throughput of each slave user can be calculated based on Shannon's theorem.

[0080] The signal-to-noise ratio of the slave user is related to the ground position of the slave user. In addition to the environmental noise, the slave user will also be affected by the interference from the main network. The position of each slave user is different, and the interference is also different. Since the transmission power of the base station G of the main network and the drone of the slave network is constant, the signal-to-noise ratio of each slave user can be obtained. The calculation formula of the signal-to-noise ratio is:

[0081]

[0082] In formula (17), γ r,u is the signal-to-noise ratio when UAV r communicates with slave user u; w r,u is a binary variable indicating whether drone r provides communication services to user u; p r is the transmission power of UAV r; gr,u is the channel gain coefficient when UAV r communicates with slave user u, which is calculated using formula (9); I CCI To identify the co-channel interference of the master network on the slave network in a cognitive wireless network; is the additive white Gaussian noise power.

[0083] I CCI =p G g1 (18)

[0084] In formula (18), p G is the transmission power of the base station G in the main network; g1 is the channel gain coefficient when the base station G in the main network communicates with the slave user u. Simply replace the drone in formula (9) with the base station G in the main network to calculate g1 using formula (9).

[0085] (2) Find the minimum throughput T of all slave users m , and find all the users whose throughput is second only to the minimum throughput T M ,as follows:

[0086]

[0087] The throughput of the slave users corresponding to the minimum throughput is gradually increased by using a cyclic improvement method. First, we need to determine in which clusters the slave users corresponding to the minimum throughput are distributed. Then, we calculate the number of slave users corresponding to the minimum throughput in each cluster, |u|. The throughput increase caused by the minimum throughput in the cluster is |u|β(T M -T m ), where β is the adjustment coefficient. Each minimum throughput is increased to obtain the adjusted throughput for each slave user. For each slave user, the signal-to-noise ratio and adjusted throughput of the slave user are used as inputs. Based on Shannon's theorem, the bandwidth of each slave user in the current iteration is calculated. Furthermore, based on the matching results between drones and slave users, the bandwidth of all slave users served by the same drone is summed to obtain the bandwidth of each drone in the current iteration.

[0088] (3) Using Shannon’s formula to express the increased throughput, it is as follows:

[0089]

[0090] In formula (20), A is the number of minimum throughput, that is, the number of minimum throughput determined by this cycle; M is the total number of drones; is the additional bandwidth that results in the increase in the throughput of the drone r; γ g,r is the signal-to-noise ratio when the temporary node g communicates with the drone r.

[0091] Based on formula (20), the calculation formula of β can be derived as follows:

[0092]

[0093] In formula (21), β is the adjustment coefficient; is the maximum bandwidth that the temporary node g can provide; M is the total number of drones; w r is the bandwidth of the current iteration of UAV r; A is the number of minimum throughput; T M is the minimum throughput; T m is the minimum throughput; γ g,r is the signal-to-noise ratio when the temporary node g communicates with the drone r.

[0094] (4) When β is less than 0 or reaches the maximum number of iterations, it means that the sum of the bandwidths of all drones is greater than the maximum bandwidth that the temporary node can provide. At this time, the optimization is stopped, the last optimization result is retained and output, that is, the bandwidth allocation plan of the last iteration is output.

[0095] (5) Check the backhaul bottleneck τ: If the total throughput exceeds the backhaul bottleneck τ, the throughput of the highest user is reduced in sequence. Specifically, the throughput of the slave user with the highest throughput is reduced first until the sum of the total throughput is less than or equal to the backhaul bottleneck. If the total throughput is still greater than the backhaul bottleneck after reducing the throughput of the slave user with the highest throughput to a preset value, a new slave user with the highest throughput is determined, and the process returns to the step of "reducing the throughput of the slave user with the highest throughput".

[0096] The specific process of the linear precision algorithm is shown in Table 2.

[0097] Table 2 Bandwidth allocation algorithm based on linear precision algorithm

[0098]

[0099]

[0100] In this embodiment, the joint optimization model is solved to obtain a joint optimization solution, which specifically includes:

[0101] (1) All slave users are divided into multiple clusters using a clustering algorithm, and each cluster corresponds to a drone. The slave users belonging to the cluster are matched with the drone corresponding to the cluster to obtain the drone location deployment plan and the drone-slave user matching optimization plan.

[0102] (2) Based on the primary user interference constraint, the UAV location deployment plan is adjusted to obtain the optimized UAV location deployment plan.

[0103] (3) Taking the drone-slave user matching optimization plan and the drone location deployment optimization plan as input, the linear precision algorithm is used to iteratively optimize and solve the joint optimization model to obtain the bandwidth allocation optimization plan.

[0104] In this embodiment, the drone position deployment plan is adjusted based on the primary user interference constraint to obtain the drone position deployment optimization plan, which specifically includes:

[0105] (1) Based on the UAV location deployment plan, determine whether each UAV meets the primary user interference constraints.

[0106] (2) If yes, the UAV location deployment plan is used as the UAV location deployment optimization plan.

[0107] (3) If not, the UAV that does not meet the primary user interference constraint is recorded as the UAV to be adjusted; for each UAV to be adjusted, the maximum channel gain coefficient when the UAV to be adjusted communicates with the primary user is calculated based on the primary user interference constraint, and the safe distance from the UAV to be adjusted to the primary user is calculated based on the maximum channel gain coefficient. The position of the UAV to be adjusted is adjusted based on the safe distance to obtain the adjusted position of the UAV to be adjusted, wherein the position of the UAV to be adjusted is determined based on the UAV position deployment plan.

[0108] Among them, adjusting the position of the drone to be adjusted based on the safety distance specifically includes: determining an adjustment circle with the main user as the center and the safety distance as the radius; and taking the position on the adjustment circle that is closest to the position of the drone to be adjusted as the adjusted position of the drone to be adjusted.

[0109] (4) Using the adjusted positions of all the drones to be adjusted to replace the positions of all the drones to be adjusted in the drone position deployment plan, the drone position deployment plan is adjusted to obtain the drone position deployment optimization plan.

[0110] In this embodiment, the UAV-slave user matching optimization solution and the UAV location deployment optimization solution are used as inputs, and a linear precision algorithm is used to iteratively optimize and solve the joint optimization model to obtain the bandwidth allocation optimization solution, which specifically includes:

[0111] (1) Based on the bandwidth allocation scheme of the previous iteration, the throughput of each slave user in the current iteration is calculated using Shannon's theorem.

[0112] (2) Sort the throughput of each slave user in the current iteration to obtain the minimum throughput and the second minimum throughput of the current iteration. The minimum throughput is the minimum value of the throughput of each slave user in the current iteration, and the second minimum throughput is the minimum value after removing the minimum throughput from the throughput of each slave user in the current iteration.

[0113] (3) Based on the minimum throughput and the second minimum throughput of the current iteration and the adjustment coefficient of the previous iteration, the minimum throughput of the current iteration is increased to obtain the adjusted throughput of each slave user in the current iteration.

[0114] The minimum throughput of the current iteration is increased based on the minimum throughput and the next minimum throughput of the current iteration and the adjustment coefficient of the previous iteration to obtain the adjusted throughput of each slave user in the current iteration, specifically including:

[0115] 1) For each minimum throughput of the current iteration, determine the minimum UAV that matches the slave user corresponding to the minimum throughput, and determine the number of users with the minimum throughput among the slave users matched with the minimum UAV; calculate the difference between the sub-minimum throughput of the current iteration and the minimum throughput, and calculate the product of the number of users, the adjustment coefficient of the previous iteration, and the difference to obtain the increase; calculate the sum of the minimum throughput and the increase to increase the minimum throughput, and obtain the increased throughput corresponding to the minimum throughput.

[0116] 2) The minimum throughput among the throughputs of each slave user in the current iteration is replaced by the increased throughput corresponding to the minimum throughput to obtain the adjusted throughput of each slave user in the current iteration.

[0117] (4) Based on the adjusted throughput of each slave user in the current iteration, the bandwidth of each slave user in the current iteration is calculated using Shannon's theorem to obtain the bandwidth allocation scheme for the current iteration.

[0118] (5) Based on the bandwidth allocation scheme of the current iteration, calculate the adjustment coefficient of the current iteration.

[0119] (6) Determine whether the iteration termination condition is met; if so, determine the bandwidth allocation scheme of the previous iteration as the initial bandwidth allocation scheme; if not, increase the current iteration number by 1, determine the bandwidth allocation scheme of the current iteration as the bandwidth allocation scheme of the previous iteration, and determine the adjustment coefficient of the current iteration as the adjustment coefficient of the previous iteration.

[0120] (7) Taking the backhaul bottleneck of the temporary node as a constraint, the initial bandwidth allocation plan is adjusted to obtain the bandwidth allocation optimization plan.

[0121] Below, this embodiment verifies the effectiveness of the proposed joint optimization algorithm through simulation. The simulation simulates the scenario of multi-UAV relay communication in a cognitive wireless network, where the network covers an area of ​​1000×1000 square meters and the UAV flies at an altitude of 100 meters. The simulation parameters are shown in Table 3 below.

[0122] Table 3 Simulation parameter settings

[0123]

[0124]

[0125] Figure 3 The results of drone-slave user matching and drone location deployment are displayed. The slave user's position is fixed, and the five-pointed star indicates the optimized drone position. This optimization ensures that the distance between each slave user and its corresponding drone is minimized. It also ensures that under the conditions of underlay spectrum reuse, the interference of the slave network drone's transmission signal to the slave user on the master user remains within a certain range. Under the premise of ensuring the communication quality of the master network, all slave users are clustered and the location deployment of each drone is completed.

[0126] Figure 4 A comparison of the throughput of each slave user before and after optimization using a linear exact algorithm is presented for a scenario with five drones and 100 slave users. The throughput setting before optimization was to set each drone's bandwidth to its minimum and evenly distribute that bandwidth to the slave users it served. The throughput setting after optimization was to calculate the throughput of each slave user using a linear exact algorithm. Due to the large number of slave users, a sample of every four slave users was selected, resulting in a throughput comparison of 25 slave users. The figure shows that users with lower throughput before optimization experienced a significant increase in throughput after optimization. Furthermore, some slave users with higher throughput before optimization experienced a significant decrease after optimization. This is because during the iteration process, the sum of the throughput of all drones exceeded the backhaul bottleneck, reducing the throughput of higher-capacity users until the sum of the throughput of the drones in the slave network fell below the bottleneck.

[0127] Figure 5The figure shows the changes in bandwidth and throughput of different drones after linear precision algorithm optimization in a scenario with 5 drones and 100 slave users. It can be seen that after optimization, the drones in cluster 3 are allocated more bandwidth and have a higher throughput. The reason is that the slave users in cluster 3 are closer to the base station of the main network. Therefore, each slave user suffers greater interference from the main network, so each slave user has a relatively lower signal-to-noise ratio. Since the bandwidth allocation before optimization is evenly distributed, the pre-optimization throughput of the slave users in cluster 3 is smaller than that of other slave users. During the optimization process, more bandwidth is allocated, so the drones in cluster 3 have higher bandwidth and throughput after optimization. In contrast, the drones in clusters 4 and 5 are farther away from the base station of the main network, so the slave users in the cluster have a larger pre-optimization throughput. Therefore, no additional bandwidth resources are allocated during the optimization process, so the throughput of these drones does not change much before and after optimization.

[0128] This example primarily studies the optimization of drone placement and bandwidth allocation within a slave network in a cognitive wireless network scenario with multiple drones acting as relays. To ensure communication quality for primary users and frequency fairness for slave users, and with the goal of maximizing the minimum throughput for slave users, an improved K-Means algorithm is used to address the drone placement problem. A linear precision algorithm is then used to optimize the bandwidth allocation of drones to slave users. Simulation results demonstrate that this algorithm significantly improves minimum throughput.

[0129] Drones (UAVs) have attracted significant attention in cognitive wireless networks due to their low cost and flexible deployment. This example addresses the spectrum allocation problem in cognitive wireless networks where multiple UAVs act as relays to provide temporary services to users. This approach considers spectrum reuse and mutual interference between master and slave networks. While ensuring the communication quality of the master user, it aims to maximize the minimum throughput of the slave users. An improved K-Means algorithm and a linear exact algorithm are proposed to optimize UAV location deployment and bandwidth allocation. Simulation results demonstrate that the proposed joint optimization method can effectively improve user transmission throughput and ensure fairness in user communications.

[0130] This embodiment studies a scenario in which multiple drones act as relays in a cognitive wireless network and reuse spectrum resources with the primary network. A joint optimization method for drone location deployment and bandwidth allocation is proposed to improve minimum throughput and achieve fairness in user frequency usage. Compared with existing technologies, this embodiment has the following advantages:

[0131] (1) This embodiment constructs a multi-UAV relay communication scenario based on a cognitive wireless network, with the goal of improving the minimum throughput. To this end, an algorithm is proposed to jointly optimize the UAV location deployment and bandwidth allocation, reduce interference, improve the minimum throughput, and ensure frequency fairness, thereby maximizing network performance and spectrum resource utilization efficiency.

[0132] (2) The algorithm proposed in this embodiment takes into account the spectrum reuse problem between the master network and the slave network. It uses an improved K-Means clustering algorithm and a linear precision algorithm to effectively reduce the mutual interference between the master network and the slave network, while optimizing the allocation of bandwidth resources and significantly improving the minimum throughput in the slave network.

[0133] (3) The simulation results show that the algorithm proposed in this embodiment can significantly improve the minimum throughput of users in the slave network while ensuring the communication quality of the primary network. This not only improves the overall performance of the communication network, but also ensures the fairness of communication between users. The simulation results also demonstrate the effectiveness and robustness of the proposed algorithm in improving the minimum throughput under different network conditions and scenarios.

[0134] Example 2

[0135] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a joint optimization method for multi-UAV relay communication in a cognitive wireless network is implemented.

[0136] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0137] In an exemplary embodiment, a computer device is also provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the joint optimization method for multi-UAV relay communication in the cognitive wireless network described in Example 1.

[0138] Example 3

[0139] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for jointly optimizing multi-UAV relay communication in a cognitive wireless network described in Example 1 is implemented.

[0140] Example 4

[0141] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the joint optimization method for multi-UAV relay communication in a cognitive wireless network described in Example 1.

[0142] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A joint optimization method for multi-UAV relay communication in a cognitive wireless network, characterized in that: The joint optimization method for multi-UAV relay communication in the cognitive wireless network includes: A joint optimization model is constructed for a multi-UAV relay communication scenario in a cognitive wireless network. The joint optimization model takes maximizing the minimum throughput of slave users as the optimization objective and takes UAV allocation constraints, minimum bandwidth constraints, bandwidth equality constraints, maximum throughput constraints, and primary user interference constraints as constraints. A primary user is a ground user in a cognitive wireless network that is provided with communication services by the primary network, and a slave user is a ground user in a cognitive wireless network that is provided with communication services by a slave network. The slave network includes a temporary node, multiple UAVs, and multiple slave users. The temporary node provides communication services to the slave users through multiple UAV relays. Solving the joint optimization model to obtain a joint optimization solution; the joint optimization solution includes a drone-slave user matching optimization solution, a drone location deployment optimization solution, and a bandwidth allocation optimization solution; The joint optimization model is: ； in, For users u Throughput; M is the total number of drones; To represent drones r Whether the service is from the user u binary variable; For drones r bandwidth; is the minimum bandwidth of the drone; For users u bandwidth; is the minimum bandwidth from the user; Temporary node g The maximum bandwidth that can be provided; K is the total number of slave users; For drones r Throughput; Temporary node g Backhaul bottleneck; For drones r The transmission power; For drones r With the main user Channel gain coefficient during communication; The maximum tolerance threshold of the primary user to single-source interference; Solving the joint optimization model to obtain a joint optimization solution specifically includes: A clustering algorithm is used to divide all slave users into multiple clusters, and each cluster corresponds to a drone. The slave users belonging to the cluster are matched with the drones corresponding to the cluster to obtain the drone location deployment plan and the drone-slave user matching optimization plan; Adjusting the UAV position deployment plan based on the primary user interference constraint to obtain an optimized UAV position deployment plan; The UAV-slave user matching optimization solution and the UAV position deployment optimization solution are used as inputs, and a linear precision algorithm is used to iteratively optimize and solve the joint optimization model to obtain a bandwidth allocation optimization solution.

2. The joint optimization method for multi-UAV relay communication in a cognitive wireless network according to claim 1, characterized in that: The UAV position deployment plan is adjusted based on the primary user interference constraint to obtain an optimized UAV position deployment plan, specifically including: Based on the UAV position deployment plan, determining whether each UAV satisfies the primary user interference constraint; If so, the UAV position deployment plan is used as the UAV position deployment optimization plan; If not, the UAV that does not meet the primary user interference constraint is recorded as a UAV to be adjusted; for each UAV to be adjusted, the maximum channel gain coefficient when the UAV to be adjusted communicates with the primary user is calculated based on the primary user interference constraint, and the safety distance between the UAV to be adjusted and the primary user is calculated based on the maximum channel gain coefficient, and the position of the UAV to be adjusted is adjusted based on the safety distance to obtain the adjusted position of the UAV to be adjusted; wherein the position of the UAV to be adjusted is determined based on the UAV position deployment plan; The positions of all the drones to be adjusted in the drone position deployment plan are replaced by the adjusted positions of all the drones to be adjusted, so as to adjust the drone position deployment plan and obtain an optimized drone position deployment plan.

3. The joint optimization method for multi-UAV relay communication in a cognitive wireless network according to claim 2, characterized in that: The calculation formula for the maximum channel gain coefficient is: ； in, For the drone to be adjusted r With the main user Maximum channel gain coefficient during communication; The maximum tolerance threshold of the primary user to single-source interference; For the drone to be adjusted r The transmission power; The calculation formula for the safety distance is: ； in, For the drone to be adjusted r To the primary user safe distance; is the air-to-ground path loss exponent; is the transmission probability of the line-of-sight link; is the attenuation factor of the non-line-of-sight link; is the transmission probability of the non-line-of-sight link; Adjusting the position of the drone to be adjusted based on the safety distance specifically includes: Determine an adjustment circle with the primary user as the center and the safety distance as the radius; The position on the adjustment circle that is closest to the position of the UAV to be adjusted is used as the adjusted position of the UAV to be adjusted.

4. The joint optimization method for multi-UAV relay communication in a cognitive wireless network according to claim 1, characterized in that: The UAV-slave user matching optimization solution and the UAV location deployment optimization solution are used as inputs, and the joint optimization model is iteratively optimized and solved using a linear precision algorithm to obtain a bandwidth allocation optimization solution, specifically including: Based on the bandwidth allocation scheme of the previous iteration, the throughput of each slave user in the current iteration is calculated using Shannon's theorem; Sorting the throughput of each slave user in the current iteration to obtain a minimum throughput and a second minimum throughput of the current iteration; the minimum throughput is the minimum value of the throughput of each slave user in the current iteration; the second minimum throughput is the minimum value after subtracting the minimum throughput from the throughput of each slave user in the current iteration; Based on the minimum throughput and the second minimum throughput of the current iteration and the adjustment coefficient of the previous iteration, the minimum throughput of the current iteration is increased to obtain the adjusted throughput of each slave user in the current iteration; Based on the adjusted throughput of each slave user in the current iteration, the bandwidth of each slave user in the current iteration is calculated using Shannon's theorem to obtain the bandwidth allocation plan for the current iteration; Calculate the adjustment coefficient of the current iteration based on the bandwidth allocation scheme of the current iteration; Determine whether the iteration termination condition is met; if so, determine the bandwidth allocation scheme of the previous iteration as the initial bandwidth allocation scheme; if not, increase the current iteration number by 1, determine the bandwidth allocation scheme of the current iteration as the bandwidth allocation scheme of the previous iteration, and determine the adjustment coefficient of the current iteration as the adjustment coefficient of the previous iteration; Taking the backhaul bottleneck of the temporary node as a constraint, the initial bandwidth allocation plan is adjusted to obtain an optimized bandwidth allocation plan.

5. The joint optimization method for multi-UAV relay communication in a cognitive wireless network according to claim 4, characterized in that: Based on the minimum throughput and the next minimum throughput of the current iteration and the adjustment coefficient of the previous iteration, the minimum throughput of the current iteration is increased to obtain the adjusted throughput of each slave user in the current iteration, which specifically includes: For each minimum throughput of the current iteration, determine the minimum UAV that matches the slave user corresponding to the minimum throughput, and determine the number of users with the minimum throughput among the slave users matched with the minimum UAV; calculate the difference between the second minimum throughput and the minimum throughput of the current iteration, and calculate the product of the number of users, the adjustment coefficient of the previous iteration, and the difference to obtain an increase; calculate the sum of the minimum throughput and the increase to increase the minimum throughput, and obtain an increased throughput corresponding to the minimum throughput; Replacing the minimum throughput of each slave user in the current iteration with the increased throughput corresponding to the minimum throughput to obtain an adjusted throughput of each slave user in the current iteration; The calculation formula of the adjustment coefficient is: ； in, is the adjustment factor; Temporary node g The maximum bandwidth that can be provided; M is the total number of drones; For the current iteration of drones r bandwidth; A is the number of minimum throughput; For the second smallest throughput; is the minimum throughput; Temporary node g With drones r Signal-to-noise ratio during communication.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the joint optimization method for multi-UAV relay communication in a cognitive wireless network according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the joint optimization method of multi-UAV relay communication in a cognitive wireless network according to any one of claims 1 to 5.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the joint optimization method of multi-UAV relay communication in a cognitive wireless network according to any one of claims 1 to 5.

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