Communication system optimization method based on multi-UAV assisted communication

Abstract

The present invention discloses a communication system optimization method based on multi-UAV assisted communication, comprising: step 1: multiple UAVs and user communication equipment form a downlink communication link of time division multiple access; step 2: taking maximizing the system communication rate as the optimization goal, establishing a target optimization problem; step 3: under the assumption that the UAV position and the transmission power are fixed, using a continuous convex approximation algorithm and an optimization function with a penalty term to determine the best user equipment and UAV communication connection allocation; step 4: adopting a block coordinate optimization method, using a continuous convex approximation algorithm with a local stable solution to optimize the hovering position of the UAV and the UAV communication power allocation, thereby realizing the communication construction based on multiple UAVs with a return link capacity limitation.

Description

Technical Field

[0001] The present invention relates to a communication system optimization method, in particular to a communication system optimization method based on multi-UAV assisted communication. Background Art

[0002] Unmanned aerial vehicle (UAV) assisted wireless communication has the characteristics of high mobility, line-of-sight air-to-ground channel and low cost. UAV wireless communication is a promising way in the sixth generation (6G) wireless network for both military and civilian fields. As an aerial base station, UAVs have the ability to increase the communication capacity of traditional ground wireless communication networks now and in the future. Due to the high mobility of UAV aerial base stations, it can be used as an important tool for temporary communication capacity expansion and post-disaster communication reconstruction; due to its inherent line-of-sight air-to-ground channel, it also has excellent performance in alleviating signal blocking; in addition, the low cost of UAVs makes it a better choice for providing communication services to a large number of Internet of Things (IoT) devices in remote areas.

[0003] As a static aerial base station, a drone hovers at a fixed position in the air to provide communication services for user devices, which is an important and widely used drone communication scenario. In this communication scenario, the appropriate drone hovering position selection can improve the communication throughput of the system. A communication network composed of multiple drones can further provide the expansion capacity of the communication network; and in a multi-drone communication system, determining the appropriate set of service user devices for each drone can better manage the co-frequency interference in communication and further improve communication performance. Controlling the transmission power of the drone dynamically adjusts the energy consumption according to the communication quality requirements of the user device to achieve a balance between communication quality and energy consumption.

[0004] Based on the advantages and characteristics of the above-mentioned UAV communication systems, UAVs as static aerial base stations to provide wireless communication services have been widely studied. In the article "Efficient 3-d placement of an aerial base station in next generation cellular networks" published by RIBor-Yaliniz et al. in 2016, the communication system of a single UAV was studied, and the problem was expressed as a mixed integer nonlinear optimization problem. In the article "Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks" published by B.Galkin et al. in 2016, the K-means clustering method was used to determine the physical location of the UAV by the location of the cell users.

[0005] Due to the size of the drone, it is energy-constrained as an aerial base station, which makes it almost impossible for it to exist as an independent communication unit. Instead, it needs to transmit information back to the ground base station through a backhaul link for processing. The wireless backhaul link is different from the ground optical fiber backhaul link. The wireless backhaul link has a distance-related communication capacity limitation, which means that the hovering position of the drone needs to be further considered based on the backhaul link capacity. In the article "Multiple UAV-mounted base station placement and user association with joint fronthaul and backhaul optimization" published by C. Qiu et al. in 2020, the system optimization problem with backhaul capacity constraints was converted into an unconstrained problem, and the gradient descent method was used to determine the position of the drone.

[0006] In summary, the problems existing in the prior art are: (1) In a multi-UAV communication system, due to the existence of co-frequency interference, placing UAVs based solely on the user’s geographical location cannot provide a better solution; (2) Considering actual application scenarios, the limited capacity of the backhaul link requires further consideration of the UAV’s location; (3) Determining the set of service users for each UAV based on the actual distribution of users is a mixed integer programming problem in mathematics and is difficult to handle. Summary of the invention

[0007] Purpose of the invention: The technical problem to be solved by the present invention is to provide a communication system optimization method based on multi-UAV assisted communication in view of the shortcomings of the prior art.

[0008] In order to solve the above technical problems, the present invention discloses a communication system optimization method based on multi-UAV assisted communication, comprising the following steps:

[0009] Step 1: Multiple drones and user communication devices form a time division multiple access downlink communication link. The wireless communication system operated by this method consists of a ground base station, J drones and K user devices. All user devices communicate with drones. The drones, as aerial base stations, forward the information of the ground base station through the backhaul link. Each drone, as a static aerial base station, forwards the information of the ground base station to its own user device set through the backhaul link, where each user device has its own communication quality requirements;

[0010] Step 2: Under the constraints of the backhaul link capacity between the UAV and the ground base station and the communication quality requirements of the user equipment, the optimization objective is to maximize the sum of the communication rates of all user equipment and establish an optimization objective problem;

[0011] Step 3: Under the assumption that the position and transmit power of the UAV are fixed, the optimal allocation of communication connections between the user equipment and the UAV is determined using a continuous convex approximation algorithm and an optimization function with a penalty term;

[0012] Step 4: Adopting the block coordinate optimization method, a continuous convex approximation algorithm with a local stable solution is used to optimize the hovering position of the UAV and the communication power allocation of the UAV, thereby realizing the communication construction based on multiple UAVs with limited backhaul link capacity.

[0013] In the present invention, the system is in a specific area, and the drones are grouped as There are J drones in total, marked as {1, 2, …, J}; the user communication device set is There are K devices in total, marked as {1,2,…,K}. K user devices have their own fixed ground positions, and the three-dimensional coordinates of the kth user device are marked as The goal is to deploy J drones to provide communication services for user devices. The entire system uses time division multiple access and considers downlink communication. The drone acts as an aerial base station to forward the information of the ground base station through the backhaul link. The jth drone is suspended in a fixed position. Provides downlink communication for the set of user devices it serves. The coordinates of the ground base station are The distance between the jth UAV and the kth user device is denoted as d j,k =||u j -u k ||2, i.e. uj and u k The Euclidean norm between them. Similarly, the distance between the ground base station and the jth UAV is recorded as d 0,j =||u j -u0||2. The UAV is connected to the ground base station through a wireless backhaul link. The ground base station communicates with the UAV through a millimeter wave channel, and the ground base station operates in the "massive multiple input multiple output (massive MIMO)" area. The beam gain at this time can be estimated as A t Indicates the number of antennas equipped by the ground base station, A g is the number of drones, i.e. J. Under the above conditions, the capacity of the wireless backhaul link between the drone and the ground base station can be expressed as:

[0014]

[0015] Among them, P GBS represents the transmission power of the ground base station, γ represents the attenuation rate of the return link related to the environment, in decibels per kilometer, and σ 2 represents the noise power density of additive Gaussian white noise. Because the UAV is suspended in the air, its channel can be regarded as a line-of-sight air-to-ground channel, so the channel power gain g between the ground base station and the jth UAV is 0,j Specifically expressed as:

[0016]

[0017] ρ0 is the channel power gain at the standard reference distance, and α is the path loss exponent. Similarly, the channel power gain between the jth UAV and the kth user equipment is recorded as:

[0018]

[0019] In the present invention, a drone provides downlink communication for at least one user device in a time division multiple access manner, using a binary variable a j,k represents the allocation relationship between the drone and the user equipment. Specifically, a j,k =1 means that the kth user equipment is assigned to the jth UAV for communication. j,k = 0 means that the kth user equipment does not belong to the service user equipment set of the jth drone. The achievable communication rate r from the jth drone to the kth user equipment j,k It is expressed as:

[0020]

[0021] pj represents the transmission power of the jth UAV. Considering the downlink communication of multiple UAVs, the user equipment will receive the same-frequency interference from other non-target UAVs. j′ represents the transmission power of the j′th UAV in the set of other UAVs excluding the jth UAV. Similarly, g j′,k represents the channel power gain of the j′th UAV, The combination represents the co-frequency interference generated by other drones to the kth user equipment. Based on the above mathematical expression, in the case of time division multiple access, the actual equivalent communication rate from the jth drone to the kth user equipment is It is expressed as:

[0022]

[0023] Among them, a j represents the number of user devices served by drone j, we can get the relationship a j =∑ k a j,k When multiple drones perform downlink communication simultaneously, it is necessary to flexibly determine the communication connection relationship between drones and user devices and the transmission power of drones according to the actual distribution of user devices to maximize the communication rate that user devices can achieve; drones, as static aerial base stations, forward information from ground base stations through backhaul links, and wireless backhaul links have capacity limitations. The sum of the communication rates from the jth drone to its assigned user device set cannot exceed the capacity of the wireless backhaul link between the jth drone and the ground base station, which can be expressed mathematically as:

[0024]

[0025] Each user device has its own communication quality requirements. represents the communication quality requirement of user equipment k, then there are constraints:

[0026]

[0027] Indicates that the kth user device needs to communicate with a certain drone, and the communication rate must be greater than The optimization goal of the system is to determine the communication connection relationship between the drone and the user equipment. j,k 、The hovering position of the drone u j And the transmission power p of the drone as an aerial base station j The optimization goal is:

[0028]

[0029] Among them, max indicates that the optimization goal is to maximize, the subsequent expressions of st indicate the constraints, and the subscripts j and k represent the jth UAV and the kth user device, respectively; r j,k represents the communication rate from UAV j to user device k, and the optimization goal is to maximize the sum of the communication rates of all user devices; the optimization variable Represents the three-dimensional hovering position vector of the drone, and the components Respectively represent the coordinate values ​​on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system. Similarly, and Indicates the lower and upper bounds that the coordinate vector can take, limiting the range of geographical locations where the drone can hover; represents the communication quality requirement of user equipment k; C j (d 0,j ) represents the upper limit of the backhaul link capacity between UAV j and the ground base station, where d 0,j is the physical distance between UAV j and the ground base station; and They represent the transmission power threshold of UAV j respectively; ∑ j a j,k = 1 means that each user device can only be connected to one drone. In addition, each drone corresponds to at least one service user device, a j represents the number of user devices served by drone j. This problem is a mixed integer nonlinear optimization problem. j,k Discretize the feasible domain of the constraints; UAV position u j At the same time, the backhaul link capacity C j (d 0,j ) and the communication rate r j,k The mutual correlation makes the related constraints non-convex, which further makes it difficult to determine the optimal solution. In order to make the problem solvable, the block coordinate descent method is used to split the problem into three small sub-problems for solution.

[0030] Assuming that the drone position and the drone transmission power are determined, the communication connection between the drone and the user equipment is first optimized. The above optimization problem becomes:

[0031]

[0032] Among them, ∑ k a j,k r j,k ≤C j a j In the original question Equivalent transformation of . The constraint can be equivalently expressed as a j,k The linear constraints are:

[0033]

[0034] It can be seen that when a j,k =1, (M-(M-1)a j,k )=1, constraint It can be equivalently expressed as And when a j,k =0, (M-(M-1)a j,k )r j,k =Mr. j,k , when M is a sufficiently large constant, This ensures that the inequality holds. In this way, we use the large constant M to unify the constraints In a j,k =1 and a j,k = 0, further transforming the non-convex constraint into j,k Linear constraints.

[0035] It can be seen that if and only if a j,k =1 and a j,k = 0, That is, the objective function can be equivalently expressed as The objective function is still non-convex, defined as And the function is about a j,k and Λ j A binary convex function, in this case, the binary variable a j,k Relaxed to a continuous variable, we further introduce a penalty term It can be seen that if and only if a j,k =1 and a j,k = 0, the penalty term is zero, which ensures that when the penalty factor λ is large enough, the variable a j,j It will converge to 0 or 1. So the final objective function is transformed into:

[0036]

[0037] The objective function is a convex function. By maximizing the lower bound of the objective function, the original objective function can be maximized. The lower bound of the objective function is expressed as:

[0038]

[0039] in and They are and At a given point The first-order Taylor expansion of . and The specific form is:

[0040]

[0041] Through the above algebraic transformation, the problem of optimizing the communication connection between the drone and the user equipment is transformed into a series of continuous variables a j,k The standard convex optimization problem is solved once by the continuous convex approximation algorithm, that is, by using the existing convex optimization problem solving tool, and then the obtained solution is used as the next problem Iterate until convergence to obtain the optimized communication connection x between the drone and the user device. j,k .

[0042] Assuming that the transmission power of the drone is determined, the connection relationship between the drone and the user equipment obtained above is used to optimize the hovering position of the drone. is a fixed value, denoted as ω j,k The UAV position optimization problem can be expressed as:

[0043]

[0044] φ is the variable introduced about the objective function. At this time, the position of the UAV u j The communication rate expression r j,k and wireless backhaul link C j By estimating r j,k and C j , this non-convex problem can be transformed into a series of convex optimization problems that can be solved using continuous convex estimation. Specifically, r j,k (u j ) can be rewritten as:

[0045]

[0046] Introducing slack variables s j,k ,v j,k . Make the distance between the drone and the user's device satisfy:

[0047]

[0048] Then we can relax r j,k (u j ),Right now:

[0049]

[0050] Based on this relaxation and the first-order Taylor expansion, we can further give r j,k(u j ) is the upper and lower bounds of r. j,k (u j ) is bounded by:

[0051]

[0052] is the symbolic representation of the lower bound, and That is r j,k (s j,k ,v j,k ) is the Taylor expansion point. Similarly, r j,k (u j ) is bounded by:

[0053]

[0054] By adding r j,k (u j ) is bounded by and These two non-convex constraints can be transformed into standard convex constraints. k ω j,k r j,k (u j )≤C j (u j ) is non-convex, further processing the backhaul link capacity C j (u j ), again giving C J (u j ) expression:

[0055]

[0056] Since the ground base station operates in a large-scale multi-antenna mode, it can be considered that the signal-to-noise ratio So it can be estimated that The backhaul link is further expanded as:

[0057]

[0058] in and is a term related to the variable drone position. Introducing variable t j , so that:

[0059]

[0060] Then there is Then we can give and d 0,j The first-order Taylor expansion of is expressed as and The specific form is:

[0061]

[0062] in, That is, it is the expansion point of the Taylor expansion, and it is also the position coordinate of the UAV at the tth iteration in the continuous convex estimation. Through the variables and mathematical transformations introduced above, the UAV position optimization problem can be expressed as follows:

[0063]

[0064] in Represents the part of the return link that is not related to the drone position. The above problem is a standard convex optimization problem. By solving the t-th drone position, the obtained drone position As the Taylor expansion point of the next iteration, until convergence, the optimized drone position can be obtained.

[0065] After determining the connection relationship between the drone and the user device and the drone's hovering position, the drone's transmission power is optimized. The problem becomes:

[0066]

[0067] Similar to the previous process, the transmission power p j Exists at communication rate r j,k (p j ), by giving r j,k (p j ), the non-convex problem can be transformed into a series of convex problems and solved using the continuous convex approximation method. Specifically, r j,k (p j ) is as follows:

[0068]

[0069] It can be further expressed as:

[0070]

[0071] This expression is the form of subtracting two convex functions. By Taylor expanding these two terms separately, we can get r j,k (p j ) are:

[0072]

[0073] Substituting the above lower bound into the constraint and In the above example, we bring the upper bound into the constraint ∑k ω j,k r j,k (p j )≤C j In the example, the original non-convex problem is transformed into a convex problem, so that the continuous convex approximation can be used to iteratively solve the UAV transmission power until convergence. The convex optimization problem obtained by the continuous convex approximation algorithm is iteratively solved until the communication rate and convergence of the user equipment. That is, all the variables to be solved can be solved. By continuously iteratively solving the above three sub-problems until the objective function converges, the optimization of the communication system based on multi-UAV assisted communication is completed.

[0074] Beneficial effects:

[0075] Based on the current development and progress of wireless communication technology, the use of drones as an auxiliary relay can significantly improve the communication quality of user communication equipment while reducing the construction cost of the entire communication system, and provide guarantees for the construction of temporary communication networks. Compared with the prior art, the advantages and positive effects of the present invention are as follows: First, the mobility of drones improves the flexibility of the entire communication system, and can improve the communication quality of the system under the premise of meeting energy consumption; secondly, the limited return capacity when drones are used as relay units in actual situations is taken into account, making the deployment of drones more practical rather than theoretical, and can flexibly respond to non-ideal channel conditions; thirdly, by optimizing the hovering position of the drone relay, the flexibility advantage of the drone is further exerted. Compared with other deployment methods that only consider the geographical location of user equipment, the present invention takes into account the return capacity limitation and co-frequency interference management, so that the system throughput has been further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0077] Figure 1 It is a schematic diagram of the UAV-assisted downlink wireless communication scenario of the present invention.

[0078] Figure 2 It is a logic block diagram based on the continuous convex approximation algorithm adopted in the embodiment of the present invention.

[0079] Figure 3 It is a flow chart of the algorithm of continuous convex approximation adopted in the embodiment of the present invention.

[0080] Figure 4 Schematic diagram illustrating the deployment position of the UAVs used in the embodiment of the present invention.

[0081] Figure 5It is a schematic diagram comparing the performance change trends of the system communication rate with the increase of the communication quality requirement of the user equipment under different deployment methods adopted in the embodiments of the present invention.

[0082] Figure 6 3 is a schematic diagram comparing the performance change trends of the system communication rate with the increase in the number of drones under different deployment methods adopted in the embodiments of the present invention. DETAILED DESCRIPTION

[0083] Unmanned aerial vehicle (UAV) assisted wireless communication has the characteristics of high mobility, line-of-sight air-to-ground channel and low cost. UAV wireless communication is a promising way in the sixth generation (6G) wireless network for both military and civilian fields. As an aerial base station, UAVs have the ability to increase the communication capacity of traditional ground wireless communication networks now and in the future. Due to the high mobility of UAV aerial base stations, it can be used as an important tool for temporary communication capacity expansion and post-disaster communication reconstruction; due to its inherent line-of-sight air-to-ground channel, it also has excellent performance in alleviating signal blocking; in addition, the low cost of UAVs makes it a better choice for providing communication services to a large number of Internet of Things (IoT) devices in remote areas.

[0084] The present invention discloses a communication system optimization method based on multi-drone assisted communication. Specifically, the present invention designs a method in which, when the drone relay has a backhaul link capacity limit, multiple drones are deployed to provide downlink communication services for multiple user devices with communication quality requirements in the target area, with maximizing the system communication rate as the optimization goal, and establishing a target optimization problem: determining the set of user devices served by each drone, determining the hovering position of each drone, and determining the transmission power of each drone, thereby realizing the communication construction based on multiple drones with backhaul link capacity limit. This embodiment designs an algorithm framework based on the above optimization problem, by decomposing the problem into three sub-problems, and then converting the problem into a problem that can be solved using a continuous convex approximation method by introducing substitution variables and mathematical transformations, and finally determining the set of user devices served by each drone, determining the hovering position of each drone, and determining the transmission power of each drone. The algorithm can achieve a near-optimal solution and remain stable. Compared with other methods, this method can achieve a higher system communication rate under the same conditions, and can handle some scenarios where other methods cannot obtain feasible solutions.

[0085] Example

[0086] This embodiment considers Figure 1 The downlink wireless communication system shown has J unmanned aerial vehicles (UAVs), one ground base station (BS) and K user communication devices. The set of UAVs is There are J drones in total, marked as {1, 2, …, J}; the user communication device set is There are K devices in total, marked as {1,2,…,K}. K user communication devices have their own fixed ground positions, and the coordinates of the kth user device are marked as The goal is to deploy J drones to provide communication services for user devices. The entire system uses time division multiple access and considers downlink communication. The drone acts as an aerial base station to forward the information of the ground base station through the backhaul link. The jth drone needs to be deployed to float in a fixed position. Provides downlink communication for the set of user devices it serves. The coordinates of the ground base station are The distance between the jth UAV and the kth user device is denoted as d j,k =||u j -u k ||2, i.e. u j and u k The Euclidean norm between them. Similarly, the distance between the ground base station and the jth UAV is recorded as d 0,j =||u j -u0||2. The UAV is connected to the ground base station through a wireless backhaul link. The ground base station communicates with the UAV through a millimeter wave channel, and the ground base station operates in the "massive multiple input multiple output (massive MIMO)" area. The beam gain at this time can be estimated as A t Indicates the number of antennas equipped by the ground base station, A g is the number of drones, i.e. H. Under the above conditions, the capacity of the wireless backhaul link between the drone and the ground base station can be expressed as:

[0087]

[0088] Among them, P GBS represents the transmission power of the ground base station, γ represents the attenuation rate of the return link related to the environment, in decibels per kilometer, and σ 2 represents the noise power density of additive Gaussian white noise. Because the UAV is suspended in the air, its channel can be regarded as a line-of-sight air-to-ground channel, so the channel power gain g between the ground base station and the jth UAV is 0,j Specifically expressed as:

[0089]

[0090] ρ0 is the channel power gain at the standard reference distance, and α is the path loss exponent. Similarly, the channel power gain between the jth UAV and the kth user equipment is recorded as:

[0091]

[0092] In the present invention, a drone provides downlink communication for at least one user device in a time division multiple access manner, using a binary variable a j,k represents the allocation relationship between the drone and the user equipment. Specifically, a j,k =1 means that the kth user equipment is assigned to the jth UAV for communication. j,k = 0 means that the kth user equipment does not belong to the service user equipment set of the jth drone. The achievable communication rate r from the jth drone to the kth user equipment j,k It is expressed as:

[0093]

[0094] p j represents the transmission power of the jth UAV. Considering the downlink communication of multiple UAVs, the user equipment will receive the same-frequency interference from other non-target UAVs. j′ represents the transmission power of other UAVs j′ in the UAV set except UAV j. Similarly, g j′,k represents the channel power gain of the j′th UAV, The combination represents the co-frequency interference generated by other drones to the kth user equipment. Based on the above mathematical expression, in the case of time division multiple access, the actual equivalent communication rate from the jth drone to the kth user equipment is It is expressed as:

[0095]

[0096] Among them, a j represents the number of user devices served by drone j, we can get the relationship a j =∑ k a j,k When multiple drones perform downlink communication simultaneously, it is necessary to flexibly determine the communication connection relationship between drones and user devices and the transmission power of drones according to the actual distribution of user devices to maximize the communication rate that user devices can achieve; drones, as static aerial base stations, forward information from ground base stations through backhaul links, and wireless backhaul links have capacity limitations. The sum of the communication rates from the jth drone to its assigned user device set cannot exceed the capacity of the wireless backhaul link between the jth drone and the ground base station, which can be expressed mathematically as:

[0097]

[0098] Each user communication device has its own communication quality requirements. represents the communication quality requirement of user equipment k, then there are constraints:

[0099]

[0100] Indicates that the kth user device needs to communicate with a certain drone, and the communication rate must be greater than The optimization goal of the system is to determine the communication connection relationship between the drone and the user. j,k 、The hovering position of the drone u j And the transmission power p of the drone as an aerial base station j The optimization goal is:

[0101]

[0102] As shown in Table 1, the parameter settings in the above system are given. Parameters without specific values ​​are used as comparison parameters with different values, and detailed descriptions are given outside:

[0103]

[0104] Table 1 Parameter setting table

[0105] This problem is a non-deterministic polynomial time difficulty (NP-hard) problem in optimization. For the proposed NP-hard problem, block coordinate descent method and continuous convex approximation method can be used to solve it.

[0106] The logic based on the continuous convex approximation algorithm adopted in this embodiment is as follows: Figure 2 As shown in the figure, multiple drones and user communication equipment are first combined into a time-division multiple access downlink communication link. Considering the limited capacity of the backhaul link and the communication quality requirements of the user equipment, a target optimization problem is established with the goal of maximizing the communication rate. The communication connection between the drone and the user equipment, the hovering position of the drone in the air, and the transmission power of the drone are solved. First, assuming that the hovering position and transmission power of the drone are fixed, the communication connection allocation between the user equipment and the drone is solved. The objective function in the target optimization problem is rewritten by introducing a penalty term, so as to obtain the communication connection allocation between the user equipment and the drone. Secondly, the block coordinate descent method is adopted, and the continuous convex approximation algorithm with a local stable solution is used. By introducing substitution variables and mathematical transformations, the hovering position of the drone and the communication power allocation of the drone are optimized. Finally, the communication system optimization scheme for multi-drone assisted communication is obtained.

[0107] The algorithm flow of the continuous convex approximation adopted in this embodiment is as follows: Figure 3As shown in the figure, after the algorithm starts, the initial values ​​of the penalty parameter λ and the optimization variables are determined, including the communication connection variables, the transmission power, and the initial position of the UAV. The corresponding backhaul link capacity and the achievable communication rate are calculated, and the number of iterations is set to 0. Then, before the number of iterations reaches the upper limit or the difference between the objective function results obtained from two iterations is less than the threshold, the following loop is performed. In the tth iteration process, the following are completed: (1) When the UAV's suspended position and transmission power are fixed, the connection between the user device and the UAV is calculated and updated. (2) When the communication connection and the UAV transmission power are determined, the hovering position of the UAV is calculated. Update the backhaul link capacity based on the obtained results

[0108] (3) When the UAV’s airborne position and communication connection are determined, the UAV’s transmission power is updated using continuous convex approximation (4) Set the number of iterations t = t + 1 and re-judge the loop condition. After the iteration is terminated, the drone position, the connection between the user device and the drone, and the drone transmission power are output. The algorithm process terminates.

[0109] In order to evaluate the proposed system algorithm, we selected two baseline methods as typical methods for comparison, one is the K-means clustering method and the other is the mean shift clustering method. These two methods are commonly used drone deployment solutions based on the geographic location of user equipment. Their disadvantage is that they do not consider the impact of wireless backhaul link capacity and co-channel interference on drone deployment and the collection of user equipment served by drones.

[0110] like Figure 4 As shown, there are 10 user devices in a square area of ​​500×500 meters. The wireless backhaul link in this scenario is in an ideal state, that is, the attenuation rate γ of the backhaul link is 0 decibels / kilometer. Since the user devices are clearly divided into two groups geographically, the connection relationship between the drone and the user device obtained by the method of the present invention and the K-means clustering method in this scenario is the same. The plus sign in the figure is the drone deployment position obtained by the method of the present invention, and the asterisk in the figure is the drone deployment position obtained by the K-means clustering method. The drone deployment positions obtained by the K-means clustering method are respectively at the geometric centers of the two groups of user devices. Since this method takes into account the influence of co-frequency interference, the two drone deployment positions obtained are far away from each other, achieving the effect of suppressing co-frequency interference. The total system communication rate of the present invention in this scenario is 12.3724 bits / second / Hz, which is higher than the 11.2826 bits / second / Hz obtained by the K-means clustering method.

[0111] like Figure 5As shown, there are 30 user devices in a square area of ​​500×500 meters, 100 random user device location distribution scenarios are taken, the average total communication rate under all scenarios is calculated, and a performance change trend comparison chart of the system total communication rate as the user device communication quality requirement increases is given. The mean shift clustering method only considers the user device geographic location, so when the user device communication quality requirement increases, the drone deployment position will not be adjusted, and due to the backhaul capacity limitation, when the user device communication quality requirement is greater than 0.6 megabits / second, the drone deployment position obtained by the mean shift clustering method cannot simultaneously meet the user device communication quality requirement and the backhaul capacity limitation. The present invention can adjust the drone hovering position according to the user device communication quality requirement and the backhaul capacity limitation, so as to always maintain a feasible solution. And compared with the mean shift clustering method, the present invention can achieve a higher total communication rate. When the backhaul link attenuation rate γ is 0 dB / km and 30 dB / km, the present invention can dynamically adjust the system to adapt to different backhaul capacity limitations.

[0112] like Figure 6 As shown, there are 50 user devices in a square area of ​​1000×1000 meters, and 100 random user device location distribution scenarios are taken. The average value of the total communication rate in all scenarios is calculated, and a performance change trend comparison chart of the system communication rate with the increase of the number of drones is given. The return link attenuation rate γ is 0 dB / km and 30 dB / km respectively. Under different numbers of drones, the total system communication rate that can be achieved by the method of the present invention is higher than that of the K-means clustering method, indicating that the present invention, which considers the impact of co-channel interference on the communication rate, is superior to the clustering method that only considers the geographical location of the user device in this indicator.

[0113] In a specific implementation, the present invention further provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0114] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0115] The present invention provides a communication system optimization method based on multi-UAV assisted communication. There are many methods and ways to implement the technical solution. The above is only a preferred implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A communication system optimization method based on multi-UAV assisted communication, characterized in that: The following steps are involved: Step 1, the communication system includes: forming a time division multiple access downlink communication link between multiple drones and user communication equipment; the communication system is a wireless communication system, which is composed of a ground base station, J drones and K user communication equipment, all user communication equipment communicate with the drones, and the drones, as air base stations, forward the information of the ground base station through a backhaul link, wherein each drone, as a static air base station, forwards the information of the ground base station to its own user communication equipment set through a backhaul link, wherein each user communication equipment has its own communication quality requirements; Step 2: Under the constraints of the backhaul link capacity between the UAV and the ground base station and the user's communication quality requirements, the optimization objective is to maximize the sum of the communication rates of all user communication devices and establish an optimization objective problem; Step 3: Under the assumption that the position and transmit power of the UAV are fixed, the optimal allocation of communication connections between the user equipment and the UAV is determined using a continuous convex approximation algorithm and an optimization function with a penalty term; Step 4: Adopting the block coordinate optimization method, the continuous convex approximation algorithm with local stable solutions is used to optimize the hovering position of the UAV and the UAV communication power allocation, and finally realize the communication construction based on multiple UAVs with limited backhaul link capacity, and complete the communication system optimization based on multi-UAV assisted communication; The method for establishing the optimization target problem in step 2 includes: The set of drones in the system is There are J drones in total, marked as {1, 2, …, J}; the user communication device set is There are K devices in total, marked as {1, 2, ..., K}; the communication connection between drone j and user device k needs to be established, marked as a j,k =1, otherwise marked as a j,k =0; the optimization goal of the system is to determine the communication connection relationship a between the drone and the user equipment j,k , the three-dimensional hovering position of the drone And the transmission power p of the drone as an aerial base station j ; The optimization objectives include: Among them, max indicates that the optimization goal is to maximize, the subsequent expressions of st indicate the constraints, and the subscripts j and k represent the jth UAV and the kth user device, respectively; r j,k represents the communication rate from UAV j to user device k, and the optimization goal is to maximize the sum of the communication rates of all user devices; the optimization variable Represents the three-dimensional hovering position vector of the drone, and the components Respectively represent the coordinate values ​​on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system. and Represents the lower and upper bounds of the coordinate vector selection, limiting the geographical location range where the drone can hover; represents the communication quality requirement of user equipment k; C j (d 0,j ) represents the upper limit of the backhaul link capacity between UAV j and the ground base station, where d 0,j is the physical distance between UAV j and the ground base station; and They represent the transmission power threshold of UAV j respectively; each UAV corresponds to at least one service user equipment, a j represents the number of user devices served by drone j.

2. The communication system optimization method based on multi-UAV assisted communication according to claim 1 is characterized in that: The communication system described in step 1 includes: the drone flexibly selects a hovering position as an aerial base station; multiple drones perform downlink communication at the same time, and the user communication equipment receives co-frequency interference from other non-target drones; multiple drones perform downlink communication at the same time, and the communication connection relationship between the drone and the user equipment and the transmission power of the drone are determined according to the actual distribution of user communication equipment to maximize the communication rate that the user communication equipment can achieve; the drone acts as a static aerial base station and forwards the information of the ground base station through a backhaul link, and the backhaul link has a capacity limit; each user communication equipment has communication quality requirements.

3. The communication system optimization method based on multi-UAV assisted communication according to claim 2 is characterized in that: The expression of the optimization objective problem in step 2 is adjusted to meet the requirement of solving the communication connection in step 3, and the method includes: for the communication connection a j,k is a binary 0-1 variable, using the penalty term introduced in the objective function The method makes the optimization variable converge to a binary variable set of {0,1}, and λ in the penalty term is the penalty coefficient.

4. The communication system optimization method based on multi-UAV assisted communication according to claim 3 is characterized in that: In the optimization objective problem established in step 2, the constraints of the communication quality requirements In a j,k = 0 does not satisfy the direction of the inequality sign. By introducing a large constant M, we can obtain the equivalent while taking into account a j,k =0 and a j,k = 1, that is, the equivalent constraint, the methods include:

5. The communication system optimization method based on multi-UAV assisted communication according to claim 4 is characterized in that: According to the equivalent constraints and the penalty term introduced in the objective function, an equivalent statement of the connection problem between the UAV and the user communication device is obtained; through the continuous convex approximation algorithm, the statement is further transformed into an iterative solution to a series of standard convex optimization problems.

6. The communication system optimization method based on multi-UAV assisted communication according to claim 5, characterized in that: The method for determining the best user equipment and drone communication connection allocation described in step 3 includes: repeatedly optimizing the connection variables between the drone and the user communication device until the communication rate of the optimized target user communication device and the difference between the two times before and after the iteration are less than a threshold, thereby obtaining the connection relationship between the drone and the user communication device.

7. The communication system optimization method based on multi-UAV assisted communication according to claim 6, characterized in that: The method of optimizing the hovering position of the UAV by using a continuous convex approximation algorithm with a local stable solution in step 4 includes: assuming that the transmission power of the UAV is determined, using the connection relationship between the UAV and the user communication device obtained in step 3 to optimize the hovering position of the UAV; by changing the communication rate of the user device r j,k A mathematical connection is established with the position of the UAV, and the mathematical relationship between the backhaul link capacity between the UAV and the ground base station and the position of the UAV is combined to obtain the hovering position of the UAV in the mathematical expression of the optimization problem.

8. The communication system optimization method based on multi-UAV assisted communication according to claim 7, characterized in that: In step 4, after establishing the mathematical expression for optimizing the hovering position of the UAV, the communication rate r of the user device is calculated. j,k The upper and lower limits of are estimated, and the non-convex optimization problem is transformed into a standard convex optimization problem through a continuous convex approximation algorithm to obtain the optimized hovering position of the UAV.

9. The communication system optimization method based on multi-UAV assisted communication according to claim 8, characterized in that: After determining the connection relationship between the drone and the user equipment and the hovering position of the drone as described in steps 3 and 4, optimize the drone transmission power; use the communication rate r j,k The mathematical expression is converted into the differential form of two convex functions related to the drone transmit power. A continuous convex approximation algorithm is used for one of the items, thereby transforming the entire drone transmit power optimization problem into a standard convex optimization problem. The convex optimization problem obtained by the continuous convex approximation algorithm is iteratively solved until the communication rate and convergence of the user equipment are achieved.

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