CN114928851A - Communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication - Google Patents

Abstract

The invention discloses a communication system optimization method based on multi-UAV auxiliary communication, comprising: step 1: forming a down communication link of time division multiple access with multiple UAVs and user communication equipment; step 2: using Maximizing the system communication rate is the optimization objective, and the objective optimization problem is established; Step 3: Under the assumption that the position of the UAV and the transmission power are fixed, use the continuous convex approximation algorithm and the optimization function with penalty term to determine the best user equipment and UAV communication connection allocation; Step 4: Using the block coordinate optimization method, using a continuous convex approximation algorithm with a locally stable solution to optimize the hovering position of the UAV and the UAV communication power distribution, so as to realize the multi-UAV-based Communication construction with backhaul link capacity constraints.

Description

Communication system optimization method based on multi-UAV auxiliary communication

technical field

The invention relates to a communication system optimization method, in particular to a communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication.

Background technique

Unmanned aerial vehicle (UAV)-assisted wireless communication is characterized by high mobility, line-of-sight air-to-ground channel, and low cost, and UAV wireless communication is promising for both military and civilian fields in sixth-generation (6G) wireless networks Way. As aerial base stations, UAVs have the ability to add communication capacity to traditional terrestrial wireless communication networks now and in the future. Due to the high mobility of the UAV air base station, it can be used as an important tool for temporary communication expansion and post-disaster communication reconstruction; due to its inherent line-of-sight air-to-ground channel, it also excels in alleviating signal blocking directions; in addition, The low cost of drones makes them an excellent choice for providing communication services to large numbers of Internet of Things (IoT) devices in remote areas.

UAV as a static air base station hovering at a fixed position in the air to provide communication services for user equipment is an important UAV communication scenario that is widely used. In this communication scenario, proper selection of the hovering position of the drone can improve the communication throughput of the system. The communication network composed of multiple UAVs can further provide the expansion capability of the communication network; in the multi-UAV communication system, determining the appropriate set of service user equipment for each UAV can better manage the same frequency in communication interference and further improve communication performance. Controlling the transmission power of the UAV dynamically adjusts the energy consumption according to the communication quality requirements of the user equipment to achieve a balance between communication quality and energy consumption.

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

Due to the size of the UAV, it is energy-constrained as an air base station, which makes it almost impossible to exist as an independent communication unit, but needs to transmit information back to the ground base station through a backhaul link to process the information. The wireless backhaul link is different from the optical fiber backhaul link on the ground. The wireless backhaul link has a distance-related communication capacity limit, so the hovering position of the UAV needs to be further considered according to the capacity of the backhaul link. "Multipleuav-mounted base station placement and user association with joint fronthaul and backhaul optimization" by C. Qiu et al., 2020 In , after the system optimization problem with backhaul capacity limitation is transformed into an unconstrained problem, gradient descent is used to determine the position of the UAV.

To sum up, the existing problems in the prior art are: (1) In a multi-UAV communication system, due to the existence of co-frequency interference, only considering the user's geographic location to place the UAV cannot obtain a better solution; (2) Considering the actual application scenario, the limited backhaul link capacity leads to further consideration of the location of the UAV; (3) The set of service users for each UAV is determined according to the actual distribution of users, which is mathematically a Mixed integer programming problems are more difficult to deal with.

SUMMARY OF THE INVENTION

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, aiming at the deficiencies of the prior art.

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

Step 1: The downlink communication link of time division multiple access is formed by multiple UAVs and user communication equipment. The wireless communication system operated by this method is composed of a ground base station, J UAVs and K user equipments. The user equipment communicates with the UAV. The UAV acts as an air base station to forward the information of the ground base station through the backhaul link. Each UAV acts as a static air base station to forward the information of the ground base station to its own users through the backhaul link. A collection of devices, where each user equipment has its own communication quality requirements;

Step 2: Under the limitation of the capacity of the backhaul link between the UAV and the ground base station and the communication quality requirements of the user equipment, the optimization goal is to maximize the sum of the communication rates of all user equipment, and the optimization goal problem is established;

Step 3: Under the assumption that the position of the UAV and the transmission power are fixed, use the continuous convex approximation algorithm and the optimization function with the penalty term to determine the optimal allocation of the communication connection between the user equipment and the UAV;

Step 4: Adopt the block coordinate optimization method, and use the continuous convex approximation algorithm with local stable solution to optimize the hovering position of the UAV and the power distribution of the UAV communication, so as to realize the capacity limitation of the backhaul link based on multiple UAVs communication construction.

共有J个无人机，分别标记为{1,2,…,J}；用户通信设备集合为

共有K个设备，分别标记为{1,2,…,K}。 K个用户设备具有自己固定的地面位置，第k个用户设备的三维坐标记为

目标是部署J个无人机为用户设备提供通信服务，整个系统采用时分多址接入，考虑下行通信。无人机作为空中基站通过回程链路转发地面基站的信息，第j个无人机悬浮于一个固定的位置

为其服务的用户设备集合提供下行通信，地面基站的坐标为

第j个无人机与第k个用户设备之间的距离记为d_j,k＝||u_j-u_k||₂，即u_j和u_k之间的欧几里得范数，类似的，地面基站与第j个无人机的距离记为d_0,j＝||u_j-u₀||₂。无人机与地面基站通过无线回程链路相连接，地面基站通过毫米波信道与无人机进行通信，并且地面基站工作在“大规模多入多出(massive Multiple Input Multiple Output,massive MIMO)”区域。此时的波束增益可以估计为

A_t表示地面基站装备的天线数，A_g为无人机的个数，即J。在上述条件下，无人机与地面基站通过无线回程链路容量可以表示为：In the present invention, the system is in a specific area, and the drones are assembled as

There are J UAVs in total, marked as {1,2,…,J}; the set of user communication equipment is

There are K devices in total, marked as {1,2,…,K}. K user equipments have their own fixed ground positions, and the three-dimensional coordinates of the kth user equipment are marked as

The goal is to deploy J UAVs to provide communication services for user equipment. The whole system adopts time division multiple access, considering downlink communication. The UAV acts as an air base station to forward the information of the ground base station through the backhaul link, and the jth UAV is suspended in a fixed position

Provides downlink communication for the set of user equipment it serves, and the coordinates of the ground base station are

The distance between the jth UAV and the kth user equipment is denoted as d _j,k =||u _j -u _k || ₂ , that is, the Euclidean norm between u _j and u _k , Similarly, the distance between the ground base station and the jth UAV is denoted as d _0,j =||u _j -u ₀ || ₂ . The UAV and the ground base station are connected through a wireless backhaul link, the ground base station communicates with the UAV through a millimeter wave channel, and the ground base station works in "massive Multiple Input Multiple Output (massive MIMO)" area. The beam gain at this time can be estimated as

_At represents the number of antennas equipped on the ground base station, and A _g is the number of UAVs, namely J. Under the above conditions, the capacity of the wireless backhaul link between the UAV and the ground base station can be expressed as:

where P _GBS is the transmit power of the ground base station, γ is the environment-dependent backhaul link attenuation rate in decibels/km, and σ ² is the noise power density of additive white Gaussian 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 _0,j between the ground base station and the j-th UAV is specifically expressed as:

ρ ₀ is the channel power gain over the standard reference distance and α is the path loss index. Similarly, the channel power gain between the jth UAV and the kth user equipment is denoted as:

In the present invention, an unmanned aerial vehicle provides downlink communication for at least one user equipment by means of time division multiple access, and binary variables a _{j, k} are used to represent the distribution relationship between the unmanned aerial vehicle and the user equipment. Specifically, a _j,k =1 means that the kth user equipment is assigned to the jth UAV for communication. Conversely, if a _j,k =0, it means that the kth user equipment does not belong to the jth drone's service user equipment set. The achievable communication rate r _j,k from the jth UAV to the kth user equipment is expressed as:

组合表示其他无人机对第k个用户设备产生的同频干扰。基于上述数学表达，在时分多址接入情况下，第j个无人机到第k个用户设备实际等效通信速率

表示为：p _j represents the transmit power of the j-th UAV. Considering the downlink communication of multiple UAVs, the user equipment will receive co-channel interference from other non-target UAVs _. The transmit power of the j'th drone in the set of drones, similarly, g _j',k represents the channel power gain of the j'th drone,

The combination represents the co-channel interference caused by other UAVs 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 UAV to the kth user equipment

Expressed as:

多无人机同时进行下行通信，需要根据实际的用户设备分布情况灵活决定无人机与用户设备的通信连接关系和无人机的发射功率以最大化用户设备所能达到的通信速率；无人机作为静态空中基站通过回程链路转发地面基站的信息，而无线回程链路具有容量限制，第j个无人机到其分配的用户设备集合上的通信速率总和不能超过第j个无人机与地面基站的无线回程链路容量，即数学表示为：Among them, a _j represents the number of user equipment served by drone j, and the relationship can be obtained

When multiple UAVs perform downlink communication at the same time, it is necessary to flexibly determine the communication connection relationship between the UAV and the user equipment and the transmission power of the UAV according to the actual distribution of user equipment to maximize the communication rate that the user equipment can achieve; As a static air base station, the aircraft forwards the information of the ground base station through the backhaul link, while the wireless backhaul link has a capacity limit, and the sum of the communication rates from the jth drone to its assigned set of user equipment cannot exceed the jth drone The wireless backhaul link capacity with terrestrial base stations, that is mathematically expressed as:

表示用户设备k的通信质量需求，则有约束：Each user equipment has its own communication quality requirements.

Represents the communication quality requirements of user equipment k, there are constraints:

系统的优化目标在于确定无人机与用户设备之间的通信连接关系a_j,k、无人机的悬停位置u_j以及无人机作为空中基站的发射功率p_j。优化目标为：Indicates that the kth user equipment needs to communicate with a drone, and the communication rate is greater than

The optimization goal of the system is to determine the communication connection a _j,k between the UAV and the user equipment, the hovering position u _j of the UAV and the transmit power p _j of the UAV as an air base station. The optimization objective is:

表示无人机的三维悬停位置向量，分量中的

分别表示三维坐标系下X轴、Y轴和Z轴上的坐标值，类似的，约束中的

和

表示坐标向量可以选取的下界和上界，限制了无人机可以悬停的地理位置范围；

表示用户设备k的通信质量需求； C_j(d_0,j)表示无人机j与地面基站的回程链路容量上限，其中d_0,j为无人机j与地面基站的物理距离；

和

分别表示无人机j的发射功率阈值；

的约束表示每一个用户设备只能与一架无人机相连。此外，每一架无人机至少对应一个服务用户设备，a_j表示无人机j服务的用户设备数量。该问题是一个混合整数非线性优化问题，由于二元变量a_j,k使得约束的可行域离散化；无人机位置u_j同时与回程链路容量 C_j(d_0,j)和通信速率r_j,k相互关联，使得相关约束非凸，进一步导致最优解难以确定。为了使问题可以求解，利用块坐标下降法，将问题拆分为三个小的子问题进行求解。Among them, max indicates that the optimization objective is maximized, the subsequent expressions of st indicate constraints, and the subscripts j and _k indicate the j-th UAV and the k-th user equipment, respectively; The communication rate of k, the optimization goal is to maximize the communication rate of all user equipments; the optimization variable

Represents the 3D hovering position vector of the drone, in the component

Represents the coordinate values on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system, similarly, in the constraint

and

Indicates the lower and upper bounds that can be selected by the coordinate vector, which limits the geographical range where the drone can hover;

Represents the communication quality requirement of the user equipment k; C _j (d _0,j ) represents the upper limit of the backhaul link capacity between the drone j and the ground base station, where d _0,j is the physical distance between the drone j and the ground base station;

and

respectively represent the transmit power threshold of UAV j;

The constraint means that each user device can only be connected to one drone. In addition, each UAV corresponds to at least one serving user equipment, and a _j represents the number of user equipment served by UAV j. The problem is a mixed-integer nonlinear optimization problem. The feasible region of constraints is discretized due to the binary variables a _j,k ; the UAV position u _j is simultaneously related to the backhaul link capacity C _j (d _0,j ) and the communication rate r _j,k are related to each other, making the related constraints non-convex, further making the optimal solution difficult to determine. In order to make the problem solvable, the block coordinate descent method is used to split the problem into three small sub-problems to solve.

Under the assumption that the position of the UAV and the transmission power of the UAV are determined, the communication connection between the UAV and the user equipment is first optimized. The above optimization problem becomes:

是原问题中

的等价变形。关于

的约束可以等价表示为一个关于a_j,k的线性约束：in,

in the original question

equivalent deformation. about

The constraint can be equivalently expressed as a linear constraint on a _j,k :

可以等价表示为

而当a_j,k＝0时，(M-(M-1)a_j,k)r_j,k＝Mr_j,k，当M取一个足够大的常数时，

可以保证不等号成立。这样我们利用大常数M统一了约束

在a_j,k＝1和a_j,k＝0的不同情况，更进一步将非凸约束转化为了关于a_j,k的线性约束。It can be seen that when a _j,k =1, (M-(M-1)a _j,k )=1, the constraint

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 takes a large enough constant,

The inequality sign is guaranteed to hold. Thus we unify the constraints with a large constant M

In the different cases of a _j,k =1 and a _j,k =0, the non-convex constraint is further transformed into a linear constraint on a _j,k .

即目标函数可以等价表示为

该目标函数仍为非凸，定义

而函数

是关于a_j,k和Λ_j的二元凸函数，此时将二元变量a_j,k松弛为连续变量，我们进一步引入惩罚项

可以看到当且仅当a_j,k＝1和a_j,k＝0时，该惩罚项为零，这样可以保证当惩罚因子λ足够大时，变量a_j,k会收敛到0或者1。于是最终的目标函数转化为：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, the definition

while the function

is a bivariate convex function about a _{j, k} and Λ _j . At this time, the binary variables a _{j, k} are relaxed into continuous variables, and we further introduce a penalty term

It can be seen that the penalty term is zero if and only when a _j,k =1 and a _j,k =0, which ensures that when the penalty factor λ is large enough, the variables a _j,k will converge to 0 or 1 . So the final objective function is transformed into:

The objective function is a convex function. By maximizing the lower bound of the objective function, the purpose of maximizing the original objective function can be achieved. The lower bound of the objective function is expressed as:

和

分别是

和

在给定点

的一阶泰勒展开。

和

具体的形式为：in

and

respectively

and

at a given point

The first-order Taylor expansion of .

and

The specific form is:

迭代至收敛，即可得出优化的无人机与用户设备之间的通信连接x_j,k。Through the above algebraic transformation, the optimization problem of determining the communication connection between the UAV and the user equipment is transformed into a series of standard convex optimization problems with respect to continuous variables a _{j, k} . Through the continuous convex approximation algorithm, that is, through the existing convex The optimization problem solver solves a standard convex optimization problem once, and then uses the obtained solution as the

After iterating until convergence, the optimized communication connection x _j,k between the UAV and the user equipment can be obtained.

为固定值，记为ω_j,k。无人机位置优化问题可以表示为：Under the assumption that the launch power of the UAV is determined, the connection relationship between the UAV and the user equipment obtained above is used to optimize the hovering position of the UAV. at this time,

is a fixed value, denoted as ω _j,k . The UAV position optimization problem can be expressed as:

φ is the introduced variable about the objective function, at this time, the position u _j of the UAV exists in the communication rate expression r _j,k and the 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 with continuous convex estimators. Specifically, r _j,k (u _j ) can be rewritten as:

满足：Introduce slack variables s _j,k ,v _j,k . Make the distance between the drone and the user equipment

Satisfy:

Then r _j,k (u _j ) can be relaxed, namely:

Based on this relaxation and first-order Taylor expansion, further upper and lower bounds of r _j,k (u _j ) can be given. The lower bound of r _j,k (u _j ) is:

是该下界的符号表示，

和

即是r_j,k(s_j,k,v_j,k)的泰勒展开点。同理，r_j,k(u_j)的上界为：

is the symbolic representation of this lower bound,

and

is the Taylor expansion point of r _j,k (s _j,k ,v _j,k ). Similarly, the upper bound of r _j,k (u _j ) is:

和

这两个非凸约束可以变为标准的凸约束。为了处理

的非凸性，进一步处理回程链路容量C_j(u_j)，再次给出C_j(u_j)的表达式：By bringing the lower bound of r _j,k (u _j ) into the constraint

and

These two non-convex constraints can be turned into standard convex constraints. in order to process

The non-convexity of , further processing the backhaul link capacity C _j (u _j ), again gives the expression for C _j (u _j ):

所以可以估计为

进一步将回程链路展开为：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 as

Further expand the backhaul link as:

和

是与变量无人机位置有关的项。引入变量t_j，使得：in

and

is the term related to the variable drone position. The variable t _j is introduced such that:

接着我们可以给出

和d_0,j的一阶泰勒展开，分别记为

和

具体形式为：then there are

Then we can give

and the first-order Taylor expansion of d _0,j , denoted as

and

The specific form is:

即是泰勒展开式的展开点，也是连续凸估计中第t次迭代时的无人机位置坐标。通过上述引入的变量和数学变换，无人机位置优化问题可以表示为如下形式：in,

It is the expansion point of the Taylor expansion, and it is also the position coordinate of the UAV at the t-th iteration in the continuous convex estimation. Through the variables and mathematical transformations introduced above, the UAV position optimization problem can be expressed in the following form:

代表回程链路中与无人机位置无关的部分。上述问题为标准的凸优化问题，通过求解第t次的无人机位置，将求解得到的无人机位置

作为下一次迭代的泰勒展开点，直到收敛，就可以得到优化后的无人机位置。in

Represents the portion of the backhaul link that is independent of the drone's location. The above problem is a standard convex optimization problem. By solving the t-th UAV position, the obtained UAV position will be solved.

As the Taylor expansion point of the next iteration, the optimized UAV position can be obtained until convergence.

After determining the connection relationship between the UAV and the user equipment and the hovering position of the UAV, optimize and solve the UAV transmit power. The problem turns into:

Similar to the previous processing, the transmit power p _j exists in the communication rate r _j,k (p _j ), by giving the linear upper and lower bounds of r _j,k (p _j ), this non-convex problem can be transformed into a The series of convex problems are thus solved using continuous convex approximations. Specifically, the detailed expression of r _j,k (p _j ) is:

It can be further expressed as:

This expression is in the form of the subtraction of two convex functions. By performing Taylor expansion on these two terms, the upper and lower bounds of r _j,k (p _j ) can be obtained as:

和

中，将上界带入约束

中，则原非凸问题被转化为凸问题，从而可以使用连续凸近似迭代求解无人机发射功率直到收敛。迭代求解连续凸近似算法得到的凸优化问题直到用户设备的通信速率和收敛。即能求解出所有的待求解变量。通过不断迭代求解上述三个子问题直到目标函数收敛，就完成了基于多无人机辅助通信的通信系统优化。Substitute the above lower bound into the constraint

and

, bring the upper bound into the constraint

, the original non-convex problem is transformed into a convex problem, so that the UAV transmit power can be solved iteratively using a continuous convex approximation until convergence. Iteratively solve the convex optimization problem obtained by the continuous convex approximation algorithm until the communication rate and convergence of the user equipment. That is, all variables to be solved can be solved. By solving the above three sub-problems iteratively until the objective function converges, the communication system optimization based on multi-UAV-assisted communication is completed.

Beneficial effects:

Based on the current development and progress of wireless communication technology, the use of UAV as an auxiliary relay can significantly improve the communication quality of user communication equipment and reduce the construction cost of the entire communication system. Build to provide security. Compared with the prior art, the advantages and positive effects of the present invention are as follows: First, the mobility of the UAV improves the flexibility of the entire communication system, which can improve the communication quality of the system on the premise of satisfying energy consumption; secondly, considering In the actual situation, the backhaul capacity of the UAV as a relay unit is limited, which makes the deployment of the UAV more practical rather than theoretical value, and can flexibly deal with non-ideal channel conditions; thirdly, by optimizing the UAV in the Compared with other deployment methods that only consider the geographical location of the user equipment, the present invention takes the backhaul capacity limitation and co-channel interference management into consideration, so that the system throughput has further improvement.

Description of drawings

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the advantages of the above and/or other aspects of the present invention will become clearer.

FIG. 1 is a schematic diagram of a UAV-assisted downlink wireless communication scenario of the present invention.

FIG. 2 is a logical block diagram based on a continuous convex approximation algorithm adopted in an embodiment of the present invention.

FIG. 3 is a flow chart of the algorithm of the continuous convex approximation adopted in the embodiment of the present invention.

FIG. 4 is a schematic diagram illustrating the deployment position of the UAV used in the embodiment of the present invention.

FIG. 5 is a schematic diagram showing a comparison of the performance change trend of the system communication rate with the increase of the communication quality requirement of the user equipment under different deployment methods adopted in the embodiment of the present invention.

FIG. 6 is a schematic diagram showing the comparison of the performance change trend of the system communication rate with the increase of the number of UAVs under different deployment methods adopted in the embodiment of the present invention.

Detailed ways

Unmanned aerial vehicle (UAV)-assisted wireless communication is characterized by high mobility, line-of-sight air-to-ground channel, and low cost, and UAV wireless communication is promising for both military and civilian fields in sixth-generation (6G) wireless networks Way. As aerial base stations, UAVs have the ability to add communication capacity to traditional terrestrial wireless communication networks now and in the future. Due to the high mobility of the UAV air base station, it can be used as an important tool for temporary communication expansion and post-disaster communication reconstruction; due to its inherent line-of-sight air-to-ground channel, it also excels in alleviating signal blocking directions; in addition, The low cost of drones makes them an excellent choice for providing communication services to large numbers of Internet of Things (IoT) devices in remote areas.

The invention discloses a communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication. Specifically, the present invention designs a method to deploy multiple UAVs to provide downlink communication services for multiple user equipments with communication quality requirements in the target area under the condition that the UAV relay has a backhaul link capacity limitation, With the optimization goal of maximizing the system communication rate, the goal optimization problem is established: determine the set of user equipment served by each UAV, determine the hovering position of each UAV, and determine the transmit power of each UAV , so as to realize multi-UAV-based communication construction with backhaul link capacity limitation. This embodiment designs an algorithm framework based on the above optimization problem. By decomposing the problem into three sub-problems, and then by introducing substitute variables and mathematical transformations, the problem is transformed into a problem that can be solved by the continuous convex approximation method, and finally each problem is determined. A collection of user equipment served by a drone, determine the hovering position of each drone, and determine the launch power of each drone. The algorithm can achieve near-optimal solutions and remain stable. Compared with other methods, this method can achieve a higher system communication rate under the same conditions, and can handle some situations where other methods cannot obtain a feasible solution.

Example

共有J个无人机，分别标记为{1,2,…,J}；用户通信设备集合为

共有K个设备，分别标记为{1,2,…,K}。K 个用户通信设备具有自己固定的地面位置，第k个用户设备的坐标记为

目标是部署J个无人机为用户设备提供通信服务，整个系统采用时分多址接入，考虑下行通信。无人机作为空中基站通过回程链路转发地面基站的信息，需要部署第j个无人机悬浮于一个固定的位置

为其服务的用户设备集合提供下行通信，地面基站的坐标为

第j个无人机与第k个用户设备之间的距离记为d_j,k＝||u_j-u_k||₂，即u_j和u_k之间的欧几里得范数，类似的，地面基站与第j个无人机的距离记为d_0,j＝||u_j-u₀||₂。无人机与地面基站通过无线回程链路相连接，地面基站通过毫米波信道与无人机进行通信，并且地面基站工作在“大规模多入多出(massive Multiple Input Multiple Output,massive MIMO)”区域。此时的波束增益可以估计为

A_t表示地面基站装备的天线数，A_g为无人机的个数，即J。在上述条件下，无人机与地面基站通过无线回程链路容量可以表示为：The present embodiment considers a downlink wireless communication system with J unmanned aerial vehicles (UAVs), one ground base station (BS) and K user communication devices as shown in FIG. 1 . A collection of drones

There are J UAVs in total, marked as {1,2,…,J}; the set of user communication equipment is

There are K devices in total, marked as {1,2,…,K}. The K user communication equipments have their own fixed ground positions, and the coordinates of the kth user equipment are marked as

The goal is to deploy J UAVs to provide communication services for user equipment. The whole system adopts time division multiple access, considering downlink communication. As the aerial base station, the UAV transmits the information of the ground base station through the backhaul link, and the jth UAV needs to be deployed in a fixed position.

Provides downlink communication for the set of user equipment it serves, and the coordinates of the ground base station are

The distance between the jth UAV and the kth user equipment is denoted as d _j,k =||u _j -u _k || ₂ , that is, the Euclidean norm between u _j and u _k , Similarly, the distance between the ground base station and the jth UAV is denoted as d _0,j =||u _j -u ₀ || ₂ . The UAV and the ground base station are connected through a wireless backhaul link, the ground base station communicates with the UAV through a millimeter wave channel, and the ground base station works in "massive Multiple Input Multiple Output (massive MIMO)" area. The beam gain at this time can be estimated as

_At represents the number of antennas equipped on the ground base station, and A _g is the number of UAVs, namely J. Under the above conditions, the capacity of the wireless backhaul link between the UAV and the ground base station can be expressed as:

where P _GBS is the transmit power of the ground base station, γ is the environment-dependent backhaul link attenuation rate in decibels/km, and σ ² is the noise power density of additive white Gaussian 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 _0,j between the ground base station and the j-th UAV is specifically expressed as:

ρ ₀ is the channel power gain over the standard reference distance and α is the path loss index. Similarly, the channel power gain between the jth UAV and the kth user equipment is denoted as:

In the present invention, an unmanned aerial vehicle provides downlink communication for at least one user equipment by means of time division multiple access, and binary variables a _{j, k} are used to represent the distribution relationship between the unmanned aerial vehicle and the user equipment. Specifically, a _j,k =1 means that the kth user equipment is assigned to the jth UAV for communication. Conversely, if a _j,k =0, it means that the kth user equipment does not belong to the jth drone's service user equipment set. The achievable communication rate r _j,k from the jth UAV to the kth user equipment is expressed as:

组合表示其他无人机对第k个用户设备产生的同频干扰。基于上述数学表达，在时分多址接入情况下，第j 个无人机到第k个用户设备实际等效通信速率

表示为：p _j represents the transmit power of the jth UAV. Considering the downlink communication of multiple UAVs, the user equipment will receive co-channel interference from other non-target UAVs, and p _j′ represents the UAVs other than UAV j. The transmit power of other UAV j' in the set, similarly, g _{j', k} represents the channel power gain of the j'th UAV,

The combination represents the co-channel interference caused by other UAVs 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 UAV to the kth user equipment

Expressed as:

多无人机同时进行下行通信，需要根据实际的用户设备分布情况灵活决定无人机与用户设备的通信连接关系和无人机的发射功率以最大化用户设备所能达到的通信速率；无人机作为静态空中基站通过回程链路转发地面基站的信息，而无线回程链路具有容量限制，第j个无人机到其分配的用户设备集合上的通信速率总和不能超过第j个无人机与地面基站的无线回程链路容量，即数学表示为：Among them, a _j represents the number of user equipment served by drone j, and the relationship can be obtained

When multiple UAVs perform downlink communication at the same time, it is necessary to flexibly determine the communication connection relationship between the UAV and the user equipment and the transmission power of the UAV according to the actual distribution of user equipment to maximize the communication rate that the user equipment can achieve; As a static air base station, the aircraft forwards the information of the ground base station through the backhaul link, while the wireless backhaul link has a capacity limit, and the sum of the communication rates from the jth drone to its assigned set of user equipment cannot exceed the jth drone The wireless backhaul link capacity with terrestrial base stations, that is mathematically expressed as:

表示用户设备k的通信质量需求，则有约束：Each user communication device has its own communication quality requirements.

Represents the communication quality requirements of user equipment k, there are constraints:

系统的优化目标在于确定无人机与用户之间的通信连接关系a_j,k、无人机的悬停位置u_j以及无人机作为空中基站的发射功率p_j。优化目标为：Indicates that the kth user equipment needs to communicate with a drone, and the communication rate is greater than

The optimization goal of the system is to determine the communication connection a _j,k between the UAV and the user, the hovering position u _j of the UAV and the transmit power p _j of the UAV as an air base station. The optimization objective is:

As shown in Table 1, the parameter settings in the above system are given. Parameters without specific values will be used as comparison parameters to have multiple different values, and detailed descriptions will be given outside:

Table 1 Parameter setting table

The problem is a nondeterministic polynomial time hard (NP-hard) problem in optimization. For the proposed NP-hard problem, the block coordinate descent method and the continuous convex approximation method can be used to solve it.

The logic based on the continuous convex approximation algorithm adopted in this embodiment is shown in Figure 2. First, multiple UAVs and user communication equipment are formed into a downlink communication link of time division multiple access, considering the capacity limitation of the backhaul link. In the case of communication quality requirements with user equipment, a target optimization problem is established with the goal of maximizing the communication rate. Solve the communication connection between the UAV and the user equipment, the hovering position of the UAV and the transmitting power of the UAV. First, assuming that the hovering position of the UAV and the transmission power are fixed, the communication connection allocation between the user equipment and the UAV is solved. By introducing a penalty term to rewrite the objective function in the objective optimization problem, the communication connection assignment between the user equipment and the UAV is obtained. Secondly, the block coordinate descent method is adopted, and the continuous convex approximation algorithm with local stable solution is used to optimize the hovering position of the UAV and the communication power distribution of the UAV by introducing substitute variables and mathematical transformations. Finally, the communication system optimization scheme of multi-UAV-assisted communication is obtained.

(2)在通信连接和无人机发射功率确定的情况下，计算得到无人机的悬停位置

根据得到的结果更新回程链路容量

(3)在无人机悬空位置和通信连接确定的情况下，利用连续凸近似更新无人机发射功率

(4) 令迭代次数t＝t+1并重新进行循环条件判断。在迭代终止后，输出无人机位置、用户设备与无人机的连接和无人机发射功率。算法流程终止。The algorithm flow of the continuous convex approximation used in this embodiment is shown in Figure 3. After the algorithm flow starts, the initial value of the penalty parameter λ and the optimization variable is first determined; including the communication connection variable, the transmission power, and the initial position of the UAV ; Calculate the corresponding backhaul link capacity and reachable communication rate; and set the number of iterations to 0. Then, before the number of iterations reaches the upper limit or the difference between the objective function results obtained by the two iterations is less than the threshold, the following cycle is performed; in the t-th iteration process, complete: (1) When the UAV is in the air and the transmission power is fixed case, calculate and update the connection between the user device and the drone

(2) Calculate the hovering position of the UAV under the condition that the communication connection and the UAV transmit power are determined.

Update the backhaul link capacity based on the results obtained

(3) Under the condition that the suspended position of the drone and the communication connection are determined, the continuous convex approximation is used to update the transmit power of the drone

(4) Set the number of iterations t=t+1 and perform the loop condition judgment again. After the iteration is terminated, the UAV position, the connection between the user equipment and the UAV, and the UAV transmit power are output. The algorithm flow terminates.

To evaluate the proposed system algorithm, we choose two baseline methods as typical methods for comparison, one is K-means clustering method and the other is mean shift clustering method, both of which are commonly used based on user equipment Geo-location drone deployment scenarios. The disadvantage is that it does not consider the impact of wireless backhaul link capacity and co-channel interference on the deployment of UAVs and the collection of user equipment that UAVs serve.

As shown in Figure 4, there are 10 user equipments in a square area of 500 × 500 meters. The wireless backhaul link in this scenario is an ideal state, that is, the backhaul link attenuation rate γ is 0 dB/km. Since the user equipment is clearly divided into two groups in terms of geographical location, the method of the present invention and the K-means clustering method obtain the same connection relationship between the UAV and the user equipment in this situation, and the plus sign in the figure is the present invention The UAV deployment position obtained by the method, the asterisk in the figure is the UAV deployment position obtained by the K-means clustering method. The UAV deployment positions obtained by the K-means clustering method are respectively in the geometric centers of the two groups of user equipment. Since this method considers the influence of co-frequency interference, the obtained two UAV deployment positions are far away from each other, and the effect of suppressing co-frequency interference is achieved. The total system communication rate of the present invention in this scenario is 12.3724 bits/sec/Hz, which is higher than 11.2826 bits/sec/Hz obtained by the K-means clustering method.

As shown in Figure 5, there are 30 user equipments in a square area of 500 × 500 meters, take 100 random user equipment location distribution scenarios, calculate the average value of the total communication rate in all scenarios, and give the total communication rate of the system as the user equipment The comparison chart of the performance change trend of the increase in equipment communication quality requirements. The mean shift clustering method only considers the geographical location of the user equipment, so when the communication quality requirements of the user equipment increase, the deployment position of the UAV will not be adjusted, and due to the limitation of backhaul capacity, in the When the communication quality requirement of user equipment is greater than 0.6 Mbit/s, the UAV deployment position obtained by the mean shift clustering method cannot meet the communication quality requirement of user equipment and the limitation of backhaul capacity at the same time. However, the present invention can adjust the hovering position of the UAV according to the communication quality requirement of the user equipment and the backhaul capacity limit, so as to keep a feasible solution all the time. And compared with the mean shift clustering method, the present invention can achieve a larger 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 constraints.

As shown in Figure 6, there are 50 user equipments in a square area of 1000×1000 meters, take 100 random user equipment location distribution scenarios, calculate the average total communication rate in all scenarios, and give the system communication rate A comparison chart of the performance change trend with the increase in the number of man-machines. The attenuation rate γ of the backhaul link is 0 dB/km and 30 dB/km respectively. In the case of different numbers of UAVs, the total communication rate of the system that can be achieved by the method of the present invention is higher than that of K-means aggregation. This method is similar to that of the clustering method that considers the influence of co-channel interference on the communication rate, which is superior to the clustering method that only considers the geographic location of the user equipment.

In a specific implementation, the present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, it may include some or all of the steps in the embodiments provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), and the like.

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 such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products may be stored in a storage medium, such as ROM/RAM , magnetic disk, optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

The present invention provides an idea and method for a communication system optimization method based on multi-UAV assisted communication. There are many specific methods and approaches for realizing the technical solution. The above are only the preferred embodiments of the present invention. It should be pointed out that for For those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can also 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 (10)

1. the communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication, is characterized in that, comprises the following steps: Step 1, the communication system includes: a down communication link of time division multiple access is formed by multiple unmanned aerial vehicles and user communication equipment; the communication system is a wireless communication system, consisting of a ground base station, J unmanned aerial vehicles It is composed of K user communication devices. All user communication devices communicate with UAVs. UAVs act as air base stations to forward the information of ground base stations through the backhaul link. Each UAV acts as a static air base station through the backhaul link. forwarding the information of the ground base station to its own set of user communication equipment, 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 communication quality requirements, the optimization goal is to maximize the sum of the communication rates of all user communication devices, and the optimization goal problem is established; Step 3: Under the assumption that the position of the UAV and the transmission power are fixed, use the continuous convex approximation algorithm and the optimization function with the penalty term to determine the optimal allocation of the communication connection between the user equipment and the UAV; Step 4: Adopt the block coordinate optimization method, and use the continuous convex approximation algorithm with local stable solution to optimize the hovering position of the UAV and the power distribution of the UAV communication, and finally realize the capacity limitation of the backhaul link based on multiple UAVs The communication construction is completed, and the communication system optimization based on multi-UAV auxiliary communication is completed. 2. The communication system optimization method based on multi-UAV-assisted communication according to claim 1, wherein the communication system in step 1 comprises: UAVs flexibly select a hovering position as an aerial base station; The man-machine performs downlink communication at the same time, and the user communication equipment receives co-channel interference from other non-target UAVs; multiple UAVs perform downlink communication at the same time, and the communication connection between the UAV and the user equipment is determined according to the actual distribution of user communication equipment. relationship and the transmit power of the UAV to maximize the communication rate that the user communication equipment can achieve; the UAV acts as a static air base station to forward the information of the ground base station through the backhaul link, and the wireless backhaul link has a capacity limit; each User communication equipment has communication quality requirements. 3. the communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication according to claim 2, is characterized in that, described in step 2, establishes the optimization target problem, the method comprises: The set of drones in the system is

There are J UAVs in total, marked as {1,2,…,J}; the set of user communication equipment is

There are K devices in total, which are marked as {1,2,...,K} respectively; if the communication connection between the drone j and the user equipment k needs to be established, it is marked as a _j,k =1, otherwise it is marked as a _{j, k} = 0; the optimization goal of the system is to determine the communication connection relationship a _j,k between the UAV and the user equipment, the three-dimensional hovering position of the UAV

and the transmit power p _j of the UAV as an air base station; the optimization objectives include:

Among them, max indicates that the optimization objective is maximized, the subsequent expressions of st indicate constraints, and the subscripts j and _k indicate the j-th UAV and the k-th user equipment, respectively; The communication rate of k, the optimization goal is to maximize the communication rate of all user equipments; the optimization variable

Represents the 3D hovering position vector of the drone, in the component

Represents the coordinate values on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system, similarly, in the constraint

and

Indicates the lower and upper bounds that can be selected by the coordinate vector, which limits the geographical 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 drone j and ground base station, where d _0,j is the physical distance between drone j and ground base station;

and

respectively represent the transmit power threshold of drone j; in addition, each drone corresponds to at least one serving user equipment, and a _j represents the number of user equipment served by drone j. 4. The communication system optimization method based on multi-UAV-assisted communication according to claim 3, wherein the expression of the optimization target problem described in step 2 is adjusted to adapt to the needs of solving the communication connection in step 3 , the method includes: for the communication connection a _{j, k} is a binary 0-1 variable, use the penalty term to be introduced into the objective function

The method makes the optimization variable converge to the binary variable set of {0,1}, and λ in the penalty term is the penalty coefficient. 5. The communication system optimization method based on multi-UAV-assisted communication according to claim 4, wherein, in the establishment of the optimization target problem described in step 2, the constraints of the communication quality requirements

When a _j,k =0 does not satisfy the direction of the inequality sign, by introducing a large number constant M, the equivalence is obtained while taking into account the constraint form of a _j,k =0 and a _j,k =1, that is, the equivalence constraint, the method includes: :

6. The communication system optimization method based on multi-UAV auxiliary communication according to claim 5, is characterized in that, according to described equivalence constraint and the penalty term introduced in objective function, obtain equivalent UAV and user A formulation of the communication equipment connection problem; this formulation is further changed to iteratively solve a series of standard convex optimization problems through a continuous convex approximation algorithm. 7. The communication system optimization method based on multi-UAV assisted communication according to claim 6, characterized in that, determining the optimal user equipment and UAV communication connection allocation method described in step 3 comprises: repeating The optimization of the connection variables between man-machine and user communication equipment, until the communication rate of the optimized target user communication equipment and the difference between the two times before and after the iteration are less than the threshold, the connection relationship between the UAV and the user communication equipment is obtained. 8. The communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication according to claim 7, is characterized in that, described in step 4, under the situation that the transmission power of assuming unmanned aerial vehicle is determined, utilize the obtained in step 3. The connection relationship between the UAV and the user communication equipment is optimized to solve the hovering position of the UAV; by establishing a mathematical connection between the communication rate r _j,k of the user equipment and the position of the UAV, combined with the backhaul link capacity between the UAV and the ground base station Mathematically related to the position of the UAV, the hovering position of the UAV is obtained in the mathematical expression of the optimization problem. 9. The communication system optimization method based on multi-unmanned aerial vehicle auxiliary communication according to claim 8, is characterized in that, in step 4, after establishing the mathematical expression that optimizes the hovering position of unmanned aerial vehicle, to user equipment communication rate r The upper and lower bounds of _j,k are estimated, and the non-convex optimization problem is transformed into a standard convex optimization problem through the continuous convex approximation algorithm, and the optimized hovering position of the UAV is obtained. 10. The communication system optimization method based on multi-UAV assisted communication according to claim 9, characterized in that, according to step 3 and step 4, determine the connection relationship between UAV and user equipment and UAV hovering After the position, optimize and solve the UAV launch power; use the mathematical expression of the communication rate r _j,k to convert it into the differential form of two convex functions related to the UAV launch power, and use a continuous convex approximation for one of them. Then, the entire UAV transmit power optimization problem is transformed into a standard convex optimization problem, and the convex optimization problem obtained by the continuous convex approximation algorithm is iteratively solved until the communication rate and convergence of the user equipment.

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