CN111835401B - Method for joint optimization of wireless resources and paths in unmanned aerial vehicle communication network - Google Patents

Abstract

The invention provides a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network, which can improve the frequency spectrum utilization rate and improve the energy efficiency of the unmanned aerial vehicle wireless communication network. The method comprises the following steps: s1, dividing the flight path of the unmanned aerial vehicle in the flight period into a plurality of sub periods; s2, allocating channels to the end users in each sub-period; s3, according to the channel distribution result, calculating the approximate closed-form solution of the terminal user power, and distributing the power for the terminal user according to the closed-form solution; s4, establishing the path optimization problem as a convex optimization problem according to the channel and power distribution result, and performing path optimization according to the established convex optimization problem; and S5, returning to S2 for iterative optimization according to the power distribution result and the optimized path until the energy efficiency value is not changed or the iteration number reaches the maximum value of the iteration number, and obtaining the optimal channel and power distribution scheme and the optimal path optimization scheme. The invention relates to the technical field of unmanned aerial vehicle communication.

Description

A method for joint optimization of wireless resources and paths in UAV communication networks

technical field

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network.

Background technique

As a newly derived wireless communication network, UAV wireless communication network has attracted more and more attention from academia and industry, and is expected to become a flexible and universal auxiliary communication method in the future. Compared with traditional wireless cellular networks, UAV communication networks have significant advantages such as low cost, easy deployment, high mobility, and wide range of uses, so they show great application potential in military and civilian fields. According to statistics, the number of drones that will be put into use next year may reach 29 million. Such a large number of drones will spawn more and more meaningful research.

The traditional UAV communication network requires manual route intervention. In the future wireless communication network scenario, the use of UAVs will be more frequent, and more flexible and free flight is required to provide auxiliary communication services for ground users. Moreover, the addition of UAV communication will inevitably intensify the competition for scarce spectrum resources, so it is necessary to improve spectrum utilization.

SUMMARY OF THE INVENTION

The embodiments of the present invention provide a method for jointly optimizing wireless resources and paths in a UAV communication network, which can improve the spectrum utilization rate and improve the energy efficiency of the UAV wireless communication network. The technical solution is as follows:

An embodiment of the present invention provides a method for jointly optimizing wireless resources and paths in a UAV communication network, including:

S1, dividing the flight path of the UAV in the flight cycle into several sub-cycles;

S2, in each sub-period, assign the channel to the end user;

S3, according to the channel allocation result, obtain the approximate closed-form solution of the power of the terminal user, and allocate power to the terminal user according to the closed-form solution;

S4, according to the channel and power allocation results, the path optimization problem is established as a convex optimization problem, and the path optimization is performed according to the established convex optimization problem;

S5, according to the power allocation result and the optimized path, return to S2 for iterative optimization, until the energy efficiency value does not change or the number of iterations reaches the maximum number of iterations, and the optimal channel and power allocation scheme and path optimization scheme are obtained.

Further, allocating the channel to the end user includes:

In each sub-period, according to the principle of channel gain maximization, the channel is allocated to the end user with the maximum gain.

Further, the gain calculation formula is:

where g _k,m is the channel gain of end user m in the kth subcycle, ε is the path loss factor when the distance is equal to 1, h _k,m is the distance between end user m and the UAV in the kth subcycle distance

Further, obtaining an approximate closed-form solution of the power of the terminal user according to the channel allocation result, and allocating power to the terminal user according to the closed-form solution includes:

According to the channel allocation result, the approximate closed-form solution of the end-user power is obtained by using the Lagrangian dual function, and the end-user power is allocated according to the closed-form solution; where,

The approximate closed-form solution for the power of end user m in the kth subcycle is expressed as:

为终端用户m在第k个子周期内迭代计算的信噪比，B为带宽，η^i-1为第i-1次迭代过程中的能量效率，形式[p]⁺＝max{p,0}，λ_k,m和ω_k都表示拉格朗日因子。where p _k,m is the power of end user m in the kth subcycle,

is the signal-to-noise ratio calculated iteratively for the end user m in the kth subcycle, B is the bandwidth, η ^i-1 is the energy efficiency during the i-1th iteration, in the form [p] ⁺ =max{p,0} , λ _{k, m} and ω _k all represent Lagrangian factors.

表示为：Further, the signal-to-noise ratio

Expressed as:

为终端用户m收到的来自终端用户m′的同信道干扰。where p _k,m' is the power of the terminal user m' in the kth subcycle, g _k,m is the channel gain of the terminal user m in the kth subcycle, ^σ2 is the noise, and _Mk is the kth subcycle The number of users occupying the channel during the period,

is the co-channel interference from end user m' received by end user m.

Further, establishing the path optimization problem as a convex optimization problem according to the channel and power allocation results, and performing the path optimization according to the established convex optimization problem includes:

According to the channel and power allocation results, using the principle of approximate convex approximation, the path optimization problem is established as a convex optimization problem as shown in the following formula:

为用户m在第k个子周期内的数据量下界；

为终端用户m在第k个子周期内的数据速率经过引入缓释变量l_k,m后转化的数据速率表达式，σ²为噪声，p_k,m′为用户m′在第k个子周期内的功率，ε为路径损耗因子，l_k,m为缓释变量，h为无人机飞行高度；B为网络带宽；η^i-1为第i-1次迭代过程中的能量效率；E为终端用户的总功率；Wherein, M represents the number of end users; K represents the number of sub-cycles;

is the lower bound of the data volume of user m in the kth subcycle;

is the data rate expression of the terminal user m in the kth sub-cycle after introducing the slow-release variable l _k,m , σ ² is the noise, p _k,m′ is the user m’ in the kth sub-cycle ε is the path loss factor, l _k,m is the slow release variable, h is the flying height of the UAV; B is the network bandwidth; η ^i-1 is the energy efficiency during the i-1th iteration; E is the the total power of the end user;

According to the obtained convex optimization problem, the path optimization is carried out using the convex optimization toolbox.

Further, the constraints of the convex optimization problem include:

为无人机在t次迭代优化过程中第k个子周期内的位置，u_m为终端用户m的位置，q_k为无人机在下次迭代中第k个子周期内的位置。where γ _min is the minimum data rate,

is the position of the UAV in the k-th sub-cycle during the t iterations of the optimization process, um is the position of the end user _m , and q _k is the position of the UAV in the k-th sub-cycle in the next iteration.

The beneficial effects of the above-mentioned technical solutions of the present invention are as follows:

In the above scheme, the flight path of the UAV in the flight cycle is divided into several sub-cycles; in each sub-cycle, the channel is allocated to the end user, and according to the channel allocation result, the Lagrange dual function is used to obtain the end user power. The approximate closed-form solution of , and allocate power to terminal users according to the closed-form solution, so as to realize the optimal allocation of wireless resources in the UAV wireless communication network in each sub-cycle, that is, the optimal allocation of wireless channel and terminal power; according to the channel, Based on the power allocation result, the path optimization problem is established as a convex optimization problem, and the path optimization is carried out according to the established convex optimization problem; according to the power allocation result and the optimized path, the channel allocation operation is returned and the network parameters used for this channel allocation are returned. All are the quantities that have been optimized. After obtaining the new channel allocation results, continue to perform the power allocation operation and the path optimization operation for cyclic optimization until the threshold is reached or the energy efficiency converges, and the optimal channel and power allocation scheme and path optimization scheme are obtained. . In this way, by iteratively optimizing the flight path of the UAV through the optimized channel and power allocation scheme, the spectrum utilization rate can be improved and the energy efficiency of the UAV wireless communication network can be improved.

Description of drawings

1 is a schematic flowchart of a method for jointly optimizing wireless resources and paths in a UAV communication network according to an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of an unmanned aerial vehicle communication network system architecture provided by an embodiment of the present invention.

Detailed ways

In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

As shown in FIG. 1 , the method for joint optimization of wireless resources and paths in a UAV communication network provided by an embodiment of the present invention includes:

S1, dividing the flight path of the UAV in the flight cycle into several sub-cycles;

S2, in each sub-period, assign the channel to the end user;

S3, according to the channel allocation result, obtain the approximate closed-form solution of the power of the terminal user, and allocate power to the terminal user according to the closed-form solution;

S4, according to the channel and power allocation results, the path optimization problem is established as a convex optimization problem, and the path optimization is performed according to the established convex optimization problem;

S5, according to the power allocation result and the optimized path, return to S2 for iterative optimization, until the energy efficiency value does not change or the number of iterations reaches the maximum number of iterations, and the optimal channel and power allocation scheme and path optimization scheme are obtained.

In the method for jointly optimizing wireless resources and paths in a UAV communication network according to the embodiment of the present invention, the flight path of the UAV in the flight cycle is divided into several sub-cycles; in each sub-cycle, channels are allocated to terminals Users, according to the channel allocation results, use the Lagrangian dual function to obtain the approximate closed-form solution of the end-user power, and allocate power to the end-users according to the closed-form solution, so as to realize the UAV wireless communication network in each sub-period. According to the channel and power allocation results, the path optimization problem is established as a convex optimization problem, and the path optimization is carried out according to the established convex optimization problem; according to the power allocation results and the optimized path, and return to perform the channel allocation operation. The network parameters used in this channel allocation are all optimized quantities. After obtaining a new channel allocation result, continue to perform the power allocation operation and the path optimization operation for cyclic optimization until When the threshold is reached or the energy efficiency converges, the optimal channel and power allocation scheme and path optimization scheme are obtained. In this way, by iteratively optimizing the flight path of the UAV through the optimized channel and power allocation scheme, the spectrum utilization rate can be improved and the energy efficiency of the UAV wireless communication network can be improved.

In this embodiment, FIG. 2 is an architecture diagram of a UAV communication network system including a UAV and end users deployed at the same frequency. The system architecture diagram includes: a UAV base station and multiple end users. Considering the UAV The complexity of dynamic changes, only one sub-channel is considered in the channel part, and the UAV only occupies this sub-channel in different sub-cycles, but the end user accessing will use the channel allocation part to maximize the gain in each sub-cycle. Choose the most appropriate end user.

In this embodiment, before S1, the path of the UAV, the position of the end user, the path loss factor, the initial power, etc. of the UAV in a complete flight cycle need to be initialized.

In this embodiment, in S1, the flight path of the UAV in the complete flight cycle is divided into several sub-cycles, so that the flight path of the UAV is set as a closed-loop path of periodic flight.

In this embodiment, the flight cycle of the UAV is subdivided into several sub-cycles, and the UAV is considered to be instantaneously stationary in each sub-cycle, and the flight paths of all sub-cycles are used to represent the dynamic position of the UAV , the end users of the UAV and the ground only consider the influence of the line-of-sight, that is, the UAV information transmission channel is only affected by the path loss.

In the specific embodiment of the aforementioned method for joint optimization of wireless resources and paths in a UAV communication network, further, the assigning channels to end users includes:

In each sub-period, according to the principle of channel gain maximization, the channel is allocated to the end user with the maximum gain.

In this embodiment, the gain calculation formula is:

where g _k,m is the channel gain of end user m in the kth subcycle, ε is the path loss factor when the distance is equal to 1, h _k,m is the distance between end user m and the UAV in the kth subcycle distance.

可以得到在每个子周期内的信道分配结果。In this embodiment, according to the formula

The channel assignment results in each sub-period can be obtained.

In the specific implementation of the method for joint optimization of wireless resources and paths in the above-mentioned UAV communication network, further, according to the channel allocation result, an approximate closed-form solution of the power of the end user is obtained, and according to the closed-form solution, End user allocated power includes:

According to the channel allocation result, the approximate closed-form solution of the end-user power is obtained by using the Lagrangian dual function, and the end-user power is allocated according to the closed-form solution; where,

The approximate closed-form solution for the power of end user m in the kth subcycle is expressed as:

and,

为终端用户m在第k个子周期内迭代计算的信噪比，B为带宽，η^i-1为第i-1次迭代过程中的能量效率，形式[p]⁺＝max{p,0}，λ_k,m和ω_k都表示拉格朗日因子，g_k,m表示终端用户m在第k个子周期内的信道增益，σ²为噪声，M_k为第k个子周期内占用信道的用户数量，

为终端用户m收到的来自终端用户m′的同信道干扰。where p _k,m and p _k,m' are the powers of end users m and m' in the kth sub-cycle,

is the signal-to-noise ratio calculated iteratively for the end user m in the kth subcycle, B is the bandwidth, η ^i-1 is the energy efficiency during the i-1th iteration, in the form [p] ⁺ =max{p,0} , λ _k,m and ω _k are Lagrangian factors, g _k,m is the channel gain of end user m in the kth sub-cycle, σ ² is the noise, M _k is the occupied channel in the k-th sub-cycle amount of users,

is the co-channel interference from end user m' received by end user m.

计算的是信道分配以后，在第k个子周期内的终端用户m的功率的近似闭式解；如果终端用户不占用第k个子周期内的信道则不求功率。In this example, the formula

What is calculated is the approximate closed-form solution of the power of the terminal user m in the kth subcycle after the channel allocation; if the end user does not occupy the channel in the kth subcycle, the power is not calculated.

In this embodiment, the form [p] ⁺ =max{p,0} is used to represent the meaning of the symbol [ ] ⁺ , that is, in the form [p] ⁺ , p is a positive value, then [p] ⁺ is equal to p, Otherwise [p] ⁺ is equal to 0.

In the specific implementation of the method for joint optimization of wireless resources and paths in the aforementioned UAV communication network, further, according to the channel and power allocation results, the path optimization problem is established as a convex optimization problem, and according to the established Convex optimization problems for path optimization include:

According to the channel and power allocation results, using the principle of approximate convex approximation, the path optimization problem is established as a convex optimization problem as shown in the following formula:

为用户m在第k个子周期内的数据量下界；

为终端用户m在第k个子周期内的数据速率经过引入缓释变量l_k,m后转化的数据速率表达式，σ²为噪声，p_k,m′为用户m′在第k个子周期内的功率，ε为路径损耗因子，l_k,m为缓释变量，h为无人机飞行高度；B为网络带宽；η^i-1为第i-1次迭代过程中的能量效率；E为终端用户的总功率；Wherein, M represents the number of end users; K represents the number of sub-cycles;

is the lower bound of the data volume of user m in the kth subcycle;

is the data rate expression of the terminal user m in the kth sub-cycle after introducing the slow-release variable l _k,m , σ ² is the noise, p _k,m′ is the user m’ in the kth sub-cycle ε is the path loss factor, l _k,m is the slow release variable, h is the flying height of the UAV; B is the network bandwidth; η ^i-1 is the energy efficiency during the i-1th iteration; E is the the total power of the end user;

According to the obtained convex optimization problem, the path optimization is carried out using the convex optimization toolbox.

计算的是信道分配以后，在第k个子周期内的终端用户m的数据速率经过引入缓释变量l_k,m后转化的数据速率；如果终端用户不占用第k个子周期内的信道，则

为0。In this example, the formula

After the channel allocation, the data rate of the end user m in the kth subcycle is transformed by introducing the slow release variable l _k,m ; if the end user does not occupy the channel in the kth subcycle, then

is 0.

In this embodiment, according to the obtained convex optimization problem, the starting and ending positions of the UAV are defined to be the same, and a specific path optimization scheme can be obtained by using the convex optimization toolbox.

In the specific implementation of the method for joint optimization of wireless resources and paths in the aforementioned UAV communication network, further, the constraints of the convex optimization problem include:

为无人机在t次迭代优化过程中第k个子周期内的位置，u_m为终端用户m的位置，q_k为无人机在下次迭代中第k个子周期内的位置。where γ _min is the minimum data rate,

is the position of the UAV in the k-th sub-cycle during the t iterations of the optimization process, um is the position of the end user _m , and q _k is the position of the UAV in the k-th sub-cycle in the next iteration.

The method for joint optimization of wireless resources and paths in a UAV communication network provided by this embodiment can effectively improve the spectrum utilization rate and the energy of the UAV wireless communication network on the premise of satisfying the minimum data rate of the user and periodic flight. efficiency.

The above are the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (6)

1. a method for joint optimization of wireless resources and paths in an unmanned aerial vehicle communication network, is characterized in that, comprising: S1, dividing the flight path of the UAV in the flight cycle into several sub-cycles; S2, in each sub-period, assign the channel to the end user; S3, according to the channel allocation result, obtain the approximate closed-form solution of the power of the terminal user, and allocate power to the terminal user according to the closed-form solution; S4, according to the channel and power allocation results, the path optimization problem is established as a convex optimization problem, and the path optimization is performed according to the established convex optimization problem; S5, according to the power allocation result and the optimized path, return to S2 for iterative optimization, until the energy efficiency value does not change or the number of iterations reaches the maximum number of iterations, and the optimal channel and power allocation scheme and path optimization scheme are obtained; Wherein, according to the channel allocation result, obtaining an approximate closed-form solution of the power of the terminal user, and allocating power to the terminal user according to the closed-form solution includes: According to the channel allocation result, the approximate closed-form solution of the end-user power is obtained by using the Lagrangian dual function, and the end-user power is allocated according to the closed-form solution; where, The approximate closed-form solution for the power of end user m in the kth subcycle is expressed as:

where p _k,m is the power of end user m in the kth subcycle,

is the signal-to-noise ratio calculated iteratively for the end user m in the kth subcycle, B is the bandwidth, η ^i-1 is the energy efficiency during the i-1th iteration, in the form [p] ⁺ =max{p,0} , λ _{k, m} and ω _k all represent Lagrangian factors. 2. The method for joint optimization of wireless resources and paths in a UAV communication network according to claim 1, wherein the assigning a channel to an end user comprises: In each sub-period, according to the principle of channel gain maximization, the channel is allocated to the end user with the maximum gain. 3. the method for joint optimization of wireless resources and path in the unmanned aerial vehicle communication network according to claim 2, is characterized in that, gain factor calculation formula is:

where g _k,m is the channel gain factor of end user m in the kth subcycle, ε is the path loss factor when the distance is equal to 1, h _k,m is the communication between end user m and the UAV in the kth subcycle the distance. 4. The method for joint optimization of wireless resources and paths in a UAV communication network according to claim 1, wherein the signal-to-noise ratio is

Expressed as:

where p _k,m' is the power of the terminal user m' in the kth subcycle, |g _k,m | ² represents the channel gain of the terminal user m in the kth subcycle, σ ² is the noise, and M _k is The number of users occupying the channel in the kth subcycle,

is the co-channel interference from end user m' received by end user m. 5. The method for joint optimization of wireless resources and paths in a UAV communication network according to claim 1, wherein the path optimization problem is established as a convex optimization problem according to channel and power allocation results, and according to The established convex optimization problem for path optimization includes: According to the channel and power allocation results, using the principle of approximate convex approximation, the path optimization problem is established as a convex optimization problem as shown in the following formula:

Wherein, M represents the number of end users; K represents the number of sub-cycles;

is the lower bound of the data volume of user m in the kth subcycle;

is the data rate expression of the terminal user m in the kth sub-cycle after introducing the slow-release variable l _k,m , σ ² is the noise, p _k,m′ is the user m’ in the kth sub-cycle ε is the path loss factor, l _k,m is the slow release variable, h is the flying height of the UAV; B is the network bandwidth; η ^i-1 is the energy efficiency during the i-1th iteration; E is the the total power of the end user; According to the obtained convex optimization problem, the path optimization is carried out using the convex optimization toolbox. 6. The method for joint optimization of wireless resources and paths in a UAV communication network according to claim 5, wherein the constraints of the convex optimization problem include:

where γ _min is the minimum data rate,

is the position of the UAV in the k-th sub-cycle during the t iterations of the optimization process, um is the position of the end user _m , and q _k is the position of the UAV in the k-th sub-cycle in the next iteration.

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