CN113825172B - Multi-unmanned aerial vehicle network load balancing method based on diffusion type deployment algorithm - Google Patents
Multi-unmanned aerial vehicle network load balancing method based on diffusion type deployment algorithm Download PDFInfo
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Abstract
The invention discloses a multi-unmanned aerial vehicle network load balancing method based on a diffusion type deployment algorithm, which comprises the following steps: establishing a wireless communication network of multiple unmanned aerial vehicles, and formulating load balancing measurement; constructing virtual force between unmanned aerial vehicles and virtual force between the unmanned aerial vehicles and users according to the load balancing measurement obtained in the step 1 and the data rate of the user terminal equipment; carrying out iterative updating on the two virtual forces obtained in the step 2 by adopting a diffusion strategy and a successive approximation strategy respectively; and (3) calculating the motion trail of the unmanned aerial vehicle according to the two virtual forces obtained in the step (3) until convergence. According to the multi-unmanned aerial vehicle network load balancing method based on the diffusion type deployment algorithm, two virtual forces are defined to control the movement of the unmanned aerial vehicle through constructing the load measurement, and better load balancing performance can be obtained. According to the invention, the deployment of the unmanned aerial vehicle, namely the position or track design of the unmanned aerial vehicle is researched, so that the coverage fairness of the unmanned aerial vehicle is improved.
Description
Technical Field
The invention belongs to the technical field of communication methods, and particularly relates to a multi-unmanned aerial vehicle network load balancing method based on a diffusion type deployment algorithm.
Background
With the development of radio access network technology, unmanned aerial vehicles (Unmanned Aerial Vehicle, UAVs) can serve as air base stations for ground User Equipment (UEs) outside the coverage area of the ground base stations. The unmanned aerial vehicle can be flexibly deployed to a communication area which is on demand, and the position of the unmanned aerial vehicle can be adjusted according to communication requirements. Because unmanned aerial vehicle operates at high altitude, sight link is the main component part of unmanned aerial vehicle-user equipment channel, has brought higher channel capacity. Therefore, unmanned aerial vehicles are deployed in hot spot areas or disaster relief areas, so that the connectivity and capacity of the network are improved, and the method has good application prospects. One of the key factors affecting the quality of multi-drone wireless network communications is load balancing among the drones.
Current unmanned aerial vehicle communication network research can be broadly divided into two categories: one is centralized control, however, in large-scale multi-drone networks, centralized algorithms may result in excessive information exchange and high control delays, and also require a central controller. Another type is distributed control, where the position of the drone is controlled by virtual forces based on the distribution of user devices, the distribution of drones, and the distance between drones, by taking into account the connectivity between the user center and the drone, to provide fair drone coverage. While these works achieve a degree of load balancing, performance is still to be improved.
Disclosure of Invention
The invention aims to provide a multi-unmanned aerial vehicle network load balancing method based on a diffusion type deployment algorithm, which solves the problem of low load balancing degree among unmanned aerial vehicles in the existing multi-unmanned aerial vehicle communication scene.
The technical scheme adopted by the invention is as follows: a multi-unmanned aerial vehicle network load balancing method based on a diffusion type deployment algorithm comprises the following steps:
step 1, establishing a wireless communication network of a plurality of unmanned aerial vehicles, and formulating load balancing measurement;
step 2, constructing virtual force between unmanned aerial vehicles and virtual force between the unmanned aerial vehicles and users according to the load balancing measurement obtained in the step 1 and the data rate of the user terminal equipment;
step 3, respectively adopting a diffusion strategy and a successive approximation strategy to iteratively update the two virtual forces obtained in the step 2;
and 4, calculating the motion trail of the unmanned aerial vehicle according to the two virtual forces obtained in the step 3 until convergence.
The present invention is also characterized in that,
the step 1 specifically comprises the following steps:
step 1.1, establishing a wireless communication network of multiple unmanned aerial vehicles, wherein the number of the unmanned aerial vehicles is as follows: UAV (unmanned aerial vehicle) 1 、UAV 2 、…UAV i …、UAV M The method comprises the steps of carrying out a first treatment on the surface of the User equipment number: UE (user Equipment) 1 、UE 2 、…UE j …、UE N The method comprises the steps of carrying out a first treatment on the surface of the The parameters of the wireless communication network of the multiple unmanned aerial vehicles are defined as follows:
T i，j [n]＝B i，j [n]log 2 (1+γ i，j [n]) (8)
in the above, d i，j [n]Indicating the distance, x, between the ith drone and the jth device i [n]And y i [n]Representing UAVs respectively i At time n, horizontal abscissa, H represents the altitude of the drone,and->Representing UE j H is a fixed height, constraint V max Maximum speed for unmanned plane to move, +.>To at time n from UAV i To UE (user Equipment) j Is the free variable, r, determined by the environment i，j [n]Is a UAV (unmanned aerial vehicle) i To UE (user Equipment) j Is a horizontal distance eta LoS And eta NLoS Represents the average additional path loss factor, g i，j [n]Representing the probability average path gain between i drone and j devices, gamma i，j [n]Represents the signal-to-dry ratio, p i [n]Is an n-moment UAV i P k [n]Is the transmitting power of other unmanned aerial vehicles, T i，j [n]For UAVs i For UE j Data transmission rate provided by device, B i，j Is the bandwidth;
step 1.2, making a load balancing measure in a multi-unmanned aerial vehicle wireless communication network as follows:
in the formula (9), U i (T i [n]) Representing UAVs i T, T i [n]Is a UAV (unmanned aerial vehicle) i Throughput vector of serving user, UE j ∈UAV i Representing UE j Currently by UAVs i Providing communication service, T i，j [n]Is UE (user equipment) j Is a high throughput.
The virtual force between unmanned aerial vehicles constructed in the step 2 is thatThe expression is as follows:
in the formula (10), the amino acid sequence of the compound,
kv is a virtual force constant between unmanned aerial vehicles and is a free variable;representing a virtual force between the i unmanned aerial vehicle and the j unmanned aerial vehicle; />Representing the virtual force direction from unmanned plane i to unmanned plane k; when the load of the i unmanned aerial vehicle and the k unmanned aerial vehicle is unbalanced, the two unmanned aerial vehicles have the same moving direction to be +.>A representation;
the virtual force between the unmanned plane and the user constructed in the step 2 is thatThe expression is as follows:
in the formula (14), the amino acid sequence of the compound,virtual force resultant between the drone and a plurality of devices that it serves.
In step 3, iterative updating of virtual force resultant force among unmanned aerial vehicles of each unmanned aerial vehicle by adopting a diffusion strategyI.e. collecting surrounding drones +.>Information and get ψ after local summation i [n-1]As the correction amount of the next virtual force update of the unmanned aerial vehicle, the cooperative optimization of the local unmanned aerial vehicle and the peripheral unmanned aerial vehicle is realized, and the related calculation formulas are shown in formulas (15) and (16):
in the step 3, the virtual force between the unmanned aerial vehicle and the user equipment is iteratively updated by adopting a successive approximation strategy as follows:
constraint conditions:
T i，j [n]＝B i，j [n]log 2 (1+γ i，j [n]) (17b)
in 17 (a), log (T) is obtained i，j [n]) For d i，j [n]The second derivative of (2) is of formula (18):
wherein the method comprises the steps ofFor the parameter which is not updated with the current iteration in the successive convex approximation iteration process, the value is x of the last iteration i [n]，y i [n]As a result, i.e.)>
In an actual communication scenario, in the above formulaIs positive, thus updating the virtual force by successive approximation strategy>The process is shown as a formula (21):
the method for calculating the motion trail of the unmanned aerial vehicle in the step 4 comprises the steps of calculating the synthetic virtual force of the unmanned aerial vehicle through a formula (22):
also comprises the step of calculating the moving speed of the unmanned aerial vehicle through a method (23)
The beneficial effects of the invention are as follows: according to the multi-unmanned aerial vehicle network load balancing method based on the diffusion type deployment algorithm, two virtual forces are defined to control the movement of the unmanned aerial vehicle through constructing the load measurement, and better load balancing performance can be obtained. According to the invention, the deployment of the unmanned aerial vehicle, namely the position or track design of the unmanned aerial vehicle is researched, so that the coverage fairness of the unmanned aerial vehicle is improved.
Drawings
FIG. 1 is a diagram of a multi-unmanned wireless communication network architecture;
FIG. 2 is a schematic illustration of virtual forces between unmanned aerial vehicles;
FIG. 3 is a schematic illustration of virtual forces between a drone and a user device;
FIG. 4 is unmanned motion control of a diffusion strategy;
fig. 5 a) is a service relationship simulation result (using the method of the present invention) between 20 drones and 200 user equipments; fig. 5 b) is a service relationship simulation result (using a baseline algorithm) between 20 drones and 200 user devices;
fig. 6 a) is a service relationship simulation result (using the method of the present invention) between 20 drones and 300 user devices; fig. 6 b) is a service relationship simulation result (using a baseline algorithm) between 20 drones and 300 user devices;
fig. 7 is the number of terminals (the number of devices 200) that each unmanned aerial vehicle provides service;
fig. 8 is the number of terminals (number of devices 300) providing services per unmanned aerial vehicle;
FIG. 9 is a balance Index Jain's Fairness Index of the unmanned aerial vehicle load in 200 Monte Carlo simulations;
fig. 10 is a CDF curve (cumulative distribution function) of the throughput of the user equipment in 200 monte carlo simulations.
Detailed Description
The invention will be described in detail with reference to the accompanying drawings and detailed description.
The invention provides a multi-unmanned aerial vehicle network load balancing method based on a diffusion type deployment algorithm, which comprises the following steps:
step 1, establishing a wireless communication network of a plurality of unmanned aerial vehicles, and formulating load balancing measurement; the method comprises the following steps:
step 1.1, as shown in fig. 1, a multi-unmanned aerial vehicle wireless communication network is established, and unmanned aerial vehicle numbers are: UAV (unmanned aerial vehicle) 1 、UAV 2 、…UAV i …、UAV M The method comprises the steps of carrying out a first treatment on the surface of the User equipment number: UE (user Equipment) 1 、UE 2 、…UE j …, UEN; the parameters of the wireless communication network of the multiple unmanned aerial vehicles are defined as follows:
T i，j [n]＝B i，j [n]log 2 (1+γ i，j [n]) (8)
in the above formula, d is used for determining the distance between the unmanned plane and the equipment i，j [n]Indicating the distance, x, between the ith drone and the jth device i [n]And y i [n]Representing UAVs respectively i At time n, horizontal abscissa, H represents the altitude of the drone,and->Representing UE j Horizontal abscissa and ordinate of (a). Since the unmanned plane moves at a high speed, it is assumed here that the UE position is unchanged during the optimization process. H is a fixed height. Constraint V max Maximum speed for unmanned plane to move, +.>To at time n from UAV i To UE (user Equipment) j Is the free variable, r, determined by the environment i，j [n]Is a UAV (unmanned aerial vehicle) i To UE (user Equipment) j Is a horizontal distance of (c). In the information interaction between the unmanned aerial vehicle and the equipment, eta LoS And eta N LoS Represents the average additional path loss factor, g i，j [n]Representing the probability average path gain between i drone and j devices, gamma i，j [n]Indicating the signal to dry ratio，p i [n]Is an n-moment UAV i P k [n]Is the transmitting power (interference) of other unmanned aerial vehicles, T i，j [n]For UAVs i For UE j Data transmission rate provided by device, B i，j Is the bandwidth;
step 1.2, making a load balancing measure in a multi-unmanned aerial vehicle wireless communication network as follows:
in the formula (9), U i (T i [n]) Representing UAVs i T, T i [n]Is a UAV (unmanned aerial vehicle) i Throughput vector of serving user, UE j ∈UAV i Representing UE j Currently by UAVs i Providing communication service, T i，j [n]Is UE (user equipment) j Is a high throughput.
Step 2, as shown in fig. 2 and fig. 3, constructing a virtual force between the unmanned aerial vehicle and a user according to the load balancing measurement obtained in the step 1 and the data rate of the user terminal equipment; the virtual force between unmanned aerial vehicles obtained by construction isThe expression is as follows:
in the formula (10), the amino acid sequence of the compound,
kv is a virtual force constant between unmanned aerial vehicles and is a free variable; virtual forces have been successfully used for unmanned in recent studiesThe motion control of the machine can be used as a practical heuristic method due to low complexity.Representing a virtual force between the i unmanned aerial vehicle and the j unmanned aerial vehicle; />Representing the virtual force direction from unmanned plane i to unmanned plane k; when the load of the i unmanned aerial vehicle and the k unmanned aerial vehicle is unbalanced, the two unmanned aerial vehicles have the same moving direction to be +.>A representation;
the virtual force between the unmanned plane and the user, which is constructed in the step 2, is thatThe expression is as follows:
in the formula (14), the amino acid sequence of the compound,virtual force resultant between the drone and a plurality of devices that it serves.
Step 3, respectively adopting a diffusion strategy and a successive approximation strategy to iteratively update the two virtual forces obtained in the step 2; wherein, the virtual force resultant force among unmanned aerial vehicles of each unmanned aerial vehicle is iteratively updated by adopting a diffusion strategyAs shown in fig. 4, i.e. collecting the +.>Information and get ψ after local summation i [n-1]As the correction amount of the next virtual force update of the unmanned aerial vehicle, the cooperative optimization of the local unmanned aerial vehicle and the peripheral unmanned aerial vehicle is realized, and the related calculation formulas are shown in formulas (15) and (16):
in the step 3, the virtual force between the unmanned aerial vehicle and the user equipment is iteratively updated by adopting a successive approximation strategy as follows:
constraint conditions:
T i，j [n]＝B i，j [n]log 2 (1+γ i，j [n]) (17b)
in formula (17 d), due to g i，j [n]Is relative to d i，j [n]Not directly solving it, but in 17 (a), log (T) i，j [n]) For d i，j [n]The second derivative of (2) is of formula (18):
wherein the method comprises the steps ofFor the parameter which is not updated with the current iteration in the successive convex approximation iteration process, the value is x of the last iteration i [n]，y i [n]As a result, i.e.)>
In the unmanned aerial vehicle deployment problem of the actual communication scene, d i，j [n]The limit of the position range of the unmanned aerial vehicle and the terminal is always more than 0, and for any given position, the simulation can prove thatIs positive. Therefore, in a real unmanned aerial vehicle deployment scenario, the approximate best of (17 a) can be found by a successive approximation strategyAnd (5) optimizing a solution. Virtual forceThe update procedure of (2) is shown in the formula (21):
and 4, calculating the movement track of the unmanned aerial vehicle according to the two virtual forces obtained in the step 3 until convergence, wherein the convergence result is shown in fig. 5 a) and fig. 6 a). The method for calculating the movement track of the unmanned aerial vehicle comprises the steps of calculating the synthetic virtual force of the unmanned aerial vehicle through a formula (22):
also comprises the step of calculating the moving speed of the unmanned aerial vehicle through a method (23)
The detailed implementation process of the unmanned aerial vehicle motion trail algorithm is shown in table 1:
table 1 motion trajectory algorithm for unmanned aerial vehicle
Compared with a baseline algorithm, the proposed algorithm improves load balancing performance among unmanned aerial vehicles, improves performance of low-data-rate user equipment and improves fairness among the user equipment.
Through the mode, the multi-unmanned aerial vehicle network load balancing method based on the diffusion type deployment algorithm can achieve load balancing in the multi-unmanned aerial vehicle network. By constructing the load metrics, two virtual forces are defined to control the motion of the drone, the two virtual forces being a drone-drone virtual force and a drone-user device virtual force, respectively. Simulation results show that compared with a baseline algorithm, the algorithm can obtain better load balancing performance. ( And (3) injection: the baseline algorithm was Zhao, h., wang, h., wu, w., & Wei, j. (2018) & Deployment Algorithms for UAV Airborne Networks Toward On-Demand coverage.ieee Journal on Selected Areas in Communications,36 (9), 2015-2031. )
Examples
The network specific parameters are shown in table 2:
table 2 network specific parameters
By using the method of the invention, according to the specific implementation of each step, the simulation results as shown in fig. 5 and 6 can be obtained, and the number of the unmanned aerial vehicle service devices after optimized deployment can be basically similar. The invention carries out simulation in 2000 m-2000 m area, terminal equipment can be regarded as stationary due to too slow moving speed in the optimization process, and the number of unmanned machines and terminal equipment is not fixed in the simulation process.
In order to obtain obvious experimental results, the invention is compared with a baseline method. The simulation selects 20 unmanned aerial vehicles, the number of users is 200 and 300 respectively for comparison analysis, and initial positions of the unmanned aerial vehicles and the users are random.
And obtaining a motion control strategy of the unmanned aerial vehicle through three-step operation: firstly, initializing, and randomly establishing a network model of the unmanned aerial vehicle and equipment, wherein the network model specifically relates to formulas (1) - (9); in a second step, the movement of the unmanned aerial vehicle is controlled iteratively, virtual forces for controlling the movement of the unmanned aerial vehicle are calculated according to equations (10) - (21), and movements of the unmanned aerial vehicle are controlled according to equations (22) and (23). And thirdly, when all the unmanned aerial vehicle moving speeds are smaller than a certain value, optimizing and stopping.
Under the optimized deployment algorithm, the simulation results are shown in fig. 5 a) and fig. 6 a), and it can be clearly seen that the number of user equipment associated with each unmanned aerial vehicle is similar through distributed optimized deployment. And simulation results under the baseline algorithm are shown in fig. 5 b) and fig. 6 b), and the number of the server terminals of part of the unmanned aerial vehicle is too large or too small. The number of the user equipment served by each unmanned aerial vehicle is shown in fig. 7 and 8, the x-axis is the serial number of the unmanned aerial vehicle, the y-axis is the number of users, and the algorithm provided by the invention can be seen to enable the number of the service user equipment of each unmanned aerial vehicle to be similar. Fig. 9 shows a Fairness result Jain's Fairness Index of the number of service users of the unmanned aerial vehicle obtained by 200 monte carlo simulations, and it can be seen that the Fairness of the algorithm is always better than that of the baseline algorithm. Fig. 10 shows a Cumulative Distribution Function (CDF) curve of user throughput obtained by 200 monte carlo simulations, and it can be seen that fairness of user throughput is improved.
Claims (2)
1. A multi-unmanned aerial vehicle network load balancing method based on a diffusion type deployment algorithm is characterized by comprising the following steps:
step 1, establishing a wireless communication network of a plurality of unmanned aerial vehicles, and formulating load balancing measurement; the method comprises the following steps:
step 1.1, establishing a wireless communication network of multiple unmanned aerial vehicles, wherein the number of the unmanned aerial vehicles is as follows: UAV (unmanned aerial vehicle) 1 、UAV 2 、…UAV i …、UAV M The method comprises the steps of carrying out a first treatment on the surface of the User equipment number: UE (user Equipment) 1 、UE 2 、…UE j …、UE N The method comprises the steps of carrying out a first treatment on the surface of the The parameters of the wireless communication network of the multiple unmanned aerial vehicles are defined as follows:
T i，j [n]＝B i，j [n]log 2 (1+γ i，j [n]) (8)
in the above, d i,j [n]Indicating the distance, x, between the ith drone and the jth device i [n]And y i [n]Representing UAVs respectively i At time n, horizontal abscissa, H represents the altitude of the drone,and->Representing UE j H is a fixed height, constraint V max Maximum speed for unmanned plane to move, +.>To at time n from UAV i To UE (user Equipment) j Is the free variable, r, determined by the environment i,j [n]Is a UAV (unmanned aerial vehicle) i To UE (user Equipment) j Is a horizontal distance eta LoS And eta NLoS Represents the average additional path loss factor, g i,j [n]Representing the probability average path gain between i drone and j devices, gamma i,j [n]Represents the signal-to-dry ratio, p i [n]Is an n-moment UAV i P k [n]Is the transmitting power of other unmanned aerial vehicles, T i,j [n]For UAVs i For UE j Data transmission rate provided by device, B i,j Is the bandwidth;
step 1.2, making a load balancing measure in a multi-unmanned aerial vehicle wireless communication network as follows:
in the formula (9), U i (T i [n]) Representing UAVs i T, T i [n]Is a UAV (unmanned aerial vehicle) i Throughput vector of serving user, UE j ∈UAV i Representing UE j Currently by UAVs i Providing communication service, T i,j [n]Is UE (user equipment) j Throughput of (2);
step 2, constructing virtual force between unmanned aerial vehicles and virtual force between the unmanned aerial vehicles and users according to the load balancing measurement obtained in the step 1 and the data rate of the user terminal equipment; the virtual force between unmanned aerial vehicles obtained by construction isThe expression is as follows:
in the formula (10), the amino acid sequence of the compound,
kv is a virtual force constant between unmanned aerial vehicles and is a free variable;representing a virtual force between the i unmanned aerial vehicle and the j unmanned aerial vehicle; />Representing the virtual force direction from unmanned plane i to unmanned plane k; when the load of the i unmanned aerial vehicle and the k unmanned aerial vehicle is unbalanced, the two unmanned aerial vehicles have the same moving direction to be +.>A representation;
the virtual force between the unmanned plane and the user constructed in the step 2 is thatThe expression is as follows:
in the formula (14), the amino acid sequence of the compound,virtual force resultant force between the unmanned aerial vehicle and a plurality of devices serving the unmanned aerial vehicle;
step 3, respectively adopting a diffusion strategy and a successive approximation strategy to iteratively update the two virtual forces obtained in the step 2; iterative updating of virtual force resultant force between unmanned aerial vehicles of each unmanned aerial vehicle by adopting diffusion strategyI.e. collecting surrounding drones +.>Information and get ψ after local summation i [n-1]As the correction amount of the next virtual force update of the unmanned aerial vehicle, the cooperative optimization of the local unmanned aerial vehicle and the peripheral unmanned aerial vehicle is realized, and the related calculation formulas are shown in formulas (15) and (16):
in the step 3, the virtual force between the unmanned aerial vehicle and the user equipment is iteratively updated by adopting a successive approximation strategy as follows:
constraint conditions:
T i，j [n]＝B i，j [n]log 2 (1+γ i，j [n]) (17b)
in 17 (a), log (T) is obtained i,j [n]) For d i,j [n]The second derivative of (2) is of formula (18):
wherein the method comprises the steps ofFor the parameter which is not updated with the current iteration in the successive convex approximation iteration process, the value is x of the last iteration i [n],y i [n]As a result, i.e.)>
In an actual communication scenario, in the above formulaIs positive, thus updating the virtual force by successive approximation strategy>The process is shown as a formula (21):
and 4, calculating the motion trail of the unmanned aerial vehicle according to the two virtual forces obtained in the step 3 until convergence.
2. The method for balancing the network load of the multiple unmanned aerial vehicles based on the diffusion deployment algorithm according to claim 1, wherein the method for calculating the movement track of the unmanned aerial vehicle in the step 4 comprises the steps of calculating the synthetic virtual force of the unmanned aerial vehicle by a formula (22):
also comprises the step of calculating the moving speed of the unmanned aerial vehicle through a method (23)
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