CN114697975A - Unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage. Firstly, randomly deploying an unmanned aerial vehicle cluster configured with directional antennas in a near-to-empty area above land multiple users; secondly, each unmanned aerial vehicle can acquire real-time three-dimensional position information of the unmanned aerial vehicle through a GPS; then, the cluster unmanned aerial vehicle network keeps connectivity, unmanned aerial vehicle communication links are influenced by noise and fading, and each unmanned aerial vehicle is communicated with a neighbor unmanned aerial vehicle through an unmanned aerial vehicle communication link; and finally, randomly selecting the unmanned aerial vehicles, interacting position information through the unmanned aerial vehicle communication link, and determining the optimal deployment position by the cluster through iteration and asynchronous updating. The invention realizes the enhancement of the wireless coverage range of the unmanned aerial vehicle cluster by depending on the local position information, and realizes the enhancement of the wireless coverage of the unmanned aerial vehicle cluster with low energy consumption and low time delay. Applicable to many wireless communication scenarios, including but not limited to: disaster relief and rescue communications, military communications, and sensor network communications.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle cluster communication, and particularly relates to an unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage to improve the wireless coverage of a downlink communication link.
Background
Unmanned aerial vehicle communication divide into unmanned aerial vehicle base station communication and networking unmanned aerial vehicle communication, because unmanned aerial vehicle communication has advantages such as flexible, the degree of freedom of deployment is high and strong stadia link, becomes the communication technology that has development potentiality in the communication network of future 6G gradually. Particularly, the ubiquitous low-altitude unmanned aerial vehicle cluster serving as a bridge for connecting space-based and foundation communication is an important component for the nation to actively promote 'air-space-earth-sea integration' important engineering and 'full coverage' for 6G mobile communication. Thanks to the advantages of cluster wide area coverage, strong survivability and the like, in recent years, unmanned aerial vehicle cluster communication has gained a rapid development in the military, civil and commercial fields, has become an important communication means for applications such as aerial scouting, emergency rescue, hot spot event relay and the like, and gradually draws wide attention in academic and industrial fields.
Initially, the wireless coverage problem of one-dimensional low-altitude flight platforms in the altitude direction was studied under a line-of-sight link channel model. Subsequently, the research range gradually extends from a one-dimensional space direction to a three-dimensional space direction, and research targets also gradually tend to be diversified, including optimization of coverage, quality of service, transmission power, and the like. Recently, optimized deployment for unmanned aerial vehicle clusters is also gradually developed. It is worth pointing out that due to the performance advantages of the unmanned aerial vehicle cluster scene, the optimized deployment based on the unmanned aerial vehicle cluster has important significance for improving the communication capability of land users and the wide area coverage capability of the network, and is very worthy of close attention. The current optimized deployment method of the unmanned aerial vehicle cluster mostly adopts a central method, and faces a plurality of problems and challenges under the influence of factors such as limited airborne energy of the unmanned aerial vehicle, sensitive cluster delay and the like.
On one hand, the central unmanned aerial vehicle cluster deployment requires frequent information interaction between the cluster unmanned aerial vehicle and a control center, occupies wireless resources and consumes a large amount of airborne energy; on the other hand, in remote areas or battlefield environments with scarce infrastructure, the central control mode hardly meets the conditions, and the unmanned aerial vehicle cluster needs to have distributed self-organizing capability to complete rapid deployment, wireless coverage and the like of the network.
Disclosure of Invention
The technical problem is as follows: in order to solve the defects of the background technology, the invention provides an unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage, and aims to solve the problem that when an unmanned aerial vehicle cluster performs downlink wireless coverage, the unmanned aerial vehicle cluster can be deployed in a distributed manner with low energy consumption and low time delay, and the wireless coverage capability of the unmanned aerial vehicle cluster to land multiple users is improved.
The technical scheme is as follows: in order to achieve the above object, the distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage of the present invention includes:
step 1, randomly deploying an unmanned aerial vehicle cluster configured with directional antennas in an overhead reachable communication range of land multiple users;
step 2, each unmanned aerial vehicle acquires real-time three-dimensional position information of the unmanned aerial vehicle through a GPS (global positioning system) and initializes the unmanned aerial vehicle;
step 3, randomly selecting an unmanned aerial vehicle in the current time slot, controlling the selected unmanned aerial vehicle and a neighbor unmanned aerial vehicle to exchange position information by an algorithm module for optimizing deployment of the unmanned aerial vehicle, selecting a behavior according to the exploration probability, calculating the income and updating a behavior selection strategy; in the process, the strategy of other unmanned aerial vehicles is kept unchanged, and asynchronous updating is finished and the unmanned aerial vehicles enter the next time slot to continue iteration; and finally determining the optimal deployment position of the unmanned aerial vehicle cluster through continuous iteration and asynchronous updating, and enhancing the land wireless coverage.
Wherein the content of the first and second substances,
the unmanned aerial vehicle cluster includes the cluster of constituteing by a plurality of unmanned aerial vehicles, and a plurality of unmanned aerial vehicles are rotor unmanned aerial vehicle, can hover in the air.
The unmanned aerial vehicle cluster network always keeps connectivity, each unmanned aerial vehicle in the cluster can only communicate with a neighbor unmanned aerial vehicle through an unmanned aerial vehicle communication link, the unmanned aerial vehicle communication link is influenced by channel fading and noise, and the network topology has time-varying property.
The unmanned aerial vehicle is provided with a directional antenna, and a user antenna is an omnidirectional antenna; the beam radiation pattern is conical.
The algorithm module for optimized deployment of the unmanned aerial vehicles controls the optimized updating of the deployment positions of the cluster unmanned aerial vehicles.
The unmanned aerial vehicle optimized deployment algorithm module is located at the rear end of the unmanned aerial vehicle directional antenna, inputs information received by the unmanned aerial vehicle, and outputs an optimized deployment strategy.
The unmanned aerial vehicle optimization deployment algorithm module comprises the following specific steps:
step 1, initializing the number of interaction times T to be 0, setting the maximum number of interaction times T, and initializing the real-time position acquired by the cluster unmanned aerial vehicle through a GPS; and each unmanned aerial vehicle is clustered to take off and randomly deployed in a near-empty area of multiple land users.
Step 2, T is 0,1, …, T is less than T, and an unmanned aerial vehicle m is selected from the cluster according to the uniform distribution probability;
step 3, the selected unmanned aerial vehicle m and the neighboring unmanned aerial vehicle thereof carry out information interaction through a wireless communication link, and the unmanned aerial vehicle m receives the information of the current neighboring unmanned aerial vehicle at a certain communication probability;
step 4, the selected unmanned aerial vehicle m limits the behavior set according to the exploration probability
In selecting a behavior, set
In order to explore the behavior of the user,
for the previous behavior, ρmFor the exploration rate, the agreed exploration probability can be expressed as
Step 5, if
The selected unmanned aerial vehicle m calculates the income according to the new exploration behavior
And
wherein the neighboring unmanned planes are integrated into
And its behavior can be expressed as
Subsequently, the behavior selection policy is updated and the process returns to step 2, in the process, the unselected drones keep the previous behavior unchanged, and the behavior selection policy update rule is expressed as:
otherwise, directly returning to the step 2 to carry out the next round of interaction until T is T or the algorithm is converged, and terminating;
through the interactive iteration of the steps 2-5, the unmanned aerial vehicle cluster can be deployed to the optimal position in a fully distributed mode, and the wireless coverage capability of land multi-users is improved.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that:
(1) in the unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage, the condition that an unmanned aerial vehicle communication link is influenced by noise and fading is considered, and when information interaction is carried out between neighboring unmanned aerial vehicles, the unmanned aerial vehicle communication link has certain connection probability and interruption probability.
(2) The method belongs to a completely distributed deployment method, an optimization control center is not needed, the network topology of an unmanned aerial vehicle cluster can be changed in real time, the method is not limited to a specific network topology framework, and the method can be deployed and used in wireless communication environments with weak infrastructure such as disaster areas and battlefields.
(3) According to the method, only local position information interaction between neighboring unmanned aerial vehicles is utilized, and the wireless coverage area of the unmanned aerial vehicle cluster is maximized through continuous iteration and asynchronous updating. Compared with the existing method, the method has lower energy consumption and communication delay, and improves the real-time controllability of the unmanned aerial vehicle cluster and the robustness to network topology changes.
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FIG. 1 is a network architecture diagram of the distributed deployment method of unmanned aerial vehicle cluster for enhanced land wireless coverage of the present invention;
fig. 2 is a flowchart of the distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage.
Detailed Description
The invention relates to an unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage, which comprises a cluster consisting of a plurality of unmanned aerial vehicles, directional antennas for configuring cluster unmanned aerial vehicles, a plurality of users distributed on land and an algorithm module for optimizing deployment of the unmanned aerial vehicles; the unmanned aerial vehicles are located in an overhead reachable communication range of land multi-users, but are not limited to overhead specific areas, and the beam radiation pattern is conical; the unmanned aerial vehicles are rotor unmanned aerial vehicles and can hover in the air, but are not limited to rotor unmanned aerial vehicles of specific models; the unmanned aerial vehicle antenna is a directional antenna, but not limited to a specific kind of directional antenna, and the user antenna is an omnidirectional antenna, but not limited to an omnidirectional antenna; the unmanned aerial vehicle optimized deployment algorithm module is located at the rear end of each unmanned aerial vehicle directional antenna, inputs information received by the unmanned aerial vehicle, and outputs an optimized deployment strategy.
The unmanned aerial vehicle optimal deployment algorithm module takes a potential game mathematical model as a theoretical basis, designs a corresponding revenue function and a potential function by combining a radiation pattern related to the position of the unmanned aerial vehicle, and establishes a fully distributed optimal deployment algorithm only using the position information of each unmanned aerial vehicle and neighboring unmanned aerial vehicles. The unmanned aerial vehicle cluster configured with the directional antenna is randomly deployed in a near-empty area above multiple land users; each unmanned aerial vehicle can acquire real-time three-dimensional position information of the unmanned aerial vehicle through a GPS; the cluster unmanned aerial vehicle network has connectivity, unmanned aerial vehicle communication links are influenced by noise and fading, and each unmanned aerial vehicle can communicate with a neighbor unmanned aerial vehicle through the unmanned aerial vehicle communication links; randomly selecting the unmanned aerial vehicle, interacting position information through the unmanned aerial vehicle communication link, and determining the optimal deployment position by the cluster through iteration and asynchronous updating.
The invention is further illustrated by the following figures and examples.
An unmanned aerial vehicle cluster distributed deployment method for enhancing land wireless coverage can effectively improve wireless coverage capability of an unmanned aerial vehicle cluster to land multiple users through complete distributed deployment of the unmanned aerial vehicle cluster. The details will be described below.
In the embodiment of the present invention, to simplify the analysis, as shown in fig. 1, a network architecture of a distributed deployment method of a cluster of unmanned aerial vehicles for enhancing land wireless coverage is provided, where M rotor unmanned aerial vehicles are provided in the cluster, and each unmanned aerial vehicle is configured with a directional antenna, so that the antenna gain of the mth unmanned aerial vehicle can be expressed as
Wherein the content of the first and second substances,
is a fan-shaped angle, and the fan-shaped angle,
and
main lobe and side lobe gains, respectively. It is assumed that the radiation pattern of the directional antenna is conical and that the receiving user is always within the main lobe direction of the transmitting user. The air-ground wireless channel adopts a free space path loss model, then
Wherein λ is wavelength, λ/4 π represents the path loss coefficient at a reference distance of 1m, dmIs the distance between the mth drone and the land user. In this case, the received signal-to-interference-and-noise ratio of land users in the coverage area of the mth drone can be expressed as
Wherein the content of the first and second substances,
in order to transmit the power, the power control unit,
set of neighbors for the mth drone, n0Is zero mean, unit variance N0Complex white gaussian noise.
Establishing a three-dimensional coordinate system x-y-z on the task area plane, the position of the mth drone may be denoted as pm＝(xm,ym,zm) Where M ∈ {1,2, …, M }, the radius of coverage can be expressed as rm＝zmtan (θ/2). Let each time-slot drone be able to select a behavior to map its corresponding position, where the moving speed is v ∈ {0, v }, and the moving direction is Ω ═ ω, Φ, and ═ ω ═ is assumed to be]ω ∈ [0,2 π) denotes the horizontal direction, φ ∈ [ - π/2, π/2)]Is in the vertical direction. Further discretizing directions into
Wherein
The behavior of the mth drone may be expressed as
Behavior corresponding to hovering is
For hover behavior, special cases are taken
And (4) showing. Based on this, the behavior space of the mth drone can be expressed as
Within a time slot, the behavior of all drones can be represented as
Wherein
Is the behavior space of the unmanned plane cluster. Based on this, at time slot t, the position of drone m
Can be expressed as
Wherein the content of the first and second substances,
the initial position of the mth unmanned aerial vehicle. The distance between the mth drone and the nth drone can be expressed as
For unmanned aerial vehicle communication links, the unmanned aerial vehicle only communicates with neighboring unmanned aerial vehicles and the communication links are affected by noise and fading, based on which the unmanned aerial vehicle cluster network can be modeled as a behavior-based random graph
Where the set of vertices is v ═ {1,2, …, M }, and the set of edges is
The impact of channel noise and fading may be represented as a weighted value of the probability of an edge in the random graph, where the probability of having a communication link between drone i and drone j is Pij(ai,aj)＝Pji(aj,ai) And is and
at each time slot, the drone communication graph is generated from a random graph, denoted as
The set of neighbor users is now denoted as
The probability that the mth drone communicates with the neighbor drone may be expressed as
Based on the random graph model, the sub-optimization problem for the mth unmanned aerial vehicle can be expressed as
γm≥γth
Hs≤zm
Wherein the optimization target represents maximizing the coverage area S formed by the mth unmanned aerial vehicle and the neighboring unmanned aerial vehiclesmThe optimization problem constraints are respectively expressed as: connectivity covered by the unmanned aerial vehicle cluster, existence of unmanned aerial vehicle communication link, collision avoidance of the unmanned aerial vehicle, service quality of the unmanned aerial vehicle and flight safety distance. According to the constraint conditions of the optimization problem, the original behavior set
Some of the behaviors in (a) cannot be selected in a particular time slot, so the set of behaviors selectable by the drone in a particular time slot belongs to a subset of the original set of behaviors, the restricted set of behaviors being used below
This case is explained.
In order to optimize the sub-problem while ensuring that the coverage of the drone cluster is maximized, the problem can be converted to a potential gaming problem,
wherein, v is 1,2, …, M is the unmanned plane set,
set of behaviors, U, for the mth unmanned aerial vehiclemFor the yield of the mth drone. In particular, the m-th drone has a revenue function of
Wherein the content of the first and second substances,
accordingly, the potential function is expressed as
According to the gain function and the potential function, the following unmanned aerial vehicle cluster distributed deployment method is constructed, and the wireless coverage area of the unmanned aerial vehicle cluster is enhanced.
The flow of the distributed deployment method of the unmanned aerial vehicle cluster for enhancing the land wireless coverage as shown in fig. 2 specifically comprises the following steps:
(1) the initialization interaction time T is equal to 0, the maximum interaction time T is set, and the real-time position of the cluster unmanned aerial vehicle acquired through the GPS is initialized; and each unmanned aerial vehicle is clustered to take off and randomly deployed in a near-empty area of multiple land users.
(2) T is 0,1, …, T is less than T, and an unmanned plane m is selected from the cluster according to the uniform distribution probability;
(3) the selected unmanned aerial vehicle m and the neighboring unmanned aerial vehicle carry out information interaction through a wireless communication link, and the unmanned aerial vehicle m receives information at a certain communication probability;
(4) selected drone m from restricted behavior set according to exploration probability
In selecting a behavior, set
In order to explore the behavior of the user,
for the previous behavior, ρmFor the exploration rate, the agreed exploration probability can be expressed as
(5) If it is not
The selected unmanned aerial vehicle m calculates the income according to the new exploration behavior
And
updating the behavior selection policy and returning to the step (2), wherein the unselected drones keep the previous behavior unchanged in the process, and the behavior selection policy updating rule is expressed as:
otherwise, directly returning to the step (2) to perform the next round of interaction, and terminating until T is T or the algorithm converges.
Through the interaction of the steps (2) - (5), the unmanned aerial vehicle cluster can be deployed to the optimal position in a fully distributed mode, and the wireless coverage capability of land multi-users is improved.
The above embodiments are merely illustrative of the technical ideas of the present invention, and the scope of the present invention should not be limited thereto; meanwhile, for a person skilled in the art, any modifications made in the specific embodiments and the application scope according to the idea of the present invention are within the protection scope of the present invention.
Claims (7)
1. A distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage is characterized in that the distributed deployment method comprises the following steps:
step 1, randomly deploying an unmanned aerial vehicle cluster configured with directional antennas in an overhead reachable communication range of land multiple users;
step 2, each unmanned aerial vehicle acquires real-time three-dimensional position information of the unmanned aerial vehicle through a GPS (global positioning system) and initializes the unmanned aerial vehicle;
step 3, randomly selecting an unmanned aerial vehicle in the current time slot, controlling the selected unmanned aerial vehicle and a neighbor unmanned aerial vehicle to exchange position information by an algorithm module for optimizing deployment of the unmanned aerial vehicle, selecting a behavior according to the exploration probability, calculating the income and updating a behavior selection strategy; in the process, the strategy of other unmanned aerial vehicles is kept unchanged, and asynchronous updating is finished and the unmanned aerial vehicles enter the next time slot to continue iteration; and finally determining the optimal deployment position of the unmanned aerial vehicle cluster through continuous iteration and asynchronous updating, and enhancing the land wireless coverage.
2. The distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage as claimed in claim 1, wherein said unmanned aerial vehicle cluster comprises a cluster of a plurality of unmanned aerial vehicles, and the plurality of unmanned aerial vehicles are rotor unmanned aerial vehicles capable of hovering in the air.
3. The distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage as claimed in claim 1, wherein said unmanned aerial vehicle cluster network always maintains connectivity, each unmanned aerial vehicle of cluster can only communicate with neighboring unmanned aerial vehicles through unmanned aerial vehicle communication link, unmanned aerial vehicle communication link is affected by channel fading and noise, and network topology has time-varying property.
4. The distributed deployment method of unmanned aerial vehicle cluster for enhancing terrestrial wireless coverage as claimed in claim 1, wherein the unmanned aerial vehicle is configured with directional antenna, and the user antenna is omni-directional antenna; the beam radiation pattern is conical.
5. The distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage as claimed in claim 1, wherein the algorithm module for optimized deployment of unmanned aerial vehicle controls the optimized update of the deployment position of cluster unmanned aerial vehicle.
6. The distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage as claimed in claim 5, wherein the unmanned aerial vehicle optimized deployment algorithm module is located at the rear end of the directional antenna of the unmanned aerial vehicle, and inputs the information received for the unmanned aerial vehicle and outputs the information as the optimized deployment strategy.
7. The distributed deployment method of unmanned aerial vehicle cluster for enhancing land wireless coverage as claimed in claim 6, wherein the algorithm module for optimizing deployment of unmanned aerial vehicle comprises the following steps:
step 1, initializing the number of interaction times T to be 0, setting the maximum number of interaction times T, and initializing the real-time position acquired by the cluster unmanned aerial vehicle through a GPS; and each unmanned aerial vehicle is clustered to take off and randomly deployed in a near-empty area of multiple land users.
Step 2, T is 0,1, …, T is less than T, and an unmanned aerial vehicle m is selected from the cluster according to the uniform distribution probability;
step 3, the selected unmanned aerial vehicle m and the neighboring unmanned aerial vehicle thereof carry out information interaction through a wireless communication link, and the unmanned aerial vehicle m receives the information of the current neighboring unmanned aerial vehicle at a certain communication probability;
step 4, the selected unmanned aerial vehicle m limits the behavior set according to the exploration probability
In selecting a behavior, set
In order to explore the behaviors, the method comprises the following steps of,
for the previous behavior, ρmFor the exploration rate, the agreed exploration probability can be expressed as
Step 5, if
The selected unmanned aerial vehicle m calculates the income according to the new exploration behavior
And
wherein the neighboring unmanned planes are integrated into
And its behavior can be expressed as
Subsequently, the behavior selection policy is updated and the process returns to step 2, in the process, the unselected drones keep the previous behavior unchanged, and the behavior selection policy update rule is expressed as:
otherwise, directly returning to the step 2 to carry out the next round of interaction until T is T or the algorithm is converged, and terminating;
through the interactive iteration of the steps 2-5, the unmanned aerial vehicle cluster can be deployed to the optimal position in a fully distributed mode, and the wireless coverage capability of land multi-users is improved.
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