A communication topology-based method for supporting communication of a UAV group

Abstract

The application belongs to the field of UAV group communication, and discloses a UAV group communication support method based on communication topology, which mainly solves the problem of UAV group communication support, and the steps are as follows: obtaining the communication topology of the UAV group; calculating the number of connections of each UAV node based on the communication topology of the UAV group; calculating the connection signal strength of each UAV node based on the signal strength of the edge in the communication topology of the UAV group and the number of connections of each UAV node; then calculating the key metric of each UAV node to the communication topology; weighting the number of connections, the connection signal strength and the key metric of each UAV node to obtain the communication support benefit of each UAV node; and preferentially performing communication support on the UAV node with high communication benefit. The application weights the number of connections, the connection signal strength and the key metric of each UAV node, and preferentially performs communication support on the UAV node with high communication support benefit, which is simple and effective.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) swarm communication support, and particularly relates to a UAV swarm communication support method based on communication topology. Background Technology

[0002] With the increasingly diversified needs of economic and social development and national and defense security, people's demands for the application of mobile communication networks in special scenarios are deepening. This has led to two important communication application scenarios: high-quality communication support for specific target groups in major and special scenarios, and targeted communication jamming. Such special scenarios include communication support for large gatherings, civil defense for major events, emergency response, disaster relief in remote or dangerous areas, and other communication support / jamming application scenarios related to national and defense security. Traditional wireless communication mostly relies on pre-built basic network infrastructure, such as base stations or access points. This fixed, centralized network is limited by infrastructure and terminal location, resulting in poor flexibility. It cannot flexibly deploy base stations to optimal locations according to application needs, nor can it change network topology or optimize communication quality as user traffic changes. Therefore, it is inadequate for the aforementioned special application scenarios. Thus, developing a mobile communication system that can provide high-quality communication support / jamming for large-scale specific targets in major and special scenarios, enabling the network to adapt to dynamic mission requirements and achieve rapid, flexible, and efficient network deployment, has become an important application problem that urgently needs to be solved.

[0003] Swarm intelligence possesses advantages such as distribution, simplicity, flexibility, and robustness, providing novel solutions to many highly challenging and complex problems. It is one of the five key intelligent forms prioritized for development in my country's "New Generation Artificial Intelligence Development Plan." With the rapid development of emerging technologies such as micro-nano electronics, computing platforms, and autonomous control, swarm systems composed of unmanned aerial vehicles (UAVs) are finding increasing applications in critical scenarios concerning national welfare, people's livelihoods, and national defense security. UAV swarm systems can rapidly achieve regional coverage in a networked, distributed, and collaborative manner, optimizing swarm resource scheduling and improving mission completion rates and response speeds. Against this backdrop, the idea of ​​using UAV swarm intelligence systems for mobile communication support / jamming missions has emerged. Compared to fixed base stations, drones offer advantages such as low cost, high mobility, the ability to carry diverse payloads, and rapid deployment. They can flexibly adapt the node communication topology to mission requirements and rapidly self-heal in the event of network outages or partial node failures. Furthermore, by carrying environmental perception payloads, they can search for targets. Relying on the distributed, self-organizing, interactive learning, and emergent swarm intelligence technologies, the entire swarm system can meet complex and ever-changing mission requirements under simple behavioral rules. It is foreseeable that drone swarm intelligence systems will bring disruptive capabilities to traditional mobile communication support / jamming systems. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a communication support method for UAV swarms based on communication topology, thereby resolving the communication support issues for UAV swarms.

[0005] To achieve the above objectives, the technical solution of the present invention includes the following steps:

[0006] A method for supporting communication in unmanned aerial vehicle (UAV) swarms based on communication topology includes the following steps:

[0007] (1) Obtain the communication topology of the UAV swarm;

[0008] (2) Based on the UAV swarm communication topology, calculate the number of connections for each UAV node;

[0009] (3) Based on the signal strength of the edges in the UAV swarm communication topology and the number of connections of each UAV node, calculate the connection signal strength of each UAV node.

[0010] (4) Based on the communication topology of the UAV swarm, calculate the key metrics of each UAV node for the communication topology;

[0011] (5) Weight the number of connections, connection signal strength and key metrics of each UAV node to obtain the communication support benefits of each UAV node.

[0012] (6) Rank the communication support benefits of each drone node in the drone swarm, and give priority to providing communication support to drone nodes with greater communication benefits.

[0013] Furthermore, the UAV swarm communication topology described in step (1) is represented as follows:

[0014]

[0015] V(G)={v1,v2,…,v n}

[0016] E(G) = {e1, e2, ..., e m}

[0017] In the formula, G represents the communication topology of the UAV swarm, V(G) represents all communication nodes of the UAV swarm, E(G) represents the edges of the communication topology, and each element in E(G) is an unordered pair of two elements in V(G), i.e., e k =(v i ,v j ), e k The signal strength is represented as I(e k ) or I(v i ,v j); where i,j=1,2,…,n, n is the total number of communication nodes in the UAV swarm, and m is the number of edges in the UAV swarm communication topology.

[0018] Furthermore, the calculation method for the number of connections for each UAV node in step (2) is as follows:

[0019]

[0020] In the formula, Nd(i) represents the number of connections for the i-th UAV node, i = 1, 2, ..., n, One(v i ,v j ) is used to determine (v i ,v j Is one of the elements in E(G)? If so, is one(v) an element in E(G)? i ,v j ) = 1, otherwise, One(v) i ,v j ) = 0.

[0021] Furthermore, the calculation method for the connectivity signal strength of each UAV node in step (3) is as follows:

[0022]

[0023] In the formula, I(v) i ) represents the connectivity signal strength of the i-th UAV node, I(v i ,v j ) represents the signal strength of the i-th drone node and the j-th drone node.

[0024] Furthermore, the calculation method for the key metric of the communication topology for each UAV node in step (4) is as follows:

[0025] (4a) Calculate the total number of communication hops between any two UAV nodes in the current communication topology G. The specific calculation method is as follows:

[0026]

[0027] In the formula, N t (V(G)) represents the total number of communication hops where any two nodes have a path, Num(v i ,v j ) represents node v i and v j The number of communication hops can be categorized into four cases:

[0028] ① If i = j, then Num(v i ,v j ) = 0;

[0029] ②If (v i ,v j If ) belongs to an element in E(G), then Num(v) i ,v j ) = 1;

[0030] ③If (v i ,v j ) is not an element in E(G), but v i and v j If a communication path exists through one or more nodes, then Num(v i ,v j ) = t, where t is v i and v j The number of nodes in the communication path between them;

[0031] ④ If v i and v j If there is no communication path between them, then Num(v) i ,v j = NumG, where NumG is a constant and is greater than the maximum number of communication hops allowed by the communication networking equipment currently used by the drone swarm;

[0032] (4b) Calculate the total number of communication hops in the current communication topology G after removing each UAV node, where any two UAV nodes in the remaining UAVs have a path;

[0033] (4c) Calculate the key metric for each UAV node in relation to the communication topology, specifically as follows:

[0034]

[0035] In the formula, GTI(i) represents the key metric for each UAV node in the communication topology, that is, the increment of the total number of communication hops between any two UAV nodes after removing the i-th UAV node, where N is the number of UAV nodes with a path to each other. t (V(G),i) represents the total number of communication hops between any two drone nodes in the remaining drones after removing the i-th drone node.

[0036] Furthermore, the calculation method for the communication support revenue of each UAV node in step (5) is as follows:

[0037] F(i) = N d (i)-λI(v i )+μGTI(i)

[0038] In the formula, F(i) represents the benefit of providing communication support to the i-th drone node, and I(v iGTI(i) represents the connectivity signal strength of the i-th UAV node, GTI(i) represents the critical metric of each UAV node to the communication topology, λ and μ are hyperparameters used to adjust the weights of the connectivity signal strength and the critical metric, respectively, and N d (i) represents the number of connections of the i-th UAV node, i = 1, 2, ..., n, where n is the total number of communication nodes in the UAV swarm.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] 1. This invention provides a method for calculating the connectivity signal strength of a UAV node, which directly calculates the average signal strength between the node and all connected nodes. The calculation method is simple and effective.

[0041] 2. This invention provides a method for calculating the key metric of communication topology for unmanned nodes, namely, the increment of the total number of communication hops between any two unmanned nodes that have a path after removing the unmanned node. The calculation method is simple and effective.

[0042] 3. This invention provides a method for calculating the benefits of communication support for UAV nodes, which involves weighting the number of connections, connection signal strength, and key metrics for each UAV node. The calculation method is simple and considers all factors.

[0043] 4. This invention provides a strategy for providing communication support to UAV nodes, namely, prioritizing communication support for UAV nodes with high communication benefits. These nodes are often connected to a large number of UAV nodes, have weak communication signal strength, and are highly critical to the communication topology. The communication support strategy is direct, effective, and reasonable. Attached Figure Description

[0044] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0045] The following will clearly and completely describe the concept, technical advantages, and resulting technical effects of the present invention with reference to embodiments, so as to fully understand the purpose, features, and effects of the present invention. It should be noted that the specific embodiments described herein are only used to explain the present invention and do not limit the present invention.

[0046] See Figure 1 A method for supporting communication in unmanned aerial vehicle (UAV) swarms based on communication topology includes the following steps:

[0047] (1) Obtaining the communication topology of the UAV swarm can be represented as:

[0048] G = (V(G), V(G))

[0049] V(G)={v1,v2,…,vn}

[0050] E(G) = {e1, e2, ..., e m}

[0051] In the formula, G represents the communication topology of the UAV swarm, V(G) represents all communication nodes of the UAV swarm, E(G) represents the edges of the communication topology, and each element in E(G) is an unordered pair of two elements in V(G), i.e., e k =(v i ,v j ), e k The signal strength can be expressed as I(e k ) or I(v i ,v j ); where i,j=1,2,…,n, n is the total number of communication nodes in the UAV swarm, and m is the number of edges in the UAV swarm communication topology.

[0052] (2) Based on the UAV swarm communication topology, calculate the number of connections for each UAV node. The calculation method is as follows:

[0053]

[0054] In the formula, N d (i) represents the number of connections for the i-th drone node, i = 1, 2, ..., n, One(v i ,v j ) is used to determine (v i ,v j Is one of the elements in E(G)? If so, is one(v) an element in E(G)? i ,v j ) = 1, otherwise, One(v) i ,v j ) = 0.

[0055] (3) Based on the signal strength of the edges in the UAV swarm communication topology and the number of connections for each UAV node, calculate the connection signal strength of each UAV node. The calculation method is as follows:

[0056]

[0057] In the formula, I(v) i ) represents the connectivity signal strength of the i-th UAV node, I(v i ,v j ) represents the signal strength of the i-th drone node and the j-th drone node.

[0058] (4) Based on the UAV swarm communication topology, calculate the key metrics of each UAV node in relation to the communication topology. The calculation method is as follows:

[0059] (4a) Calculate the total number of communication hops between any two UAV nodes in the current communication topology G. The specific calculation method is as follows:

[0060]

[0061] In the formula, N t (V(G)) represents the total number of communication hops where any two nodes have a path, Num(v i ,v j ) represents node v i and v j The number of communication hops can be categorized into four cases:

[0062] ① If i = j, Num(v i ,v j ) = 0;

[0063] ②If (v i ,v j ) belongs to the elements in E(G), Num(v) i ,v j ) = 1;

[0064] ③If (v i ,v j ) is not an element in E(G), but v i and v j There is a communication path, for example (v i ,v l ) and (v l ,v j If all of them are elements in E(G), then v i and v j It can be accessed through node v l If connected, then v i and v j There is a communication path with a hop count of 2; assuming v i and v j If no communication path with a hop count of 2 exists, then a communication path with a hop count of 3 needs to be found, i.e., (v i ,v l1 ), (v l1 ,v l2 ), (v l2 ,v j All of these belong to elements in E(G); and so on.

[0065] ④ If v i and v j There is no communication path between them, Num(v i ,v j= NumG, where NumG is a constant and is greater than the maximum number of communication hops allowed by the current communication networking equipment used by the drone swarm;

[0066] (4b) Calculate the total number of communication hops in the current communication topology G after removing each UAV node, where any two UAV nodes in the remaining UAVs have a path. Let N be the sum of these hops. t (V(G),i) represents the total number of communication hops between any two drone nodes in the remaining drones after removing the i-th drone node. The calculation method is the same as the calculation method for the total number of communication hops between any two drone nodes in the current communication topology G.

[0067] (4c) Calculate the key metric for each UAV node in relation to the communication topology, specifically as follows:

[0068]

[0069] In the formula, GTI(i) represents the key metric of each UAV node to the communication topology, that is, the increment of the total number of communication hops between any two UAV nodes in the remaining UAVs after removing the i-th UAV node.

[0070] (5) The communication support benefit of each UAV node is obtained by weighting the number of connections, connection signal strength, and key metrics. The calculation method is as follows:

[0071] F(i) = N d (i)-λI(v i )+μGTI(i)

[0072] In the formula, F(i) represents the benefit of providing communication support to the i-th UAV node, and λ and μ are hyperparameters used to adjust the weights of connectivity signal strength and key metrics, respectively.

[0073] (6) Rank the communication support benefits of each drone node in the drone swarm, and give priority to providing communication support to drone nodes with greater communication benefits.

Claims

1. A method for supporting communication in unmanned aerial vehicle (UAV) swarms based on communication topology, characterized in that, Includes the following steps: (1) Obtain the communication topology of the UAV swarm; (2) Based on the UAV swarm communication topology, calculate the number of connections for each UAV node; (3) Based on the signal strength of the edges in the UAV swarm communication topology and the number of connections of each UAV node, calculate the connection signal strength of each UAV node; (4) Based on the UAV swarm communication topology, calculate the key metrics of each UAV node for the communication topology; (5) Weight the number of connections, connection signal strength and key metrics of each UAV node to obtain the communication support benefit of each UAV node; (6) Rank the communication support benefits of each UAV node in the UAV swarm, and give priority to UAV nodes with greater communication benefits; The UAV swarm communication topology described in step (1) is represented as follows: ； In the formula, This indicates the communication topology of the drone swarm. This represents all communication nodes of the drone swarm. Edges representing the communication topology Each element in is An unordered pair of two elements in a given set, i.e. , The signal strength is expressed as or ;in , This represents the total number of communication nodes in the drone swarm. The number of edges in the UAV swarm communication topology; The calculation method for the key metric of the communication topology for each UAV node in step (4) is as follows: (4a) Calculate the current communication topology The total number of communication hops between any two UAV nodes that have a path is calculated as follows: ； In the formula, This represents the total number of communication hops between any two nodes that have a path. Represents a node and The number of communication hops can be categorized into four cases: ① If ,but ; ② If belong The elements in, then ; ③ If Not belonging to The elements in, but and If a communication path exists through one or more nodes, then , for and The number of nodes in the communication path between them; ④ If and If there is no communication path between them, then , It is a constant and is greater than the maximum number of communication hops allowed by the communication networking equipment currently used by the drone swarm; (4b) Calculate the current communication topology The sum of the total communication hops between any two drone nodes that have a path after removing each drone node; (4c) Calculate the key metric for each UAV node in relation to the communication topology, specifically as follows: ； In the formula, This represents the key metric for each drone node in relation to the communication topology, i.e., removing the first... After the first drone node, the increment of the total communication hops between any two remaining drone nodes that have a path is... This means removing the first... After a certain number of drone nodes, the sum of the total communication hops between any two drone nodes that have a path.

2. The method for supporting UAV swarm communication based on communication topology according to claim 1, characterized in that, The calculation method for the number of connections for each UAV node in step (2) is as follows: ； In the formula, Indicates the first The number of connections for each drone node , Used for judgment Is it? If the element in is, ,otherwise, .

3. The method for supporting UAV swarm communication based on communication topology according to claim 2, characterized in that, The calculation method for the connectivity signal strength of each UAV node in step (3) is as follows: ； In the formula, Indicates the first The connectivity signal strength of each drone node Indicates the first The drone node and the first Signal strength of each drone node.

4. The method for supporting UAV swarm communication based on communication topology according to claim 1, characterized in that, The calculation method for the communication support benefit of each UAV node in step (5) is as follows: ； In the formula, Indicates the first Benefits of providing communication support to individual drone nodes Indicates the first The connectivity signal strength of each drone node This represents a key metric for each drone node in relation to the communication topology. and These are hyperparameters used to adjust the weights of the connectivity signal strength and the key metric, respectively. Indicates the first The number of connections for each drone node , This represents the total number of communication nodes in the drone swarm.

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