An optimal selection method for ground node subsets in multi-UAV wireless power transmission

Abstract

This invention relates to an optimal selection method for ground node subsets in multi-UAV wireless power transmission, belonging to the field of UAV-assisted wireless communication. The method includes the following steps: S1: Dividing the ground nodes into multiple circular regions of equal radius according to the power transmission range of the UAVs, as ground node subsets; S2: Constructing an optimal selection model for the ground node subsets; S3: Optimizing the hovering points of UAVs within each ground node subset, enabling UAVs to charge the ground nodes at the optimal hovering point. This invention is applicable to large-scale low-power ground node charging scenarios. By optimizing the selection of ground node subsets, it reduces the number of hovering charging operations for UAVs, reduces UAV flight energy consumption, and thus improves the energy reception efficiency and fairness of ground nodes.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) assisted wireless communication and relates to an optimal selection method for a subset of ground nodes in multi-UAV wireless power transmission. Background Technology

[0002] In drone-based wireless power transmission systems, the limited battery capacity of individual drones severely restricts their range, making it impossible to simultaneously meet the energy replenishment needs of a large number of ground nodes under limited resource conditions. Therefore, employing multiple drones in collaboration to jointly charge ground nodes has become one of the best solutions.

[0003] Currently, research on multi-UAV cooperative wireless power transmission has significant guiding value in assisted communication applications, primarily employing two approaches: First, the UAV swarm concept is used, grouping multiple UAVs according to certain rules to jointly charge ground nodes. This approach essentially transforms the multi-UAV problem into a single-UAV problem, introducing the coupling and interference issues of energy transmission between multiple UAVs. Second, ground nodes are divided into areas equal to the number of UAVs, with each UAV responsible for a specific area, completing wireless power transmission within its designated area. This scheme essentially introduces multi-task allocation into multi-UAV operations, dividing the overall task to obtain the service area for each UAV, and then transforming it into a single-UAV wireless power transmission problem. Summary of the Invention

[0004] In view of this, the purpose of this invention is to avoid coupling and interference in energy transmission between multiple UAVs, and to provide an optimal selection method for the subset of ground nodes in wireless power transmission between multiple UAVs, thereby reducing the number of ground node subsets, reducing the number of hovering charging times of UAVs, reducing losses in the wireless power transmission process, and improving the energy reception efficiency and balance of nodes.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An optimal selection method for a subset of ground nodes in multi-UAV wireless power transmission includes the following steps:

[0007] S1: Divide the ground nodes into multiple circular regions with the same radius according to the power transmission range of the UAV, and use them as a subset of ground nodes;

[0008] S2: Construct an optimal selection model for a subset of ground nodes;

[0009] S3: Optimize the hovering points of drones within each subset of ground nodes so that drones can charge ground nodes at the optimal hovering point.

[0010] Furthermore, the specific steps for dividing the ground nodes into multiple circular regions of equal radius according to the power transmission range of the UAV, as described in step S1, are as follows:

[0011] Guarantee the received power P of the nodes within the subset of ground nodes gn_r ≥0, the received power of the node is expressed as:

[0012] P gn_r =P uav +G uav +G gn_r -P loss (1)

[0013] Among them, P uav It is the drone's transmission power, G uav It is the gain of the drone's transmitting antenna, G gn_r It is the receiving antenna gain of the node, P loss It is wireless power transmission loss;

[0014] Assume that the farthest transmission distance that can be obtained from the drone within the current subset of nodes is d, and the circular charging radius projected onto the ground by the drone at a fixed flight altitude H.

[0015] Furthermore, the optimal selection model for the ground node subset described in step S2 follows the following principles during the selection process:

[0016] 1) Minimize the number of ground node subsets to save energy consumption for drone hovering and charging;

[0017] 2) The subset regions of each surface node do not overlap;

[0018] 3) All nodes should be included in the subset of ground nodes;

[0019] 4) Nodes in each ground node sub-set can receive the wireless energy emitted by the UAV at the hovering point in their respective area.

[0020] Furthermore, the optimal selection model for the ground node subset mentioned in step S2 is specifically selected through the following steps:

[0021] The hovering point of the drone is fixed at the center of each subset of ground nodes, and the entire network is divided into K subsets, with the total set of network nodes being S; the center of each subset, i.e., the set of drone hovering points, is denoted as P = {P1, ..., P2}. k ,…,P K}, P k The coordinates of the point are (x k ,y k H); Let the region of the k-th subset be set A. kThe set of nodes is S k The coordinates of the j-th node within its region are P. kj (x kj ,y kj The energy collected is E (0). kj The optimization problem for the subset of ground nodes is then:

[0022] min(K) (2)

[0023] The constraints are:

[0024]

[0025] S1∪...∪S k ∪...∪S K =S (4)

[0026]

[0027] E kj >0, 1≤k≤K, 1≤j≤n k (6)

[0028] A greedy algorithm is used to solve this optimization problem, and a set of approximately optimal ground node subsets is obtained.

[0029] Furthermore, step S3, which optimizes the hovering points of UAVs within each subset of ground nodes to enable UAVs to charge ground nodes at the optimal hovering point, includes the following steps:

[0030] Optimize the hovering point position of the UAV in each subset of ground nodes, changing it from hovering point A to B. Then, for a single subset of ground nodes, the following optimization subproblem is established:

[0031]

[0032] The constraints are:

[0033] B∈A k (8)

[0034]

[0035]

[0036] E' kj >0 (11)

[0037] 1≤j≤n k (12)

[0038] Where η represents the conversion efficiency from radio frequency to DC. It is the received power of the j-th node in the k-th subset. This refers to the charging time within the k-th subset. The constraints state that if the newly found hovering point simultaneously satisfies the following conditions: within the region of subset k, the distance to all nodes within the subset is less than or equal to R; the energy collected by all nodes within the subset is greater than 0; and the energy value of the node with the least collected energy in the subset is greater than the energy value collected by the UAV when hovering at the center, then the new hovering point is the optimized optimal hovering point. Otherwise, the center of subset k is the optimal hovering point of that subset.

[0039] By solving the optimization subproblem of a single subset using intelligent algorithms, the optimal hovering point within the single subset is obtained, and finally the optimal node subset of the ground nodes is obtained.

[0040] The beneficial effects of this invention are as follows: In a UAV-based wireless power transmission system, to address network blind spots caused by the inability of some ground nodes to transmit data back in a large-scale network, multiple UAVs collaborate to complete the charging and replenishment task, enabling the system to return to normal operation. This invention avoids coupling and interference in energy transmission between multiple UAVs by employing an optimal subset selection method for ground nodes, reducing the number of UAV hovering charging operations, and optimizing the hovering charging position of a single UAV, thereby improving the energy reception efficiency and balance of nodes within the subset.

[0041] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0042] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0043] Figure 1 This is a model diagram of the multi-UAV collaborative wireless power transmission system described in this invention;

[0044] Figure 2 This is a schematic diagram of the wireless power transmission energy range of a drone;

[0045] Figure 3 This is an optimized schematic diagram of hover points within a subset of nodes. Detailed Implementation

[0046] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0047] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0048] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0049] This embodiment is based on a wireless power transmission system for multi-UAV cooperation, such as... Figure 1 As shown, assume that M (M>1) drones cooperate to replenish energy to N identical ground nodes, and the drone sets are U={u m |m=1,2,…,M}. Assume all drones depart from the central base station, fly at a minimum flight altitude H (H≥1m) at a constant speed v, ensuring they can avoid all obstacles at this altitude and that the altitude remains constant throughout the flight; each drone carries the same amount of energy, E, from the central base station. load Each drone uses the same transmit power P uav The energy transmission range is projected onto the ground as a circular area with radius R, within which all ground nodes can receive the energy transmitted by the drone; the wireless charging time for each drone at a single hovering charging position is T. cAll drones collaborate to charge all ground nodes and then safely return to the central base station. In actual drone-based wireless power transmission systems, some ground nodes may not receive enough energy from the base station or may experience insufficient energy when transmitting large amounts of data, leading to network blind spots in certain areas of the system. To address the issue of data loss due to insufficient energy at a large number of ground nodes, a multi-drone collaborative approach is implemented to wirelessly charge a large number of ground nodes, including the following steps.

[0050] like Figure 2 As shown, considering the power transmission range of the UAV, the ground nodes are divided into multiple circular regions with the same radius according to the transmission range of the UAV, which are regarded as subsets of ground nodes. The specific steps are as follows: In order to ensure that all nodes in the subset of ground nodes can receive energy from the UAV, the received power P of the nodes must be guaranteed. gn_r ≥0, meaning the node's received power can be expressed as

[0051] P gn_r =P uav +G uav +G gn_r -P loss (1)

[0052] Among them, P uav It is the drone's transmission power, G uav It is the gain of the drone's transmitting antenna, G gn_r It is the receiving antenna gain of the node, P loss This refers to wireless power transmission loss. Assuming the furthest transmission distance achievable by the drone within the current subset of nodes is d, which can be calculated using the wireless power transmission loss formula, then the circular charging radius projected onto the ground by the drone at a fixed flight altitude H is...

[0053] The following principles must be followed when selecting a subset of ground nodes:

[0054] 1) Minimize the number of ground node subsets to save energy consumption for drone hovering and charging;

[0055] 2) The subset regions of each surface node do not overlap;

[0056] 3) All nodes should be included in the subset of ground nodes;

[0057] 4) Nodes in each ground node sub-set can receive the wireless energy emitted by the UAV at the hovering point in their respective area.

[0058] The optimal selection model for the ground node subset is constructed, and the specific selection steps are as follows:

[0059] To simplify the analysis, we first fix the hovering point of the UAV at the center of each subset of ground nodes, and divide the entire network into K subsets, with the total set of network nodes being S. The center of each subset, i.e., the set of UAV hovering points, is denoted as P = {P1, ..., P2}. k ,…,P K}, P k The coordinates of the point are (x k ,y k H). Let the region of the k-th subset be set A. k The set of nodes is S k The coordinates of the j-th node within its region are P. kj (x kj ,y kj The energy collected is E (0). kj The optimization problem for the subset of ground nodes is then:

[0060] min(K) (2)

[0061] The constraints are:

[0062]

[0063] S1∪...∪S k ∪...∪S K =S (4)

[0064]

[0065] E kj >0, 1≤k≤K, 1≤j≤n k (6)

[0066] This optimization problem belongs to the set covering class, which is a typical non-deterministic polynomial complete (NPC) optimization problem. Therefore, we use a greedy algorithm to solve it and obtain a set of approximately optimal ground node subsets.

[0067] like Figure 3 As shown, the hovering points of UAVs within each subset of ground nodes are optimized to enable UAVs to charge ground nodes at the optimal hovering point, thereby improving the energy reception efficiency of nodes within the subset. The specific steps are as follows:

[0068] Considering the scenario where ground nodes are concentrated in a subset and close to one side of the circular area, hovering the drone at the center is not the optimal charging position. To further improve the efficiency and evenness of energy reception among the nodes in the ground node subset, the hovering point of the drone in each subset needs to be optimized, changing from hovering point A to B. Therefore, for a single subset, the following optimization subproblem is established:

[0069]

[0070] The constraints are:

[0071] B∈A k (8)

[0072]

[0073]

[0074] E' kj >0 (11)

[0075] 1≤j≤n k (12)

[0076] Where η represents the conversion efficiency from radio frequency to DC. It is the received power of the j-th node in the k-th subset. This refers to the charging time within the k-th subset. The constraints state that if the newly found hovering point simultaneously satisfies the following conditions: within the region of subset k, the distance to all nodes within the subset is less than or equal to R; the energy collected by all nodes within the subset is greater than 0; and the energy value of the node with the least collected energy in the subset is greater than the energy value collected by the UAV when hovering at the center, then the new hovering point is the optimized optimal hovering point. Otherwise, the center of subset k is the optimal hovering point of that subset.

[0077] By solving the optimization subproblem of a single subset using intelligent algorithms, the optimal hovering point within the single subset is obtained, and finally the optimal node subset of the ground nodes is obtained.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimal selection of a subset of ground nodes in multi-UAV wireless power transmission, characterized in that: Includes the following steps: S1: Divide the ground nodes into multiple circular regions with the same radius according to the power transmission range of the UAV, and use them as a subset of ground nodes; S2: Construct an optimal selection model for a subset of ground nodes; S3: Optimize the hovering points of drones within each subset of ground nodes so that drones can charge ground nodes at the optimal hovering point; The optimal selection model for the ground node subset mentioned in step S2 is specifically selected as follows: The hovering point of the drone is fixed at the center of a subset of ground nodes, and the entire network is divided into... A subset, the entire set of network nodes is The center of each subset, i.e., the set of drone hovering points, is set as follows: , The coordinates of the point are ;No. The regions of each subset are set as a set. The set of nodes is The first in its region The coordinates of each node are The collected energy is The optimization problem for the subset of ground nodes is then: (2) The constraints are: (3) (4) (5) (6) R represents the drone's altitude at a fixed flight level. The radius of the circular charging circle projected onto the ground at that time; A greedy algorithm is used to solve this optimization problem, and a set of approximately optimal ground node subsets is obtained. Step S3, which optimizes the hovering points of UAVs within each subset of ground nodes to enable UAVs to charge ground nodes at the optimal hovering point, includes the following steps: The hovering point position of the UAV in each ground node subset is optimized, from the original hovering point... Become For a single subset of ground nodes, the following optimization subproblem is established: (7) The constraints are: (8) (9) (10) (11) (12) in This represents the conversion efficiency from radio frequency to DC. It is the first Subset The received power of each node, It is the first The charging time within each subset, and the constraint condition states that if the newly found hovering point simultaneously satisfies: within the subset Within the specified area, the distance between the new hovering point and all nodes in the subset is less than or equal to R, the energy collected by all nodes in the subset is greater than 0, and the energy value of the node with the least collected energy in the subset is greater than the energy value collected by the UAV when hovering at the center of the circle. In this case, the new hovering point is the optimized optimal hovering point. Otherwise, subset The center of the circle is the optimal hovering point for this subset. ; By solving the optimization subproblem of a single subset using intelligent algorithms, the optimal hovering point within the single subset is obtained, and finally the optimal node subset of the ground nodes is obtained.

2. The optimal selection method for a subset of ground nodes in multi-UAV wireless power transmission according to claim 1, characterized in that: The steps in step S1, which involve dividing the ground nodes into multiple circular regions of equal radius according to the power transmission range of the UAV, and using these regions as subsets of the ground nodes, are as follows: Guarantee the received power of nodes within the subset of ground nodes. The received power of a node is expressed as: (1) in, It is the drone's transmission power. It is the gain of the drone's transmitting antenna. It is the receiving antenna gain of the node. It is wireless power transmission loss; Assuming the furthest transmission distance that can be obtained from the drone within the current subset of nodes is... The drone flies at a fixed altitude The circular charging radius projected onto the ground at that time .

3. The optimal selection method for a subset of ground nodes in multi-UAV wireless power transmission according to claim 1, characterized in that: The optimal selection model for the ground node subset described in step S2 follows the following principles during the selection process: 1) Minimize the number of ground node subsets to save energy consumption for drone hovering and charging; 2) The subset regions of each surface node do not overlap; 3) All nodes should be included in the subset of ground nodes; 4) Nodes in each ground node sub-set can receive the wireless energy emitted by the UAV at the hovering point in their respective area.

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