Wireless perception system energy and information transmission method of unmanned aerial vehicle (UAV) swarm

A wireless perception system energy and information transmission method of an unmanned aerial vehicle (UAV) swarm, comprising the following steps:

• building a wireless perception system architecture based on autonomous and collaborative energy supply of a UAV swarm, wherein the wireless perception system architecture comprises K wireless powered sensors pre-arranged at perception positions and a freely movable UAV swarm, the UAV swarm comprises N UAVs, and each perception subnet comprises a UAV and M sensors managed by the UAV;
• in the wireless perception system architecture, the UAVs are all provided with A antennas, and the sensors are only provided with a single antenna due to limitations of size and cost;
• in the wireless perception system architecture, when perception is required, the UAV swarm arrives at a designated region to perform wireless energy transmission and wireless information transmission on the sensors;
• each transmission process comprises two stages:
• the wireless energy harvesting (WEH) stage and the wireless information transmission (WIT) stage;
• based on the wireless perception system architecture, establishing a joint optimization model by combining a power allocation weight W, a receiving beam forming weight B and a time division factor τ in the UAV swarm, and solving optimal system configuration to obtain an optimal transmission strategy;
• maximizing the total throughput of a sensor uplink under the constraints of data throughput in the UAV swarm;
• the joint optimization model is:
• max
• W
• ,
• B
• ,
• τ
• ∑
• i
• =
• 1
• N
• ξ
• i
• ⁢
• C
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• (
• W
• ,
• B
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• ,
• τ
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• s
• .
• t
• .
• 
• C
• ⁢
• 1
• :
• W
• ±
• W
• lb
• ,
• C
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• 2
• :
• WW
• H
• =
• 1
• ,
• C
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• 3
• :
• B
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• 1
• ×
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• 4
• :
• τ
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• ≤
• τ
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• 1.
• where ξi is an importance factor and ξi>0;
• Ci(W, Bij, τ) is the total throughput in the ith subnet;
• Bij is a receiving beam forming weight vector;
• wilb is a lower bound of an ith element of a power allocation weight vector W, WH represents the conjugate transpose of the vector TV;
• and τlb is a lower bound of the time division factor τ;
• C1, C2 and C3 are constraints;
• C1 is a data throughput constraint in the UAV swarm;
• Wlb∈└1×N is a vector with wilb as an element, and represents the lower bound of W;
• C2 constrains the total power of the wireless energy harvesting (WEH) stage to be constant P;
• C3 means that the receiving beam forming weight cannot exceed 1 because there is no extra energy for signal amplification, where 11×A represents an all-1 vector of size 1×A;
• C4 limits the upper and lower bounds of the time division factor;
• the step of solving optimal system configuration comprises:
• S1. setting the maximum number of optimizations and a convergence threshold, setting initial values of TV, B and τ, and calculating a common parameter;
• S2. conducting optimal value search in a feasible region [τlb, 1) through a golden section search algorithm to obtain an optimal solution τ* of the time division factor, and updating the common parameter;
• optimizing W under a new common parameter to obtain an optimal solution W* of the power allocation weight, and updating the common parameter;
• optimizing B under a new common parameter to obtain an optimal solution B* of the beam forming weight, and updating the common parameter;
• obtaining a new τ* again through the golden section search algorithm under a new common parameter, and updating the common parameter;
• S3. repeating S2 until a difference between two adjacent optimizations is less than the convergence threshold or reaches the maximum number of optimizations, to obtain final W*, B* and τ*.