多重优化的分布式无线覆盖探测算法

为满足无线通信网络信号覆盖有效性的实时实地可重复探测的需求,提出一种基于传感器网络的分布式无线覆盖探测算法。通过随机部署于目标区域内的无线传感器节点对无线通信网接收信号强度进行感知和预处理;利用变异函数构造新的BP神经网络目标函数,通过改进粒子群算法优化其初始权值和阈值;利用训练好的网络模型对存在探测盲区的目标区域进行插值估计,并联合传感器节点采集到的数据生成无线通信网络等信号强度线。仿真结果表明,所提算法比其他经典算法具有更高的精度,可有效探测目标区域无线通信网络的信号覆盖情况。

Distributed wireless coverage detection algorithm based on multiple optimization

In order to meet the real-time field and repeatable detection requirements of wireless communication network signal coverage effectiveness, a distributed wireless coverage detection algorithm based on wireless sensor network is proposed. First, perceive and preprocess the received signal strength of the wireless communication network by wireless sensor nodes randomly deployed in the target area. Second, the variogram is used to construct a new BP neural network objective function, and the initial weight and threshold are optimized by the modified particle swarm optimization algorithm. Finally, the trained network model is used to estimate the interpolation of the target area with detection blind zone, and the data collected by the sensor nodes is combined to generate the equal signal strength line of the wireless communication network. The simulation results show that the proposed algorithm has higher prediction accuracy than other classical algorithms and can effectively detect the signal coverage situation of the wireless communication network in the target area.