具有脉冲聚能的WMN网络测度增益预测算法

WMN Network Measurement Information Gain Prediction Algorithm with Pulse Energy Shaped

传统方法中对WMN网络测度的信息度增益预测采用线性时频分析预测方法,当网络数据吞吐量出现非线性增益时,网格能量发散,预测性能不好。提出一种具有脉冲聚能效应的WMN网络测度信息增益预测方法,以活跃状态节点为顶点,通信范围内的节点之间的边为权形成网络图,以估计到的WMN网格方位角作为初值进行前向跟踪,结合随机近场测度信息增益特征指标,得到脉冲聚能的混叠谱估计值,计算网络测度信号起始时刻码相位和载波频率相位的脉冲聚能路径,实现脉冲聚能,改进预测算法。仿真结果表明,该算法能保证WMN网格能量的聚敛,提高预测性能,提高网络寿命和连通性,较传统方法优越。

In the traditional method to measure WMN network information gain predicted by using linear prediction method for the analysis of frequency, when the data throughput of the network in the nonlinear gain, grid energy divergence, forecast performance is not good. WMN network measurement information gain prediction algorithm with pulse energy shaped is proposed, the active state nodes for the vertices, edges between nodes are within communication range for the right form the network diagram, WMN grid orientation to estimate to angle as the initial value of tracking, combined with random nearfield measurement information gain feature index, get aliasing spectrum estimate pulse energy, pulse shaped path computation starting time of network measurement signal code phase and carrier frequency and phase, pulse energy, improved prediction algorithm. The simulation results show that, the algorithm can guarantee the WMN grid convergence, improve the prediction performance, enhance the network lifetime and connectivity, it is better than the conventional methods.