应用于认知无线电频谱预测的小波神经网络模型

Wavelet Neural Network Model for Cognitive Radio Spectrum Prediction

精确的频谱预测能够有效地降低认知无线电系统的能耗,还有助于提高认知无线电系统的吞吐量。针对频谱预测方法的预测精度问题,提出了一种小波神经网络频谱预测模型,以预测通道占用状态情况。该模型利用离散小波变换产生分析信号的时频分布,使用一个时间序列来表示某子信道的占用状态;对预测精度、利用率和参数初始化之间的权衡进行了分析,以便选择一个近于最优的模型。实验测量结果表明,与基于BP神经网络算法的模型相比,所提模型在预测精度和能耗方面均表现出较优的性能。

Accurate spectrum prediction can effectively reduce the energy consumption of cognitive radio system and improve the throughput of cognitive radio system.In order to solve the problem of the prediction accuracy of spectrum prediction method,a wavelet neural network model was proposed to predict the state of channel occupancy.The discrete wavelet transform was used to generate the time-frequency distribution of the signal,and a time series was used to represent the state of a sub channel.The tradeoff between prediction accuracy,utilization and parameter initialization was analyzed to select a near optimal model.The experimental results show that,compared with the model based on BP neural network,the proposed model shows better performance in terms of prediction accuracy and energy consumption.