基于空时压缩感知算法的蜂窝流量预测

Prediction of Cellular Traffic Based on Space-time Compression Sensing

针对蜂窝网络中,基站发射功率不能有效根据小区内峰值流量实时调整而造成蜂窝网络能量浪费这一问题,提出一种基于阈值控制的正则化正交匹配追踪(BT-ROMP)蜂窝流量预测算法。该算法利用小区内用户行为在时间与空间上的周期平稳变化特性,构建蜂窝流量块稀疏模型;利用阈值有效筛选出正则化漏掉的次优原子,扩大原子候选集,达到减少算法迭代次数与提高重构精度的目的。仿真结果表明,本文算法较正则化正交匹配追踪算法(ROMP),算法预测精度平均提高0. 01。

A cellular network traffic prediction algorithm based on Threshold Regularized Orthogonal Matching Pursuit(BT-ROMP)is proposed to solve the problem of cellular network energy waste,where Bases transmitted power cannot be effectively adjusted according to the peak flow rate in cells.The block sparse model of cellular traffic is constructed by using the characteristics of periodic and stable changes.And the algorithm uses the threshold to effectively screen the suboptimal atoms which are regularized,and to expand the candidate set to reduce the number of iterative times and improve the accuracy of the reconstruction.Simulation results show that compared with regularized orthogonal matching pursuit algorithm(ROMP),the prediction accuracy of the proposed algorithm can be improved by 0.01 on average.