Discrete-time Markov-based dynamic control approach for compressed sampling  

压缩采样中基于离散时间马尔科夫链的动态控制机制(英文)

作　　者：安春燕[1] 纪红[1] 李屹[1] 张晓亮[1] 

机构地区：[1]北京邮电大学泛网无线通信教育部重点实验室,北京100876

出　　处：《Journal of Southeast University(English Edition)》2012年第3期287-291,共5页东南大学学报（英文版）

基　　金：Innovation Funds for Outstanding Graduate Students in School of Information and Communication Engineering in BUPT;the National Natural Science Foundation of China(No.61001115, 61271182)

摘　　要：To solve the problem that the signal sparsity level is time-varying and not known as a priori in most cases,a signal sparsity level prediction and optimal sampling rate determination scheme is proposed.The discrete-time Markov chain is used to model the signal sparsity level and analyze the transition between different states.According to the current state,the signal sparsity level state in the next sampling period and its probability are predicted.Furthermore,based on the prediction results,a dynamic control approach is proposed to find out the optimal sampling rate with the aim of maximizing the expected reward which considers both the energy consumption and the recovery accuracy.The proposed approach can balance the tradeoff between the energy consumption and the recovery accuracy.Simulation results show that the proposed dynamic control approach can significantly improve the sampling performance compared with the existing approach.针对信号稀疏度在大多数情况下时变且未知的问题,提出了一种实时信号稀疏度预测及最优采样速率确定机制.利用离散时间马尔科夫链对信号稀疏度进行建模,分析信号稀疏度各状态之间变化的规律,根据当前状态预测下一个采样周期内信号的稀疏度状态及概率.此外,基于预测结果,综合考虑采样过程中的能量消耗和信号重构的精确度,以最大化预期收益为目的,提出一种控制机制来确定最优采样速率.该机制能够达到能量消耗和精确度之间的折中.仿真证明,所提出的基于离散时间马尔科夫链的动态控制机制与现有控制机制相比在采样性能方面具有较大的优势.

关 键 词：compressed sampling signal sparsity level prediction discrete-time Markov chain 

分 类 号：TN91[电子电信—通信与信息系统]