Majorization-Minimization-Based Sparse Signal Recovery Method Using Prior Support and Amplitude Information for the Estimation of Time-varying Sparse Channels

作     者：Wang, Chen Fang, Yong 

作者机构：Shanghai Univ Shanghai Inst Adv Commun & Data Sci Joint Int Res Lab Specialty Fiber Opt & Adv Commu Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200444 Peoples R China 

出 版 物：《KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS》 (KSII Trans. Internet Inf. Syst.)

年 卷 期：2018年第12卷第10期

页      面：4835-4855页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation (NSF) of China [61271213, 61673253] Ph.D. Programs Foundation of Ministry of Education of China 

主　　题：Channel estimation non-convex optimization Majorization-Minimization method prior information particle filer sparse signal recovery 

摘      要：In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.