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

• Chen Wang,
• Yong Fang,

Vol. 12, No. 10, October 30, 2018
10.3837/tiis.2018.10.012 , Download Paper (Free):

• channel estimation
• non-convex optimization
• Majorization-Minimization method
• prior information
• particle filer
• sparse signal recovery

Abstract

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.

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