Zhejiang Sci-Tech University
In this paper, the authors propose a low-complexity algorithm for the blind identification of multiple sparse channels. The proposed algorithm exploits the sparse structure of the channel impulse responses by posing the channel estimation as a convex optimization problem with L1-minimization. Unlike previous work in the literature for sparse channel estimation, they formulate the available information from multiple receivers as additional linear and quadratic constraints, which greatly simplifies the task of parameter selection. Utilizing the special matrix structures in their setup, they develop an efficient projected sub-gradient method for solving the proposed optimization problem.