This paper considers the problem of finding sparse solutions from multiple measurement vectors (MMVs) with joint sparsity. The solutions share the same sparsity structure, and the locations of the common nonzero support contain important information of signal features. When the measurement vectors are collected from spatially distributed users, the issue of decentralized support detection arises. This paper develops a decentralized row-based Lasso (DR-Lasso) algorithm for the distributedMMVproblem. A penalty term on row-based total energy is introduced to enforce joint sparsity for the MMVs, and consensus constraints are formulated such that users can consent on the total energy, and hence the common nonzero support, in a decentralized manner. As an illustrative example, the problem of cooperative spectrum occupancy detection is solved in the context of wideband cognitive radio networks.
['Qing Ling', 'Zhi Tian']
Decentralized support detection of multiple measurement vectors with joint sparsity