1-비트 압축 센싱을 위한 효율적인 알고리즘

초록

In this paper, we consider a compressed sensing (CS) problem with multiple measurement vector (MMV), in which a set of sparse signals with the same support are recovered simultaneously from the corresponding measurements. Many practical problems (e.g., active user detection for massive connectivity, communication-efficient federated learning, and so on) have been formulated in this problem. Also, it is necessary to investigate one-bit CS under the MMV framework, in order to boost communication efficiency. Unfortunately, the best-known one-bit CS algorithms such as Bayesian matching pursuit (BMP) is not applicable to the emerging one-bit MMV problems. In these problems, we propose novel algorithms, named Turbo-BMP as nontrivial extensions of BMP, respectively. Simulation results demonstrate the superiority of the proposed algorithm.

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