  In this letter, a binary sparse Bayesian learning (BSBL) algorithm is
proposed to slove the one-bit compressed sensing (CS) problem in both single
measurement vector (SMV) and multiple measurement vectors (MMVs). By utilising
the Bussgang-like decomposition, the one-bit CS problem can be approximated as
a standard linear model. Consequently, the standard SBL algorithm can be
naturally incorporated. Numerical results demonstrate the effectiveness of the
BSBL algorithm.
