LAUSR

In this letter, we utilize the penalty dual decomposition (PDD) framework and develop a novel PDD decoding algorithm for binary linear codes. Instead of relaxing the discrete constraints to continuous ones, we take an alternative by transforming them into equivalent equality constraints. This idea leads to a double-loop parallel algorithm: In the outer loop, we update the dual variables and certain penalty parameters, while in the inner loop, we divide the primal variables into several blocks and employ the block successive upper-bound minimization method to iteratively optimize each block variable in closed form. Every limit point generated by the proposed algorithm is guaranteed to be a stationary point of the maximum likelihood decoding problem. Simulation results demonstrate that the proposed algorithm shows great error rate performance at both low and high signal-to-noise ratios.