  In this paper, we develop an efficient detector for massive multiple-input
multiple-output (MIMO) communication systems via penalty-sharing alternating
direction method of multipliers (PS-ADMM). Its main content are as follows:
first, we formulate the MIMO detection as a maximum-likelihood optimization
problem with bound relaxation constraints. Then, the higher modulation signals
are decomposed into a sum of multiple binary variables through their inherent
structures, by exploiting introduced binary variables as penalty functions, the
detection optimization model is equivalent to a nonconvex sharing minimization
problem. Second, a customized ADMM algorithm is presented to solve the
formulated nonconvex optimization problem. In the implementation, all variables
can be solved analytically and parallelly. Third, it is proved that the
proposed PS-ADMM algorithm converges if proper parameters are chosen.
Simulation results demonstrate the effectiveness of the proposed approach.
