Designing Massive MIMO Detector via PS-ADMM approach
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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.
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