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An alternate direction method of multipliers (ADMM)-based detectors canachieve good performance in both small and large-scale multiple-inputmultiple-output (MIMO) systems. However, due to the difficulty of choosing theoptimal penalty parameters, their performance is limited. This paper presents adeep neural network (DNN)-based massive MIMO detection method which canovercome the above limitation. It exploits the unfolding technique and learnsto estimate the penalty parameters. Additionally, a computationally cheaperdetector is also proposed. The proposed methods can handle the higher-ordermodulation signals. Numerical results are presented to demonstrate theperformances of the proposed methods compared with the existing works.

A Low-Complexity ADMM-based Massive MIMO Detectors via Deep Neural Networks