  An alternate direction method of multipliers (ADMM)-based detectors can
achieve good performance in both small and large-scale multiple-input
multiple-output (MIMO) systems. However, due to the difficulty of choosing the
optimal penalty parameters, their performance is limited. This paper presents a
deep neural network (DNN)-based massive MIMO detection method which can
overcome the above limitation. It exploits the unfolding technique and learns
to estimate the penalty parameters. Additionally, a computationally cheaper
detector is also proposed. The proposed methods can handle the higher-order
modulation signals. Numerical results are presented to demonstrate the
performances of the proposed methods compared with the existing works.
