  The paper presents a deep learning-aided iterative detection algorithm for
massive overloaded MIMO systems. Since the proposed algorithm is based on the
projected gradient descent method with trainable parameters, it is named as
trainable projected descent-detector (TPG-detector). The trainable internal
parameters can be optimized with standard deep learning techniques such as back
propagation and stochastic gradient descent algorithms. This approach referred
to as data-driven tuning brings notable advantages of the proposed scheme such
as fast convergence. The numerical experiments show that TPG-detector achieves
comparable detection performance to those of the known algorithms for massive
overloaded MIMO channels with lower computation cost.
