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

Deep Learning-Aided Projected Gradient Detector for Massive Overloaded MIMO Channels