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In this paper, an efficient massive multiple-input multiple-output (MIMO)detector is proposed by employing a deep neural network (DNN). Specifically, wefirst unfold an existing iterative detection algorithm into the DNN structure,such that the detection task can be implemented by deep learning (DL) approach.We then introduce two auxiliary parameters at each layer to better cancelmultiuser interference (MUI). The first parameter is to generate the residualerror vector while the second one is to adjust the relationship among previouslayers. We further design the training procedure to optimize the auxiliaryparameters with pre-processed inputs. The so derived MIMO detector falls intothe category of model-driven DL. The simulation results show that the proposedMIMO detector can achieve preferable detection performance compared to theexisting detectors for massive MIMO systems.

A Model-Driven Deep Learning Method for Massive MIMO Detection