Deep Neural Network Based Detection Algorithm for High-Order Modulation in Uplink Massive MIMO

For the 6th generation (6G) communications, the interference exploitation generated from a growing number of intelligent factors with ultra scale is great challenge for the low complexity detection algorithms in the uplink multiuser massive multiple input and multiple output (MIMO) systems, especially for detecting high order quadrature amplitude modulation (QAM) signals. The deep learning technology is one of the key technical solutions for 6G. In this paper, a deep neural network based semidefinite relaxation (DNNSR) detection algorithm is proposed on the basis of the graphical detection model for uplink multiuser massive MIMO systems. Compared with the counterpart detection algorithms, along with a low polynomial average per symbol computational complexity, the proposed DNNSR detection algorithm requires lower average received signal to noise ratios to obtain better bit error rate performance as well as achieve theoretical spectral efficiency for graphical high order QAM signals in the uplink multiuser massive MIMO systems.