Deep Learning Detection for Massive MIMO Systems

This study explores a technique for addressing detection challenges in large multiple-input multipleoutput (MIMO) systems using deep learning (DL) and mathematical principles. The research focuses on enhancing the effectiveness of a smart detection method called Fast-Convergence Sparsely Connected Detection Network. To accomplish this, a novel deep neural network is introduced by modifying its structure and incorporating mathematical tools such as eigenvalues and eigenvectors to improve the initial estimation. The numerical results demonstrate that the proposed approach produces superior Bit Error Rate (BER) performance for two QPSK module scenarios. Given BER =1$0^{-2}$, the proposed methods performed ldesibel better than Fast-Convergence Sparsely Connected Detection Network. Improved performance is achieved by leveraging this method with threequarters of the layers from the Fast-Convergence Sparsely Connected Detection Network.