DLNet: Deep learning-aided massive MIMO decoder

Abstract

Traditional MIMO decoding schemes are complex, impractical, and perform poorly for massive multiple-input multiple-output (M-MIMO) systems. Deep learning (DL) has recently emerged to perform many complex operations more efficiently within a shorter time. This paper proposes a learning-based network (DLNet) to design an M-MIMO decoder. The DLNet network architecture is designed by iteratively unfolding the gradient descent algorithm. The proposed DLNet decoder consists of 15 neural networks (NN) layers with some trainable parameters. This work considered uplink Rayleigh and correlated M-MIMO channels, which are perfectly known to the receiver. With the knowledge of the received signals and the M-MIMO channels, the proposed DLNet decoder decodes the messages of all the users. In the M-MIMO perspective, the proposed DLNet has been evaluated for symbol-error-rate (SER) performance, algorithm complexity, and run-time requirement. The simulations show that the proposed DLNet converges faster than other available decoders and performs better than other M-MIMO decoding schemes, by at least 2 dB in SER and at least 11 times faster than the baseline (OAMP-Net) and nine times less complex.