A data-driven deep learning network for massive MIMO detection with high-order QAM

Abstract

Massive multiple-input multiple-output (MIMO) can provide higher spectral efficiency and energy efficiency compared to conventional MIMO systems. Unfortunately, as the numbers of modulation orders and antennas increase, the computational complexity of conventional symbol detection algorithms becomes unaffordable and their performance deteriorates. However, deep learning (DL) techniques can provide flexibility, nonlinearity and computational parallelism for massive MIMO detection to address these challenges. We propose an efficient data-driven detection network, i.e., accelerated multiuser interference cancellation network (AMIC-Net), for uplink massive MIMO systems. Specifically, we first introduce an extrapolation factor regarded as a learnable parameter into the multiuser interference cancellation (MIC) algorithm for iterative sequential detection (ISD) detector through extrapolation technique to enhance the convergence performance. Then we unfold the above accelerated iterative algorithm and adopt a sparsely connected approach, instead of fully connected, to obtain a relatively simple deep neural network (DNN) structure to enhance the detection performance through the data-driven DL approach. Furthermore, in order to accommodate communication scenarios with higher-order modulation, a novel activation function is proposed, which is composed of multiple softsign activation functions with additional learnable parameters to implement a multi-segment mapping of the set of constellation points with different modulations. Numerical results show that the proposed DL network can bring significant performance gain to ISD detector with various massive antenna settings and outperform the existing detectors with the same or lower computational complexity, especially in high-order QAM modulation scenarios.