Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals

Abstract: In this paper, we investigate learning-based maximum likelihood (ML) detection for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). To overcome the significant dependency of learning-based detection on the training length, we propose two one-bit ML detection methods: a biased-learning method and a dithering-and-learning method. The biased-learning method keeps likelihood functions with zero probability from wiping out the obtained information through learning, thereby providing more robust detection performance. Extending the biased method to a system with knowledge of the received signal-to-noise ratio, the dithering-and-learning method estimates more likelihood functions by adding dithering noise to the quantizer input. The proposed methods are further improved by adopting the post likelihood function update, which exploits correctly decoded data symbols as training pilot symbols. The proposed methods avoid the need for channel estimation. Simulation results validate the detection performance of the proposed methods in symbol error rate.