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Reinforcement-learning-aided ML detector for uplink massive MIMO systems with low-precision ADCs

This paper considers an uplink massive multiple-input multiple-output (MIMO) system with low-precision analog-to-digital converters (ADCs). In this system, a robust maximum-likelihood detection (MLD) method is proposed under imperfect channel state information at a receiver (CSIR). Inspired by reinforcement learning theory, the idea of the proposed method is to enhance the accuracy of a likelihood function estimated at the receiver, by exploiting associations between correctly detected data symbols and quantized received signals. The proposed method utilizes these associations as training examples to learn the true likelihood function of the system, which provides a more accurate estimate for the likelihood function that can overcome the effect of imperfect CSIR. Simulation results show that the proposed MLD considerably outperforms the conventional MLD in terms of the detection performance under imperfect CSIR.