Adaptive Learning-Based Maximum Likelihood and Channel-Coded Detection for Massive MIMO Systems with One-Bit ADCs
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In this paper, we propose a learning-based detection framework for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog-to-digital converters. The learning-based detection only requires counting the occurrences of the quantized outputs of -1 and +1 for estimating a likelihood probability at each antenna. Accordingly, the key advantage of this approach is to perform maximum likelihood detection without explicit channel estimation which has been one of the primary challenges of one-bit quantized systems. The learning in the high signal-to-noise ratio (SNR) regime, however, needs excessive training to estimate the extremely small likelihood probabilities. To address this drawback, we propose a dither-and-learning technique to estimate likelihood functions from dithered signals. First, we add a dithering signal to artificially decrease the SNR and then infer the likelihood function from the quantized dithered signals by using an SNR estimate derived from a deep neural network-based offline estimator. We extend our technique by developing an adaptive dither-and-learning method that updates the dithering power according the patterns observed in the quantized dithered signals. The proposed framework is also applied to state-of-the-art channel-coded MIMO systems by computing a bit-wise and user-wise log-likelihood ratio from the refined likelihood probabilities. Simulation results validate the detection performance of the proposed methods in both uncoded and coded systems.
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