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.

翻译：本文提出一种针对采用单比特模数转换器的上行大规模多输入多输出（MIMO）系统的学习型检测框架。该学习型检测仅需统计每根天线处量化输出为-1和+1的发生次数，即可估计似然概率。该方法的核心优势在于无需显式信道估计即可实现最大似然检测，而信道估计恰恰是单比特量化系统面临的主要挑战之一。然而，在高信噪比（SNR）条件下，该学习方法需要大量训练才能估计极小的似然概率。为解决此缺陷，我们提出一种抖动学习技术，通过抖动信号估计似然函数。首先添加抖动信号人为降低信噪比，然后利用基于深度神经网络离线估计器获得信噪比估计值，从量化抖动信号中推断似然函数。我们进一步开发自适应抖动学习方法，根据量化抖动信号的观测模式动态调整抖动功率，从而扩展了该技术的适用范围。通过从精细化似然概率中计算逐比特和逐用户的似然比，所提框架还可应用于先进信道编码MIMO系统。仿真结果验证了所提方法在未编码和编码系统中的检测性能。