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. However, due to the quasi-deterministic reception in the high signal-to-noise ratio (SNR) regime, one-bit observations in the high SNR regime are biased to either +1 or -1, and thus, the learning requires excessive training to estimate the 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 estimator which is trained offline. We extend our technique by developing an adaptive dither-and-learning method that updates the dithering power according to the patterns observed in the quantized dithered signals. The proposed framework is also applied to channel-coded MIMO systems by computing a bit-wise and user-wise log-likelihood ratio from the refined likelihood probabilities. Simulation results validate the performance of the proposed methods in both uncoded and coded systems.

翻译：本文针对配备单比特模数转换器的大规模多输入多输出（MIMO）系统上行链路，提出一种基于学习的检测框架。该检测方法仅需统计每个天线上量化输出-1和+1的出现次数，即可估计似然概率。其核心优势在于无需显式信道估计即可实现最大似然检测——而信道估计正是单比特量化系统的主要技术挑战之一。然而，由于高信噪比（SNR）条件下的准确定性接收特性，单比特观测值会偏向+1或-1，导致算法需大量训练才能估计极小似然概率。为解决此缺陷，我们提出抖动学习技术：首先通过添加抖动信号人为降低信噪比，再利用离线训练的深度神经网络估计器获取信噪比估计值，进而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模式动态调整抖动功率。该框架通过从修正后的似然概率计算逐比特和逐用户的对数似然比，可扩展应用于信道编码MIMO系统。仿真结果验证了所提方法在无编码与编码系统中的有效性。