The use of one-bit analog-to-digital converter (ADC) has been considered as a viable alternative to high resolution counterparts in realizing and commercializing massive multiple-input multiple-output (MIMO) systems. However, the issue of discarding the amplitude information by one-bit quantizers has to be compensated. Thus, carefully tailored methods need to be developed for one-bit channel estimation and data detection as the conventional ones cannot be used. To address these issues, the problems of one-bit channel estimation and data detection for MIMO orthogonal frequency division multiplexing (OFDM) system that operates over uncorrelated frequency selective channels are investigated here. We first develop channel estimators that exploit Gaussian discriminant analysis (GDA) classifier and approximated versions of it as the so-called weak classifiers in an adaptive boosting (AdaBoost) approach. Particularly, the combination of the approximated GDA classifiers with AdaBoost offers the benefit of scalability with the linear order of computations, which is critical in massive MIMO-OFDM systems. We then take advantage of the same idea for proposing the data detectors. Numerical results validate the efficiency of the proposed channel estimators and data detectors compared to other methods. They show comparable/better performance to that of the state-of-the-art methods, but require dramatically lower computational complexities and run times.

翻译：采用单比特模数转换器（ADC）已被视为实现和商业化大规模多输入多输出（MIMO）系统中高分辨率器件的可行替代方案。然而，单比特量化器丢弃幅度信息的问题必须加以补偿。由于传统方法无法直接适用，因此需要精心设计适用于单比特信道估计与数据检测的定制化方法。针对这些问题，本文研究了在非相关频率选择性信道上工作的MIMO正交频分复用（OFDM）系统中的单比特信道估计与数据检测。我们首先开发了利用高斯判别分析（GDA）分类器及其近似版本作为自适应提升（AdaBoost）方法中所谓弱分类器的信道估计器。特别地，将近似GDA分类器与AdaBoost相结合，能够以线性计算复杂度实现可扩展性——这在大规模MIMO-OFDM系统中至关重要。随后，我们利用相同思想提出了数据检测器。数值结果验证了所提信道估计器与数据检测器相较于其他方法的有效性。它们展现出与现有最优方法相当甚至更优的性能，但所需计算复杂度和运行时间显著降低。