The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine ( SVM ), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

中文翻译：

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用于一位大规模MIMO系统的基于SVM的信道估计和数据检测

低分辨率模数转换器（ADC）的使用是降低大规模多输入多输出（MIMO）系统成本和功耗的实用解决方案。但是，低分辨率ADC的严重非线性会导致接收信号出现严重失真，并使信道估计和数据检测任务更具挑战性。在本文中，我们展示了如何支持向量机 （ 支持向量机 ）是机器学习中的一种著名的监督学习技术，可以利用它在具有一比特ADC的大规模MIMO系统中提供有效而强大的信道估计和数据检测。首先，将不相关信道的信道估计问题表述为常规的SVM问题。然后修改此SVM问题的目标函数，以估计空间相关的通道。接下来，提出了一种两阶段检测算法，其中在第一阶段进一步利用SVM。当完全知道信道时，所提出的数据检测方法的性能非常接近最大似然（ML）数据检测的性能。我们还提出了一种基于SVM的联合信道估计和数据检测（CE-DD）方法，它同时利用待解码数据向量和导频数据向量来提高估计和检测性能。最后，提出了将所提出的方法扩展到具有频率选择衰落信道的OFDM系统的方法。仿真结果表明，所提出的方法高效，鲁棒，并且优于现有方法。