SVM-based Channel Estimation and Data Detection for Massive MIMO Systems with One-Bit ADCs

Low-resolution Analog-to-Digital Converters (ADCs) have emerged as 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 is formulated as an SVM problem, and then 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. Finally, we 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. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.