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

Abstract

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 \textit{Support Vector Machine} (\textit{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.

11 pages, 12 figures, submitted for a journal publication

References in corpus (3)

• Massive MIMO with 1-bit ADC
• Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
• Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs

Cited by in corpus (6)

• LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
• On the Mean Square Error Optimal Estimator in One-Bit Quantized Systems
• One-Bit Channel Estimation for IRS-aided Millimeter-Wave Massive MU-MISO System
• Pseudo-Random Quantization Based Two-Stage Detection in One-Bit Massive MIMO Systems
• Massive MIMO Channel Estimation with Low-Resolution Spatial Sigma-Delta ADCs
• Differential Modulation in Massive MIMO With Low-Resolution ADCs