Blind Detection for MIMO Systems With Low-Resolution ADCs Using Supervised Learning

Abstract

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we present a novel blind detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the estimated nonlinear function from labeled training data to detect which data symbols were transmitted. We propose three blind detection methods, which are connected to a \(K\)-nearest neighbors classification and a nearest-centroid classification. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. One major observation is that the symbol-vector-error probability decreases exponentially with the inverse of the number of transmit antennas, the operating signal-to-noise ratio, and the minimum distance that can increase with the number of receive antennas. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.