Semi-Supervised Learning Detector for MU-MIMO Systems with One-Bit ADCs

Abstract

We study an uplink multiuser multiple-input multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). In this system, we recently proposed a supervised-learning (SL) detector by modeling a non-linear end-to-end system function into a parameterized Bernoulli-like model. Despite its attractive performance, the SL detector requires a large amount of labeled data (i.e., pilot signals) to accurately estimate the parameters of the underlying model, since the amount of the parameters grow exponentially with the number of users. In this paper, we address this problem by presenting a semi-supervised learning (SSL) detector where both pilot signals (i.e., labeled data) and some part of data signals (i.e., unlabeled data) are exploited to estimate the parameters via expectation-maximization (EM) algorithm. Simulation results demonstrate that the proposed SSL detector can achieve the performance of the existing SL detector with significantly lower pilot-overhead.