Deep learning-based channel prediction for TDD MIMO systems with imperfect channel reciprocity

Abstract

In wireless communication systems, accurate channel state information plays a fundamental role in achieving optimal transmission efficiency at the base station (BS). We introduce a deep learning-based channel prediction designed to address the challenges posed by imperfect channel reciprocity in time-division duplex multiple-input multiple-output systems. We propose two models that not only facilitate accurate channel prediction but also perform channel calibration that can alleviate the impact of imperfect channel reciprocity between BS and users. We evaluate the performance through the simulations in line-of-sight and non-line-of-sight scenarios, demonstrating efficacy in enhancing the accuracy of predicted future downlink channels.