In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) within the coverage area of a base station (BS) in an offline phase. We propose to leverage the same GMM for channel prediction in the online phase. Our proposed approach does not require signal-to-noise ratio (SNR)-specific training and allows for parallelization. Numerical simulations for both synthetic and measured channel data demonstrate the effectiveness of our proposed GMM-based channel predictor compared to state-ofthe-art channel prediction methods.
@article{arxiv.2402.08351,
title = {Wireless Channel Prediction via Gaussian Mixture Models},
author = {Nurettin Turan and Benedikt Böck and Kai Jie Chan and Benedikt Fesl and Friedrich Burmeister and Michael Joham and Gerhard Fettweis and Wolfgang Utschick},
journal= {arXiv preprint arXiv:2402.08351},
year = {2024}
}