Wireless Channel Prediction via Gaussian Mixture Models

2024/02/13 by Turan, Nurettin, Böck, Benedikt, Chan, Kai Jie +5 · 1 citation
#FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2402.08351

Abstract

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.

Cited by

• Efficient Channel Prediction based on Gram-Square-Root Factorization using GMMs

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