Wireless Channel Prediction via Gaussian Mixture Models
2024 27th International Workshop on Smart Antennas (WSA)(2024)
摘要
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.
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关键词
Gaussian mixture models,machine learning,channel prediction,time-varying channels
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