Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach

arXiv:2101.10300v5 Announce Type: replace-cross
Abstract: In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-labeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does …

abstract analysis arxiv communication cs.lg denoising domain eess.sp general literature multiple reinforcement reinforcement learning systems type via