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Practical Denoising Autoencoder for CSI Feedback without Clean Target in Massive MIMO Networks
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- Authors
- Anseok Lee, Hanjun Park, Yongjin Kwon, Heesoo Lee, Song Chong
- Issue Date
- 2024-02
- Citation
- IEEE Wireless Communications Letters, v.13, no.2, pp.525-529
- ISSN
- 2162-2337
- Publisher
- IEEE Communications Society
- Language
- English
- Type
- Journal Article
- Abstract
- In this letter, we present a novel approach for denoising channel state information (CSI) feedback in massive multiple-input multiple-output (MIMO) cellular networks. Our method utilizes Deep Learning (DL) techniques to compress and remove noise from measured CSI. Traditional DL-based denoising requires pairs of noisy input and corresponding clean targets, which are impractical to obtain in real-world wireless networks. To address this challenge, we propose a training method of denoising autoencoder using pairs of noisy CSIs and practical data acquisition strategies. Extensive evaluations demonstrate the superior reconstruction performance of our method compared to a vanilla autoencoder and legacy codebook-based CSI feedback.
- KSP Keywords
- CSI Feedback, Cellular networks, Channel State Information(CSI), Data Acquisition(DAQ), MIMO networks, Multiple input multiple output(MIMO), Novel approach, Real-world, Reconstruction performance, Wireless network, deep learning(DL)