Compressed Representation of High Dimensional Channels using Deep Generative Networks

Date

2020-05-01

Author

Doshi, Akash
Balevi, Eren
Andrews, Jeffrey G.

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© 2020 IEEE.This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks constrains the reconstructed channel to lie in the range of the generative model, which allows it to outperform conventional channel estimation techniques in the presence of limited number of pilots. It also eliminates the need for explicit knowledge of the sparsifying basis for mmWave multiple-input multiple-output (MIMO) channel matrices, such as the DFT basis, and the associated compressed sensing based strategies for optimal choice of training precoders and combiners. Our approach significantly outperforms sparse signal recovery methods that employ Basis Pursuit Denoising(BPDN) algorithms for narrowband mmWave channel reconstruction.

Subject Keywords

Channel estimation, CS, GAN, MIMO

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090395629&origin=inward
https://hdl.handle.net/11511/100259

DOI

https://doi.org/10.1109/spawc48557.2020.9154297

Conference Name

21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

A. Doshi, E. Balevi, and J. G. Andrews, “Compressed Representation of High Dimensional Channels using Deep Generative Networks,” Georgia, Amerika Birleşik Devletleri, 2020, vol. 2020-May, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090395629&origin=inward.