High Dimensional Channel Estimation Using Deep Generative Networks

Date

2021-01-01

Author

Balevi, Eren
Doshi, Akash
Jalal, Ajil
Dimakis, Alexandros
Andrews, Jeffrey G.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires fewer pilots. It also eliminates the need of a priori knowledge of the sparsifying basis, instead using the structure captured by the deep generative model as a prior. Using this prior, we also perform channel estimation from one-bit quantized pilot measurements, and propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator's channel estimate while minimizing the rank of the channel estimate. Our approach significantly outperforms sparse signal recovery methods such as Orthogonal Matching Pursuit (OMP) and Approximate Message Passing (AMP) algorithms such as EM-GM-AMP for narrowband mmWave channel reconstruction, and its execution time is not noticeably affected by the increase in the number of received pilot symbols.

Subject Keywords

Channel estimation, Training, Receiving antennas, Radio frequency, Estimation, Optimization, Matching pursuit algorithms, MIMO channel estimation, generative adversarial networks (GAN), compressed sensing, one-bit receivers, WAVE MIMO SYSTEMS

URI

https://hdl.handle.net/11511/100316

Journal

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS

DOI

https://doi.org/10.1109/jsac.2020.3036947

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

E. Balevi, A. Doshi, A. Jalal, A. Dimakis, and J. G. Andrews, “High Dimensional Channel Estimation Using Deep Generative Networks,” IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 39, no. 1, pp. 18–30, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100316.