High Dimensional Channel Estimation Using Deep Generative Networks

Date

2021-01-01

Author

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

Metadata

Show full item record

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

Item Usage Stats

136
views

0
downloads

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

Suggestions

OpenMETU
Core

Compressed Representation of High Dimensional Channels using Deep Generative Networks Doshi, Akash; Balevi, Eren; Andrews, Jeffrey G. (2020-05-01) © 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...
An Efficient Hybrid Beamforming and Channel Acquisition for Wideband mm-Wave Massive MIMO Channels Kurt, Anıl; Güvensen, Gökhan Muzaffer (2019-01-01) In this paper, an efficient hybrid beamforming architecture together with a novel spatio-temporal receiver processing is proposed for single-carrier (SC) mm-wave wideband massive MIMO channels in time-domain duplex (TDD) mode. The design of two-stage beamformers is realized by using a virtual sectorization via second-order channel statistics based user grouping. The novel feature of the proposed architecture is that the effect of both inter-group-interference (due to non-orthogonality of virtual angular sec...
One-Bit OFDM Receivers via Deep Learning Balevi, Eren; Andrews, Jeffrey G. (2019-06-01) This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the fre...
Simplified MAP estimator for OFDM systems under fading Cueruek, Selva Muratoglu; Tanık, Yalçın (2007-04-25) This paper presents a simplified Maximum A Posteriori (MAP) estimator, which yields channel taps in OFDM systems under fading conditions using a parametric correlation model, assuming that the channel is frequency selective, slowly time varying and Gaussian. Expressions for the variance of estimation error are derived to evaluate the performance of the MAP estimator. The relation between the correlation of subchannels taps and error variance and the effect of Signal to Noise Ratio (SNR) are investigated. Th...
Reliable Low Resolution 01-4DM Receivers via Deep Learning Balevi, Eren; Andrews, Jeffrey G. (2018-01-01) This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption, but makes accurate data detection difficult. This is particularly true for multicarrier waveforms, which have high peak-to-average ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe dist...

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.