Compressed Representation of High Dimensional Channels using Deep Generative Networks

Date

2020-05-01

Author

Doshi, Akash
Balevi, Eren
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

164
views

0
downloads

© 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

Suggestions

OpenMETU
Core

High Dimensional Channel Estimation Using Deep Generative Networks Balevi, Eren; Doshi, Akash; Jalal, Ajil; Dimakis, Alexandros; Andrews, Jeffrey G. (2021-01-01) 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 ...
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...
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...
Robust Attitude Estimation Using IMU-Only Measurements Candan, Batu; Söken, Halil Ersin (2021-01-01) © 1963-2012 IEEE.This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning proce...
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

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.