Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
One-Bit OFDM Receivers via Deep Learning
Date
2019-06-01
Author
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
189
views
0
downloads
Cite This
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 frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver-specifically, an autoencoderjointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.
Subject Keywords
Deep learning
,
OFDM
,
channel estimation
,
data detection
,
one-bit quantization
,
MIMO SYSTEMS
,
CHANNEL ESTIMATION
,
DETECTOR
,
UPLINK
URI
https://hdl.handle.net/11511/100217
Journal
IEEE TRANSACTIONS ON COMMUNICATIONS
DOI
https://doi.org/10.1109/tcomm.2019.2903811
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
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...
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...
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 ...
A General framework on adaptive hybrid beamformingand channel acquisition for wideband mm-wave massive MIMO systems
Kurt, Anıl; Güvensen, Gökhan Muzzaffer.; Department of Electrical and Electronics Engineering (2019)
In this thesis, 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 beamformer 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...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Balevi and J. G. Andrews, “One-Bit OFDM Receivers via Deep Learning,”
IEEE TRANSACTIONS ON COMMUNICATIONS
, vol. 67, no. 6, pp. 4326–4336, 2019, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100217.