Imperial College London
Portrait Picture

ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mashhadi:2021:10.1109/TWC.2021.3073309,
author = {Mashhadi, MB and Gunduz, D},
doi = {10.1109/TWC.2021.3073309},
journal = {IEEE Transactions on Wireless Communications},
pages = {6315--6328},
title = {Pruning the pilots: deep learning-based pilot design and channel estimation for MIMO-OFDM systems},
url = {http://dx.doi.org/10.1109/TWC.2021.3073309},
volume = {20},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. Our proposed NN architecture uses a non-local attention module to learn longer range correlations in the channel matrix to further improve the channel estimation performance.We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense NN layers during training. This constitutes a novel application of NN pruning to reduce the pilot transmission overhead. Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.
AU  - Mashhadi,MB
AU  - Gunduz,D
DO  - 10.1109/TWC.2021.3073309
EP  - 6328
PY  - 2021///
SN  - 1536-1276
SP  - 6315
TI  - Pruning the pilots: deep learning-based pilot design and channel estimation for MIMO-OFDM systems
T2  - IEEE Transactions on Wireless Communications
UR  - http://dx.doi.org/10.1109/TWC.2021.3073309
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000704824800009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9410430
UR  - http://hdl.handle.net/10044/1/92899
VL  - 20
ER  -
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