Imperial College London
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DrWeiDai

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6333wei.dai1 Website

 
 
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Location

 

811Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gao:2016:10.1109/TCOMM.2015.2508809,
author = {Gao, Z and Dai, L and Dai, W and Shim, B and Wang, Z},
doi = {10.1109/TCOMM.2015.2508809},
journal = {IEEE Transactions on Communications},
pages = {601--617},
title = {Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO},
url = {http://dx.doi.org/10.1109/TCOMM.2015.2508809},
volume = {64},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.
AU  - Gao,Z
AU  - Dai,L
AU  - Dai,W
AU  - Shim,B
AU  - Wang,Z
DO  - 10.1109/TCOMM.2015.2508809
EP  - 617
PY  - 2016///
SN  - 0090-6778
SP  - 601
TI  - Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO
T2  - IEEE Transactions on Communications
UR  - http://dx.doi.org/10.1109/TCOMM.2015.2508809
UR  - https://ieeexplore.ieee.org/document/7355354
UR  - http://hdl.handle.net/10044/1/40310
VL  - 64
ER  -
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