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{Liu:2016:10.1109/TWC.2016.2608342,
author = {Liu, A and Lau, VKN and Dai, W},
doi = {10.1109/TWC.2016.2608342},
journal = {IEEE Transactions on Wireless Communications},
pages = {7820--7830},
title = {Exploiting burst-sparsity in massive MIMO with partial channel support information},
url = {http://dx.doi.org/10.1109/TWC.2016.2608342},
volume = {15},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - How to obtain accurate channel state information at the base station (CSIT) is a key implementation challenge behind frequency-division duplex massive MIMO systems. Recently, compressive sensing (CS) has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, brute-force applications of standard CS may not lead to good performance in massive MIMO systems, because standard sparse recovery algorithms have quite a stringent requirement on the sparsity level for robust recovery and this severely limits the operating regime of the solution. Moreover, since the channel support is usually correlated across time, it is possible to obtain partial channel support information (P-CSPI) from previously estimated channel support. Motivated by the above observations, we propose a P-CSPI aided burst Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to exploit both the P-CSPI and additional structured properties of the sparsity, namely, the burst sparsity in massive MIMO channels. We also accurately characterize the asymptotic channel estimation error of the P-CSPI aided burst LASSO algorithm. Both the analysis and simulations show that the P-CSPI aided burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.
AU  - Liu,A
AU  - Lau,VKN
AU  - Dai,W
DO  - 10.1109/TWC.2016.2608342
EP  - 7830
PY  - 2016///
SN  - 1536-1276
SP  - 7820
TI  - Exploiting burst-sparsity in massive MIMO with partial channel support information
T2  - IEEE Transactions on Wireless Communications
UR  - http://dx.doi.org/10.1109/TWC.2016.2608342
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000388674700044&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/7564429
UR  - http://hdl.handle.net/10044/1/83410
VL  - 15
ER  -
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