Reference : Superimposed Training based Estimation of Sparse MIMO Channels for Emerging Wireless ...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/10993/28308

Title :	Superimposed Training based Estimation of Sparse MIMO Channels for Emerging Wireless Networks
Language :	English
Author, co-author :	Mansoor, Babar [COMSATS Institute of Information Technology, Islamabad, Pakistan]
	Nawaz, Syed Junaid [COMSATS Institute of Information Technology, Islamabad, Pakistan.]
	Amin, Bilal [COMSATS Institute of Information Technology, Lahore, Pakistan.]
	Sharma, Shree Krishna [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Patwary, Mohammad [Staffordshire University, Stoke-on-trent, UK > FCES]
Publication date :	May-2016
Main document title :	Proceedings of ICT 2016
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	23rd International Conference on Telecommunications (ICT)
Event date :	16-06-2016 to 18-06-2016
Event organizer :	IEEE
Event place (city) :	Thessaloniki
Event country :	Greece
Keywords :	[en] Superimposed training ; Channel estimation ; MIMO ; First order statistics ; Compressed sensing
Abstract :	[en] Multiple-input multiple-output (MIMO) systems constitute an important part of todays wireless communication standards and these systems are expected to take a fundamental role in both the access and backhaul sides of the emerging wireless cellular networks. Recently, reported measurement campaigns have established that various outdoor radio propagation environments exhibit sparsely structured channel impulse response (CIR). We propose a novel superimposed training (SiT) based up-link channels’ estimation technique for multipath sparse MIMO communication channels using a matching pursuit (MP) algorithm; the proposed technique is herein named as superimposed matching pursuit (SI-MP). Subsequently, we evaluate the performance of the proposed technique in terms of mean-square error (MSE) and bit-error-rate (BER), and provide its comparison with that of the notable first order statistics based superimposed least squares (SI-LS) estimation. It is established that the proposed SI-MP provides an improvement of about 2dB in the MSE at signal-to-noise ratio (SNR) of 12dB as compared to SI-LS, for channel sparsity level of 21.5%. For BER = 10^−2, the proposed SI-MP compared to SI-LS offers a gain of about 3dB in the SNR. Moreover, our results demonstrate that an increase in the channel sparsity further enhances the performance gain
Target :	Researchers ; Professionals ; Students ; Others
Permalink :	http://hdl.handle.net/10993/28308
DOI :	10.1109/ICT.2016.7500477

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