Reference : Superimposed Training based Estimation of Sparse MIMO Channels for Emerging Wireless ...
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Engineering, computing & technology : Electrical & electronics engineering
Superimposed Training based Estimation of Sparse MIMO Channels for Emerging Wireless Networks
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 mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Patwary, Mohammad [Staffordshire University, Stoke-on-trent, UK > FCES]
Proceedings of ICT 2016
23rd International Conference on Telecommunications (ICT)
16-06-2016 to 18-06-2016
[en] Superimposed training ; Channel estimation ; MIMO ; First order statistics ; Compressed sensing
[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
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