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International Journal of Scientific Development and Research - IJSDR
(An International Open Access Journal)
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Issue: July 2019

Volume 4 | Issue 7

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Paper Title: CHANNEL ESTIMATION FOR MASSIVE MIMO USING SPARSE BAYESIAN LEARNING METHOD
Authors Name: M.KEERTHI , T.RAVI BABU , MANAS RANJAN BISWAL
Unique Id: IJSDR1901017
Published In: Volume 4 Issue 1, January-2019
Abstract: Pilot contamination creates a fundamental limit to the potential benefits on the performance of massive multiple-input multiple-output (MIMO) systems. Due to failure in accurate channel estimation the author proposed spares Bayesian learning (SBL) method on Gaussian framework.. To address this problem, we propose to estimate channel coefficient by its own hyper parameter and also to its adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper the simulation results show that the channel coefficients can be estimated more efficiently in contrast to the conventional channel estimators in terms of channel estimation with pilot contamination. A pilot design criterion is proposed to design the optimal pilot to improve the estimation accuracy of the proposed algorithm using the Lagrange multiplier optimization method. Results show that we can reduce the MSE of the SBL estimator by employing the optimal pilot sequence. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero.
Keywords: Spares Bayesian learning, Channel estimation, Gaussian Process, Massive MIMO, OFDM Modulation, Pilot contamination.
Cite Article: "CHANNEL ESTIMATION FOR MASSIVE MIMO USING SPARSE BAYESIAN LEARNING METHOD", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 1, page no.97 - 107, January-2019, Available :http://www.ijsdr.org/papers/IJSDR1901017.pdf
Downloads: 0001362
Publication Details: Published Paper ID: IJSDR1901017
Registration ID:190011
Published In: Volume 4 Issue 1, January-2019
DOI (Digital Object Identifier):
Page No: 97 - 107
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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