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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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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: | 000337072 |
| 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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