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Title: Pilot contamination mitigation for massive MIMO system using novel channel estimation techniques
Author: Al-Salihi, Hayder Qahtan Kshash
ISNI:       0000 0004 7224 1479
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2018
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Massive multiple input multiple output (MIMO) is a promising technique to achieve the targets of the fifth generation of mobile communications. However, the pilot contamination problem creates a limitation to the potential benefits of massive MIMO systems. To mitigate the pilot contamination, in this thesis. novel channel estimation schemes are proposed and analyzed. First, an efficient channel estimation approaches based on Bayesian learning namely Bayesian compressed sensing (BCS) that rely on prior knowledge of statistical information about the channel sparsity is proposed for massive MIMO systems. Further enhancement has been proposed to the proposed technique of BCS through the principle of thresholding. Also, the multi-task BCS is also proposed to exploit the common sparsity distribution of the system channel. Furthermore, the Cramer Rao bound (CRB) has been derived as a reference line. Second, a novel channel estimation for massive MIMO systems using sparse Bayesian learning (SBL) is proposed. In the proposed technique, the sparsity of each channel coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The mean square error (MSE) analytical expression for the proposed technique is derived. Based on that MSE expression, a pilot design criteria is proposed to design the optimal pilot to improve the estimation accuracy of the proposed algorithm. Next, the optimal pilot for massive MIMO system has been investigated. The optimal pilots are designed by minimising the MSE of the minimum mean square error (MMSE) using the semidefinite programming (SDP) optimisation approach. Then, the conventional channel estimation is considered for Massive MIMO in a correlated Rician fading and correlated Nakagami-m fading channel models. Our analysis reveals that by increasing the line-of-sight (LOS) component the pilot contamination can be eliminated. Finally, the discrete Fourier transform (DFT) based channel estimation is proposed for massive MIMO, our simulation results show the effectiveness of the DFT channel estimation techniques for reducing the pilot contamination in comparison with the conventional based channel estimation.
Supervisor: Nakhai, Mohammad Reza ; Nallanathan, Arumugam Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available