Document Type

Thesis

Degree

Master of Science (MS)

Department

Electrical Engineering

First Advisor's Name

Ismail Guvenc

First Advisor's Committee Title

Committee chair

Second Advisor's Name

A. Selcuk Uluagac

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Ahmed Ibrahim

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Hai Deng

Fourth Advisor's Committee Title

Committee member

Keywords

5G, angular spread, CRLB, massive MIMO, maximum likelihood, MMSE channel estimation, noise variance estimation, pilot contamination

Date of Defense

11-10-2016

Abstract

Future fifth generation (5G) cellular networks have to cope with the expected ten-fold increase in mobile data traffic between 2015 and 2021. To achieve this goal, new technologies are being considered, including massive multiple-input multiple-output (MIMO) systems and millimeter-wave (mmWave) communications. Massive MIMO involves the use of large antenna array sizes at the base station, while mmWave communications employ frequencies between 30 and 300 GHz. In this thesis we study the impact of these technologies on the performance of channel estimators.

Our results show that the characteristics of the propagation channel at mmWave frequencies improve the channel estimation performance in comparison with current, low frequency-based, cellular networks. Furthermore, we demonstrate the existence of an optimal angular spread of the multipath clusters, which can be used to maximize the capacity of mmWave networks. We also propose efficient noise variance estimators, which can be employed as an input to existing channel estimators.

Identifier

FIDC001207

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