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Massive MIMO for 5G wireless networks : an energy efficiency perspective

University of British Columbia

As we progress towards the fifth generation (5G) of wireless networks, the bit-per-joule energy efficiency (EE) metric becomes an important design criterion because it allows for operation at practically affordable energy consumption levels. In this regard, one of the key technology enablers for 5G is the recently proposed massive multiple-input multiple-output (MIMO) technology, which is a special case of multiuser MIMO with an excess of base station (BS) antennas. However, techniques for extracting large EE gains from massive MIMO (MM) networks have not been actively investigated so far. We seek to address the above limitation in this thesis by (i) reviewing MM technology from an EE perspective, (ii) critically analyzing the state-of-the-art and proposing new research directions for EE-maximization in “hybrid MM” networks, where massive MIMO operates alongside other emerging 5G technologies, and (iii) proposing a novel resource allocation scheme for EE-maximization in MM networks. The thesis consists of three main parts. In the first part, we motivate the need for EE and explain why massive MIMO is promising as an energy-efficient technology enabler for 5G networks. In the second part, we critically analyze opportunities for EE-maximization in three types of hybrid MM networks, namely, millimeter wave based MM networks, MM-based heterogeneous networks, and energy har- vesting based MM networks. We analyze limitations in the state-of-the-art and propose several promising research directions which, if pursued, will immensely help network opera- tors in designing hybrid MM networks. In the third part, we propose a novel EE-maximization scheme which optimizes resource allocation in an MM network. Three communication resources, namely, the number of BS antennas, pilot power, and data power are optimized for EE. Since the optimization problem is difficult to solve in its original form, we propose a novel solution approach where each iteration solves a sequence of difference of convex programming subproblems. Simulation results render few interesting guidelines for network designers. For example, using higher pilot power than data power can improve the system EE, particularly when SNR is high. Also, the number of BS antennas should be optimized with the available power budget to ensure operation at peak EE.

Text

2016-01-19

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/56553

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/