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Analysis and design of mimo heterogeneous cellular networks

University of British Columbia

The structure of cellular networks is under disruptive innovations as a response to the growth of data traffic demands and the emergence of new applications. On the one hand, cellular networks are evolving into complex infrastructures comprising of several tiers of base stations (BS), known as heterogenous cellular networks (HetNets). On the other hand, multiple-input multiple-output (MIMO) multi-stream (multiplexing) communications are deployed to improve the communication reliability and increase the transmission rate. A comprehensive network-level analysis of MIMO multiplexing HetNets in terms of influential system parameters is therefore required. The dissertation focuses on this matter and studies the networl performance of several prominent MIMO multiplexing HetNets by adopting the powerful tool of stochastic geometry. Unfortunately, the current literature lacks an accurate definition of the coverage probability in multiplexing systems, often considers simplistic cell association (CA) scenarios, and commonly provides the analytical results in numerically expensive forms. In general, these drawbacks render complexities in performance evaluation and hinder scalable system design. With these regards, this thesis aims at 1) analyzing the coverage performance from a link-level perspective; 2) considering the maximum signal-to-interference-plus-noise ration (max-SINR) CA rule; 3) deriving the network performance through easy-to-compute formulas. Our analytical results are insightful and permits us to further explore various practical design issues. Specifically, thanks to compact formats and manageable computational costs of our analytical results, we are able to 1) comprehensively study the correlation across data streams of a given communication link, and prove that this correlation undermines the coverage performance; 2) prove that MIMO multiplexing HetNets growing the multiplexing gains reduces the coverage probability, thus diversity systems stands as the best option to maximize coverage probability; 3) investigate the relationship between spectral efficiency, multiplexing gains, and densification from a network-level perpective; 4) optimize the network in order to maximize aggregated multiplexing gains under prescribed coverage loss against the best possible coverage performance; and 5) explore the spectral efficiency optimization of the network subject to prescribed constraints.

Text

2018-08-14

Attribution 4.0 International

http://hdl.handle.net/2429/66767

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/4.0/