Examination Committee

Prof Gang WANG, CIVL/HKUST (Chairperson)
Prof Khaled BEN LETAIEF, ECE/HKUST (Thesis Supervisor)
Prof Chengshan XIAO, Department of Electrical and Computer Engineering, Lehigh University (External Examiner)
Prof Chi Ying TSUI, ECE/HKUST
Prof James SHE, ECE/HKUST
Prof Brahim BENSAOU, CSE/HKUST

Abstract

The deluge of mobile data is motivating the deployment of dense wireless networks to boost the capacity of next-generation cellular networks. However, deploying more and more base stations (BSs) will impose heavy burdens on existing backhaul links, and may also require considerable operating expenditures. Caching popular contents at BSs has recently been proposed as a potential and cost-effective solution. To fully exploit caches, critical design issues need to be carefully addressed in different phases.
 
In the deployment phase, the cache size allocation problem is investigated under a given budget by considering wireless channel statistics, backhaul conditions and file popularity distributions. A closed-form expression for the user success probability (USP) is derived, and an efficient allocation algorithm is developed to maximize the USP.  As for the content prefetching phase, the caching placement for minimizing the average download delay is studied. To tackle the mixed-integer nonlinear programming (MINLP) design problems, we propose efficient algorithms based on successive convex approximation. Simulation results shall show that the proposed algorithms outperform conventional strategies adopted in prior works.
 
In the content delivery phase, besides the unicast beamforming, we also study a joint design which unifies active BS selection, backhaul data assignment and adaptive multicast beamforming, while taking into account the quality-of-service (QoS) requirements and backhaul capacity limitations. We shall propose a three-stage layered group sparse beamforming (LGSBF) framework, which is a generalization of the power efficiency problems in prior works. Simulation results will validate the effectiveness of the proposed algorithms, and demonstrate that caching plays a more significant role in networks with higher user densities and less-power-efficient backhaul links.