Collaborative communications, caching, and computing for cloud-assisted 5G wireless networks
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Tran, Tuyen X..
Collaborative communications, caching, and computing for cloud-assisted 5G wireless networks. Retrieved from
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TitleCollaborative communications, caching, and computing for cloud-assisted 5G wireless networks
Date Created2018
Other Date2018-05 (degree)
Extent1 online resource (xii, 171 p. : ill.)
DescriptionOver the last few years, the proliferation of data-intensive applications on mobile devices has contributed to the overwhelming mobile traffic volume that is pushing against the boundary of the current communication networks' capacity. Additionally, the rapidly growing popularity of computation-intensive and latency-sensitive mobile services has placed severe demands on cloud infrastructures and wireless access networks such as ultra-low latency, user experience continuity, and high reliability. To keep up with these surging demands, network operators have to spend enormous efforts to improve users' experience while maintaining healthy revenue growth. While several solutions have been proposed to improve network capacity such as the deployment of ultra-dense small cells and massive antenna arrays as well as the utilization of millimeter wave spectrum bands, these approaches are fundamentally constrained by the limited spectrum resources, inter-cell interference, and control signaling overheads. Therefore, in order to support the foreseen massive demands from data- and computation-hungry users in the upcoming Fifth Generation (5G) of wireless systems in an affordable way, improving network capacity alone is not sufficient and has to be accompanied by innovations at higher layers. To overcome the limitations of current connection-centric Radio Access Networks (RANs), cloud-assisted wireless networks are promising solutions that unite wireless networks and cloud-computing to deliver cloud services directly from the network edges. The two emerging paradigms for cloud-assisted wireless networks are Cloud Radio Access Network (C-RAN), which aims at the centralization of base station (BS) functionalities via network virtualization and optical fronthaul technologies, and Mobile-Edge Computing (MEC), which proposes to empower the network edge by providing computing, storage, and networking resources within the edge of the mobile RAN. These two paradigms are complementary and have unique justifications within the 5G ecosystem: the centralized nature of C-RAN provides higher degree of cooperation in the network to address the capacity fluctuation and to increase the spectral and energy efficiency; on the other hand, the MEC paradigm is useful in reducing service latency and improving localized user experience. The goal of this research is to leverage the emerging C-RAN and MEC paradigms to design disruptive innovations for the wireless access network that always make best use of the resources available to satisfy service requests from the users. To this end, novel cooperative frameworks are proposed to make optimized decisions for communications, caching, and computation in 5G wireless systems. The proposed innovative solutions include: (i) a joint user-centric radio clustering and beamforming scheme that maximizes the downlink sum throughput of a C-RAN system, (ii) a cooperative hierarchical caching framework that aims at minimizing the network cost of content delivery and at improving users' Quality of Experience (QoE) in a C-RAN, (iii) a joint collaborative caching and processing framework that enhances Adaptive Bitrate (ABR)-video streaming in a MEC network, and (iv) a joint computation offloading and resource allocation framework that helps improve users' computation experience by offloading their computation tasks to the MEC servers. The proposed innovations in this research can benefit a wide range of mobile applications and services such as video streaming, augmented reality (AR)/virtual reality (VR), Internet-of-Things (IoTs), public safety operations and real-time healthcare data analytics.
NotePh.D.
NoteIncludes bibliographical references
Noteby Tuyen X. Tran
Genretheses, ETD doctoral
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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