Li, Kin Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 95-99). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction / Motivation --- p.1 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Dynamic Spectrum Access Networks --- p.8 / Chapter 2.3 --- Reliability --- p.10 / Chapter 2.3.1 --- Reliability in Wireless Networks --- p.10 / Chapter 2.3.2 --- Reliability in Wireline Networks --- p.11 / Chapter 2.4 --- Capacity Planning in Wireless Mesh Networks --- p.14 / Chapter 2.4.1 --- Interference Model --- p.14 / Chapter 2.4.2 --- Link Capacity Constraint --- p.15 / Chapter 2.4.3 --- Feasible Path --- p.16 / Chapter 2.4.4 --- Optimal Capacity Allocation in DSA Net- works and Wireless Mesh Networks --- p.16 / Chapter 2.5 --- Chapter Summary --- p.18 / Chapter 3 --- Lifetime Aware Routing without Backup --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- System Model --- p.20 / Chapter 3.3 --- Lifetime Distribution of a Path without Backup Protection --- p.22 / Chapter 3.3.1 --- Exact Lifetime Distribution --- p.23 / Chapter 3.3.2 --- The Chain Approximation --- p.24 / Chapter 3.4 --- Route Selection without Backup Protection --- p.26 / Chapter 3.4.1 --- NP-Hardness of Finding Maximum Lifetime Path --- p.26 / Chapter 3.4.2 --- The Minimum Weight Algorithm --- p.28 / Chapter 3.4.3 --- Greedy Algorithm --- p.28 / Chapter 3.4.4 --- GACA - The Greedy Algorithm using the Chain Approximation --- p.32 / Chapter 3.5 --- Simulation Results --- p.33 / Chapter 3.5.1 --- Tightness of the Chain Approximation Bound for Vulnerable Area --- p.33 / Chapter 3.5.2 --- Comparison between Greedy and GACA using Guaranteed Lifetime --- p.36 / Chapter 3.5.3 --- Factors impacting the performance of GACA --- p.37 / Chapter 3.6 --- Chapter Summary --- p.43 / Chapter 4 --- Prolonging Path Lifetime with Backup Channel --- p.44 / Chapter 4.1 --- Introduction --- p.44 / Chapter 4.2 --- Non-Shared Backup Protection --- p.45 / Chapter 4.2.1 --- Lifetime of a Path with Non-Shared Backup --- p.45 / Chapter 4.2.2 --- Route Selection for paths with Non-Shared Backup --- p.46 / Chapter 4.3 --- Shared Backup Protection --- p.47 / Chapter 4.3.1 --- Sharing of Backup Capacity --- p.48 / Chapter 4.3.2 --- Lifetime of a Path with Shared Backup --- p.48 / Chapter 4.3.3 --- Route Selection for paths with Shared Backup --- p.50 / Chapter 4.4 --- Simulation Results --- p.50 / Chapter 4.4.1 --- Tightness of Failure Probability Upper Bound for Backup Protection --- p.51 / Chapter 4.4.2 --- Comparison between the Shared Backup and Non Shared Backup schemes --- p.53 / Chapter 4.5 --- Chapter Summary --- p.54 / Chapter 5 --- Finding Capacity-Feasible Routes --- p.55 / Chapter 5.1 --- Introduction --- p.55 / Chapter 5.2 --- Constructing an Edge graph --- p.56 / Chapter 5.3 --- Checking Capacity Feasibility under each Protec- tion Scheme --- p.58 / Chapter 5.3.1 --- No Backup Protection --- p.59 / Chapter 5.3.2 --- Non-Shared Backup Protection --- p.59 / Chapter 5.3.3 --- Shared Backup Protection --- p.60 / Chapter 5.4 --- Chapter Summary --- p.62 / Chapter 6 --- Performance Evaluations and Adaptive Protec- tion --- p.63 / Chapter 6.1 --- Introduction --- p.63 / Chapter 6.2 --- Tradeoffs between Route Selection Algorithms --- p.64 / Chapter 6.3 --- Adaptive Protection --- p.66 / Chapter 6.3.1 --- Route Selection for Adaptive Protection --- p.67 / Chapter 6.3.2 --- Finding a Capacity-Feasible Path for Adaptive Protection --- p.68 / Chapter 6.4 --- Comparison between No Protection and Adaptive Protection --- p.69 / Chapter 6.5 --- Chapter Summary --- p.71 / Chapter 7 --- Restoration Capacity Planning and Channel Assignment --- p.72 / Chapter 7.1 --- Introduction --- p.72 / Chapter 7.2 --- System Model --- p.74 / Chapter 7.2.1 --- Channel Assignment Model --- p.74 / Chapter 7.2.2 --- Presence of Primary Users --- p.75 / Chapter 7.2.3 --- Link Flow Rates --- p.76 / Chapter 7.2.4 --- Problem Formulation --- p.77 / Chapter 7.3 --- Simulation Results --- p.79 / Chapter 7.3.1 --- "Comparison between ""Shared Backup"" and “No Restore Plan"" using Guarantee Percentage and Reduced Capacity" --- p.80 / Chapter 7.3.2 --- Comparison using Traffic Demand Scaling Factor g and Guarantee Fraction p --- p.81 / Chapter 7.3.3 --- Comparison between Optimal Channel Assignment and Random Channel Assignment --- p.84 / Chapter 7.4 --- Chapter Summary --- p.86 / Chapter 8 --- Conclusion and Future Works --- p.87 / Chapter A --- Proof of Theorem (3.1) in Chapter3 --- p.90 / Chapter B --- Proof of Theorem (4.1) in Chapter4 --- p.92 / Bibliography --- p.95

Thesis (M.Phil.)--City University of Hong Kong, 2009. / "Submitted to Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Master of Philosophy." Includes bibliographical references (leaves 66-74)

M. Tech. Electrical Engineering / The radio frequency (RF) spectrum is a natural resource used by wireless network operators to provide radio communication and transmission systems. The availability of RF spectrum to various wireless network operators using large bandwidth and more than one channel, as demanded by their offered services, is challenging, due to RF spectrum scarcity and lack of optimal channel selection. On the other hand, the under-utilisation of licensed spectrum by some primary users' (PU's) networks may be improved by the use of dynamic spectrum access (DSA) techniques. Recent research has shown that a large portion of the assigned spectrum is used irregularly, leading to under-utilisation and waste of valuable frequency resources. The notion of cognitive radio (CR) was introduced to increase the effectiveness and efficiency of spectrum consumption. In a cognitive radio network (CRN), each secondary user (SU) is expected to select the best RF spectrum band for opportunistic use when the PUs have temporarily vacated the spectrum allocated to them. The literature has proposed many spectrum selection methods for selecting vacant spectra in CRNs. However, most of these methods do not consider the channel usage pattern over time by PUs and also do not adequately consider the effect that frequent channel-switching might have on the quality of service requirements of the SUs and the throughput of the CR system. Thus, the proposed scheme, which is heuristic-based spectrum selection scheme for minimal channel switching (HBSSS-MCS) and heuristic-based spectrum selection scheme for maximum throughput (HBSSS-MT) addresses these issues. The main study objectives were as follows: 1) Minimise the frequent switching of channels using HBSS-MCS; 2) Maximise the throughput of the CR system using HBSSS-MT. To achieve the objectives of this research work, the following sub-problems were addressed: 1) To investigate the spectrum selection schemes in cognitive radio network (CRN); 2) To design a learning mechanism to facilitate the selection of a suitable channel in the primary network.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 102-111). Also available in electronic version.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 105-110).

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Akyildiz, Ian; Committee Member: Ingram, Mary Ann; Committee Member: Blough, Douglas; Committee Member: Dovrolis, Konstantinos; Committee Member: Li, Ye. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Includes bibliographical references (p. 124-131).

Thesis (M.S.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 28, 2008) Includes bibliographical references.

Thesis (LL. M.)--University of Toronto, 2005. / Includes bibliographical references (leaves 49-52).

Includes bibliographical references (p. 56-60).