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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 (M.A.)--University of Wisconsin--Madison, 1970. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

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

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper describes research aimed at investigating how to help decision makers devise optimized frequency scheduling and management strategies, both for advanced planning and real-time metrics adjustment. Part of these investigations include research to (i) define the metrics, objectives, and constraints involved in optimal frequency allocation decision-making; (ii) harmonize competing, orthogonal goals when devising candidate solutions; and (iii) devise an architectural strategy for dynamic spectrum allocation and management.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Cognitive radio is a reasonably new branch of research aimed at more fully utilizing the RF spectrum. This is accomplished by allowing wireless communication systems to dynamically choose a frequency band, and a modulation technique, based on the current state of the RF spectrum as perceived by the cognitive radio network. This paper will give a brief introduction of cognitive radio networks, and describe a hardware platform designed at the IFT/UMR Telemetry Learning Center. The test-bed will accommodate future research into cognitive networks, by allowing the user to dynamically change both its carrier frequency and modulation technique through software. A general description of the design of the platform is provided.

In this thesis a distributed, dynamic, load aware, joint power and frequency allocation protocol for 4G networks along with system-level simulated results are presented. In all cellular networks, a key limiting factor for throughput is inter-cell interference, especially at the cell edges. Several methods have been proposed and adopted in each mobile network generation to cancel or suppress its effects, whereas each method has its drawbacks in terms of receiver complexity or additional control nodes. However, the proposed protocol presented here does not impose any architectural changes.  In 4G networks such as LTE, the choice of OFDMA for the air interface has paved the way for selective frequency and power allocation in the available spectrum. Taking advantage of this opportunity, fractional frequency reuse (FFR) has been proposed in OFDMA based mobile networks in order to reduce the throughput loss at the cell edges due to inter-cell interference. In FFR, center users lose part of available spectrum that is dedicated to the edge users.  Our protocol aims to minimize this loss of center users incurred by FFR, at the cost of minimal degradation at the edges. An eNodeB, only when overloaded, requests its neighbours’ edge band to be used for its center users at a reduced power level. This is done via small message exchange between the eNodeBs. The neighbors of the overloaded eNodeBs solve a small local knapsack problem to decide whether band lending is feasible or not. A distinguishing feature of this protocol is the power level adjustment for the borrowed band, which is mutually decided by the borrower and lender. The band is released when it is not needed or it is causing unacceptable loss to the lender. The implementation is done in a Matlab based LTE system level simulator. For the implementation of our protocol in the simulator, starting point was implementation of FFR-3 functionality, a prerequisite and a baseline for comparison with our protocol. Results are compared among three different setups of re-use1, FFR-3 and our protocol by varying number of overloaded eNodeBs for various numbers of scenarios and the comparison is made based on the center users’ throughput, edge users’ throughput. An estimation of time and protocol overhead is also presented. We have observed center users’ throughput gain up to 46%, at the cost of 9% edge users’ throughput loss, when compared to the classic FFR-3 scheme. The overall system throughput goes up to 26 % in heavily loaded scenario.   The utility of the protocol for an LTE system is evident from the results, which is supported by the dynamic and decentralized nature of the protocol. This ensures better utilization of spectrum, by temporarily allocating more bandwidth where it is needed more.