Session reliability and capacity allocation in dynamic spectrum access networks.

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.iAcknowledgement --- p.ivChapter 1 --- Introduction / Motivation --- p.1Chapter 2 --- Literature Review --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Dynamic Spectrum Access Networks --- p.8Chapter 2.3 --- Reliability --- p.10Chapter 2.3.1 --- Reliability in Wireless Networks --- p.10Chapter 2.3.2 --- Reliability in Wireline Networks --- p.11Chapter 2.4 --- Capacity Planning in Wireless Mesh Networks --- p.14Chapter 2.4.1 --- Interference Model --- p.14Chapter 2.4.2 --- Link Capacity Constraint --- p.15Chapter 2.4.3 --- Feasible Path --- p.16Chapter 2.4.4 --- Optimal Capacity Allocation in DSA Net- works and Wireless Mesh Networks --- p.16Chapter 2.5 --- Chapter Summary --- p.18Chapter 3 --- Lifetime Aware Routing without Backup --- p.19Chapter 3.1 --- Introduction --- p.19Chapter 3.2 --- System Model --- p.20Chapter 3.3 --- Lifetime Distribution of a Path without Backup Protection --- p.22Chapter 3.3.1 --- Exact Lifetime Distribution --- p.23Chapter 3.3.2 --- The Chain Approximation --- p.24Chapter 3.4 --- Route Selection without Backup Protection --- p.26Chapter 3.4.1 --- NP-Hardness of Finding Maximum Lifetime Path --- p.26Chapter 3.4.2 --- The Minimum Weight Algorithm --- p.28Chapter 3.4.3 --- Greedy Algorithm --- p.28Chapter 3.4.4 --- GACA - The Greedy Algorithm using the Chain Approximation --- p.32Chapter 3.5 --- Simulation Results --- p.33Chapter 3.5.1 --- Tightness of the Chain Approximation Bound for Vulnerable Area --- p.33Chapter 3.5.2 --- Comparison between Greedy and GACA using Guaranteed Lifetime --- p.36Chapter 3.5.3 --- Factors impacting the performance of GACA --- p.37Chapter 3.6 --- Chapter Summary --- p.43Chapter 4 --- Prolonging Path Lifetime with Backup Channel --- p.44Chapter 4.1 --- Introduction --- p.44Chapter 4.2 --- Non-Shared Backup Protection --- p.45Chapter 4.2.1 --- Lifetime of a Path with Non-Shared Backup --- p.45Chapter 4.2.2 --- Route Selection for paths with Non-Shared Backup --- p.46Chapter 4.3 --- Shared Backup Protection --- p.47Chapter 4.3.1 --- Sharing of Backup Capacity --- p.48Chapter 4.3.2 --- Lifetime of a Path with Shared Backup --- p.48Chapter 4.3.3 --- Route Selection for paths with Shared Backup --- p.50Chapter 4.4 --- Simulation Results --- p.50Chapter 4.4.1 --- Tightness of Failure Probability Upper Bound for Backup Protection --- p.51Chapter 4.4.2 --- Comparison between the Shared Backup and Non Shared Backup schemes --- p.53Chapter 4.5 --- Chapter Summary --- p.54Chapter 5 --- Finding Capacity-Feasible Routes --- p.55Chapter 5.1 --- Introduction --- p.55Chapter 5.2 --- Constructing an Edge graph --- p.56Chapter 5.3 --- Checking Capacity Feasibility under each Protec- tion Scheme --- p.58Chapter 5.3.1 --- No Backup Protection --- p.59Chapter 5.3.2 --- Non-Shared Backup Protection --- p.59Chapter 5.3.3 --- Shared Backup Protection --- p.60Chapter 5.4 --- Chapter Summary --- p.62Chapter 6 --- Performance Evaluations and Adaptive Protec- tion --- p.63Chapter 6.1 --- Introduction --- p.63Chapter 6.2 --- Tradeoffs between Route Selection Algorithms --- p.64Chapter 6.3 --- Adaptive Protection --- p.66Chapter 6.3.1 --- Route Selection for Adaptive Protection --- p.67Chapter 6.3.2 --- Finding a Capacity-Feasible Path for Adaptive Protection --- p.68Chapter 6.4 --- Comparison between No Protection and Adaptive Protection --- p.69Chapter 6.5 --- Chapter Summary --- p.71Chapter 7 --- Restoration Capacity Planning and Channel Assignment --- p.72Chapter 7.1 --- Introduction --- p.72Chapter 7.2 --- System Model --- p.74Chapter 7.2.1 --- Channel Assignment Model --- p.74Chapter 7.2.2 --- Presence of Primary Users --- p.75Chapter 7.2.3 --- Link Flow Rates --- p.76Chapter 7.2.4 --- Problem Formulation --- p.77Chapter 7.3 --- Simulation Results --- p.79Chapter 7.3.1 --- "Comparison between ""Shared Backup"" and “No Restore Plan"" using Guarantee Percentage and Reduced Capacity" --- p.80Chapter 7.3.2 --- Comparison using Traffic Demand Scaling Factor g and Guarantee Fraction p --- p.81Chapter 7.3.3 --- Comparison between Optimal Channel Assignment and Random Channel Assignment --- p.84Chapter 7.4 --- Chapter Summary --- p.86Chapter 8 --- Conclusion and Future Works --- p.87Chapter A --- Proof of Theorem (3.1) in Chapter3 --- p.90Chapter B --- Proof of Theorem (4.1) in Chapter4 --- p.92Bibliography --- p.9

D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks

The Fifth Generation (5G) wireless service of sensor networks involves significant challenges when dealing with the coordination of ever-increasing number of devices accessing shared resources. This has drawn major interest from the research community as many existing works focus on the radio access network congestion control to efficiently manage resources in the context of device-to-device (D2D) interaction in huge sensor networks. In this context, this paper pioneers a study on the impact of D2D link reliability in group-assisted random access protocols, by shedding the light on beneficial performance and potential limitations of approaches of this kind against tunable parameters such as group size, number of sensors and reliability of D2D links. Additionally, we leverage on the association with a Geolocation Database (GDB) capability to assist the grouping decisions by drawing parallels with recent regulatory-driven initiatives around GDBs and arguing benefits of the suggested proposal. Finally, the proposed method is approved to significantly reduce the delay over random access channels, by means of an exhaustive simulation campaign.Comment: First submission to IEEE Communications Magazine on Oct.28.2017. Accepted on Aug.18.2019. This is the camera-ready versio