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Title: Interference-aware adaptive spectrum management for wireless networks using unlicensed frequency bands
Author: Pediaditaki, Sofia
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2012
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The growing demand for ubiquitous broadband network connectivity and continuously falling prices in hardware operating on the unlicensed bands have put Wi-Fi technology in a position to lead the way in rapid innovation towards high performance wireless for the future. The success story of Wi-Fi contributed to the development of widespread variety of options for unlicensed access (e.g., Bluetooth, Zigbee) and has even sparked regulatory bodies in several countries to permit access to unlicensed devices in portions of the spectrum initially licensed to TV services. In this thesis we present novel spectrum management algorithms for networks employing 802.11 and TV white spaces broadly aimed at efficient use of spectrum under consideration, lower contention (interference) and high performance. One of the target scenarios of this thesis is neighbourhood or citywide wireless access. For this, we propose the use of IEEE 802.11-based multi-radio wireless mesh network using omnidirectional antennae. We develop a novel scalable protocol termed LCAP for efficient and adaptive distributed multi-radio channel allocation. In LCAP, nodes autonomously learn their channel allocation based on neighbourhood and channel usage information. This information is obtained via a novel neighbour discovery protocol, which is effective even when nodes do not share a common channel. Extensive simulation-based evaluation of LCAP relative to the state-of-the-art Asynchronous Distributed Colouring (ADC) protocol demonstrates that LCAP is able to achieve its stated objectives. These objectives include efficient channel utilisation across diverse traffic patterns, protocol scalability and adaptivity to factors such as external interference. Motivated by the non-stationary nature of the network scenario and the resulting difficulty of establishing convergence of LCAP, we consider a deterministic alternative. This approach employs a novel distributed priority-based mechanism where nodes decide on their channel allocations based on only local information. Key enabler of this approach is our neighbour discovery mechanism. We show via simulations that this mechanism exhibits similar performance to LCAP. Another application scenario considered in this thesis is broadband access to rural areas. For such scenarios, we consider the use of long-distance 802.11 mesh networks and present a novel mechanism to address the channel allocation problem in a traffic-aware manner. The proposed approach employs a multi-radio architecture using directional antennae. Under this architecture, we exploit the capability of the 802.11 hardware to use different channel widths and assign widths to links based on their relative traffic volume such that side-lobe interference is mitigated. We show that this problem is NP-complete and propose a polynomial time, greedy channel allocation algorithm that guarantees valid channel allocations for each node. Evaluation of the proposed algorithm via simulations of real network topologies shows that it consistently outperforms fixed width allocation due to its ability to adapt to spatio-temporal variations in traffic demands. Finally, we consider the use of TV-white-spaces to increase throughput for in-home wireless networking and relieve the already congested unlicensed bands. To the best of our knowledge, our work is the first to develop a scalable micro auctioning mechanism for sharing of TV white space spectrum through a geolocation database. The goal of our approach is to minimise contention among secondary users, while not interfering with primary users of TV white space spectrum (TV receivers and microphone users). It enables interference-free and dynamic sharing of TVWS among home networks with heterogeneous spectrum demands, while resulting in revenue generation for database and broadband providers. Using white space availability maps from the UK, we validate our approach in real rural, urban and dense-urban residential scenarios. Our results show that our mechanism is able to achieve its stated objectives of attractiveness to both the database provider and spectrum requesters, scalability and efficiency for dynamic spectrum distribution in an interference-free manner.
Supervisor: Marina, Mahesh ; Franke, Bjoern Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: adaptive spectrum management ; unlicensed spectrum use ; multi-radio wireless mesh networks ; cognitive radios ; learning automata ; neighbour discovery ; distributed channel allocation