Radio Resource Management for Wireless Mesh Networks Supporting Heterogeneous Traffic

Radio Resource Management for Wireless Mesh Networks Supporting Heterogeneous Traffic
Author :
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Total Pages : 245
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ISBN-10 : OCLC:612351595
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Book Synopsis Radio Resource Management for Wireless Mesh Networks Supporting Heterogeneous Traffic by : Ho Ting Cheng

Download or read book Radio Resource Management for Wireless Mesh Networks Supporting Heterogeneous Traffic written by Ho Ting Cheng and published by . This book was released on 2009 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless mesh networking has emerged as a promising technology for future broadband wireless access, providing a viable and economical solution for both peer-to-peer applications and Internet access. The success of wireless mesh networks (WMNs) is highly contingent on effective radio resource management. In conventional wireless networks, system throughput is usually a common performance metric. However, next-generation broadband wireless access networks including WMNs are anticipated to support multimedia traffic (e.g., voice, video, and data traffic). With heterogeneous traffic, quality-of-service (QoS) provisioning and fairness support are also imperative. Recently, wireless mesh networking for suburban/rural residential areas has been attracting a plethora of attentions from industry and academia. With austere suburban and rural networking environments, multi-hop communications with decentralized resource allocation are preferred. In WMNs without powerful centralized control, simple yet effective resource allocation approaches are desired for the sake of system performance melioration. In this dissertation, we conduct a comprehensive research study on the topic of radio resource management for WMNs supporting multimedia traffic. In specific, this dissertation is intended to shed light on how to effectively and efficiently manage a WMN for suburban/rural residential areas, provide users with high-speed wireless access, support the QoS of multimedia applications, and improve spectrum utilization by means of novel radio resource allocation. As such, five important resource allocation problems for WMNs are addressed, and our research accomplishments are briefly outlined as follows: Firstly, we propose a novel node clustering algorithm with effective subcarrier allocation for WMNs. The proposed node clustering algorithm is QoS-aware, and the subcarrier allocation is optimality-driven and can be performed in a decentralized manner. Simulation results show that, compared to a conventional conflict-graph approach, our proposed approach effectively fosters frequency reuse, thereby improving system performance; Secondly, we propose three approaches for joint power-frequency-time resource allocation. Simulation results show that all of the proposed approaches are effective in provisioning packet-level QoS over their conventional resource allocation counterparts. Our proposed approaches are of low complexity, leading to preferred candidates for practical implementation; Thirdly, to further enhance system performance, we propose two low-complexity node cooperative resource allocation approaches for WMNs with partner selection/allocation. Simulation results show that, with beneficial node cooperation, both proposed approaches are promising in supporting QoS and elevating system throughput over their non-cooperative counterparts; Fourthly, to further utilize the temporarily available radio spectrum, we propose a simple channel sensing order for unlicensed secondary users. By sensing the channels according to the descending order of their achievable rates, we prove that a secondary user should stop at the first sensed free channel for the sake of optimality; and Lastly, we derive a unified optimization framework to effectively attain different degrees of performance tradeoff between throughput and fairness with QoS support. By introducing a bargaining floor, the optimal tradeoff curve between system throughput and fairness can be obtained by solving the proposed optimization problem iteratively.


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