Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.646397
Title: Optimisation analytics for bandwidth resource management in converted IP networks
Author: Sheykhkanloo, Naghmeh Moradpoor
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2012
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Abstract:
The Internet Protocol (IP) based converged Next Generation Networks (NGN) [130] appears in order to provide an efficient, cost-aware and reliable network infrastructure in support of emerging sophisticated and bandwidth hungry applications and services [129]. Addressing the International Telecommunications Union - Telecommunication Standardisation Sector (ITU-T) [132], the NGN brings significant advantages to telecom companies as well as Subscriber Stations (SSs) such as support for End-to-End (ETE) Quality of Service (QoS), mobility features, converged services and applications as well as converged infrastructure between fixed and mobile networks. The ultimate goal of the NGN is to provide the Internet applications and services wherever, whenever and in whatever format with reasonable costs for both SSs and telecom companies as well as the satisfactory coverage, capacity, speed and maintenance. Optical technology, as a best nominee for the next generation fixed broadband access networks, is tied up and restricted to the fixed infrastructure but wherever it goes it provides the huge bandwidth with relatively lower cost for both SSs and telecom companies. On the other hand, wireless technology supports flexibility as well as mobility features and is not tied up to the fixed infrastructure but it is highly restricted to the capacity, transmission power as well as the transmission range. Taking into consideration the converged infrastructure of the NGN [132], the future broadband applications and services must leverage on both fixed and wireless technologies which forms the idea for development of the integrated fixed, particularly optical, and wireless access networks. However, in order to successfully integrated these two technologies there are some technical concerns in terms of architectural aspects, physical layer issues and Media Access Control (MAC) related topics which need to be addressed effectively and efficiently in order to provide the smooth End-to-End (ETE) integrated structure and optimum or near optimum utilisation of network resources. This thesis takes up the challenge of addressing these issues by providing a detailed converged framework with support of a distributed, real-time, dynamic, scalable and intelligent wavelength and bandwidth allocation algorithm for the converged scenario of the NGN. The conventional works related to optical and wireless technology, where a traditional single channel optical network has been employed as a backhaul solution for the wireless counterpart, do have some shortcomings in providing the level of capacity, scalability and intelligence which is required in the current NGN environment [131]. The integrated scenario between the multi-channel optical network and wireless counterpart has gained popularity as the foundation of providing the higher bandwidth and capacity due to employing the multi wavelengths over a same fibre infrastructure with great security and protocol transparency [24]. On the other hand optimisation techniques [84] have attracted huge attention particularly in telecommunication field as the foundation of compilation speed, real-time support, low error level, scalability, CPU overhead and memory usage. Once appropriately coded they can provide the selection of the optimum or near optimum elements from some set of the available alternatives with relatively low error levels. Hence, the overall objective of this thesis is design, development and evaluation of an intelligent and dynamic resource (wavelength/bandwidth) allocation algorithm for multi-channel optical network integration with wireless technology with the support of optimisation techniques. In the pursuit of fulfilling the addressed objectives of this thesis, a Genetic Algorithm (GA) optimisation technique emerged as an efficient solution to the identified resource allocation problems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.646397  DOI: Not available
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