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Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1

Ben Ghorbel, Mahdi, Berscheid, Brian, Mohamed, Ebrahim Bedeer, Hossain, Jahangir, Howlett, Colin and Cheng, Julian (2018) Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1. IEEE Transactions on Networks and Service Management, na . pp. 1-12. [Journal article]

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Abstract

Data over cable service interface specification (DOCSIS) introduced the possibility of a variable bit-loading over the subcarriers within a channel in its release DOCSIS 3.1. This variable bit-loading will improve the data rates. However, to limit the encoding processing overhead, the concept of profiles was introduced. Each profile defines the modulation per subcarrier for a given channel while the number of allowed profiles is limited. Thus, an efficient profile assignment scheme, which determines the best set of profiles based on the users’ channel conditions, is needed. Although various profile assignment algorithms have been proposed in the literature, realistic evaluation of these schemes has been difficult, as channel quality measurements of real DOCSIS 3.1 systems has not previously been available. In this paper, we exploit DOCSIS 3.1 measurement data to evaluate performance of the proposed algorithms. We propose to employ principal component analysis to derive low-dimensional clustering variables in order to ensure efficient profile optimization. We show how this technique can be employed with different clustering algorithms to improve the spectrum efficiency of the profiles by extracting the most important information of the channels in low-dimensional vectors. This not only reduces the complexity of the clustering, but also ensures better throughput. Moreover, we adapt the clustering algorithms to tailor them to the profile optimization problem. Finally, we present an exhaustive simulation-based performance analysis to compare the different algorithms for various scenarios using extrapolation of the measurements data.

Item Type:Journal article
Keywords:Adaptive modulation, clustering, data over cable networks, profile optimization.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Engineering
ID Code:39979
Deposited By: Dr Ebrahim Bedeer Mohamed
Deposited On:20 Apr 2018 14:05
Last Modified:20 Apr 2018 14:05

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