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

Mahdi Ben Ghorbel, Brian Berscheid, Ebrahim Bedeer Mohamed, Jahangir Hossain, Colin Howlett, Julian Cheng

Research output: Contribution to journalArticle

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.
LanguageEnglish
Pages1-12
JournalIEEE Transactions on Networks and Service Management
Volumena
Publication statusPublished - 19 Apr 2018

Fingerprint

Cables
Specifications
Clustering algorithms
Extrapolation
Principal component analysis
Throughput
Modulation
Processing

Keywords

  • Adaptive modulation
  • clustering
  • data over cable networks
  • profile optimization.

Cite this

Ben Ghorbel, M., Berscheid, B., Mohamed, E. B., Hossain, J., Howlett, C., & Cheng, J. (2018). Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1. na, 1-12.
Ben Ghorbel, Mahdi ; Berscheid, Brian ; Mohamed, Ebrahim Bedeer ; Hossain, Jahangir ; Howlett, Colin ; Cheng, Julian. / Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1. 2018 ; Vol. na. pp. 1-12.
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Ben Ghorbel, M, Berscheid, B, Mohamed, EB, Hossain, J, Howlett, C & Cheng, J 2018, 'Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1', vol. na, pp. 1-12.

Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1. / Ben Ghorbel, Mahdi; Berscheid, Brian; Mohamed, Ebrahim Bedeer; Hossain, Jahangir; Howlett, Colin; Cheng, Julian.

Vol. na, 19.04.2018, p. 1-12.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1

AU - Ben Ghorbel, Mahdi

AU - Berscheid, Brian

AU - Mohamed, Ebrahim Bedeer

AU - Hossain, Jahangir

AU - Howlett, Colin

AU - Cheng, Julian

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Ben Ghorbel M, Berscheid B, Mohamed EB, Hossain J, Howlett C, Cheng J. Principal Component-based Approach for Profile Optimization Algorithms in DOCSIS 3.1. 2018 Apr 19;na:1-12.