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Rateless coding scheme based on Autoregressive-moving-average model over ka-band links

  • Harbin Institute of Technology Shenzhen
  • The University of Sydney

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Future deep space explorations and spatial information networks (SINs) are with the increasing demands for high data rate services, the application of Ka-band channel is viewed as a primary solution. However, the Ka-band channel is much more sensitive to the weather conditions than S/C/X-band channels. In this paper, the noise temperature on Ka-band channel is modeled as N-states Markov channel, and a practical time-varying rain attenuation prediction model is proposed based on the Autoregressive-Moving-Average (ARMA) model. By considering the tradeoffs between transmission latency, reliability and power allocations consumptions, we design an adaptive rateless coding scheme based on the analog fountain codes. Simulation results show that our proposed scheme can effectively improve the throughput and efficiency.

Original languageEnglish
Title of host publicationCommunications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems
EditorsQilian Liang, Min Jia, Jiasong Mu, Wei Wang, Xuhong Feng, Baoju Zhang
PublisherSpringer Verlag
Pages1191-1198
Number of pages8
ISBN (Print)9789811065705
DOIs
StatePublished - 2019
Externally publishedYes
Event6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 - Harbin, China
Duration: 14 Jul 201716 Jul 2017

Publication series

NameLecture Notes in Electrical Engineering
Volume463
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017
Country/TerritoryChina
CityHarbin
Period14/07/1716/07/17

Keywords

  • Adaptive rateless coding
  • Autoregressive-Moving-Average model
  • Ka-band
  • Spatial information networks

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