Efficient bit-interleaved APSK scheme for LDPC codes

  • Meixiang Zhang*
  • , Sooyoung Kim
  • , Won Yong Kim
  • , Yong Hoon Cho
  • *Corresponding author for this work

Research output: Contribution to conferenceConference paperpeer-review

Abstract

This paper presents a new bit-interleaved higher-order APSK scheme for LDPC codes. Although the maximum decoding performance can be achieved using the maximum likelihood (ML) soft detection scheme, the complexity increases exponentially with the modulation orders. We approximate the ML soft detection scheme by applying the hard decision threshold (HDT) based method for a modified constellation of 32-APSK. In order to further improve the BER performance, we combined an efficient bit interleaving scheme, by considering that BER performance of each bit contained in a symbol is not uniform. Simulation results demonstrate that the proposed bit-interleaved scheme with very simple HDT based soft detection can achieve better performance compared to the ML detection of conventional scheme.

Original languageEnglish
Title of host publication2012 6th Advanced Satellite Multimedia Systems Conference, ASMS 2012 and 12th Signal Processing for Space Communications Workshop, SPSC 2012
Pages49-53
Number of pages5
DOIs
StatePublished - 2012
Event2012 6th Advanced Satellite Multimedia Systems Conference, ASMS 2012 and 12th Signal Processing for Space Communications Workshop, SPSC 2012 - Baiona, Spain
Duration: 2012.09.52012.09.7

Publication series

Name2012 6th Advanced Satellite Multimedia Systems Conference, ASMS 2012 and 12th Signal Processing for Space Communications Workshop, SPSC 2012

Conference

Conference2012 6th Advanced Satellite Multimedia Systems Conference, ASMS 2012 and 12th Signal Processing for Space Communications Workshop, SPSC 2012
Country/TerritorySpain
CityBaiona
Period12.09.512.09.7

Keywords

  • APSK
  • Bit-interleaving
  • DVB-S2
  • LDPC
  • soft demapping

Quacquarelli Symonds(QS) Subject Topics

  • Computer Science & Information Systems
  • Communication & Media Studies
  • Data Science

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