A useful lemma for bit error rate of the noise imbalanced MRC combining over correlated and uncorrelated fading channels

Ferkan Yilmaz, Mohamed Slim Alouini

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

7 Scopus citations

Abstract

In the literature, much attention has been devoted to the analysis of maximal-ratio combining (MRC) whose diversity branches are subjected to the balanced noise levels. However, this paper considers the noise imbalanced MRC (NI-MRC), i.e., the multi-branch MRC whose diversity branches are together subjected to some significant imbalance of noise levels due to the estimation error of the noise power knowledges used to maximize combining the signals from diversity branches. To the best of our knowledge, this paper proposes the first analytical approach in the literature to exactly evaluate the bit error rate (BER) of binary modulation schemes over either correlated or uncorrelated fading channels. Based on moment generating function (MGF), the proposed approach is exemplified for the well-known correlated fading conditions, and verified by computer based simulations to elucidate the effect of noise imbalance (NI) on the BER performance.

Original languageEnglish (US)
Title of host publication26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538615010
DOIs
StatePublished - Jul 5 2018
Event26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey
Duration: May 2 2018May 5 2018

Publication series

Name26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/TerritoryTurkey
CityIzmir
Period05/2/1805/5/18

Keywords

  • Imperfect noise variance knowledge
  • Maximal ratio combining
  • Moment-generating function
  • Noise imbalance
  • Symbol error probability

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Computer Networks and Communications
  • Computer Science Applications

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