PAPR Minimization Technique in MIMO V2V Transmission using SVM-based Channel Estimation

Sonam Jain, Ranjan Bose

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

Abstract

In this paper, linear support vector machine (SVM), a well known machine learning algorithm is exploited for estimating the channel. Further, a linear precoding scheme for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) system is proposed for V2V transmission. The key idea of the scheme is to generate the precoded signal by the use of the estimated channel information. Selection of the optimum precoding matrix criteria directly relates to minimizing the probability of symbol error rate, whereas, codebook designing is based on Grassmanian subspace packing. Theoretical analysis and simulation result validate that the proposed scheme has the ability to provide large PAPR reduction and low computational complexity in space time block code (STBC) based MIMOOFDM system, and therefore, leads to improvement in the system performance.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728192901
DOIs
StatePublished - Dec 14 2020
Externally publishedYes
Event2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020 - New Delhi, India
Duration: Dec 14 2020Dec 17 2020

Publication series

NameInternational Symposium on Advanced Networks and Telecommunication Systems, ANTS
Volume2020-December
ISSN (Print)2153-1684

Conference

Conference2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020
Country/TerritoryIndia
CityNew Delhi
Period12/14/2012/17/20

Keywords

  • multiple-input multiple-output (MIMO)
  • orthogonal frequency division multiplexing (OFDM)
  • peak to average power ratio (PAPR)
  • space time block code (STBC)
  • support vector machine (SVM)

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