Sequence Design for Frame Detection Based on Autocorrelation

Ana Belen Martinez, Atul Kumar, Marwa Chafii, Gerhard Fettweis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autocorrelation (AC) is one of the most frequently used methods for initial acquisition. Usually, algorithms that employ a metric based on AC for this purpose, focus on the estimation of synchronization parameters, ignoring the detection performance. This paper analyses the relationship between the structure of the reference sequence that serves to compute the AC and the corresponding attainable detection performance. The distributions of test statistics based on AC for frame detection are derived and validated through simulations in a frequency-flat fading channel. It is shown that the use of AC can outperform, in terms of detection performance, the more complex matched filtering in a certain signal-to-noise ratio region, at the cost of significantly longer required sequences. Besides, we show that a set of sequences, with different periodic structures, can provide the same target detection probability, allowing a flexible sequence design.

Original languageEnglish (US)
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189642
DOIs
StatePublished - Apr 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: Apr 25 2021Apr 28 2021

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-April
ISSN (Print)1550-2252

Conference

Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online
Period4/25/214/28/21

Keywords

  • autocorrelation
  • Detection probability
  • matched filtering
  • reference sequence
  • synchronization

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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