Canonic real-valued radix-2n FFT computations

Yingjie Lao, Keshab K Parhi

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

2 Scopus citations

Abstract

Fast Fourier transform (FFT) is an important digital signal processing (DSP) algorithm for analysis of the phase and frequency components of a time-domain signal. Canonic real-valued FFT (RFFT) approach improves the computation performance by completely eliminating arithmetic redundancies. The major advantage of the canonic RFFTs is that these require the least butterfly operations and only involve real datapath when mapped to architectures. In this paper, we study the performances of canonic RFFT computations for different radix factorizations. We compare various radices RFFTs along with their canonic variants from both arithmetic and architectural perspectives. It is shown that decimation-in-frequency (DIF) RFFT structures require less twiddle factor operations than their decimation-in-time (DIT) counterparts. However, we also show that canonic RFFTs may not be desirable when taking the cost of twiddle factor operations as the major consideration.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages441-446
Number of pages6
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove
Period11/8/1511/11/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Fingerprint

Dive into the research topics of 'Canonic real-valued radix-2n FFT computations'. Together they form a unique fingerprint.

Cite this