Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

327
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
327
Discrete Fourier Transform01:15

Discrete Fourier Transform

360
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
360
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

441
The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the...
441
Fast Fourier Transform01:10

Fast Fourier Transform

420
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
420
Design Example01:23

Design Example

350
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
350
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

420
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
420

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering.

Sensors (Basel, Switzerland)·2022
Same author

Quantitative proteomics identifies surfactant-resistant alpha-synuclein in cerebral cortex of Parkinsonism-dementia complex of Guam but not Alzheimer's disease or progressive supranuclear palsy.

The American journal of pathology·2007
Same author

Different supramolecular assemblies in two 1:1 proton-transfer compounds of sulfobenzoic acids with aromatic amines.

Acta crystallographica. Section C, Crystal structure communications·2007
Same author

Identification of proteins involved in microglial endocytosis of alpha-synuclein.

Journal of proteome research·2007
Same author

Biomarkers for Alzheimer's disease.

Expert review of neurotherapeutics·2007
Same author

[Role of sympathetic nerve activity and arterial endothelial function in pathogenesis of hypertension in patients with obstructive sleep apnea-hypopnea syndrome].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2007

Related Experiment Video

Updated: Aug 10, 2025

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.1K

FDSS-Based DFT-s-OFDM for 6G Wireless Sensing.

Lu Chen1, Jianxiong Pan2,3, Jing Zhang4

  • 1School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

Frequency-domain spectral shaping enhances discrete Fourier transform spread orthogonal frequency-division multiplexing for integrated sensing and communications. This boosts sensing accuracy and reduces signal power, improving 6G system performance.

Keywords:
DFT-s-OFDMFDSSISACPAPRambiguity functionestimation error

More Related Videos

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
09:03

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

7.2K
Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K

Related Experiment Videos

Last Updated: Aug 10, 2025

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.1K
A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
09:03

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

7.2K
Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Telecommunications

Background:

  • Integrated Sensing and Communications (ISAC) is a key 6G technology.
  • Discrete Fourier Transform spread Orthogonal Frequency-Division Multiplexing (DFT-s-OFDM) offers advantages for ISAC, including low Peak-to-Average Power Ratio (PAPR) and suitability for high-frequency transmission.
  • However, DFT-s-OFDM exhibits lower sensing accuracy compared to traditional Orthogonal Frequency-Division Multiplexing (OFDM).

Purpose of the Study:

  • To enhance the sensing accuracy and reduce the PAPR of DFT-s-OFDM for ISAC applications.
  • To investigate the impact of frequency-domain signal characteristics on sensing performance.
  • To propose a novel framework for improving DFT-s-OFDM in ISAC systems.

Main Methods:

  • A signal model for ISAC systems was established.
  • Frequency-Domain Spectral Shaping (FDSS) was employed to adjust signal correlations.
  • Two types of FDSS filters were designed: a pre-equalization filter and an Isotropic Orthogonal Transform Algorithm (IOTA) filter.

Main Results:

  • The proposed FDSS-enhanced DFT-s-OFDM scheme achieved approximately 4 dB performance gain in sensing accuracy compared to standard DFT-s-OFDM.
  • Significant reduction in PAPR was observed.
  • Improved power amplifier efficiency was demonstrated.

Conclusions:

  • FDSS is an effective technique for enhancing DFT-s-OFDM performance in ISAC systems.
  • The proposed FDSS-enhanced DFT-s-OFDM framework offers a promising solution for improving both sensing accuracy and power efficiency in 6G communications.
  • This approach addresses key limitations of DFT-s-OFDM, paving the way for more robust ISAC deployments.