Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Aliasing

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...

Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....

You might also read

Related Articles

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

Sort by

Same author

EPRS: Experience-Prioritized Reinforcement Scheduler in Edge Clusters.

Sensors (Basel, Switzerland)·2026

Same author

Graph Neural Networks for Fault Diagnosis in Photovoltaic-Integrated Distribution Networks with Weak Features.

Sensors (Basel, Switzerland)·2025

Same author

Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing.

Sensors (Basel, Switzerland)·2025

Same author

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing.

Sensors (Basel, Switzerland)·2024

Same author

Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing.

Sensors (Basel, Switzerland)·2023

Same author

Arithmetic Optimization AOMDV Routing Protocol for FANETs.

Sensors (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Jul 19, 2025

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

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

Published on: June 25, 2021

An Improved SAMP Algorithm for Sparse Channel Estimation in OFDM System.

Hao Hu¹, Xu Zhao², Shiyong Chen¹

¹School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This summary is machine-generated.

This study introduces an Improved SAMP (ImpSAMP) algorithm for better sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. The new method enhances both efficiency and accuracy in channel state information estimation.

Keywords:

channel estimation compressed sensing denoise step-size adjustment

More Related Videos

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Related Experiment Videos

Last Updated: Jul 19, 2025

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

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

Published on: June 25, 2021

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Area of Science:

Signal Processing
Wireless Communications
Information Theory

Background:

Orthogonal Frequency Division Multiplexing (OFDM) systems require efficient channel estimation to optimize spectrum utilization.
Traditional algorithms like Sparse Adaptive Matching Pursuit (SAMP) face limitations in estimation efficiency and accuracy due to fixed step sizes.
Pilot overhead in OFDM systems can be reduced through compressed sensing techniques.

Purpose of the Study:

To propose an Improved SAMP (ImpSAMP) algorithm for enhanced sparse channel estimation in OFDM systems.
To address the limitations of traditional SAMP algorithms regarding estimation efficiency and accuracy.
To improve the utilization rate of spectrum resources by reducing pilot overhead.

Main Methods:

Implemented a denoising step using energy detection to mitigate interference during channel estimation.
Introduced a dynamic step size adjustment mechanism based on the l2 norm of adjacent sparse channel coefficient differences.
Utilized a double threshold judgment strategy to further improve estimation efficiency.

Main Results:

The proposed ImpSAMP algorithm demonstrated superior performance compared to the traditional SAMP algorithm.
Significant improvements in both estimation efficiency and accuracy were observed with the ImpSAMP algorithm.
The denoising and dynamic step size adjustments effectively reduced interference and accelerated convergence.

Conclusions:

The ImpSAMP algorithm offers a more efficient and accurate approach to channel estimation in OFDM systems.
Dynamic step size adjustment and denoising are key factors in the enhanced performance of ImpSAMP.
This improved method contributes to better spectrum resource utilization in wireless communication systems.