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

Cascaded Op Amps01:16

Cascaded Op Amps

Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Energy Stored In A Coaxial Cable01:31

Energy Stored In A Coaxial Cable

A coaxial cable consists of a central copper conductor used for transmitting signals, followed by an insulator shield, a metallic braided mesh that prevents signal interference, and a plastic layer that encases the entire assembly.
In the simplest form, a coaxial cable can be represented by two long hollow concentric cylinders in which the current flows in opposite directions. The magnetic field inside and outside the coaxial cable is determined by using Ampère's law. The magnetic field inside...

You might also read

Related Articles

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

Sort by
Same author

Hybrid Deployment Optimization Algorithm for Reconfigurable Intelligent Surface.

Sensors (Basel, Switzerland)·2025
Same author

Robust Beamforming Design for Energy Efficiency and Spectral Efficiency Tradeoff in Multi-STAR-RIS-Aided C-HRSMA.

Sensors (Basel, Switzerland)·2025
Same author

Adaptive Channel Estimation for Semi-Passive IRS with Optimized Sensor Deployment.

Sensors (Basel, Switzerland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Two-Stage Sparse Recovery for Off-Grid Cascaded Channel Estimation in RIS-Assisted mmWave Systems.

Zhiyu Han1, Qiuyan Liu2, Yanxia Cao2

  • 1The Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the TS-PO algorithm for accurate channel estimation in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems. It effectively reduces estimation errors and improves accuracy, even with limited pilot data.

Keywords:
channel estimationenergy leakagelinear bregman iterationpilot overheadreconfigurable intelligent surface (RIS)

Related Experiment Videos

Last Updated: May 28, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Area of Science:

  • Wireless communication systems
  • Signal processing
  • Electromagnetics

Background:

  • Accurate channel estimation is vital for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems.
  • Conventional compressive sensing estimators face performance degradation due to off-grid leakage in spatial angle estimation.

Purpose of the Study:

  • To propose a novel two-stage channel estimation framework to address off-grid leakage in RIS-assisted mmWave systems.
  • To develop an algorithm, TS-PO, that enhances channel estimation accuracy and mitigates performance degradation.

Main Methods:

  • A two-stage framework involving support selection and amplitude recovery.
  • Utilizing a preconditioned linear Bregman iteration (PLBI) for channel support identification.
  • Employing localized orthogonal matching pursuit (OMP) for accurate physical channel gain recovery.

Main Results:

  • The TS-PO algorithm effectively suppresses off-grid energy leakage.
  • Significant mitigation of the estimation error floor was observed.
  • High reconstruction accuracy was achieved under strict pilot overhead constraints.
  • Demonstrated strong robustness in dense multipath environments.

Conclusions:

  • The proposed TS-PO framework offers a robust solution for channel estimation in RIS-assisted mmWave systems.
  • It overcomes limitations of conventional methods by effectively handling off-grid effects.
  • The algorithm provides accurate and reliable channel state information, crucial for system performance.