Search research articles

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

A Dual-FBG Sensor with Machine Learning for Microstrain-Temperature Decoupling Under Cyanoacrylate Bonding Toward Catheter Applications.

Micromachines·2026

Same author

Pulmonary Infarction Secondary to Prostatitis-induced Venous Thromboembolism Mimicking Lobar Pneumonia in a Young Patient: A Diagnostic Pitfall.

Internal medicine (Tokyo, Japan)·2026

Same author

Development of a monoclonal antibody-based approach for selective enrichment of target <i>Bifidobacterium longum</i> from a complex fecal community.

Gut microbes reports·2026

Same author

Clinical practice guideline for pediatric IgA vasculitis in Japan 2023: a digest-secondary publication.

Clinical and experimental nephrology·2026

Same author

Dual-positive anti-aminoacyl-tRNA synthetase and anti-melanoma differentiation-associated gene 5 antibody-related rapidly progressive interstitial lung disease in a patient with severe coronavirus disease 2019 complicated by probable pulmonary aspergillosis and pneumothorax: a case report.

BMC pulmonary medicine·2026

Same author

Peripheral blood UBA1 variant burden predicts poor outcomes in VEXAS syndrome: a nationwide prospective study.

Annals of the rheumatic diseases·2026

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

Search research articles

Related Experiment Video

Updated: Jul 4, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

Reinforcement learning-based adaptive beam alignment in a photodiode-integrated array antenna module.

Jyun-Wei Li, Yu-Chieh Teng, Shinji Nimura

|February 1, 2024

Summary

This summary is machine-generated.

We developed an intelligent adaptive beam alignment system using reinforcement learning (RL) for millimeter wave (MMW) communications. This system optimizes antenna phase for superior signal quality, meeting 3GPP standards.

More Related Videos

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Published on: June 13, 2023

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

Related Experiment Videos

Last Updated: Jul 4, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Published on: June 13, 2023

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

Area of Science:

Wireless Communications
Antenna Technology
Artificial Intelligence

Background:

Millimeter wave (MMW) communication systems require precise beam alignment for optimal performance.
Traditional beam alignment methods can be complex and time-consuming.
Adaptive antenna systems are crucial for dynamic wireless environments.

Purpose of the Study:

To demonstrate an intelligent adaptive beam alignment scheme using reinforcement learning (RL).
To integrate RL with an 8x8 photonic array antenna for MMW applications.
To autonomously optimize beam alignment for enhanced signal quality.

Main Methods:

Utilized a reinforcement learning (RL) algorithm with Q-table for decision-making.
Represented RL states as phase values, actions as phase changes, and rewards as error vector magnitude (EVM).
Integrated the RL scheme with a 40 GHz photonic array antenna.

Main Results:

The RL scheme autonomously achieved optimal phase for the best EVM performance.
Successfully demonstrated beam alignment in both single- and multiple-user scenarios.
Experimental results consistently met the 3rd Generation Partnership Project (3GPP) criterion for 64-QAM OFDM.

Conclusions:

The proposed intelligent adaptive beam alignment scheme effectively optimizes MMW communication.
Reinforcement learning provides an autonomous and efficient method for beam alignment.
The system ensures reliable signal quality meeting stringent industry standards.