Revolutionizing Microwave Circuit Design with Machine Learning: Challenges and Opportunities

The progress of wireless communication systems is highly dependent on microwave technologies. With the escalating adoption of higher frequency bands in communication systems and the exponential growth of transistor densities in line with Moore's Law, microwave circuit design complexity and costs are mounting significantly. Machine learning (ML) has recently arisen as a transformative tool for improving wireless communications through various means. This research reviews existing ML-aided microwave circuit behavior analysis techniques, assesses the performance through case studies, and elaborates on various design methods to streamline microwave circuit design. This article also highlights the challenges and potential solutions for improving the efficiency and cost-effectiveness of design processes. Future research needs to focus on developing advanced ML architectures and integrating them into existing design methodologies to revolutionize microwave design and accelerate the advancement of wireless communication systems.

Authors

Institutions

Publication Details

Journal
IEEE Communications Magazine
Published
2024-08-01
DOI
https://doi.org/10.1109/mcom.001.2400209
Citations
8
Primary Topic
Microwave Engineering and Waveguides
Type
article
Field-Weighted Citation Impact
1.67

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Revolutionizing Microwave Circuit Design with Machine Learning: Challenges and Opportunities

Jiteng Ma, Gavin T. Watkins, Robert J. Piechocki, Shuping Dang et al.
8 citations
IEEE Communications Magazine
Microwave Engineering and Waveguides
1.67
article

Revolutionizing Microwave Circuit Design with Machine Learning: Challenges and Opportunities

Jiteng Ma, Gavin T. Watkins, Robert J. Piechocki, Shuping Dang, Mark A Beach, John Haine, Liang Qiao
article en
8 citations

Abstract

The progress of wireless communication systems is highly dependent on microwave technologies. With the escalating adoption of higher frequency bands in communication systems and the exponential growth of transistor densities in line with Moore's Law, microwave circuit design complexity and costs are mounting significantly. Machine learning (ML) has recently arisen as a transformative tool for improving wireless communications through various means. This research reviews existing ML-aided microwave circuit behavior analysis techniques, assesses the performance through case studies, and elaborates on various design methods to streamline microwave circuit design. This article also highlights the challenges and potential solutions for improving the efficiency and cost-effectiveness of design processes. Future research needs to focus on developing advanced ML architectures and integrating them into existing design methodologies to revolutionize microwave design and accelerate the advancement of wireless communication systems.

IEEE Communications MagazineVol. 62(8)
University of Bristol (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 16%
Microwave Engineering and Waveguides
1.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Revolutionizing Microwave Circuit Design with Machine Learning: Challenges and Opportunities — Jiteng Ma, Gavin T. Watkins, et al. · IEEE Communications Magazine (2024) | TGRS Research Map | TGRS