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Advanced Millimeter-Wave and Microwave Transceivers for FMCW Radar and 5/6G Beamforming Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 695

Special Issue Editor

Prof. Dr. Jeong-Geun Kim

E-Mail Website
Guest Editor
Department of Electronic Engineering, Kwangwoon University, Seoul, Republic of Korea
Interests: automotive radar front-end RFIC; beamforming RFIC; GaN Front-end IC; Si-based mm-wave circuit and systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in mm-wave and microwave IC technologies are rapidly transforming high-resolution sensing and high-capacity wireless communication systems. The convergence of FMCW radar, MIMO signal processing, and 5G/6G beamforming has created integrated sensing-and-communication (ISAC) system able to deliver high spatial resolution, low latency, and robust environmental awareness. Modern mm-wave transceivers—enabled by CMOS, SiGe, and III-V semiconductor technologies—now support compact phased arrays, wide instantaneous bandwidth, precise frequency synthesis, and digitally assisted calibration, making them highly suitable for autonomous vehicles, robotics, indoor positioning, smart manufacturing, and next-generation cellular networks.

This Special Issue aims to gather contributions on advanced architectures, circuits, algorithms, and system-level innovations that push the boundaries of mm-wave sensing and communication. Topics include integrated phased-array transceivers, digitally controlled beamforming, high-linearity RF front-ends, multi-antenna FMCW radar, and joint radar-communication frameworks. We invite original research papers, reviews, and application-oriented studies addressing both theoretical and practical challenges in next-generation mm-wave radar and beamforming systems.

Prof. Dr. Jeong-Geun Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • millimeter-wave transceivers
  • FMCW radar
  • phased-array and beamforming
  • MIMO radar systems
  • 5G/6G integrated sensing and communication (ISAC)
  • RF front-end IC
  • frequency synthesis and calibration
  • microwave sensing technologies

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Published Papers (1 paper)

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Research

19 pages, 2191 KB  
Article
Mask-Aware Spatiotemporal Classification of Millimeter-Wave Radar Point Cloud Sequences Using DGCNN and Transformer for Child–Pet Recognition in Enclosed Spaces
by Yehui Shi and Jianhong Shi
Sensors 2026, 26(5), 1580; https://doi.org/10.3390/s26051580 - 3 Mar 2026
Viewed by 499
Abstract
Applications in enclosed spaces such as vehicle cabin on-site detection, human–pet separation, and pet care have put forward higher requirements for non-contact target recognition. Millimeter-wave radar point clouds have advantages such as privacy friendliness and robustness against low light and occlusion. However, their [...] Read more.
Applications in enclosed spaces such as vehicle cabin on-site detection, human–pet separation, and pet care have put forward higher requirements for non-contact target recognition. Millimeter-wave radar point clouds have advantages such as privacy friendliness and robustness against low light and occlusion. However, their point clouds are generally sparse, with obvious noise and multipath interference. Moreover, the fluctuation of point numbers over time makes alignment and feature learning difficult, which leads to performance degradation of existing point cloud classification methods in complex environments. To this end, this paper proposes a spatiotemporal joint classification framework for millimeter-wave point cloud sequences: An effective point mask mechanism is introduced in the spatial dimension to suppress the interference of invalid points generated by alignment on the neighborhood composition and feature aggregation and improve the reliability of local geometric representation; and to integrate attention-based time series modeling in the time dimension and enhance category separability by using cross-frame dynamic patterns. The experimental results show that the proposed method can achieve an accuracy rate of 97.8% in the three-classification tasks of Child, Cat and Dog and the ablation analysis verifies the key contributions of the mask mechanism and time series modeling to robust recognition. This framework provides a deployable and more generalized millimeter-wave point cloud solution for the identification of life forms in confined spaces. Full article
(This article belongs to the Special Issue Advanced Millimeter-Wave and Microwave Transceivers for FMCW Radar and 5/6G Beamforming Systems)
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