Machine Learning for 6G Integrated Sensing, Computing, and Communications (ML-ISCC)
This Special Session is part of the RIVF 2026 programme. Papers are submitted through EDAS and reviewed to the same standard as the main conference track. See Special Sessions for the full list of accepted sessions.
| Paper Submission Deadline | September 30, 2026 |
|---|---|
| Acceptance Notification | October 30, 2026 |
| Camera-Ready Submission | To be announced |
| Conference Dates | December 18–20, 2026 |
Session Rationale and Overview
The upcoming sixth generation (6G) of wireless networks represents a paradigm shift from pure communication systems to hyper-connected infrastructures capable of Integrated Sensing and Communications (ISAC). To fully realize this vision, networks must simultaneously act as high-precision radar sensors and ubiquitous computing platforms. However, processing complex integrated radar-communication (IRC) signals at the network edge presents severe bottlenecks. Standard machine learning models are often too resource-intensive for real-time edge deployment, creating a critical need for highly parameter-efficient, low-latency deep learning architectures.
This Special Session, ML-ISCC, explores the crucial intersection of advanced machine learning and 6G integrated systems. We focus on developing and deploying lightweight neural networks designed specifically for edge-based IoT devices, enabling real-time waveform classification, intelligent resource allocation, and robust signal processing without compromising accuracy.
While the main programme of RIVF traditionally covers core networking and computing advances, the specialized convergence of deep learning, radar-communication integration, and edge-native processing requires a dedicated forum. ML-ISCC provides a timely platform for researchers to address the algorithmic challenges of deploying intelligence directly at the radio edge. By focusing on practical, lightweight AI solutions rather than purely theoretical models, this session complements RIVF 2026's focus on applied computing and technological innovation.
Topics of Interest
This Special Session seeks original, high-quality research on the design, optimization, and deployment of machine learning algorithms for 6G ISAC systems. We particularly encourage submissions focusing on parameter-efficient models, low-latency inference, and advanced signal processing techniques. Topics include, but are not limited to:
Edge and Lightweight Models
- Lightweight deep learning architectures for edge-based IoT devices
- Frameworks optimizing inference latency, energy efficiency, and memory constraints for real-time network deployments
Signal Processing and Waveforms
- Machine learning for integrated radar-communication (IRC) waveform classification and real-time semantic segmentation of signal spectrograms
- Diffusion models and generative frameworks for signal enhancement, latent correction, and channel estimation
Distributed and End-to-End Learning
- Federated learning, split learning, and multi-agent reinforcement learning for decentralized ISAC networks
- End-to-end AI frameworks for joint ISAC waveform design, precoding, and resource allocation
Emerging ISAC Directions
- Machine learning for RIS-assisted ISAC, over-the-air (OTA) computation, and semantic communications
- Digital Twins, remote sensing for environmental monitoring, and privacy-preserving AI in 6G sensing networks
Session Organizers
Dr. Thien Huynh-The — Ho Chi Minh City University of Technology and Engineering (HCM-UTE), Vietnam
Role: Principal Organizer and Session Chair, Department of Electronics and Information Engineering
Email: thienht@hcmute.edu.vn
Thien Huynh-The (Senior Member, IEEE) received his B.S. (2011) and M.Sc. (2013) from Ho Chi Minh City University of Technology and Engineering (HCM-UTE), Vietnam, and his Ph.D. in Computer Science and Engineering from Kyung Hee University, South Korea, in 2018 (Superior Thesis Prize). Following postdoctoral fellowships in South Korea, he is currently an Assistant Professor in the Department of Electronics and Information Engineering at HCM-UTE. A highly cited researcher with over 10,200 citations and an h-index of 44, his research focuses on machine learning and deep learning for wireless communications, radio signal processing, and computer vision. His notable honours include the 2020 Golden Globe Award for Vietnamese Young Scientists and Best Paper Awards at ATC (2023–2025). Dr. Huynh-The serves as an Editor for IEEE Communications Letters, earning Exemplary Editor (2025) and Exemplary Reviewer (2023) recognitions. He holds active leadership roles as Technical Track Chair for SOICT 2025, ATC, and RIVF, and frequently serves as a Technical Programme Committee member for premier conferences, including IEEE GLOBECOM and IEEE ICC.
Dr. Toan-Van Nguyen — San Diego State University, USA
Role: Department of Electrical and Computer Engineering
Email: tnguyen58@sdsu.edu
Toan-Van Nguyen (Member, IEEE) received the B.S. degree in 2011 and the M.Sc. degree in 2013 from Ho Chi Minh City University of Technology and Education (HCM-UTE), Vietnam, and the Ph.D. degree in Electronics and Computer Engineering from Hongik University, South Korea, in 2021. He is currently a Postdoctoral Researcher with the Department of Electrical and Computer Engineering at San Diego State University, CA, USA. His research covers signal processing and machine learning applications for wireless communications. Dr. Nguyen has received three Best Paper Awards at ATC (2023, 2024) and ICAIIC (2024). He serves as an Editor for IEEE Communications Letters, where he was recognized as an Exemplary Editor in 2025 and an Exemplary Reviewer in 2023 and 2025. He is also a Technical Programme Committee member for international conferences, such as ATC, RIVF, IEEE GLOBECOM, IEEE ICC, and IEEE VTC.
Potential Participants
Technical Programme Committee
- Dr. Toan-Van Nguyen, San Diego State University, USA
- Dr. Thai-Hoc Vu, VSB-Technical University of Ostrava, Czech Republic
- Dr. Nguyen Cong Luong, Phenikaa University, Vietnam
- Dr. Van-Sang Doan, Vietnam Naval Academy, Vietnam
- Assoc. Prof. Vo Thi Luu Phuong, International University – VNU-HCM, Vietnam
- Assoc. Prof. Nguyen Tien Hoa, Hanoi University of Science and Technology, Vietnam
Review and Management Process
Submission and Review
The session adheres to the RIVF 2026 peer-review guidelines. Submissions are managed via the conference submission system (EDAS). Each paper undergoes a rigorous double-blind review process, receiving a minimum of three independent reviews from the Special Session Technical Programme Committee to ensure scientific excellence and relevance.
Session Format
The session is planned as a half-day programme of approximately three hours: a ten-minute introduction by the Session Chair, six to eight high-quality papers in twenty-minute slots (fifteen minutes for presentation and five for questions), and a ten-minute closing discussion on future research directions in resilient, AI-driven wireless networks.
Paper Submission
Authors are invited to submit full papers formatted according to the standard RIVF 2026 IEEE template, to a maximum of six pages. Submit through EDAS, making sure to select this Special Session track: EDAS Track SS2
At least one author of each accepted paper must register and present at the conference for the paper to be included in the proceedings.
Contact
For questions about this Special Session, please contact the session organizers directly. For general inquiries, please contact: rivf2026@vinuni.edu.vn