Industry Track Panels
Panel #1
Title: The New Paradigm of Gigantic MIMO
Moderators:
Boyu Ning (Principal Research Engineer / Huawei)
Panelists:
Fengwei Liu (Technical Expert / Huawei)
Guangyi Liu (Chief Expert / CMCC)
Xiangyang Duan (Chief Expert / ZTE)
Gen Cao (Chief Expert / China Unicom)
Emil Björnson (Professor / KTH)
Emil Björnson’s virtual presentation: https://kth-my.sharepoint.com/personal/emilbjo_ug_kth_se/_layouts/15/stream.aspx?id=%2Fpersonal%2Femilbjo%5Fug%5Fkth%5Fse%2FDocuments%2FDocuments%2FgMIMO%5FVTC%2Emp4&ga=1&referrer=StreamWebApp%2EWeb&referrerScenario=AddressBarCopied%2Eview%2E7bb1ddb7%2Dc9e6%2D45cc%2Db96c%2D0baa9b39c18c
Abstract:
The Upper 6 GHz (U6G) and 7-8 GHz frequency bands are critical for the next generation of communication systems. These bands provide more spectrum resources compared to sub-6 GHz bands and superior propagation characteristics to mmWave bands. The successful deployment of upper mid-band frequencies in next-generation systems depends on achieving enhanced spectral efficiency with similar coverage capability as in current networks.
The implementation of U6G and 7-8 GHz bands necessitates the adoption of novel Gigantic MIMO (gMIMO) technologies with at least 4x the antenna elements and ports than in the 3 GHz band, which introduce specific challenges and research opportunities in the areas of air-interface and algorithm technologies, as well as system architecture technologies. This industry panel focuses on the following practical challenges for deploying future gMIMO systems:
- Advanced Channel Acquisition Techniques: How to effectively utilize the measurement capabilities of both the base station (BS) and user equipment (UE), along with the potentially sparse characteristics of the channel, to accurately obtain high-dimensional channels at minimal cost when the coherence time shrinks.
- Advanced Precoding Techniques: Discuss on high-performance precoding techniques for gMIMO, under various single-panel and distributed architectures, with the goal of fully exploiting the performance potential of gMIMO while maintaining manageable computational and hardware complexity.
- Advanced Architecture Techniques: Development of low-cost, high-performance gMIMO hardware architectures, including but not limited to hybrid beamforming architecture with phase-shifters or metamaterial. This approach can help address the cost and power consumption issues associated with traditional digital beamforming systems.
By addressing these challenges, we can pave the way for the successful deployment of gMIMO technologies in the U6G and 7-8 GHz bands, leading to more efficient and effective next-generation communication systems.
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Panel #2
Title: The Future of Large Language Models in Mobile Networks: Enabling Intelligence in Autonomous Vehicular Systems
Moderators:
Dusit Niyato, Nanyang Technological University, Singapore
Ruichen Zhang, Nanyang Technological University, Singapore
Panelists:
Xiao Lu, Ericsson, Canada
Lin Cai, University of Victoria, Canada
Abbas Jamalipour, The University of Sydney, Australia
Sumei Sun, Institute for Infocomm Research (I2R), A*STAR, Singapore
Dusit Niyato, Nanyang Technological University, Singapore
Abstract:
The rise of Large Language Models (LLMs) marks a paradigm shift in artificial intelligence, with profound implications for mobile and wireless communication systems. In the context of autonomous vehicular networks, LLMs offer unprecedented capabilities for enabling intelligent, adaptive, and cooperative behavior across connected vehicles, infrastructure, and edge computing nodes.
This industry panel will bring together leading researchers and industry experts to explore the evolving role of LLMs in shaping the future of autonomous vehicular systems. With the increasing complexity and mobility of vehicular networks, traditional rule-based or narrowly trained AI systems struggle to keep pace. LLMs, with their powerful reasoning, contextual understanding, and multimodal capabilities, offer a compelling foundation for applications such as natural language-based vehicle-to-everything (V2X) communication, real-time situational awareness, cooperative perception, traffic prediction, and autonomous decision-making.
The panel will cover key research frontiers, including model optimization for low-latency inference at the edge, federated and continual learning across vehicular nodes, secure and privacy-preserving LLM deployment, and the integration of LLMs with 5G/6G and Mobile Edge Computing (MEC) infrastructures. It will also address the challenges of deploying LLMs in highly dynamic, bandwidth-constrained, and safety-critical environments.
Participants will gain insights into cutting-edge research, practical deployment strategies, and future roadmaps for embedding LLMs into the fabric of intelligent transportation systems. This seminar aims to foster cross-disciplinary dialogue and collaboration to realize the vision of AI-native, autonomous, and human-centric vehicular networks.
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Panel #3
Title: Toward the realization of low altitude economy: Key technologies and challenges
Moderators:
Shuguang Cui (CUHK-SZ)
Huang Chuan (CUHK-SZ)
Panelists:
Liuqing Yang (Hong Kong University of Science & Technology (Guangzhou)),
Xiang Cheng (Peking University),
Rongke Liu (Beihang University),
Yuanwei Liu (The University of Hong Kong),
Yuan Wu (University of Macau).
Abstract coming soon:
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Panel #4
Title: Industry Perspectives on Low Altitude Intelligent Networks
Moderators:
Zhisheng Niu (Tsinghua University)
Sheng Zhou (Tsinghua University)
Panelists:
Qi Bi (China Telecom),
Shanzhi Chen (China Information and Communication Technology Group Co.,Ltd. (CICT)),
Yinian Mao (Meituan),
Yu Su (China Mobile).
Abstract:
Low-altitude intelligent networks are emerging as a critical enabler for the next generation of urban mobility, logistics, and public services. As drone delivery, low-altitude air traffic management, and unmanned aerial vehicle (UAV) communications move from pilot projects to large-scale deployment, the demand for reliable, secure, and intelligent network infrastructure has never been greater.
This industry panel will convene thought leaders from China Telecom, China Mobile, Meituan, and China Information and Communication Technology Group to discuss the evolving ecosystem of low-altitude networks. Panelists will share their perspectives on connectivity requirements, spectrum allocation, and the integration of terrestrial, satellite, and aerial networks to support high-density UAV operations. The discussion will also highlight advances in AI-driven airspace management, edge-cloud collaboration for real-time decision-making, and the role of 5G/6G in enabling ultra-reliable low-latency communications (URLLC) for safety-critical applications.
Key topics will include network architecture design, interoperability standards, security and privacy considerations, and lessons learned from early commercial deployments such as drone-based logistics. Attendees will gain a comprehensive view of both the technological opportunities and regulatory challenges shaping this fast-growing sector, as well as insight into how cross-industry collaboration can accelerate the realization of a safe, efficient, and intelligent low-altitude economy.
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Panel #5
Title: AI based 6G System Architecture and Procedure
Moderators:
Yang Ning, OPPO
Panelists:
Huawei – Zhang Wanqiang, 3GPP SA2 Vice Chair
ZTE – Gao Yin, Standardization Director
CMCC – Sun Tao, 3GPP SA Vice Chair
China Telecom – XiaXu, Standardization Director
CATT – Erlin Zeng, 3GPP RAN2 Vice Chair
China Unicom – Cao Gen, Standardization Director
Abstract:
In 2025 June, 3GPP approved the 6G study items for RAN and System Architecture. This marks the official start of the 6G standardization work.
The study of 6G RAN aims to develop one non-backward compatible radio access technology, with the newly design 6G radio interface architecture and procedure. AI is a promising tool which can be potentially used to enhance the protocol and procedures. Meanwhile, new services based on AI may bring potential new requirements on the design of the 6G air interface protocol and procedure.
The study of 6G System Architecture aims to define a system architecture for 6G mobile networks for improvement of existing services and support of new services. How to support and enable use of AI in 6G (e.g. AI agent, framework) is an important aspect to enable native AI in the 6G system architecture.