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The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science and AI, preventing a consistent framework. A five-layer nested…
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Low-altitude wireless networks (LAWNs) are widely regarded as a cornerstone of the emerging low-altitude economy, yet they face significant challenges, including rapidly varying channels, diverse functional requirements, and dynamic…
With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to…
Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed…
The convergence of Artificial Intelligence (AI) and the Internet of Things has accelerated the development of distributed, network-sensitive applications, necessitating ultra-low latency, high throughput, and real-time processing…
Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future beyond 5G (B5G) and 6G networks. In Air-FEEL, distributed edge devices use their local data to…
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To support on-device inference, the next-generation mobile networks are expected to support real-time model downloading services to mobile users. However, powerful AI models typically have large model sizes, resulting in excessive…
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