Emerging Trends in UAVs: From Placement, Semantic Communications to Generative AI for Mission-Critical Networks

Abstract

Unmanned Aerial Vehicles (UAVs) have gained popularity across academia and various industries due to their ability to operate in challenging and complex environments. They are particularly valuable in emergency response scenarios where conventional communication infrastructure is unavailable. However, deploying UAVs in such situations poses challenges, requiring intelligent decision-making to meet mission-critical network (MCN) constraints such as latency, quality-of-service, and reliability. To overcome those challenges, this paper aims to provide a comprehensive overview ranging from knowledge driven (Semantic) approaches to generative artificial intelligence (GAI) by integrating UAVs into MCNs. Initially, existing technologies used in UAV-assisted communication are reviewed. While previous literature often emphasizes UAVs’ general applications, it tends to overlook their specific role in MCN scenarios and lacks depth in discussing emerging technologies. Therefore, state-of-the-art data-driven and intelligent future approaches are presented as key enablers for future UAV-assisted MCNs. Representative technologies include semantic communication, Native AI, GAI, joint sensing and communication (JSAC), open-radio access networks (O-RAN), and digital twins. For instance, these technologies prove beneficial when traditional networks fail, allowing UAVs equipped with advanced intelligent systems to establish resilient links, collect real-time data, transmit meaningful information with minimal delay, and optimize resource allocation. Finally, research challenges are outlined, and potential research directions are proposed to encourage further investigation.

Topics & Concepts