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Multi-UAV networks are increasingly deployed for large-scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular,…
Adequate strategizing of agents behaviors is essential to solving cooperative MARL problems. One intuitively beneficial yet uncommon method in this domain is predicting agents future behaviors and planning accordingly. Leveraging this…
This paper introduces the deployment of unmanned aerial vehicles (UAVs) as lightweight wireless access points that leverage the fixed infrastructure in the context of the emerging open radio access network (O-RAN). More precisely, we…
The emergence of dense, mission-driven aerial networks supporting the low-altitude economy presents unique communication challenges, including extreme channel dynamics and severe cross-tier interference. Traditional reactive communication…
The integration of emerging uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands…
In the realm of wireless communications in 5G, 6G and beyond, deploying unmanned aerial vehicle (UAV) has been an innovative approach to extend the coverage area due to its easy deployment. Moreover, reconfigurable intelligent surface (RIS)…
Low-altitude wireless networks (LAWNs) represent a promising architecture that integrates unmanned aerial vehicles (UAVs) as aerial nodes to provide enhanced coverage, reliability, and throughput for diverse applications. However, these…
This work tackles the physical layer security (PLS) problem of maximizing the secrecy rate in heterogeneous UAV networks (HetUAVNs) under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook…
Spatial task allocation in systems such as multi-robot delivery or ride-sharing requires balancing efficiency with fair service across tasks. Greedy assignment policies that match each agent to its highest-preference or lowest-cost task can…
In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure…
Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows promise in managing these networks, through topological…
Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and…
Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN), particularly when terrestrial networks are unavailable. In such scenarios, the environmental topology is typically unknown; hence,…
Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination. Crucially, enhancing policy expressiveness is pivotal for achieving superior performance. Diffusion-based generative models are…
Despite the growing interest in low-altitude economy (LAE) applications, including UAV-based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal-constrained environments. These…
Efficient spectrum allocation has become crucial as the surge in wireless-connected devices demands seamless support for more users and applications, a trend expected to grow with 6G. Innovations in satellite technologies such as SpaceX's…
The low-altitude economy (LAE) is reshaping the industrial landscape by deploying unmanned aerial vehicles (UAVs) to facilitate a wide range of applications demanding flexible aerial mobility. Integrating edge artificial intelligence (AI)…
Unmanned aerial vehicles (UAVs) are increasingly used to support time-critical medical supply delivery, providing rapid and flexible logistics during emergencies and resource shortages. However, effective deployment of UAV fleets requires…
AI-RAN consolidates AI services and Radio Access Network (RAN) functions onto a unified, GPU-accelerated infrastructure at the network edge. However, compute sharing between real-time RAN functions and highly heterogeneous AI services…
Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things applications. The limited spectrum and coverage area of terrestrial base stations pose…