TL;DR
This study considers a UAV-RIS-assisted MEC system and proposes a joint optimization scheme for local computation, task offloading, and RIS phase control, while taking into account stochastic task arrivals and channel variations, which outperforms conventional centralized DDPG, TD3, and MADDPG schemes.
This paper investigates a phased sensing-assisted mobile edge computing system composed of multiple unmanned aerial vehicles (UAVs). A framework is proposed to operate in three sequential phases: local user sensing, global state aggregation, and centralized decision making for distributed offloading. To achieve efficie...
Unmanned aerial vehicle (UAV)-assisted edge intelligence networks have emerged as a promising paradigm for supporting semantic-aware tasks, which rely on the joint orchestration of communication, computation, and caching (3C) resources. This paper investigates a multi-user collaborative urban sensing scenario and aims...
The growing demand for multimedia services in Internet of Things (IoT) networks has significantly increased the traffic load on backhaul links, making Mobile Edge Caching (MEC) a key technology for reducing content delivery latency. Unmanned Aerial Vehicles (UAVs) can serve as mobile aerial caching nodes that complemen...
This paper surveys the U-MEC landscape through the lens of method–scenario mapping, and compares optimization approaches across six categories—convex optimization, heuristic algorithms, game theory, deep reinforcement learning, multi-agent reinforcement learning, and federated learning—in terms of applicability and tec...
With the rapid development of Vehicle-to-Everything (V2X) and Vehicular Edge Computing (VEC), the massive computation-intensive tasks generated by intelligent vehicles during driving, including environmental perception, path planning, and autonomous driving decision-making, impose extremely high requirements on real-ti...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
