TL;DR
Experimental results show that the proposed P-LLM framework outperforms traditional scheduling methods and existing reinforcement learning baselines, and maintains stable and consistent performance across different real-world scenarios, time periods, fleet sizes, and order volumes.
The incorporation of Mobile Edge Computing into satellite systems is a highly promising approach to enabling large-scale intelligent Internet of Things services in remote areas. However, the high-speed mobility of satellites and the extreme scarcity of on-board resources pose significant challenges, as traditional reac...
The scheduling efficiency of airport special vehicles directly determines ground-handling quality and flight punctuality. Conventional methods rely heavily on human experience and static rules, lacking adaptability to dynamic uncertainties involving flights, vehicles, environments, and human-machine interactions. This...
This paper argues that context-situational factors that give meaning to sensor signals and constrain decisions-should be treated as a first-class principle for next-generation vehicle systems, and operationalized as a unified, shared state for learning, risk assessment, and closed-loop control across the software stack...
The proposed Forecast-SAC framework demonstrates that unified predictive-control learning enables safe and efficient UAV-BS navigation under dynamic uncertainty, achieving a strong safety–throughput balance that reactive methods cannot match in high-risk environments.
Federated learning (FL) has become a promising paradigm for privacy-preserving and communication-efficient model training in vehicular networks. With the continuous expansion of vehicular networks, multiple FL tasks are often initiated concurrently by moving vehicles, which poses substantial challenges to the conventio...
Cloud computing has emerged as a new paradigm, which entrusts task scheduling to ensure the satisfaction of stringent constraints on latency, energy, and resources for sustainably running real-time applications. State-of-the-art natural DRL-based scheduling solutions mainly rely heavily on DRL techniques and are either...
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