LA-DRQN is proposed, a novel hierarchical decision-making framework that synergistically integrates Learning Automata at the strategic level for macro-policy selection with a Deep Recurrent Q-Network (DRQN) at the tactical level for fine-tuning computation offloading ratios.
The growing smart devices (SDs) in the Industrial Internet of Things (IIoT) generate complex computations that strain the performance and energy of local processing. Mobile Edge Computing (MEC) addresses this by providing nearby computing resources for low-latency offloading. However, achieving efficient computation of...
Xinmin Cheng, Chengquan Yu, Lu Gao et al.· IEEE Transactions on Green C...· 0 citations
Networks with highly dynamic data transmission demands and network topologies are common in real world. A fundamental problem in such networks is achieving scalable traffic allocation to maximize long-term total throughput under link capacity constraints. However, state-of-the-art (SOTA) works lack scalability. This is...
Zhaoxing Yang, Guiyun Fan, An-Jie Cao et al.· IEEE Transactions on Network...· 0 citations
JATO is presented, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning, and offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality.
G. Purnama, Irma Amelia Dewi, A. Langi et al.· Journal of ICT Research and...· 0 citations
This work presents a predictive quality-of-service (QoS) framework that jointly forecasts latency and packet delivery rate using dual-output recurrent neural networks, enabling proactive RAT selection across 5G NR, C-V2X, and DSRC.
Lucas Bréhon–Grataloup, Rahim Kacimi· International Conference on...· 0 citations
With the advancement of autonomous driving and smart navigation, Internet of Vehicles (IoV) systems face stringent requirements for real-time data delivery and processing reliability. Traditional metrics cannot fully capture information timeliness due to network dynamics and packet loss. Existing approaches also strugg...
Chao He, Wanting Wang, Dongfeng Fu et al.· 2026 International Conferenc...· 0 citations
This paper investigates the integration of deep learning algorithms into 5G traffic optimization frameworks, proposes an intelligent AI-driven optimization architecture, evaluates major performance metrics, and discusses implementation challenges, scalability issues, and future research directions toward autonomous nex...
Kamal N, Thirupathi Sundararajulu, A. R et al.· International journal of com...· 0 citations
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