2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 10719-10732· 0 citations· 38 references
Computer Science
Abstract
Smart factories are evolving into agentic control systems powered by wireless connectivity, edge computing, and artificial intelligence. This evolution alleviates computational limits and enhances production efficiency. However, the heterogeneity of spatiotemporal control logic, coupled with indeterminate wireless conditions, makes it challenging to coordinate control tasks and radio resources. To overcome these challenges, this paper presents a mixed graph-driven model to characterize spatiotemporal dependencies among control tasks and proposes a semi-centralized multi-agent collaborative framework. This paper employs an improved heterogeneous twin delayed deep deterministic policy gradient algorithm to jointly optimize task scheduling and radio resource allocation, thereby minimizing the average processing delay of industrial control processes. Simulation results demonstrate that the proposed algorithm achieves outstanding performance compared to benchmarks, improving execution success rate and data processing rate, as well as reducing model training time.
An improved Multi‐Agent Proximal Policy Optimization (MAPPO)‐based scheduling framework is proposed to enable distributed and cooperative decision‐making under partial observability and achieves lower task latency and energy consumption, improved load balancing, and more stable convergence compared with state‐of‐the‐ar...
Chen-Qian Fang, Xiu-Guo Zhang, Yan-Xing Wen et al.· Concurrency and Computation· 0 citations
Distributed Radio Resource Management (RRM) is a fundamental challenge in 6G wireless networks, particularly under dynamic user demands, multilayer inter-cell interference, and heterogeneous QoS constraints. Centralized optimization solutions are often infeasible in practice due to signaling delay and the lack of syste...
T. Vu, Cuong Le, S. Chatzinotas· IEEE International Symposium...· 0 citations
A hierarchical Graph Attention Transformer-Proximal Policy Optimization (GAT-PPO) framework is proposed, where a high-level agent selects aggregation nodes and a low-level agent performs packet-level routing and offset control.
This paper proposes a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices and UAVs act as heterogeneous agents and utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers.
Ming Cheng, Canlin Zhu, Jian-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
To address the requirement of latency‐sensitive intelligent transportation applications, vehicular fog computing has proven to be a promising paradigm to push the computation towards the vehicles. However, efficient task offloading and scheduling is a challenging optimization problem because of the highly dynamic natur...
Alaguvathana Paravel, Dhurgadevi Muthusamy· International Journal of Net...· 0 citations
