IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONSIEEE通信选题杂志
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS(英文缩写 IEEE J SEL AREA COMM),ISSN 0733-8716,eISSN 1558-0008,中文译名:IEEE通信选题杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 13.081 | Q1 |
| 2022 | 16.400 | Q1 |
| 2023 | 13.800 | Q1 |
| 2024 | 17.200 | Q1 |
| 2025 | 16.800 | Q1 |
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 最新收录文献
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1. Reinforcement Learning-Based Control and Networking Co-design for Industrial Internet of Things.
PMID:期刊:日期:2020-05Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networking and computing systems) are synthesized into IIoT systems interactively to achieve the operator's design goals. The interactions among different systems is a non-negligible factor that affects the IIoT design and requirements, such as automation, especially under dynamic industrial operations. In this paper, we leverage reinforcement learning techniques to automatically configure the control and networking systems under a dynamic industrial environment. We design three new policies based on the characteristics of industrial systems so that the reinforcement learning can converge rapidly. We implement and integrate the reinforcement learning-based co-design approach on a realistic wireless cyber-physical simulator to conduct extensive experiments. Our experimental results demonstrate that our approach can effectively and quickly reconfigure the control and networking systems automatically in a dynamic industrial environment.