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首页> 外文期刊>IEEE transactions on industrial informatics >Toward ML-Based Energy-Efficient Mechanism for 6G Enabled Industrial Network in Box Systems
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Toward ML-Based Energy-Efficient Mechanism for 6G Enabled Industrial Network in Box Systems

机译：在箱体系统中实现基于ML的Libeled Industrib Network的基于ML的节能机制

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Machine learning (ML) techniques in association to emerging sixth generation (6G) technologies, i.e., massive Internet of Things (IoT), big data analytics have caught too much attention from academia to the business world since last few years due to their high and fast computing capabilities. The role of ML-based 6G techniques is to reshape the imaginary idea into physical world for resolving the challenging issues of energy, quality of service (QoS), and quality of experience (QoE). Besides, ML techniques with better association to 6G reshapes the industrial network in box (NIB) platform. In the mean-time rapidly increasing market of the IoT devices to deliver multimedia content has caught the attention of various fields such as, industrial, and healthcare. The challenging issue that end-users are facing is the unsatisfactory and annoyed performance of portable devices while surfing the video, and image to/from desired entity, i.e., low QoE. To resolve these issues this research first, proposes a novel ML-driven mobility management method for the efficient communication in industrial NIB applications. Second, a novel architecture of 6G-based intelligent QoE and QoS optimization in industrial NIB is proposed. Third, a 6G-based NIB framework is proposed in association to the long-term evolution. Forth, use-case for 6G-empowered industrial NIB is recommended for an energy efficient communication. Experimental results are extracted with high energy efficiency, better QoE, and QoS in 6G-based industrial NIB.
机译：机器学习（ML）技术与新兴的第六代（6G）技术，即大规模的物联网（物联网），大数据分析由于其高度历史以来，大数据分析从学术界到商业世界的关注太多了快速计算能力。基于ML的6G技术的作用是将虚构的想法重塑成物理世界，以解决能源，服务质量（QoS）和经验质量（QoE）的挑战性问题。此外，具有更好关联的ML技术与6G重塑盒子（NIB）平台的工业网络。在IOT设备的平均迅速增加市场，以提供多媒体内容，引起了各种领域，如工业和医疗保健。最终用户面临的具有挑战性的问题是便携式设备在冲浪视频时不满意和生气的性能，以及从期望实体的图像，即低QoE。为了解决这些问题，首先提出了一种新的ML驱动的移动性管理方法，用于工业笔尖应用中的有效通信。其次，提出了一种新颖的建筑，在工业笔尖的工业笔尖中的6G基础智能QoE和QoS优化建筑。第三，提出了基于6G的NIB框架与长期演进。第四，推荐用于6G动力的工业笔尖的用例，以节能通信。实验结果用高能效，更好的QoE和QoS提取，在基于6G的工业笔尖中提取。

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