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首页> 外文期刊>IEEE transactions on industrial informatics >Deep Federated Q-Learning-Based Network Slicing for Industrial IoT
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Deep Federated Q-Learning-Based Network Slicing for Industrial IoT

机译：基于Federated基于Q学习的工业IOT网络切片

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摘要

Fifth generation and beyond networks are envisioned to support multi industrial Internet of Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the network-as-infrastructure transition to the network-as-service. Motivated by the increasing IIoT computational capacity, and taking into consideration the QoS satisfaction and private data sharing challenges, federated reinforcement learning (RL) has become a promising approach that distributes data acquisition and computation tasks over distributed network agents, exploiting local computation capacities and agent's self-learning experiences. This article proposes a novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks. This involves IIoT slices resource allocation in terms of transmission power (TP) and spreading factor (SF) according to the slices QoS requirements. Toward this goal, the proposed deep federated Q-learning (DFQL) is reached into two main steps. First, we propose a multiagent deep Q-learning-based dynamic slices TP and SF adjustment process that aims at maximizing self-QoS requirements in term of throughput and delay. Second, the deep federated learning is proposed to learn multiagent self-model and enable them to find an optimal action decision on the TP and the SF that satisfy IIoT virtual network slice QoS reward, exploiting the shared experiences between agents. Simulation results show that the proposed DFQL framework achieves efficient performance compared to the traditional approaches.
机译：设想第五代和超越网络，以支持具有多样化的服务质量（QoS）要求的多工业物联网（IIOT）应用。网络切片被识别为一个旗舰技术，通过允许网络 - 作为网络 - 作为服务的网络和基础架构过渡，通过多服务和资源要求实现IIT网络。由于IIOT计算能力增加，并考虑到QoS满意度和私人数据分享挑战，联邦强化学习（RL）已成为一种有希望的方法，可以通过分布式网络代理分发数据采集和计算任务，利用本地计算能力和代理商自学经验。本文提出了一种新的DEAD RL计划，为未来IIT网络提供联合和动态网络管理和资源分配，以便在未来的IIOT网络中进行差异化的QoS服务。这涉及IIOT切片根据切片QoS要求的传输功率（TP）和扩展因子（SF）的资源分配。对此目标，提出的深度联邦Q-Learning（DFQL）被达到了两个主要步骤。首先，我们提出了一种基于Q的基于Q学习的动态切片TP和SF调整过程，其目的在吞吐量和延迟期间最大化自QoS要求。其次，建议深入联合学习来学习多层自我模型，并使它们能够在TP和满足IIT虚拟网络切片QoS QoS奖励的TP和SF上找到最佳行动决策，利用代理之间的共享体验。仿真结果表明，与传统方法相比，建议的DFQL框架实现了高效的性能。

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