Zero-Touch AI-Driven Distributed Management for Energy-Efficient 6G Massive Network Slicing

Chergui, Hatim; Blanco, Luis; Garrido, Luis A.; Ramantas, Kostas; Kukliński, Sławomir; Ksentini, Adlen; Verikoukis, Christos

doi:10.1109/mnet.111.2100322

Cited by 38 publications

(9 citation statements)

References 6 publications

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“…of DQN agents) is not specified in the paper, introducing ambiguity in the results. In [33], Chergui et al refer to 6G massive slicing, in which slices span multiple technological domains -the radio access network, core, edge and cloud. The authors emphasize the large monitoring overhead involved in centralized MANO, and the associated delay and subsequent SLA violation.…”

Section: Distributed Intelligence and Identified Gapsmentioning

confidence: 99%

Towards Massive Distribution of Intelligence for 6G Network Management using Double Deep Q-Networks

Majumdar¹,

Schwarzmann²,

Trivisonno³

et al. 2023

Preprint

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<p>Our solution evaluation is based on synthetic data-sets generated by our internally-developed simulation platform. This means we do not use any stand-alone, external or internal data-sets. Our platform code is not public, but we describe in detail the design of the platform and the data-set generator module, for other researchers to reproduce our results. The code is written in 100% Python, leveraging object-oriented approaches for code reusability.</p>

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Section: Distributed Intelligence and Identified Gapsmentioning

confidence: 99%

Towards Massive Distribution of Intelligence for 6G Network Management using Double Deep Q-Networks

Majumdar¹,

Schwarzmann²,

Trivisonno³

et al. 2023

Preprint

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“…The authors of [254] propose an architectural and artificial intelligence-based algorithm to achieve energy efficiency in 6G networks along with the analysis of their trade-offs. The authors then introduce a novel statistical federated learningbased analytic engine for zero-touch 6G massive network slicing.…”

Section: End-to-end Aspectsmentioning

confidence: 99%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

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Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

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“…The most critical demand for 6G wireless networks is the ability to handle massive volumes of data at a very data rate per device [10]. Backscatter communications might enable battery-free or zero-energy devices in 6G, allowing huge data collection for analytics and closed-loop control [11].…”

Section: Introductionmentioning

confidence: 99%

6G Driven Fast Computational Networking Framework for Healthcare Applications

et al. 2022

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New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone framework to deliberate the time-intensive services. This paper proposes a reference layered communication framework for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services.

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Zero-Touch AI-Driven Distributed Management for Energy-Efficient 6G Massive Network Slicing

Cited by 38 publications

References 6 publications

Towards Massive Distribution of Intelligence for 6G Network Management using Double Deep Q-Networks

Towards Massive Distribution of Intelligence for 6G Network Management using Double Deep Q-Networks

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

6G Driven Fast Computational Networking Framework for Healthcare Applications

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