mlsanityData to 7 Oct 2026

A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet

arXiv 2009.02771 ↗First posted 6 Sep 2020cs.NI cs.SY eess.SYcited by 2

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Abstract (arXiv)

Future integrated terrestrial-aerial-satellite networks will have to exhibit some unprecedented characteristics for the provision of both communications and computation services, and security for a tremendous number of devices with very broad and demanding requirements across multiple networks, operators, and ecosystems. Although 3GPP introduced the concept of self-organization networks (SONs) in 4G and 5G documents to automate network management, even this progressive concept will face several challenges as it may not be sufficiently agile in coping with the immense levels of complexity, heterogeneity, and mobility in the envisioned beyond-5G integrated networks. In the presented vision, we discuss how future integrated networks can be intelligently and autonomously managed to efficiently utilize resources, reduce operational costs, and achieve the targeted Quality of Experience (QoE). We introduce the novel concept of "self-evolving networks (SENs)" framework, which utilizes artificial intelligence, enabled by machine learning (ML) algorithms, to make future integrated networks fully automated and intelligently evolve with respect to the provision, adaptation, optimization, and management aspects of networking, communications, computation, and infrastructure nodes' mobility. To envisage the concept of SEN in future integrated networks, we use the Intelligent Vertical Heterogeneous Network (I-VHetNet) architecture as our reference. The paper discusses five prominent scenarios where SEN plays the main role in providing automated network management. Numerical results provide an insight on how the SEN framework improves the performance of future integrated networks. The paper presents the leading enablers and examines the challenges associated with the application of SEN concept in future integrated networks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2020.

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

2 papers.