mlsanityData to 7 Oct 2026

Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

arXiv 2205.08347 ↗First posted 26 Apr 2022cs.NI cs.AIcited by 2

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Abstract (arXiv)

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 156 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

41 of 156 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  2. 2026Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Predictioncited by 0