# Towards Self-learning Edge Intelligence in 6G

arXiv: https://arxiv.org/abs/2010.00176 · HTML: https://mlsanity.com/paper/2010.00176/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 14 of 14)

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- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach](https://mlsanity.com/paper/2302.00207/) (2023)
- [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023)
- [Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors](https://mlsanity.com/paper/2402.16858/) (2024)
- [Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks](https://mlsanity.com/paper/2103.07636/) (2021)
- [Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.10400/) (2021)
- [A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks](https://mlsanity.com/paper/2211.12851/) (2022)

## Abstract (arXiv)

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. It has been considered to be one of the key missing components in the existing 5G network and is widely recognized to be one of the most sought-after functions for tomorrow's wireless 6G cellular systems. In this article, we identify the key requirements and challenges of edge-native AI in 6G. A self-learning architecture based on self-supervised Generative Adversarial Nets (GANs) is introduced to bludemonstrate the potential performance improvement that can be achieved by automatic data learning and synthesizing at the edge of the network. We evaluate the performance of our proposed self-learning architecture in a university campus shuttle system connected via a 5G network. Our result shows that the proposed architecture has the potential to identify and classify unknown services that emerge in edge computing networks. Future trends and key research problems for self-learning-enabled 6G edge intelligence are also discussed.