The improved data transportation network of the 5G network can be further improved to create 6G. It will be based on ubiquitous AI, a hyper-flexible human-like intelligence. One of the possible ways to spread the development of AI in wireless systems is to employ edge intelligence which integrates AI, communication networks, and mobile edge computing.

5G technology - abstract image. Image credit: ADMC via Pixabay (free Pixabay licence)” data-src=”https://www.technology.org/texorgwp/wp-content/uploads/2020/05/the-internet-4899254_960_720-720×540.jpg” alt=”5G technology – abstract image. Image credit: ADMC via Pixabay (free Pixabay licence)” width=”500″ height=”378″>

5G technology – abstract image. Image credit: ADMC via Pixabay (free Pixabay licence)

A recent study on arXiv.org suggests self-learning for addressing the issues of 6G. It can reduce the human efforts involved in data processing and model development. A self-supervised generative adversarial net was proposed and evaluated in a campus shuttle system connected to edge servers via 5G.

The results show that a self-learning-based system can improve the data classification and synthesizing performance without requiring any human labeled dataset. The architecture also adapts to the changes of the environment or networks caused by human usage.

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 blu{demonstrate 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.

Link: https://arxiv.org/abs/2010.00176


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