# AI for 5G: Research Directions and Paradigms

arXiv: https://arxiv.org/abs/1807.08671 · HTML: https://mlsanity.com/paper/1807.08671/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Design of JiuTian Intelligent Network Simulation Platform](https://mlsanity.com/paper/2310.06858/) (2023)
- [Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.14152/) (2022)
- [Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications](https://mlsanity.com/paper/2205.10750/) (2022)
- [Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G](https://mlsanity.com/paper/2206.04967/) (2022)

## Abstract (arXiv)

The 5th wireless communication (5G) techniques not only fulfil the requirement of 1,000 times increase of internet traffic in the next decade, but also offer the underlying technologies to the entire industry and ecology for internet of everything. Compared to the existing mobile communication techniques, 5G techniques are more-widely applicable and the corresponding system design is more complicated. The resurgence of artificial intelligence (AI) techniques offers as an alternative option, which is possibly superior over traditional ideas and performance. Typical and potential research directions to which AI can make promising contributions need to be identified, evaluated, and investigated. To this end, this overview paper first combs through several promising research directions of AI for 5G, based on the understanding of the 5G key techniques. Also, the paper devotes itself in providing design paradigms including 5G network optimization, optimal resource allocation, 5G physical layer unified acceleration, end-to-end physical layer joint optimization, and so on.