# Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

**Authors:** Dinh C. Nguyen, Peng Cheng, Ming Ding, David Lopez-Perez, Pubudu N. Pathirana, Jun Li, Aruna Seneviratne, Yonghui Li, H. Vincent Poor  
**arXiv:** [2003.00866](https://arxiv.org/abs/2003.00866)  
**Date:** 2020-02-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2003.00866v2/

## Key points

- The rapid deployment of mobile computing and IoT networks has been driven by wireless systems' communication and sensing capabilities.
- Data-driven AI is envisioned to be deployed on top of IoT networks, creating a new world powered by AI.
- Wireless AI combines AI and wireless communications, enabling future intelligent network evolution.
- A comprehensive survey was conducted on the latest studies in wireless AI from a data-driven perspective.
- The survey proposes a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks: Sensing AI, Network Device AI, Access AI, User Device AI, and Data-provenance AI.
- For each theme, an overview is presented on how AI approaches can solve emerging data-related problems and empower wireless network functionalities.
- This study provides an in-depth discussion on Wireless AI applications in various data-driven domains where it proves extremely useful for wireless network design and optimization compared to other related survey papers.
- Research challenges and future visions are discussed to stimulate further research in this promising area.
- The study highlights the importance of incorporating Wireless AI into future wireless networks to enable efficient data processing at the network edge.

## Summary

The rapid deployment of mobile computing and Internet of Things (IoT) networks in recent years has been driven by the increasing communication and sensing capabilities of wireless systems. This has led to the emergence of data-driven artificial intelligence (AI) that is envisioned to be deployed on top of IoT networks, creating a new world powered by AI. Wireless AI, which combines AI and wireless communications, is widely regarded as a key enabler for future intelligent network evolution. To further explore this paradigm, a comprehensive survey was conducted on the latest studies in wireless AI from a data-driven perspective. The survey proposes a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks: Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. For each theme, an overview is presented on how AI approaches can solve emerging data-related problems and empower wireless network functionalities. Compared to other related survey papers, this study provides an in-depth discussion on Wireless AI applications in various data-driven domains where it proves extremely useful for wireless network design and optimization. Moreover, research challenges and future visions are discussed to stimulate further research in this promising area. The study highlights the importance of incorporating Wireless AI into future wireless networks to enable efficient data processing at the network edge. This study was accepted at IEEE Communications Surveys & Tutorials and authored by Dinh C. Nguyen, Peng Cheng from La Trobe University's Department of Computer Science and Information Technology; Ming Ding from Beijing University of Posts and Telecommunications; David Lopez-Perez from Nokia Bell Labs; Pubudu N. Pathirana from Deakin University's School of Information Technology; Jun Li from Nanjing University's School of Electronic Science and Engineering; Aruna Seneviratne from The University of New South Wales' School of Electrical Engineering & Telecommunications; Yonghui Li from Imperial College London's Department of Electrical & Electronic Engineering; and H. Vincent Poor from Princeton University's Department of Electrical Engineering.