Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G
| Autor: | Rubayet Shafin, Jianzhong Charlie Zhang, Hao Chen, Lingjia Liu, Jeffrey H. Reed, Vikram Chandrasekhar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Network complexity
Computer science business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology Overlay Computer Science Applications Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Cellular network Artificial intelligence Electrical and Electronic Engineering Macro business Critical path method 5G |
| Zdroj: | IEEE Wireless Communications. 27:212-217 |
| ISSN: | 1558-0687 1536-1284 |
| DOI: | 10.1109/mwc.001.1900323 |
| Popis: | Mobile network operators (MNOs) are in the process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in fifth generation (5G) and beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top five challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond- 5G and sixth generation (6G) networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|