Artificial Intelligence in Open Radio Access Network
arXiv:2104.09445 · doi:10.1109/MAES.2022.3186966
Abstract
This tutorial seeks to outline the proposed Open Radio Access Network (O-RAN) deployment for Fifth generation (5G) wireless networks. O-RAN seeks to supplant hardware-specific Radio Access Network (RAN) components (e.g., the mobility management entity (MME) or base station (gNB)) with generic hardware, specialized software, and open signaling interfaces. The virtualization and network slicing features of 5G allow for software to replace previously hardware specific functions. Software further provides faster analytics, thus supporting 5Gs latency requirements and advanced usage scenarios (i.e., enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communications (uRLLC)). Furthermore, as software annexes control of the RAN, there is freedom to integrate Artificial Intelligence/Machine Learning (AI/ML) algorithms into RAN management (particularly at the user plane). This integration is one of the goals of O-RAN. Lastly, relying on generic hardware and specialized, open-source software eliminates reliance upon specific device manufacturers. This paper will provide questions regarding the future of O-RAN, with a focus on 5G network device security.
9 pages, 4 figures, 1 Table. Paper has been edited to conform to journal submission standards. This work has been submitted to the IEEE for possible publication
References in corpus (3)
Cited by in corpus (4)
- A Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challenges
- Misconfiguration in O-RAN: Analysis of the impact of AI/ML
- Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization
- REAL: Reinforcement Learning-Enabled xApps for Experimental Closed-Loop Optimization in O-RAN with OSC RIC and srsRAN