# Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning

arXiv: https://arxiv.org/abs/2309.01730 · HTML: https://mlsanity.com/paper/2309.01730/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Corralling a Band of Bandit Algorithms](https://mlsanity.com/paper/1612.06246/) (2016)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning](https://mlsanity.com/paper/2202.04005/) (2022)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [Wireless Caching: Technical Misconceptions and Business Barriers](https://mlsanity.com/paper/1602.00173/) (2016)
- [OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN](https://mlsanity.com/paper/2202.10318/) (2022)
- [Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2208.09956/) (2022)
- [CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming (Extended Version)](https://mlsanity.com/paper/1901.05712/) (2019)
- [The Complexity-Rate Tradeoff of Centralized Radio Access Networks](https://mlsanity.com/paper/1503.08585/) (2015)
- [Computationally Aware Sum-Rate Optimal Scheduling for Centralized Radio Access Networks](https://mlsanity.com/paper/1507.01971/) (2015)
- [Multi-Objective Energy Efficient Resource Allocation and User Association for In-band Full Duplex Small-Cells](https://mlsanity.com/paper/2007.01117/) (2020)

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

- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [CHOMET: Conditional Handovers via Meta-Learning](https://mlsanity.com/paper/2507.07581/) (2025)
- [A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration](https://mlsanity.com/paper/2312.16142/) (2023)
- [Meta-Learning-Based Handover Management in NextG O-RAN](https://mlsanity.com/paper/2512.22022/) (2025)

## Abstract (arXiv)

Open Radio Access Network systems, with their virtualized base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. Optimizing the allocation of resources in a vBS is challenging since it requires knowledge of the environment, (i.e., "external'' information), such as traffic demands and channel quality, which is difficult to acquire precisely over short intervals of a few seconds. To tackle this problem, we propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments; for instance, non-stationary or adversarial traffic demands. We also develop a meta-learning scheme, which leverages the power of other algorithmic approaches, tailored for more "easy'' environments, and dynamically chooses the best performing one, thus enhancing the overall system's versatility and effectiveness. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments. The performance of the algorithms is evaluated with real-world data and various trace-driven evaluations, indicating savings of up to 64.5% in the power consumption of a vBS compared with state-of-the-art benchmarks.