# Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

arXiv: https://arxiv.org/abs/2208.09956 · HTML: https://mlsanity.com/paper/2208.09956/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 21 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [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)
- [CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming (Extended Version)](https://mlsanity.com/paper/1901.05712/) (2019)
- [Computationally Aware Sum-Rate Optimal Scheduling for Centralized Radio Access Networks](https://mlsanity.com/paper/1507.01971/) (2015)
- [DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN](https://mlsanity.com/paper/1812.00852/) (2018)

## Built on by (most cited first, 6 of 6)

- [Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2309.01730/) (2023)
- [Fair Resource Allocation in Virtualized O-RAN Platforms](https://mlsanity.com/paper/2402.11285/) (2024)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [CHOMET: Conditional Handovers via Meta-Learning](https://mlsanity.com/paper/2507.07581/) (2025)
- [EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN](https://mlsanity.com/paper/2602.09206/) (2026)
- [Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN](https://mlsanity.com/paper/2507.18111/) (2025)

## Abstract (arXiv)

The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we propose an online learning algorithm for balancing the performance and energy consumption of a vBS. This algorithm provides performance guarantees under unforeseeable conditions, such as non-stationary traffic and network state, and is oblivious to the vBS operation profile. We study the problem in its most general form and we prove that the proposed technique achieves sub-linear regret (i.e., zero average optimality gap) even in a fast-changing environment. By using real-world data and various trace-driven evaluations, our findings indicate savings of up to 74.3% in the power consumption of a vBS in comparison with state-of-the-art benchmarks.