# Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis

arXiv: https://arxiv.org/abs/2210.04604 · HTML: https://mlsanity.com/paper/2210.04604/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues](https://mlsanity.com/paper/2103.04536/) (2021)
- [OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN](https://mlsanity.com/paper/2202.10318/) (2022)
- [Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks](https://mlsanity.com/paper/1808.07647/) (2018)
- [Dynamic CU-DU Selection for Resource Allocation in O-RAN Using Actor-Critic Learning](https://mlsanity.com/paper/2110.00492/) (2021)
- [Prototyping Next-Generation O-RAN Research Testbeds with SDRs](https://mlsanity.com/paper/2205.13178/) (2022)
- [Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks](https://mlsanity.com/paper/2102.00145/) (2021)

## Built on by (most cited first, 5 of 5)

- [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN](https://mlsanity.com/paper/2407.03068/) (2024)
- [Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis](https://mlsanity.com/paper/2411.03365/) (2024)
- [Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps](https://mlsanity.com/paper/2506.12812/) (2025)

## Abstract (arXiv)

Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time. Despite the opportunities provided by this new architecture, the progress of practical artificial intelligence (AI)-based solutions for network control and automation has been slow. This is mostly because of the lack of an endto-end solution for designing, deploying, and testing AI-based xApps fully executable in real O-RAN network. In this paper we introduce an end-to-end O-RAN design and evaluation procedure and provide a detailed discussion of developing a Reinforcement Learning (RL) based xApp by using two different RL approaches and considering the latest released O-RAN architecture and interfaces.