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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 network controllers. xApps are the applications that can leverage machine learning (ML) algorithms for near-real time control. 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. There is a lack of end-to-end solutions for designing, deploying, and testing AI-based xApps in production-like network settings. This paper introduces an end-to-end O-RAN design and evaluation procedure using the latest O-RAN architecture and interface releases. We provide details on the development of a reinforcement learning (RL)-based xApp, considering two RL approaches and present numerical results to validate the xApp.
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This content will become publicly available on January 1, 2026
Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps
The open radio access network (O-RAN) architecture introduces RAN intelligent controllers (RICs) to facilitate the management and optimization of the disaggregated RAN. Reinforcement learning (RL) and its advanced form, deep RL (DRL), are increasingly employed for designing intelligent controllers, or xApps, to be deployed in the near-real time (near-RT) RIC. These models often encounter local optima, which raise concerns about their reliability for RAN intelligent control. We therefore introduce Federated O-RAN enabled Neuroevolution (NE)-enhanced DRL (F-ONRL) that deploys an NE-based optimizer xApp in parallel to the RAN controller xApps. This NE-DRL xApp framework enables effective exploration and exploitation in the near-RT RIC without disrupting RAN operations. We implement the NE xApp along with a DRL xApp and deploy them on Open AI Cellular (OAIC) platform and present numerical results that demonstrate the improved robustness of xApps while effectively balancing the additional computational load.
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- Award ID(s):
- 2120442
- PAR ID:
- 10639035
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Communications Magazine
- ISSN:
- 0163-6804
- Page Range / eLocation ID:
- 1 to 7
- Subject(s) / Keyword(s):
- Open RAN Optimization Computer architecture Genetic algorithms Artificial intelligence Robustness Resource management Performance metrics Encoding Adaptation models
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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