More Like this

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

   https://doi.org/10.1109/GCWkshps56602.2022.10008713  

   Kouchaki, Mohammadreza; Marojevic, Vuk (December 2022, 2022 IEEE Globecom Workshops (GC Wkshps))

   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. 

   more » « less
2. OpenAI dApp: An Open AI Platform for Distributed Federated Reinforcement Learning Apps in O-RAN

   https://doi.org/10.1109/FNWF58287.2023.10520642  

   Kouchaki, Mohammadreza; Abdalla, Aly S.; Marojevic, Vuk (November 2023, IEEE)

   The evolution of open architectures for Radio Ac-cess Networks (RANs) is revolutionizing network management and optimization. This transformation, fostered by O-RAN, expedites data acquisition and examination by exploiting newly established open interfaces. Moreover, it has led to the rise of near real-time RAN Intelligent Controllers (RICs), instigating a wave of AI-driven applications, or xApps, that employ Artificial Intelligence (AI)/Machine Learning (ML) methods. Nevertheless, deploying xApps as centralized applications presents substantial challenges, such as handling vast data transactions, potential delays, and security vulnerabilities, which are notably prominent within the multifaceted, decentralized, multivendor, and trustless nature of open networks. To alleviate these predicaments, a transition from centralized apps operating in near real-time to distributed real-time apps is imperative for augmented security and efficiency. This paper addresses these complexities by introducing an open platform that integrates a federated reinforcement learning algorithm to operate as distributed Apps (dApps) within the next-generation O-RAN architecture. We present evaluation results in a specific test environment. 

   more » « less
3. Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach

   https://doi.org/10.1109/WCNC61545.2025.10978358  

   Zolghadr, Arshia; Santos, Joao F; DaSilva, Luiz A; Kibilda, Jacek (March 2025, IEEE)

   The Open Radio Access Network (O-RAN) architecture enables the deployment of third-party applications on the RAN Intelligent Controllers (RICs). However, the operation of third-party applications in the Near Real-Time RIC (Near-RT RIC), known as xApps, may result in conflicting interactions. Each xApp can independently modify the same control parameters to achieve distinct outcomes, which has the potential to cause performance degradation and network instability. The current conflict detection and mitigation solutions in the literature assume that all conflicts are known a priori, which does not always hold due to complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we introduce the first data-driven method for reconstructing and labeling conflict graphs in O-RAN. Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, parameters, and KPIs. Our numerical results, based on a conflict model used in the O-RAN conflict management literature, demonstrate that our proposed method can effectively reconstruct conflict graphs and identify the conflicts defined by the O-RAN Alliance. 

   more » « less
4. Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps

   https://doi.org/10.1109/MCOM.001.2400491  

   Kouchaki, Mohammadreza; Abdalla, Aly Sabri; Marojevic, Vuk (January 2025, IEEE Communications Magazine)

   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. 

   more » « less
5. ZTRAN: Prototyping Zero Trust Security xApps for Open Radio Access Network Deployments

   https://doi.org/10.1109/MWC.001.2300419  

   Abdalla, Aly S; Moore, Joshua; Adhikari, Nisha; Marojevic, Vuk (April 2024, IEEE Wireless Communications)

   The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this architecture introduces new security challenges. This article proposes leveraging zero trust principles for O-RAN security. We introduce zero trust RAN (ZTRAN), which embeds service authentication, intrusion detection, and secure slicing subsystems that are encapsulated as xApps. We implement ZTRAN on the open artificial intelligence cellular (OAIC) research platform and demonstrate its feasibility and effectiveness in terms of legitimate user throughput and latency figures. Our experimental analysis illustrates how ZTRAN's intrusion detection and secure slicing microservices operate effectively and in concert as part of O-RAN Alliance's containerized near-real time RIC. Research directions include exploring machine learning and additional threat intelligence feeds for improving the performance and extending the scope of ZTRAN. 

   more » « less