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1. 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. 

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2. ShapeShifter: Resolving the Hidden Latency Contention Problem in MEC

   https://doi.org/10.1109/SEC54971.2022.00026  

   Rakovic, Valentin; Hsu, Ke-Jou; Bhardwaj, Ketan; Gavrilovska, Ada; Gavrilovska, Liljana (December 2022, IEEE)

   Mobile Edge Computing (MEC) creates new infrastructure at the edges of the mobile networks, thus providing transformative opportunities for applications seeking latency benefits by operating closer to end-users and devices. However, the reduced network distance between the application endpoints of the MEC flows causes pattern shifts in the packet bursts exchanged at the network edges. The longer and denser bursts create a new source of contention that is not considered by current solutions. As a result, naively collocating applications onto the MEC tier can negatively affect latency-critical workloads, resulting in up to 73% packets experiencing as much as 3.8x increased latency. This makes it impossible to support latency-centric SLOs in MEC, obviating its expected benefits from MEC. This paper is the first to describe this new contention point in mobile networks and its potentially crippling impact on the achievable latency benefit from MEC. We propose ShapeShifter, a new component in the MEC architecture which solves the MEC latency contention problem through adaptive latency-centric burst management of MEC flows. ShapeShifter is effective - it eliminates SLO violations for latency-critical applications and improves application performance in multi-tenant scenarios by up to 3.8 x – and practical – it can be deployed with minimal disruption to the current mobile network ecosystem. 

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3. Poster Abstract: O-RAN Signaling Optimizations for Improved IoT Handover Performance in 5G Networks

   https://doi.org/10.1145/3576842.3589165  

   Riccio, Eduardo Lichtenfels; Mangipudi, Pavan Kumar; McNair, Janise (May 2023, IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation)

   IoT systems require a wireless infrastructure that supports 5G devices, including handovers between heterogeneous and/or small cell radio access networks. These networks are subject to increased radio link failures and loss of IoT network function. 3GPP new radio (NR) applications include multihoming, i.e., simultaneously connecting devices, and handover, i.e., changing the point of access to the network. This work leverages the open radio access network (O-RAN) alliance, which specifies a new open architecture with intelligent controllers, to improve handover management. A new feedback-based time-to-trigger (TTT) handover mechanism is introduced. Improved throughput and reduced radio link failures over other techniques were achieved. 

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4. Demo: SSxApp: Secure Slicing for O-RAN Deployments

   https://doi.org/10.1109/MILCOM58377.2023.10356385  

   Moore, Joshua; Abdalla, Aly; Zhang, Minglong; Marojevic, Vuk (December 2023, MILCOM IEEE Military Communications Conference)

   This demonstration explores the security concerns in 5G and beyond networks within open radio access network (O-RAN) deployments, focusing on active attacks disrupting cellular communications. An xApp developed on the open artificial intelligence cellular (OAIC) platform enables on-the-fly creation and management of network slices to mitigate such attacks. The xApp is hosted in the near-real time RAN intelligent controller (RIC) and establishes secure slices for the software radio network it controls. This solution presents a practical approach for resilient and secure network management in dynamic environments. 

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5. Distributed Graph-Based Learning for User Association and Beamforming Design in Multi-RIS Multi-Cell Networks

   https://doi.org/10.1109/TWC.2025.3551763  

   Lim, Byungju; Vu, Mai (July 2025, IEEE Transactions on Wireless Communications)

   We propose a novel graph neural network (GNN) architecture for jointly optimizing user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase shift in a multi-RIS aided multi-cell network. The proposed architecture represents BSs and users as nodes in a bipartite graph where the same type of nodes shares the same neural networks for generating messages and updating its representations, allowing for distributed implementation. In addition, we utilize a composite reflected channel estimation integrated between layers of the GNN structure to significantly reduce the signaling overhead and complexity required for channel estimation in a multi-RIS network. To avoid BS overload, load balancing is regularized in the training of the GNN and we further develop a collision avoidance algorithm to ensure strict load balancing at every BS. Numerical results show that the proposed GNN architecture is significantly more efficient than existing approaches. The results further demonstrate its strong scalability with network size and achieving a throughput performance approaching that of a centralized traditional optimization algorithm, without requiring individual RIS-reflected channels estimation and without the need for re-training or fine-tuning. 

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