Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN

Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access Network (O-RAN) framework provides a promising foundation for embedding machine learning intelligence through its disaggregated architecture and programmable interfaces. This work applies a Neural Architecture Search (NAS)-based framework that dynamically selects and orchestrates efficient Long Short-Term Memory (LSTM) architectures for traffic prediction in O-RAN environments. Our approach leverages the O-RAN paradigm by separating architecture optimisation (via non-RT RIC rApps) from real-time inference (via near-RT RIC xApps), enabling adaptive model deployment based on traffic conditions and resource constraints. Experimental evaluation across six LSTM architectures demonstrates that lightweight models achieve $R^2 \approx 0.91$--$0.93$ with high efficiency for regular traffic, while complex models reach near-perfect accuracy ($R^2 = 0.989$--$0.996$) during critical scenarios. Our NAS-based orchestration achieves a 70-75\% reduction in computational complexity compared to static high-performance models, while maintaining high prediction accuracy when required, thereby enabling scalable deployment in real-world edge environments.

Joint Optimisation of Load Balancing and Energy Efficiency for O-RAN Deployments

Open Radio Access Network (O-RAN) architecture provides an intrinsic capability to exploit key performance monitoring (KPM) within Radio Intelligence Controller (RIC) to derive network optimisation through xApps. These xApps can leverage KPM knowledge to dynamically switch on/off the associated RUs where such a function is supported over the E2 interface. Several existing studies employ artificial intelligence (AI)/Machine Learning (ML) based approaches to realise such dynamic sleeping for increased energy efficiency (EE). Nevertheless, most of these approaches rely upon offloading user equipment (UE) to carve out a sleeping opportunity. Such an approach inherently creates load imbalance across the network. Such load imbalance may impact the throughput performance of offloaded UEs as they might be allocated a lower number of physical resource blocks (PRBs). Maintaining the same PRB allocation while addressing the EE at the network level is a challenging task. To that end, in this article, we present a comprehensive ML-based framework for joint optimisation of load balancing and EE for ORAN deployments. We formulate the problem as a multi-class classification system that predictively evaluates potential RU configurations before optimising the EE, mapping network conditions to three load balance categories (Well Balanced, Moderately Balanced, Imbalanced). Our multi-threshold approach (Conservative, Moderate, Aggressive) accommodates different operational priorities between energy savings and performance assurance. Experimental evaluation using 4.26 million real network measurements from simulations demonstrates that our Random Forest model achieves 98.3% F1-macro performance, representing 195% improvement over traditional baseline strategies.

Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.

Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks

The disaggregated, distributed and virtualised implementation of radio access networks allows for dynamic resource allocation. These attributes can be realised by virtue of the Open Radio Access Networks (O-RAN) architecture. In this article, we tackle the issue of dynamic resource allocation using a data-driven approach by employing Machine Learning (ML). We present an xApp-based implementation for the proposed ML algorithm. The core aim of this work is to optimise resource allocation and fulfil Service Level Specifications (SLS). This is accomplished by dynamically adjusting the allocation of Physical Resource Blocks (PRBs) based on traffic demand and Quality of Service (QoS) requirements. The proposed ML model effectively selects the best allocation policy for each base station and enhances the performance of scheduler functionality in O-RAN - Distributed Unit (O-DU). We show that an xApp implementing the Random Forest Classifier can yield high (85\%) performance accuracy for optimal policy selection. This can be attained using the O-RAN instance state input parameters over a short training duration.