Reinforcement learning for 5G O-RAN

This video demonstrates an O-RAN (Open Radio Access Network) testbed with xApps in Near-RT RIC for online network resource optimization.

The O-RAN architecture is designed to disaggregate traditional, monolithic radio access networks into modular, open, and interoperable components. This paradigm shift fosters innovation, flexibility, and vendor diversity by breaking vendor lock-in and enabling the integration of best-in-class solutions across different layers of the RAN. At the core of this architecture lies the Near-RT RAN Intelligent Controller (Near-RT RIC), which operates at timescales on the order of 10 milliseconds to 1 second. The Near-RT RIC provides a standardized platform for hosting modular applications, known as xApps, that execute advanced control and optimization functions. Through standardized interfaces, the Near-RT RIC continuously interacts with distributed RAN nodes, collecting network state information, analyzing real-time conditions, and enforcing optimized control policies. By doing so, it introduces a level of programmability and adaptability that is difficult to achieve in legacy RAN designs.

xApps running on the Near-RT RIC enable near-real-time network optimization by dynamically tuning parameters in response to traffic dynamics, user mobility, and varying channel conditions. This capability extends beyond rule-based adaptations: by leveraging AI/ML, particularly DRL, xApps can learn and adapt policies that optimize network behavior under uncertainty. Unlike static heuristics, DRL-based xApps are capable of making sequential, long-horizon decisions that balance multiple objectives such as throughput maximization, latency minimization, energy efficiency, and fairness among users. For instance, an AI-driven xApp could proactively allocate spectrum resources, adjust transmission power levels, or optimize handover strategies, all while accounting for evolving traffic patterns and interference conditions.

The integration of DRL and other AI techniques into the Near-RT RIC transforms O-RAN into more than just an open and flexible RAN management framework; it positions O-RAN as a platform for autonomous, data-driven network operation. This approach not only improves short-term performance but also enables the network to continuously learn and adapt in real-world deployments. As a result, O-RAN with Near-RT RIC and AI-powered xApps represents a critical enabler for the next generation of wireless networks—offering resilience, scalability, and intelligence that are essential for meeting the demands of 5G and beyond.