PreNS: A Hybrid Predictive and Real-Time Resource Allocation Framework for 5G and Beyond RAN Network Slicing

The rapid evolution of 5G and beyond-5G (B5G) networks, coupled with the emergence of network slicing, has introduced unprecedented challenges in resource allocation for Radio Access Networks (RAN). Existing methods primarily fall into two categories: i) reactive approaches, which fail to anticipate sudden traffic changes; ii) predictive methods, which struggle to adapt to real-time conditions. These limitations result in inefficient resource utilization, degraded Quality of Service (QoS), and increased latency. To address these challenges, we propose PreNS, a hybrid framework that integrates predictive modeling with real-time decision-making to optimize resource allocation. PreNS combines an attention-based Bi-LSTM model for accurate traffic demand forecasting with a Double Deep Q-Network (DDQN) for real-time adjustments. Its key innovation lies in the aggregated state representation, which fuses predicted demands and real-time data, enabling adaptive and efficient resource management. By balancing proactive foresight and reactive adaptability, PreNS addresses fluctuating network conditions and the rigidity of static optimization. Experimental evaluations on a realistic 5G testbed demonstrate that PreNS improves resource utilization by approximately 14%, reduces latency by 20%, and enhances QoS satisfaction by 5%, compared to state-of-the-art methods—demonstrating its potential for efficient, scalable resource allocation in future wireless networks.