Deep Transfer Learning: A Smarter Approach to Wireless Communication Networks

Next-generation cellular networks are evolving into more complex and virtualized systems, utilizing machine learning (ML) for enhanced optimization while leveraging higher frequency bands and denser deployments to meet diverse service demands. Although this evolution brings numerous benefits, it also introduces significant challenges, particularly in radio resource management (RRM). In such environments, effective RRM becomes increasingly difficult due to several factors: more intricate interference patterns, the need for rapid decision making across a growing number of base stations (BSs) and the highly dynamic nature of user mobility. In addition, the requirements of different types of services further complicate resource allocation, necessitating more advanced and adaptive RRM strategies to achieve optimal performance and maintain high quality of service (QoS). To address these challenges, we propose a ML algorithm that predicts the optimal future serving cell using sequential user equipment (UE) measurements. Conventional ML models require retraining for each environmental change, leading to high complexity and energy consumption. Thus, we also introduce the transfer learning (TL) approach to accelerate model adaptation to dynamic networks and evolving channel conditions, significantly reducing retraining time and improving efficiency. Furthermore, it optimizes key network objectives, such as load balancing and energy efficiency through TL techniques. Our framework complies fully with the O-RAN specifications and is designed to be deployable in a Near-Rea-lTime RAN Intelligent Controller (RIC).