LTE WiFi

LTE-WiFi coexistence, focusing on efficient spectrum sharing and resource management in increasingly dense networks, is a key research area driven by the need to meet growing mobile data demands. Current research employs machine learning techniques, including graph neural networks, transformers, and reinforcement learning (e.g., Q-learning), to optimize resource allocation and predict network behavior, often within the framework of Open Radio Access Networks (O-RAN). These advancements aim to improve network performance metrics such as capacity, signal strength, and latency, ultimately enhancing the user experience and enabling seamless integration of LTE and WiFi technologies.