The objective of project is to develop an intelligent system concept that uses deep reinforcement learning (DRL) techniques to optimize resource allocation , traffic management in 5G networks.
Video link : https://drive.google.com/file/d/1v2UF-JKxg-2uHrrkywgv_LWeVAMo-VXt/view?usp=sharing
With the explosive growth of mobile data demand, 5G networks need to support diverse applications like enhanced mobile broadband (eMBB), ultra-reliable low-latency com munications (URLLC), and massive machine-type commu nications (mMTC). Efficient management of these networks requires adaptive, intelligent solutions. Traditional methods, relying on static, rule-based techniques, are no longer sufficient to handle the dynamic, heterogeneous environments of 5G. Deep Reinforcement Learning (DRL) emerges as a promising solution, offering the ability to continuously adapt and opti mize resource allocation and traffic management. This report dives into the specific challenges of resource management in 5G, such as network slicing, dynamic resource allocation, and the complexities of traffic management, and explores how DRL algorithms can address these