Machine Learning based Dynamic Resource Allocation in 6G Network

The 6G network is a new generation of wireless technology that builds on the $\mathbf{5 G}$ network’s advancements. It aims to improve speed, connectivity, and performance, with high data rates and broad frequency bands. It offers higher capacity and lower latency. Dynamic resource allocation in 6G networks will likely heavily leverage machine learning (ML) techniques due to the complexity and scale of these networks. The resource requirements of devices in 6G network are variable in nature. Dynamic resource allocation is crucial in 6G networks, and machine learning algorithms help to manage resource allocation efficiently. By leveraging machine learning techniques in dynamic resource allocation, $\mathbf{6 G}$ networks can achieve higher efficiency, flexibility, and adaptability to meet the diverse requirements of emerging applications and services. A machine learning algorithm used in this paper showed higher resource utilization and success rate than existing methods.