Quality of Service (QoS) Optimization in 5G/6G Networks Using Neural Networks

Charis E Shiny, S Annapurna, C Lakshana, Anusha Fakirappa Bogur, S Ramesh, G R Naik

Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, traditional quality of service (QoS) policy support mechanisms are faced with significant limits in identifying and managing dynamic heterogeneous traffic types in the next generation wireless networks. Traffic demands will vary widely and traditional QoS will not provide the flexibility to adopt mechanisms quickly to arbitrary network conditions, along with providing a very different service requirement. This research proposes an innovative neural network based QoS optimization framework that predicts the traffic parameters from intelligence resource allocation. Even a variety of deep learning models will be used to predict the key performance indicators or latency, jitter and packet loss for different scenarios. Neural network slicing is an automation technique that manages bandwidth allocation autonomously, adapting in real time to traffic demands and service provisioning requirements. The proposed approach is expected to improve QoS by reducing latency and packet loss, and also to enhance resource utilization and service reliability in 5G and 6G networks.