QoS-based Intelligent multi-connectivity for B5G networks

TL;DR

This paper introduces a machine learning-based multi-connectivity framework for B5G networks that intelligently allocates resources to meet diverse QoS requirements, significantly improving QoS success rates and spectrum efficiency.

Contribution

It presents a novel deep learning approach for QoS-aware multi-connectivity, optimizing user association and resource allocation in B5G networks.

Findings

Achieves a 98% QoS success rate.

Improves spectrum efficiency by 30%.

Enhances network performance over existing methods.

Abstract

The rapid advancement of communication technologies has established cellular networks as the backbone for diverse applications, each with distinct quality of service requirements. Meeting these varying demands within a unified infrastructure presents a critical challenge that can be addressed through advanced techniques such as multi-connectivity. Multiconnectivity enables User equipments to connect to multiple BSs simultaneously, facilitating QoS differentiation and provisioning. This paper proposes a QoS-aware multi-connectivity framework leveraging machine learning to enhance network performance. The approach employs deep neural networks to estimate the achievable QoS metrics of BSs, including data rate, reliability, and latency. These predictions inform the selection of serving clusters and data rate allocation, ensuring that the User Equipment connects to the optimal BSs to meet its QoS needs. Performance evaluations demonstrate that the proposed algorithm significantly enhances Quality of Service (QoS) for applications where traditional and state-of-the-art methods are inadequate. Specifically, the algorithm achieves a QoS success rate of 98%. Furthermore, it improves spectrum efficiency by 30% compared to existing multi-connectivity solutions.