Performance Improvement of Time-Sensitive Fronthaul Networks in 5G Cloud-RANs Using Reinforcement Learning-Based Scheduling Scheme

Abstract

The rapid surge in internet-driven smart devices and bandwidth-hungry multimedia applications demands high-capacity internet services and low latencies during connectivity. Cloud radio access networks (C-RANs) are considered the prominent solution to meet the stringent requirements of fifth-generation (5G) and beyond networks by deploying the fronthaul transport links between baseband units (BBUs) and remote radio heads (RRHs). High-capacity optical links could be conventional mainstream technology for deploying the fronthaul in C-RANs. But densification of optical links significantly increases the cost and imposes several design challenges on fronthaul architecture which makes them impractical. Contrary, Ethernet-based fronthaul links can be lucrative solutions for connecting the BBUs and RRHs but are inadequate to meet the rigorous end-to-end delays, jitter, and bandwidth requirements of fronthaul networks. This is because of the inefficient resource allocation and congestion control schemes for the capacity constraint Ethernet-based fronthaul links. In this research, a novel reinforcement learning-based optimal resource allocation scheme has been proposed which eradicates the congestion and improves the latencies to make the capacity-constraints low-cost Ethernet a suitable solution for the fronthaul networks. The experiment results verified a notable 50% improvement in reducing delay and jitter as compared to the existing schemes. Furthermore, the proposed scheme demonstrated an enhancement of up to 70% in addressing conflicting time slots and minimizing packet loss ratios. Hence, the proposed scheme outperforms the existing state-of-the-art resource allocation techniques to satisfy the stringent performance demands of fronthaul networks.

Keywords

• 5G
• end-to-end delays
• fronthaul networks
• jitter
• resource allocation