The rapid surge in internet-driven smart devices and bandwidth-hungry backpacks 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 Cushion 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.