Traffic-Estimation-Based Low-Latency XGS-PON Mobile Front-Haul for Small-Cell C-RAN Based on an Adaptive Learning Neural Network

Abstract

In this paper, we propose a novel method for low-latency 10-Gigabit-capable symmetric passive optical network (XGS-PON) mobile front-haul for small cell cloud radio access network (C-RAN) based on traffic estimation. In this method, the number of packets that arrive to the optical network unit (ONU) buffer from the remote radio unit (RRU) link is predicted using an adaptive learning neural network function integrated into the dynamic bandwidth allocation (DBA) module at the optical line terminal (OLT). By using this predictive method, we are able to eliminate the additional DBA processing delay and the delay required for reporting ONU buffer occupancy to the OLT. As a result, the latency is as low as required for mobile front-haul in C-RAN architecture. The performance of the new method is evaluated by means of simulation under XGS-PON standard. The simulation results confirmed the capability of the proposed method of achieving the latency requirement for mobile front-haul while outperforming some other XGS-PON standard compliant algorithms that are optimized to support mobile front-haul and backhaul traffic.

Keywords

• C-RAN
• front-haul
• traffic estimation
• neural network
• XGS-PON