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Appl. Sci. 2018, 8(7), 1097;

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

State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University, No. 800, Road Dongchuan, Shanghai 200240, China
Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54590, Pakistan
Authors to whom correspondence should be addressed.
Received: 6 May 2018 / Revised: 28 June 2018 / Accepted: 28 June 2018 / Published: 6 July 2018
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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. View Full-Text
Keywords: C-RAN; front-haul; traffic estimation; neural network; XGS-PON C-RAN; front-haul; traffic estimation; neural network; XGS-PON

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Mikaeil, A.M.; Hu, W.; Hussain, S.B.; Sultan, A. Traffic-Estimation-Based Low-Latency XGS-PON Mobile Front-Haul for Small-Cell C-RAN Based on an Adaptive Learning Neural Network. Appl. Sci. 2018, 8, 1097.

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