Next Article in Journal
Molecular Imaging with 68Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
Previous Article in Journal
Estimation of the Longitudinal Elasticity Modulus of Braided Synthetic Fiber Rope Utilizing Classical Laminate Theory with the Unit N/tex
Previous Article in Special Issue
Dimensioning Models of Optical WDM Rings in Xhaul Access Architectures for the Transport of Ethernet/CPRI Traffic
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(7), 1097; https://doi.org/10.3390/app8071097

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

1
State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University, No. 800, Road Dongchuan, Shanghai 200240, China
2
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
View Full-Text   |   Download PDF [1744 KB, uploaded 6 July 2018]   |  

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. View Full-Text
Keywords: C-RAN; front-haul; traffic estimation; neural network; XGS-PON C-RAN; front-haul; traffic estimation; neural network; XGS-PON
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top