Gan, Z; Shevchenko, M; Nallaperuma Herzberg, S; Savory, S J (2024) Fast neural network inverse model to maximize throughput in ultra-wideband WDM systems. Optics Express, 32 (22). pp. 38642-38654. ISSN 1094-4087

Preview

Text eid10357.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

Ultra-wideband systems expand the optical bandwidth in wavelength-division multiplexed (WDM) systems to provide increased capacity using the existing fiber infrastructure. In ultra-wideband transmission, power is transferred from shorter-wavelength WDM channels to longer-wavelength WDM channels due to inelastic inter-channel stimulated Raman scattering. Thus, managing launch power is necessary to improve the overall data throughput. While the launch power optimization problem can be solved by the particle swarm optimization method it is sensitive to the objective value and requires intensive objective calculations. Hence, we first propose a fast and accurate data-driven deep neural network-based physical layer in this paper which can achieve 99%−100% throughput compared to the semi-analytical approach with more than 2 orders of magnitude improvement in computational time. To further reduce the computational time, we propose an iterative greedy algorithm enabled by the inverse model to well approximate a sub-optimal solution with less than 6% performance degradation but almost 3 orders of magnitude reduction in computational time.

Item Type:	Article
Keywords:	ultrawideband communication, stimulated Raman scattering, nonlinear interference, WDM, launch power control, neural network, optimisation
Subjects:	Optical Radiation and Photonics > Optical Comms. And Data
Divisions:	Materials and Mechanical Metrology
Identification number/DOI:	10.1364/OE.536632
Last Modified:	24 Mar 2026 15:01
URI:	https://eprintspublications.npl.co.uk/id/eprint/10357