The reduction of system margin in open optical line systems (OLSs) requires the capability to predict the quality of transmission (QoT) within them. This quantity is given by the generalized signal-to-noise ratio (GSNR), including both the effects of amplified spontaneous emission (ASE) noise and nonlinear interference accumulation. Among these, estimating the ASE noise is the most challenging task due to the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFAs), which depend on the spectral load, given the overall gain profile. An accurate GSNR estimation enables control of the power optimization and the possibility to automatically deploy lightpaths with a minimum margin in a reliable manner. We suppose an agnostic operation of the OLS, meaning that the EDFAs are operated as black boxes and rely only on telemetry data from the optical channel monitor at the end of the OLS. We acquire an experimental data set from an OLS made of 11 EDFAs and show that, without any knowledge of the system characteristics, an average extra margin of 2.28 dB is necessary to maintain a conservative threshold of QoT. Following this, we applied deep neural network machine-learning techniques, demonstrating a reduction in the needed margin average down to 0.15 dB.
Precise computation of the quality of transmission (QoT) of lightpaths (LPs) in transparent optical networks has techno-economic importance for any network operator. The QoT metric of LPs is defined by the generalized signal-to-noise ratio (GSNR), which includes the effects of both amplified spontaneous emission noise and nonlinear interference accumulation. Generally, the physical layer of a network is characterized by nominal values provided by vendors for the operational parameters of each network element (NE). Typically, NEs suffer a variation in the working point that implies an uncertainty from the nominal value, which creates uncertainty in the GSNR computation and requires the deployment of a system margin. We propose the use of a machine learning agent trained on a dataset from an in-service network to reduce the uncertainty in the GSNR computation on an unused sister network, based on the same optical transport equipment and thus following the transfer learning paradigm. We synthetically generate datasets for both networks using the open-source library GNPy and show how the proposed deep neural network based on TensorFlow may substantially reduce the GSNR uncertainty and, consequently, the needed margin. We also present a statistical analysis of the observed GSNR fluctuations, showing that the per-wavelength GSNR distribution is always well-approximated as Gaussian, enabling a statistical closed-form approach to the margin setting.
This paper introduces a novel 9-shaped multiband frequency reconfigurable monopole antenna for wireless applications, using 1.6 mm thicker FR4 substrate and a truncated metallic ground surface. The designed antenna performs in single and dual frequency modes depending on switching states. The antenna works in a single band (WiMAX at 3.5 GHz) when the switch is in the OFF state. The dual band frequency mode (Wi-Fi at 2.45 GHz and WLAN at 5.2 GHz) is obtained when the switch is turned ON. The directivities are: 2.13 dBi, 2.77 dBi and 3.99 dBi and efficiencies: 86%, 93.5% and 84.4% are attained at frequencies 2.45 GHz, 3.5 GHz and 5.2 GHz respectively. The proposed antenna has VSWR< 1.5 for all the three frequencies. The scattering and far-field parameters of the designed antenna are analyzed using computer simulation technology CST 2014. The performance of the proposed antenna is analyzed on the basis of VSWR, efficiency, gain, radiation pattern and return loss.
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