Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning

Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, we propose an optical spectrum anomaly detection scheme that exploits computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals. The proposed scheme achieves 100% detection accuracy even without prior knowledge of the anomalies. Furthermore, operation with encoded images of constellation diagrams reduces the runtime by up to 200 times

Experimental evaluation of impairments in unrepeatered DP-16QAM link with distributed raman amplification

The transmission impairments of a Raman amplified link using dual-polarization 16-quadrature amplitude modulation (DP-16QAM) are experimentally characterized. The impact of amplitude and phase noise on the signal due to relative intensity noise (RIN) transfer from the pump are compared for two pumping configurations: first-order backward pumping and bi-directional pumping. Experimental results indicate that with increased Raman backward pump power, though the optical signal-to-noise ratio (OSNR) is increased, so is the pump-induced amplitude and phase noise. The transmission performance is firstly improved by the enhanced OSNR at a low pump power until an optimum point is reached, and then the impairments due to pump-induced noise start to dominate. However, the introduction of a low pump power in the forward direction can further improve the system's performance

Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation

We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks (CNN) for error vector magnitude (EVM) estimation in coherent optical communications. We build a regression model to extract EVM information from complex signal constellation diagrams using a small number of received symbols. For the additive white Gaussian noise (AWGN) impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase shift keying (QPSK), 2.2% for 16-ary quadrature amplitude modulation (16QAM), and 1.1% for 64QAM signals, requiring only 100 symbols per constellation cluster in each observation period. Therefore, it can be used as a low-complexity alternative to conventional bit-error-rate (BER) estimation, enabling solutions for intelligent optical performance monitoring

Total Cost of Ownership of Digital vs. Analog Radio-Over-Fiber Architectures for 5G Fronthauling

The article analyzes the total cost of ownership (TCO) of 5G fronthauling solutions based on analog and digital radio-over-fiber (RoF) architectures in cloud radio access networks (C-RANs). The capital and operational expenditures (CAPEX, OPEX) are assessed, for a 10-year period, considering three different RoF techniques: intermediate frequency analog RoF (IF-A-RoF), digital signal processing (DSP) assisted analog RoF (DSP-A-RoF), and digital RoF (D-RoF) based on the common public radio interface (CPRI) specifications. The greenfield deployment scenario under exam includes both fiber trenching (FT) and fiber leasing (FL) options. The TCO is assessed while varying (i) the number of aggregated subcarriers, (ii) the number of three-sector antennas located at the base station, and (iii) the mean fiber-hop length. The comparison highlights the significance that subcarrier aggregation has on the cost efficiency of the analog RoF solutions. In addition, the analysis details the contribution of each cost category to the overall CAPEX and OPEX values. The obtained results indicate that subcarrier aggregation via DSP results in high cost efficiency for a mobile fronthaul network, while a CPRI-based architecture together with FL brings the highest OPEX value

Laser Linewidth Tolerant EVM Estimation Approach for Intelligent Signal Quality Monitoring Relying on Feedforward Neural Networks

Robustness against the large linewidth semiconductor laser-induced impairments in coherent systems is experimentally demonstrated for a feedforward neural network-enabled EVM estimation scheme. A mean error of 0.4% is achieved for 28 Gbaud square and circular QAM signals and linewidths up to 12.3 MHz