Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a variety of anomalies resulting from hard failures (e.g., fiber cuts) and malicious physical attacks [e.g., optical eavesdropping (fiber tapping)]. Such anomalies may cause network disruption, thereby inducing huge financial and data losses, compromising the confidentiality of optical networks by gaining unauthorized access to the carried data, or gradually degrading the network operations. Therefore, it is highly required to implement efficient anomaly detection, diagnosis, and localization schemes for enhancing the availability and reliability of optical networks. In this paper, we propose a data-driven approach to accurately and quickly detect, diagnose, and localize fiber fault anomalies, including fiber cuts and optical eavesdropping attacks. The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder. We verify the efficiency of our proposed approach by experiments under various attack anomaly scenarios using real operational data. The experimental results demonstrate that (i) the autoencoder detects any fiber fault or anomaly with an F1 score of 96.86%, and (ii) the attention-based bidirectional gated recurrent unit algorithm identifies the detected anomalies with an average accuracy of 98.2% and localizes the faults with an average root mean square error of 0.19 m.

Combating the destructive effects of power-fading in fiber-optic systems using direct-detection is the key for increasing the transmission rates and/or reach. Some of the solutions used in the past include additional components, e.g. a dispersion compensating fiber or an IQ-modulator, which increase the costs of the entire system. Another solution, the compensation of the channel distortions using decision-feedback equalization, is limited to its error-propagation effect. In this paper, we introduce Tomlinson-Harashima precoding for the compensation of power-fading, which allows to use the efficiency of feedback equalization for channels with spectral zeros without the disadvantage of error propagation. In an experimental investigation with different scenarios, including transmission over 25 km up to 100 km of standard single-mode fiber with gross data rates ranging from 56 to 560 Gb/s, we show that Tomlinson Harashima precoding significantly outperforms decision-feedback equalization in all cases. Using Tomlinson Harashima precoding, power-fading can be efficiently compensated for using digital signal processing only, keeping the system flexible and at the same time cost-effective.

Intensity modulation and direct detection (IM/DD) together with pulse amplitude modulation (PAM) is a cost-efficient solution for intra-datacenter links. However, sophisticated digital signal processing (DSP) is inevitable to reach the demanded high data rates for these short-reach applications. In this paper, we compare different DSP schemes that enable rates of 200 Gb/s/ and beyond. Those schemes include Tomlinson-Harashima precoding, partial response signaling, Viterbi equalization and post-filtering together with Volterra nonlinear equalization. Extensive experimental measurements are shown in which the DSP approaches are compared at gross rates between 180 Gb/s and 300 Gb/s. The experiments are done in C-band for fiber links of up to 2 km length. As modulation formats, PAM4, PAM6 and PAM8 are considered. Net bit rates well over 200 Gb/s are shown for all formats at a range of 1 km single mode fiber using a transmission system consisting of currently available commercial components. The optimum DSP configuration as well as the best modulation format varies along the investigated data rates. The best solution is shown to change from partial response PAM4 for lower rates to PAM6 and PAM8 at high bit rates.

The ability to transmit signals with high information spectral density (ISD) using low-complexity and cost-effective transceivers is essential for short- and medium-haul optical communication systems. Consequently, spectrally efficient direct detection transceiver-based solutions are attractive for such applications. In this paper, we experimentally demonstrate the wavelength-division multiplexed (WDM) transmission of 7×12 GHz-spaced dispersion pre-compensated Nyquist pulse-shaped 16-QAM subcarrier modulated channels operating at a net bit rate of 24 Gb/s per channel, and achieving a net optical ISD of 2.0 b/s/Hz. The direct detection receiver used in our experiment consisted of a single-ended photodiode and a single analog-to-digital converter. The carrier-to-signal power ratio at different values of optical signal-to-noise ratio was optimized to maximize the receiver sensitivity performance. The transmission experiments were carried out using a recirculating fiber loop with uncompensated standard single-mode fiber and EDFA-only amplification. The maximum achieved transmission distances for single channel and WDM signals were 727 and 323 km below the bit-error ratio of 3.8 × 10 ^-3 , respectively. To the best of our knowledge, this is the highest achieved ISD for WDM transmission in direct detection links over such distances.

Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distorted by different kinds of noise, causing blurring of the backscattered signals, and thereby leading to a misleading interpretation and a more cumbersome event detection task. To address this problem, a novel method combining a denoising convolutional autoencoder (DCAE) and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the former is used for noise removal of OTDR signals and the latter for fault detection, localization, and diagnosis with the denoised signal as input. The proposed approach is applied to noisy OTDR signals of different levels of input SNR ranging from −5 dB to 15 dB. The experimental results demonstrate that: (i) the DCAE is efficient in denoising the OTDR traces and it outperforms other deep learning techniques and the conventional denoising methods; and (ii) the BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an improvement of 13.74% compared to the performance of the same model trained with noisy OTDR signals.

State-of-the-art, prospects, and challenges of next-generation optical access technology based on tunable lasers are discussed. These considerations can also be applied to various backhaul and mobile fronthaul applications since these have similar characteristics and requirements, and have to share at least part of the optical distribution networks. Potential advantages of tunable lasers over competing approaches (e.g., seeded reflective transmitters) include higher bit-rate × reach products and better tolerance to reflections in the fiber plant. Today, the main issue is still the lack of available low-cost tunables with sufficient tuning range and launch power. However, significant progress has been made in the last few years to bring such lasers closer to commercialization. In this paper, we will review low-cost tunable laser approaches and report on latest technical results. We will also give an overview on current standardization activities and give an outlook on next-generation optical access, backhaul and fronthaul networks which also support active-site consolidation.