Environmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

Monolithic silicon photonic 32x32 thin-CLOS AWGR for all-to-all interconnections.

This paper reports the design, fabrication, and experimental demonstration of a monolithic silicon photonic (SiPh) 32×32 Thin-CLOS arrayed waveguide grating router (AWGR) for scalable SiPh all-to-all interconnection fabrics. The 32×32 Thin-CLOS makes use of four 16-port silicon nitride AWGRs, which are compactly integrated and interconnected by a multi-layer waveguide routing method. The fabricated Thin-CLOS has 4 dB insertion loss, < -15 dB adjacent channel crosstalk, and < -20 dB non-adjacent channel crosstalk. System experiments operated on the 32×32 SiPh Thin-CLOS demonstrate error-free communication at 25 Gb/s.

Photonic spiking neural networks with event-driven femtojoule optoelectronic neurons based on Izhikevich-inspired model.

Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits in terms of event-driven computing capability. While state-of-the-art PSNN designs require a continuous laser pump, this paper presents a monolithic optoelectronic PSNN hardware design consisting of an MZI mesh incoherent network and event-driven laser spiking neurons. We designed, prototyped, and experimentally demonstrated this event-driven neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of two photodetectors for excitatory and inhibitory optical spiking inputs, electrical transistors' circuits providing spiking nonlinearity, and a laser for optical spiking outputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. The nanoscale neuron designed in our monolithic PSNN utilizes quantum impedance conversion. It shows that estimated 21.09 fJ/spike input can trigger the output from on-chip nanolasers running at a maximum of 10 Gspike/second in the neural network. Utilizing the simulated neuron model, we conducted simulations on MNIST handwritten digits recognition using fully connected (FC) and convolutional neural networks (CNN). The simulation results show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. The benchmark shows our PSNN can achieve 50 TOP/J energy efficiency, which corresponds to 100 × throughputs and 1000 × energy-efficiency improvements compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid.

Demonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks.

This paper proposes a distributed collaborative learning approach for cognitive and autonomous multi-domain elastic optical networking (EON). The proposed approach exploits a knowledge-defined networking framework which leverages a broker plane to coordinate the operations of multiple EON domains and applies machine learning (ML) to support autonomous and cognitive inter-domain service provisioning. By employing multiple distributed ML blocks learning domain-level features and working with broker plane aggregation ML blocks (through the chain rule-based training), the proposed approach enables to develop cognitive networking applications that can fully exploit the multi-domain EON states while obviating the need for the raw and confidential intra-domain data. In particular, we investigate end-to-end quality-of-transmission estimation application using the distributed learning approach and propose three estimator designs incorporating the concepts of multi-task learning (MTL) and transfer learning (TL). Evaluations with experimental data demonstrate that the proposed designs can achieve estimation accuracies very close to (with differences less than 0.5%) or even higher than (with MTL/TL) those of the baseline models assuming full domain visibility.

Demonstration of a carrier frequency offset estimator for 16-/32-QAM coherent receivers: a hardware perspective.

We propose and implement a hardware-efficient frequency offset estimator (FOE) optimized for 16- and 32-QAM coherent optical receivers with low hardware cost and high estimation accuracy. The proposed FOE combines a wide-range coarse estimator and a narrow-range highly accurate estimator in a feedforward architecture. We numerically and experimentally investigate the performance of the proposed estimator by using a field-programmable-logic-array (FPGA) based real-time coherent receiver. Compared with other state-of-the-art estimators in literature, the proposed method reduces over 40% of hardware utilizations while maintaining the same level of estimation accuracy in terms of mean-squared-error (MSE) and optical signal-to-noise ratio (OSNR) sensitivity. These results enable the development of next generation DSP circuit capable of supporting high capacity coherent optical communication link with advanced modulation formats.

Blind modulation format identification using nonlinear power transformation.

This paper proposes and experimentally demonstrates a blind modulation format identification (MFI) method delivering high accuracy (> 99%) even in a low OSNR regime (< 10 dB). By using nonlinear power transformation and peak detection, the proposed MFI can recognize whether the signal modulation format is BPSK, QPSK, 8-PSK or 16-QAM. Experimental results demonstrate that the proposed MFI can achieve a successful identification rate as high as 99% when the incoming signal OSNR is 7 dB. Key parameters, such as FFT length and laser phase noise tolerance of the proposed method, have been characterized.

A scalable silicon photonic chip-scale optical switch for high performance computing systems.

This paper discusses the architecture and provides performance studies of a silicon photonic chip-scale optical switch for scalable interconnect network in high performance computing systems. The proposed switch exploits optical wavelength parallelism and wavelength routing characteristics of an Arrayed Waveguide Grating Router (AWGR) to allow contention resolution in the wavelength domain. Simulation results from a cycle-accurate network simulator indicate that, even with only two transmitter/receiver pairs per node, the switch exhibits lower end-to-end latency and higher throughput at high (>90%) input loads compared with electronic switches. On the device integration level, we propose to integrate all the components (ring modulators, photodetectors and AWGR) on a CMOS-compatible silicon photonic platform to ensure a compact, energy efficient and cost-effective device. We successfully demonstrate proof-of-concept routing functions on an 8 × 8 prototype fabricated using foundry services provided by OpSIS-IME.

Rapid and complete hitless defragmentation method using a coherent RX LO with fast wavelength tracking in elastic optical networks.

This paper demonstrates a rapid and full hitless defragmentation method in elastic optical networks exploiting a new technique for fast wavelength tracking in coherent receivers. This technique can be applied to a single-carrier connection or each of the subcarriers forming a super-channel. A proof-of-concept demonstration shows hitless defragmentation of a 10 Gb/s QPSK single-carrier connection from 1547.75 nm to 1550.1 nm in less than 1 µs. This was obtained using a small (0.625 kB) link-layer transmitter buffer without the need for any additional transponder. We also demonstrated that the proposed defragmentation technique is capable of hopping over an existing connection, i.e. 10 Gb/s OOK at 1548.5 nm, without causing any degradation of its real-time Bit Error Rate (BER) value. The proposed scheme gives advantages in terms of overall network blocking probability reduction up to a factor of 40.

Experimental demonstration of flexible bandwidth networking with real-time impairment awareness.

We demonstrate a flexible-bandwidth network testbed with a real-time, adaptive control plane that adjusts modulation format and spectrum-positioning to maintain quality of service (QoS) and high spectral efficiency. Here, low-speed supervisory channels and field-programmable gate arrays (FPGAs) enabled real-time impairment detection of high-speed flexible bandwidth channels (flexpaths). Using premeasured correlation data between the supervisory channel quality of transmission (QoT) and flexpath QoT, the control plane adapted flexpath spectral efficiency and spectral location based on link quality. Experimental demonstrations show a back-to-back link with a 360-Gb/s flexpath in which the control plane adapts to varying link optical signal to noise ratio (OSNR) by adjusting the flexpath's spectral efficiency (i.e., changing the flexpath modulation format) between binary phase-shift keying (BPSK), quaternary phase-shift keying (QPSK), and eight phase-shift keying (8PSK). This enables maintaining the data rate while using only the minimum necessary bandwidth and extending the OSNR range over which the bit error rate in the flexpath meets the quality of service (QoS) requirement (e.g. the forward error correction (FEC) limit). Further experimental demonstrations with two flexpaths show a control plane adapting to changes in OSNR on one link by changing the modulation format of the affected flexpath (220 Gb/s), and adjusting the spectral location of the other flexpath (120 Gb/s) to maintain a defragmented spectrum.

System feasibility of using stimulated Brillouin scattering in self coherent detection schemes.

We demonstrate the first self-coherent detection of 10 Gbit/s BPSK signals based on narrow-band amplification of the optical carrier by means of Stimulated Brillouin effect in a common fiber. We found that this technique is very effective only if it is combined with proper line coding and high-pass electrical filtering at the receiver. In this case we obtain OSNR-performance close to the ideal coherent receiver.

A 80 km reach fully passive WDM-PON based on reflective ONUs.

We propose a novel line coding combination (Inverse RZ coding in downlink and RZ in uplink) that extends the reach of WDM Passive Optical Networks based on Reflective SOAs with no in-line amplification. We achieved full downstream remodulation even when feeding the reflective SOA with power levels as low as -35 dBm, thus increasing the system power budget. We experimentally assessed this scheme for a fully passive, full-duplex and symmetrical 1.25 Gb/s WDM-PON over a 80 km G.652 feeder.