Thomas Shun Rong Shen

Optical Performance Monitoring Using Artificial Neural Network Trained With Asynchronous Amplitude Histograms

We propose an optical performance monitoring technique for simultaneous monitoring of optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) using an artificial neural network trained with asynchronous amplitude histograms (AAHs). Simulations are conducted to demonstrate the technique for both 40-Gb/s return-to-zero differential quadrature phase-shift keying (RZ-DQPSK) and 40-Gb/s noneturn-to-zero 16 quadrature amplitude modulation (16-QAM) systems. The OSNR, CD, and PMD monitoring range and root-mean-square (rms) errors are 10-30 and 0.43 dB, 0-400 and 9.82 ps/nm, and 0-10 and 0.92 ps, respectively, for RZ-DQPSK systems. For 16-QAM system, the monitoring range and rms errors are 10-30 and 0.2 dB, 0-400 and 9.66 ps/nm, and 0-30 and 0.65 ps for OSNR, CD, and PMD, respectively. As the generation of AAH does not require any clock or timing recovery, the proposed technique can serve as a low-cost option to realize in-service multiparameter monitoring for the next-generation transparent optical networks.

UltraFlow access networks: A dual-mode solution for the access bottleneck

Optical Flow Switching (OFS) is promised to be an efficient solution for large Internet data transfers. In this paper, we introduce UltraFlow Access, a novel optical access network architecture that offers dual-mode service to its end-users: IP and OFS. With UltraFlow Access, we design and implement a new control plane and a novel dual-mode network stack to ensure efficient connection setup, and reliable and optimal data transmission. Experimental testbed results demonstrate concurrent error-free transmission of 10 Gbps per-wavelength OFS and 1.25 Gbps conventional IP, delivered over the same infrastructure.

Optical Performance Monitoring Using Artificial Neural Networks Trained With Empirical Moments of Asynchronously Sampled Signal Amplitudes

We propose a low-cost technique for simultaneous and independent optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) monitoring in 40/56-Gb/s return-to-zero differential quadrature phase-shift keying (RZ-DQPSK) and 40-Gb/s RZ-DPSK systems, using artificial neural networks (ANN) trained with empirical moments of asynchronously sampled signal amplitudes. The proposed technique employs an extremely simple hardware and digital signal processing to enable multi-impairment monitoring at different data rates and for various modulation formats without necessitating hardware changes. Simulation results demonstrate wide dynamic ranges and good monitoring accuracies.

UltraFlow access testbed: Experimental exploration of dual-mode access networks

Electrical packet switching is well known as a flexible solution for small data transfers, whereas optical flow switching (OFS) might be an effective solution for large Internet file transfers. The UltraFlow project, a joint effort of three universities, Stanford, Massachusetts Institute of Technology, and University of Texas-Dallas, aims at providing an efficient dual-mode solution (i.e., IP and OFS) to the current network. In this paper, we propose and experimentally demonstrate UltraFlow Access, a novel optical access network that enables dual-mode service to the end users: IP and OFS. The new architecture cooperates with legacy passive optical networks (PONs) to provide both IP and novel OFS services. The latter is facilitated by a novel optical flow network unit (OFNU) that we have proposed, designed, and experimentally demonstrated. Different colored and colorless OFNU designs are presented, and their impact on the network performance is explored. Our testbed experiments demonstrate concurrent bidirectional 1.25 Gbps IP and 10 Gbps per-wavelength Flow error-free communication delivered over the same infrastructure. The support of intra-PON OFS communication, that is, between two OFNUs in the same PON, is also explored and experimentally demonstrated.

Reconfigurable Long-Reach UltraFlow Access Network: A Flexible, Cost-Effective, and Energy-Efficient Solution

In this paper, we propose and experimentally demonstrate a reconfigurable long-reach (R-LR) UltraFlow access network to provide flexible dual-mode (IP and Flow) service with lower capital expenditure (CapEx) and higher energy efficiency. UltraFlow is a research project involves the collaboration of Stanford, MIT, and UT-Dallas. The design of the R-LR UltraFlow access network enables seamless integration of the Flow service with IP passive optical networks deployed with different technologies. To fulfill the high-wavelength demand incurred by the extended service reach, we propose the use of multiple feeder fibers to form subnets within the UltraFlow access network. Two layers of custom switching devices are installed at the central office (CO) and remote node to provide flexibility in resource allocation and user grouping. With a centralized software-defined network (SDN) controller at the CO to control the dual-mode service, numerical analysis indicates that the reconfiguration architecture is able to reduce the CapEx during initial deployment by about 30%. A maximum of around 50% power savings is also achieved during low traffic period. The feasibility of the new architecture and the operation of the SDN controller are both successfully demonstrated on our experimental testbed.

OSNR Monitoring for Higher Order Modulation Formats Using Asynchronous Amplitude Histogram

We propose an asynchronous-amplitude-histogram-based optical signal-to-noise ratio monitoring technique for higher order modulation formats. Simulation results show that the proposed technique enables a wide monitoring range for 25-GSym/s nonreturn-to-zero/return-to-zero 16- and 64-quadrature amplitude modulation systems with high monitoring sensitivity. The tolerance of the monitoring technique to other signal impairments such as chromatic dispersion and polarization-mode dispersion is also investigated. The proposed technique can be used along transmission links with simple low-cost hardware and/or in digital signal processing (DSP)-based coherent receivers without additional hardware, thus enabling next-generation optical networks using higher order modulation formats to achieve transmission rates of 100 Gb/s per channel and beyond.

A Novel Quasi-Passive, Software-Defined, and Energy Efficient Optical Access Network for Adaptive Intra-PON Flow Transmission

In this paper, we propose, design, and demonstrate a novel Intra-PON Flow transmission with optical reroute using a Quasi-PAssive Reconfigurable (QPAR) node. The network can be reconfigured adaptively according to the monitored traffic status in a software-defined manner. Simulations show that PON with reroute architecture can achieve ~20% higher network capacity comparing to PON without reroute case with the same traffic waiting time or blocking probability requirement. PON with reroute consistently outperforms PON without reroute configuration with 20% larger throughput and 24% less power consumption with the Intra-PON traffic ratio of 0.3. In addition, adaptive Intra-wavelength assignment with a QPAR node can adapt to the subscription rate growth with time, and provide cost and power savings compared to PON without reroute and fixed PON with reroute architectures by approximately 20% and 10%. Moreover, adaptive Intra-PON architecture with a QPAR node can facilitate efficient multicast transmission for video or file backup among multiple serves located in different access networks, which can provide lower traffic waiting time, 14% power saving, and support roughly 30% higher traffic comparing to the fixed PON with reroute design with a multicast ratio of 0.5.

Experimental Demonstration of Reconfigurable Long-Reach UltraFlow Access: Software-Defined Dual-Mode Networks

We propose and experimentally demonstrate a novel reconfigurable long-reach software-defined UltraFlow access network that provides flexible, robust and energy efficient optical Flow switched and legacy IP services to end-users located in wide areas.

OSNR Monitoring for PM-QPSK Systems With Large Inline Chromatic Dispersion Using Artificial Neural Network Technique

We propose an optical signal-to-noise ratio (OSNR) monitoring technique for polarization-multiplexed (PM) quadrature phase shift keying (QPSK) systems with large inline chromatic dispersion (CD) based on artificial neural network (ANN) techniques. The transmitted signal is directly detected, and part of the corresponding radio frequency spectrum is used as the input to ANN. Simulation results for 112-Gb/s PM return-to-zero QPSK systems demonstrate an OSNR monitoring range of 12–24 dB in presence of CD up to 27000 ps/nm with a maximum monitoring error of 0.84 dB. Tolerance of the proposed technique on polarization-mode dispersion effects is also investigated.

UltraFlow Access Network With Remotely Powered and Controlled Quasi-Passive Reconfigurable Remote Node

In this paper, we propose an UltraFlow access network enabled by a remotely powered and controlled quasi-passive reconfigurable (QPAR) node. Residing at the remote node (RN), QPAR can dynamically split and route optical channels to any users attached to its outputs, thereby improving bandwidth efficiency and ensuring harmonic coexistence of different network services in the UltraFlow access network. We experimentally demonstrate the proposed UltraFlow access network with a 2 × 4 × 4 QPAR. The QPAR module is powered by a local supercapacitor that is remotely charged by remote laser power. Remote switching control in the QPAR has also been demonstrated with a self-designed control circuit. Scalability of the system is studied in the context of channel power budget and switching control in QPAR. Compared to other RN architectures, QPAR enabled UltraFlow access network uses about 50% less channels on average during low traffic time, and significantly reduces IP service delays in presence of unbalanced IP traffics. Simulation results also indicate that QPAR helps to mitigate the impact of multicast Flow traffic on Flow service delays by a maximum of 90%.