800 Gb/s (80 × 10 Gb/s) capacity WDM-PON based on ASE injection seeding

Kim, J.; Moon, Sang Heup; Yoo, Seung‐Min; Lee, Changhee

doi:10.1364/oe.22.010359

Cited by 17 publications

(4 citation statements)

References 19 publications

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“…Moreover, it requires well designed optical fiber lengths according the channels to detect the reflected light with a delayed time. However, for the case of the proposed WDM scheme, the channel capacity can be increased easily by utilizing another wavelength band of BLS with the cyclic characteristic of the AWG [34] or reducing the channel bandwidth of the AWG [35]. Furthermore, it is possible to accommodate the same length of fiber cable for the OFT.…”

Section: Discussionmentioning

confidence: 99%

A Simple All-Optical Water Level Monitoring System Based on Wavelength Division Multiplexing with an Arrayed Waveguide Grating

Lee

Choo

Shin

2019

Sensors

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We propose and demonstrate a simple water level monitoring system based on the wavelength division multiplexing (WDM) for the spent fuel pool (SFP) at a nuclear power plant. The basic principle is based on the measurement of the optical power spectra by the Fresnel reflection according to the change of the refractive index at the end facet of the optical fiber tip (OFT). An arrayed waveguide grating (AWG) is employed to achieve multi-channel sensing capability with a C-band broadband light source (BLS) based on amplified spontaneous emission (ASE). The feasibility of the proposed scheme is investigated with a simulation and experimentation. We also investigate the limiting factor for remote transmission. The system performance is degraded by the Rayleigh backscattering of the BLS light, but it can be operated over long distances within 10 km with 5 dB of difference peak power margin.

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Section: Discussionmentioning

confidence: 99%

A Simple All-Optical Water Level Monitoring System Based on Wavelength Division Multiplexing with an Arrayed Waveguide Grating

Lee

Choo

Shin

2019

Sensors

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“…However, this problem will be even more intense when the number of SUs is increased to enhance the spatial resolution. It should be noted that the spatial resolution can be easily increased by utilizing another wavelength band of BLS with a cyclic characteristic of AWG [34] and/or reducing the channel bandwidth of AWG [35]. Hence, we introduce a signal processing technique based on feature extraction from the received optical signal.…”

Section: Level Determination With Signal Processing Technique Based O...mentioning

confidence: 99%

Passive IoT Optical Fiber Sensor Network for Water Level Monitoring with Signal Processing of Feature Extraction

et al. 2023

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This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., <10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).

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“…The proposed DWDM-based OFS network can provide a strong advantage compared to other various multiplexing techniques in terms of channel scalability. The channel capacity can be easily increased by utilizing another wavelength band of BLS with a cyclic characteristic of AWG [33] and/or reducing the channel bandwidth of AWG [34]. Moreover, it can provide a very simple architecture with a self-referencing function for the determination of water level.…”

Section: Architecture Of Dwdm-passive Ofs Network Based On Optical Po...mentioning

confidence: 99%

16 Ch × 200 GHz DWDM-Passive Optical Fiber Sensor Network Based on a Power Measurement Method for Water-Level Monitoring of the Spent Fuel Pool in a Nuclear Power Plant

Lee

Choo

Kim

2021

Sensors

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This paper presents a remote 16 Ch × 200 GHz dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network. We particularly investigate the remote water-level monitoring capability of the OFS network based on an optical power measurement that features simplicity and a fast processing speed. The OFS network utilizes a seeded amplified spontaneous emission (ASE) light that is spectrum-sliced and distributed by an arrayed waveguide grating (AWG) towards multiple sensing units (SU), where each SU is installed at a different height in the water pool. Then, each SU reflects either of the two different optical powers according to the medium (air vs. water) back to the monitoring station. Therefore, the total received optical power at the monitoring station linearly changes according to the water level. We can simply recognize the water level by utilizing the optical power meter (OPM) at the monitoring station rather than the optical spectrum analyzer (OSA), which is bulky and expensive and requires a relatively long processing time. Consequently, we can reduce the system complexity, processing time, and cost (both installation and maintenance). However, the OPM-based OFS network requires a new methodology to derive the water level from the measured optical power. Thus, we come up with the reference-to-power ratio () analysis, which can be used for the maximum distance analysis as well as water level recognition. Based on the new reception architecture supported by the new post-processing scheme, the OFS network can distinguish 17 different water levels of the SFP at the monitoring station, which is > 40 km away from the SFP, without using any active devices (such as optical amplifiers) at the remote places.

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800 Gb/s (80 × 10 Gb/s) capacity WDM-PON based on ASE injection seeding

Cited by 17 publications

References 19 publications

A Simple All-Optical Water Level Monitoring System Based on Wavelength Division Multiplexing with an Arrayed Waveguide Grating

A Simple All-Optical Water Level Monitoring System Based on Wavelength Division Multiplexing with an Arrayed Waveguide Grating

Passive IoT Optical Fiber Sensor Network for Water Level Monitoring with Signal Processing of Feature Extraction

16 Ch × 200 GHz DWDM-Passive Optical Fiber Sensor Network Based on a Power Measurement Method for Water-Level Monitoring of the Spent Fuel Pool in a Nuclear Power Plant

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