Search

A cost-effective optical performance monitoring (OPM) scheme is proposed to realize modulation format identification (MFI), baud rate identification (BRI), chromatic dispersion identification (CDI), and optical signal-to-noise ratio (OSNR) estimation of optical signals simultaneously. This technique is based on multi-task learning (MTL) neural network model with adaptive asynchronous delay tap plot (AADTP) and asynchronous amplitude histogram (AAH) by direct detection in the intermediate nodes of optical networks. The generation of AADTP depends on the sampling rate but not the symbol rate, which makes the scheme transparent to the baud rate. The combined inputs of AADTP with AAH improve accuracies of the neural network, compared with a single input. This scheme is verified experimentally where signals with two formats, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM), two baud rates, 14 GBaud and 28 GBaud, and three CD situations, 0 ps/nm, 858.5 ps/nm, and 1507.9 ps/nm, are adopted. The best accuracies of MFI, BRI, CDI are 100%, 99.81%, and 99.83%, respectively. Meanwhile, the lowest average mean absolute error (MAE) of OSNR estimation is 0.2867 dB over the range of 10–24 dB (QPSK) and 15–29 dB (16QAM). It is cost-effective and practical for the proposed OPM technique to be applied in the intermediate nodes to construct smart optical networks since it uses only one photodetector assisted with an advanced deep learning algorithm.

Ballistocardiography (BCG) is a vibration signal related to cardiac activity, which can be obtained in a non-invasive way by optical fiber sensors. In this paper, we propose a modified generative adversarial network (GAN) to reconstruct BCG signals by solving signal fading problems in a Mach-Zehnder interferometer (MZI). Based on this algorithm, additional modulators and demodulators are not needed in the MZI, which reduces the cost and hardware complexity. The correlation between reconstructed BCG and reference BCG is 0.952 in test data. To further test the model performance, we collect special BCG signals including sinus arrhythmia data and post-exercise cardiac activities data, and analyze the reconstructed results. In conclusion, a BCG reconstruction algorithm is presented to solve the signal fading problem in the optical fiber interferometer innovatively, which greatly simplifies the BCG monitoring system.

A novel optical performance monitoring (OPM) method based on Fourier transform spectrum analysis (FTSA) is designed for optical signal-to-noise ratio (OSNR) monitoring, modulation format and baud rate recognition in the presence of fiber nonlinearities. The interference intensities, which reflect spectral features of signals, are obtained by exploiting the FTSA consisting of two-stage Mach-Zehnder interferometer (MZI) arrays. Then, the mapping between the OPM parameters and modulated interference intensity (MII) is characterized using neural networks without prior knowledge of the configuration of the communication network. Results show that optical performance parameters are monitored simultaneously. Meanwhile, the accuracy of modulation format and baud rate recognition is 94.8% and most (over 86%) OSNR monitoring errors are less than ±1 dB under complex transmission conditions in presence of frequency offset and delay jitter. Besides, the FTSA can be fabricated on a silicon on insulator (SOI) platform with a large fabrication tolerance, and it has broad working bandwidth to support the full optical communication band. Therefore, the proposed OPM method is capable of integration and miniaturization, which can be ubiquitously applied in network intermediate nodes to support the construction of smart optical networks.

To increase the transmission capacity, ultra-wideband wavelength-division multiplexing (UWB WDM) has been exploited to enlarge the spectral range. However, inter-channel stimulated Raman scattering (ISRS) results in power transition from high-frequency channels to low-frequency channels in wideband scenarios, which degrades the Q-factor of signals. Hence, we modify the optimization method of power control by applying the simulated annealing (SA) algorithm to search for the optimal power slopes and offsets of three bands to construct an optimum distribution of launch powers over channels. High transmission capacity can be reached by carrying 384 channels (96+96+192) in the C+L+S band with the consideration of dynamic Raman gain and channel-dependent parameters. We show that compared to using brute-force searching (BFS), a comparable and even higher transmission capacity can be achieved by the SA algorithm. Meanwhile, the searching speed of the SA algorithm is much faster. Also, different optimizing strategies can be selected to balance the trade-off between capacity and spectral flatness. This method can be used for designing arbitrary optical fiber UWB WDM systems before practical testing.

Recently, ultrathin metalenses have attracted dramatically growing interest in optical imaging systems due to the flexible control of light at the nanoscale. In this paper, we propose a dual-wavelength achromatic metalens that will generate one or two foci according to the polarization of the incident. Based on geometric phase modulation, two unit cells are attentively selected for efficient operation at distinct wavelengths. By patterning them to two divided sections of the metalens structure plane, the dual-wavelength achromatic focusing effect with the same focal length is realized. In addition, the holographic concept is adopted for polarization-dependent bifocal generation, in which the objective wave is originated from two foci that are respectively formed by two orthogonal polarization states of circularly polarized light, namely Left-handed circularly polarized (LCP) light and Right-handed circularly polarized (RCP) light. The incident light is considered as the reference light. The achromatic focusing and polarization-dependent bifocusing are numerically verified through simulations. The proposed design opens the path for the combination of multi-wavelength imaging and chiral imaging, which may find potential applications, such as achromatic optical devices and polarization-controlled biomedical molecular imaging systems.

A miniature Fourier transform spectrometer is designed with tailored optical path differences for visible spectral reconstruction. The combination of intelligent algorithms, the convex optimization algorithm (CVX) and the one-dimension modified convolutional neural network (Unet), is used to retrieve spectra under measurement with noise. This scheme recovers four kinds of spectra accurately with the mean-square error of 8.32 × 10−4.

We optimize the number and delays of MZIs in Fourier transform spectrum analyzer for optical performance monitoring. We find it gets good performance when MZI number is 3 and delays are 12ps, 18ps, and 31ps.

Our proposed optical performance monitoring (OPM) technique, based on the multi-task learning, is able to realize modulation format identification (MFI), baud rate identification (BRI), chromatic dispersion identification (CDI), and optical signal-to-noise ratio (OSNR) estimation simultaneously. This OPM technique can be used in the intermediate nodes of optical networks, which is cost-effective since it can monitor systems by direct detection. To further reduce the cost, PD and ADC with low bandwidth are used. Therefore, we investigate the effect of different bandwidths of direct detection receiver on the performance of OPM, to find the optimal low bandwidth of receiver to achieve relatively high OPM performance. The comparison experiment between different bandwidths of receivers has been carried out where signals with two formats, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM), two baud rates, 14 GBaud and 28 GBaud, and three CD situations, 0 ps/nm, 858.5 ps/nm, and 1507.9 ps/nm, are adopted. And the test bandwidths of receiver are 0.5 GHz, 1 GHz, 2.5 GHz, 5 GHz, 7.5 GHz, 10 GHz, and the original 33 GHz. It is found that the model with 5 GHz bandwidth receiver has the relatively highest performance, except the model with original 33 GHz bandwidth receiver. The identification accuracies of model with 5 GHz receiver are 99.92%, 99.11%, and 99.94% for MFI, BRI, and CDI, respectively. The OSNR estimation error of this model is 0.594 dB.

This paper proposes a simple and efficient approach for the estimation of carrier frequency offset (CFO) in coherent optical OFDM (CO-OFDM) systems. By zero-padding the first received OFDM symbol and performing discrete Fourier transform (DFT), the obtained spectrum will preserve the shape of the original transmitted spectrum, but with finer frequency intervals. Leveraging this feature and the matched filter concept, we can develop a CFO estimator that can achieve the optimum estimation performance whose estimation error variance can reach the Cramer-Rao lower bound (CRLB). However, to obtain the accurate the estimation of CFO, the zero-padding rate should be large enough and a search step is required in matched filtering. In order to reduce the implementation complexity of the proposed algorithm, we further simplify the cost function after matched filtering to an approximated cosine function. Given three test values, the CFO estimate can be directly calculated from the test functions, without complicated search. Simulation results verify the feasibility and simplicity of the proposed simplified CFO estimator.

We proposed a two-dimensional vector displacement sensor with the capability of distinguishing the direction and amplitude of the displacement simultaneously, with improved performance assisted by random forest, a powerful machine learning algorithm. The sensor was designed based on a seven-core multi-core fiber inscribed with Bragg gratings, with a displacement direction range of 0-360° and the amplitude range related to the length of the sensor body. The displacement information was obtained under a random circumstance, where the performances with theoretical model and random forest model were studied. With the theoretical model, the sensor performed well over a shorter linear range (from 0 to 9 mm). Whereas the sensor assisted with random forest algorithm exhibits better performance in two aspects, a wider measurement range (from 0 to 45 mm) and a reduced measurement error of displacement. Mean absolute errors of direction and amplitude reconstruction were decreased by 60% and 98%, respectively. The proposed displacement sensor shows the possibility of machine learning methods to be applied in point-based optical systems for multi-parameter sensing.