A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra that are dependent on multiple parameters, we adopt the machine learning algorithm instead of massive asymptotic solutions of resonators. Employing the proposed multi-mode sensing approach, a two-parameter SIMRR sensor is designed. Assuming that two gases have different wavelength dependence of refractive indices, the feasibility and effectiveness of the two-parameter sensing strategy are verified numerically. Moreover, the dependence of parameter estimation accuracy on the laser intensity noises is also investigated. The numerical results indicate that our scheme of multi-parameter sensing in a multimode SIMRR holds great potential for practical high-sensitive sensing platforms compared with the single-mode sensing based on whispering gallery mode (WGM) resonators.

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wide band transmission spectra that consist of multiple resonant modes. It is different from previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.

A novel carrier phase recovery algorithm is proposed for coherent optical systems with circular multilevel quadrature amplitude modulation (C-mQAM). After amplitude segmentation and phase rotation, the particular distribution of the constellation points in a C-mQAM signal is exploited to construct a special cost function to provide a rough estimate of phase noise. The cost function can be replaced with a simple cosine function, and only two or three test phases are required for the calculation of its parameters. The performances of different cost functions are compared and simulation results show that the cost function can be chosen with the high phase noise tolerance and low computational complexity. Maximum-likelihood (ML) phase noise estimation algorithm is combined with the proposed algorithm to mitigate residual phase noise. Compared to the single-stage blind phase search (BPS) and n-phase shift keying (n-PSK) partitioning algorithms, the numerical results demonstrate that the proposed two-stage algorithm shares same phase noise tolerance and offers a lower computational complexity for a 28GBaud back-to-back coherent optical QAM transmission system.

A multidimensional vector quantization-based fast statistical-estimation (VQ-FSE) algorithm is proposed to enhance data compression performance in digitalized radio over fiber (D-RoF) systems. The original samples with Gaussian distribution are first transformed into these with uniform distribution via companding transformation. After the companding transformation operation, the signal vector is constructed by grouping multiple samples in a certain way so that there is little correlation among them. The constructed signal vector may follow approximately multidimensional uniform distribution, and then multidimensional uniform quantization can be easily carried out, where the complex optimized process in nonuniform quantization is not required. For the proposed two-dimensional (2D) VQ-FSE algorithm, the proposed scheme is numerically verified in a 20 km D-RoF system with 2 Gbit/s RF wireless signal. Compared with the scalar-quantization-based FSE algorithm, its compression ratio is significantly enhanced. In comparison to the 2D $k$-means-clustering-based VQ algorithm, the proposed scheme shares a similar compression ratio and offers lower computational complexity. Therefore, the proposed algorithm has the ability to provide better compression and lower complexity for the digitized D-RoF system when the original sample follows Gaussian distribution.

A blind discrete-cosine-transform-based phase noise compensation (BD-PNC) is proposed to compensate the inter-carrier-interference (ICI) in the coherent optical offset-quadrature amplitude modulation (OQAM)-based filter-bank multicarrier (CO-FBMC/OQAM) transmission system. Since the phase noise sample can be approximated by an expansion of the discrete cosine transform (DCT) in the time-domain, a time-domain compensation model is built for the transmission system. According to the model, phase noise compensation (PNC) depends only on its DCT coefficients. The common phase error (CPE) compensation is firstly performed for the received signal. After that, a pre-decision is made on a part of compensated signals with low decision error probability, and the pre-decision results are used as the estimated values of transmitted signals to calculate the DCT coefficients. Such a partial pre-decision process reduces not only decision error but also the complexity of the BD-PNC method while keeping almost the same performance as in the case of the pre-decision of all compensated signals. Numerical simulations are performed to evaluate the performance of the proposed scheme for a 30 GBaud CO-FBMC/OQAM system. The simulation results show that its bit error rate (BER) performance is improved by more than one order of magnitude through the mitigation of the ICI in comparison with the traditional blind PNC scheme only aiming for CPE compensation.

Artificial intelligence (AI) algorithms enhance distributed acoustic sensing (DAS) signal interpretation by leveraging large-scale acoustic data. However, heterogeneous deployment environments hinder model generalization ability and exacerbate label scarcity. To overcome these challenges, we propose MAEPD, a foundation model for DAS signal recognition trained via masked autoencoder pre-training on large-scale, unlabeled DAS data collected from diverse domains. The pre-trained model is subsequently adapted to downstream tasks using adapter-based prompt tuning (APT) with only minimal labeled samples. In the DAS gait identity recognition task, with only 240 image signals per class, APT achieves 94.75% accuracy, a 4.46% improvement over full fine-tuning while updating only 2.77% of parameters. Inference latency of 2.74 ms per image meets real-time requirements. Compared to pre-training with gait data only (35.6 k samples), MAEPD improves accuracy by 3.88%, demonstrating the advantage of diverse pre-training data. The method shows robust performance across water pipe leakage, perimeter security, and public datasets, with low sensitivity to labeled data quantity. Results demonstrate an efficient and scalable solution for DAS signal recognition.