arXiv subjects
Publications and source records attributed to Darko Zibar.
We demonstrate a new lower bound on the sensitivity of $\phi$-OTDR by estimating phase using Kalman filtering. Numerical analysis shows a $\sim$35-dB reduction in the minimum detectable SNR, compared to when using conventional phase estimation.
Signal fading creates points along the fiber where phase cannot be extracted, so they are conventionally discarded. Instead, we propose a Kalman-based solution for {\phi}-OTDR full-fiber monitoring. Experiments demonstrate phase and temperature estimation with approximately 15 times better spatial density than if removing these points.
Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training data efficiency and limited generalizability. To address this trade-off, physics-informed data-driven modeling has emerged as a powerful hybrid paradigm. This paper presents a comparative analysis of these three modeling paradigms across three benchmark use cases: optical amplifiers, directly modulated lasers, and interferometer meshes. By evaluating model complexity, data efficiency, generalizability, and modularity, this work provides a detailed analysis of the respective trade-offs and highlights the advantages of combining physical insight with data-driven learning.
Phase noise limits the coherence and stability of soliton microcombs, yet its origin is difficult to trace because multiple noise sources act simultaneously. It is often represented by common-mode and repetition-rate components, but how each physical source contributes to these components remains unclear. We combine subspace tracking with multi-source Ikeda-map simulations, switching each source and the Raman nonlinearity on and off to isolate its contribution. Without Raman, pump phase noise is purely common-mode, while shot noise and amplified spontaneous emission drive the repetition rate noise. With Raman, the nonlinearity coherently converts pump phase noise from common-mode into repetition-rate noise without introducing an independent noise source, yielding a parabolic linewidth profile with a quiet-point minimum below the pump linewidth. When all noise sources are present, shot noise, ASE, and RIN raise the common-mode floor and shift this minimum toward the pump, setting the achievable noise floor. The intracavity dynamics thus do not merely carry noise but actively partition it, providing a mechanistic basis for low-noise microcomb design.
We present a theoretical, algorithmic, and experimental study of temperature sensing using $ϕ$-OTDR with coherent detection. A physics-based model is developed to relate the measured Rayleigh backscattered signal to temperature variations along the fiber, showing that the phase evolution encodes the cumulative temperature change between the interrogator and the sensing location, while the amplitude exhibits only local sensitivity. Based on this insight, we propose robust algorithms for temperature-event detection and temperature-profile reconstruction. Experimental results demonstrate reliable recovery of temperature-induced perturbations in standard single-mode fibers using coherently detected $ϕ$-OTDR.
Practical hardware limitations, including finite transmitter and receiver filter lengths as well as the finite resolution of digital-to-analog and analog-to-digital converters, lead to mode mismatch and degrade the performance of continuous-variable quantum key distribution systems. To address this, we develop a machine learning-based end-to-end optimization framework that jointly optimizes transmitter pulse shaping and receiver matched filtering. The approach employs reinforcement learning under realistic hardware constraints, including a limited number of filter taps, finite digital-to-analog and analog-to-digital converter resolution, analog low-pass filtering, and the optimal mean photon number. By mitigating mode mismatch and accounting for implementation constraints, the proposed method improves overall system performance. Simulation results demonstrate enhanced secure key rates compared to conventional approaches, demonstrating the effectiveness of the proposed framework.
Using reinforcement learning, we optimize for practical hardware constraints, including limited FIR filter taps at the transmitter and receiver, mean photon number and finite DAC/ADC resolution. Under these realistic conditions, the proposed approach achieves significant performance improvements.
Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dynamics make the modeling and optimization of DML-based systems challenging. In this paper, we study the end-to-end optimization of DML-based systems based on a data-driven surrogate model trained on experimental data. The end-to-end optimization includes the pulse shaping and equalizer filters, the bias current and the modulation radio-frequency (RF) power applied to the laser. The performance of the end-to-end optimization scheme is tested on the experimental setup and compared to 4 different benchmark schemes based on linear and nonlinear receiver-side equalization. The results show that the proposed end-to-end scheme is able to deliver better performance throughout the studied symbol rates and transmission distances while employing lower modulation RF power, fewer filter taps and utilizing a smaller signal bandwidth.
The introduction of digital signal processing (DSP) assisted coherent detection has been a cornerstone of modern fiber-optic communication systems. The ability to digitally, i.e. after analogue-to-digital converter, compensate for chromatic dispersion, polarization mode dispersion, and phase noise has rendered traditional analog feedback loops largely obsolete. While analog techniques remain prevalent for phase noise characterization of single-frequency lasers, the phase noise characterization of optical frequency combs presents a greater challenge. This complexity arises from different number of phase noise sources affecting an optical frequency comb. Here, we show how a phase noise measurement techniques method based on multi-heterodyne coherent detection and DSP-based subspace tracking can be used to identify, measure and quantify various phase noise sources associated with an optical frequency comb.
Electro-optic modulation is an attractive approach for generating flat, stable, and low-noise optical frequency combs with relatively high power per comb line. However, a key limitation of electro-optic combs is the restricted number of comb lines imposed by the available RF source power. To overcome this limitation, a nonlinear spectral broadening stage is typically employed. The phase noise characteristics of an electro-optic comb are well described by the standard phase noise model, which depends on two parameters: the seed laser and the RF source phase noise. A fundamental question that arises is how nonlinear broadening processes affect the phase noise properties of the expanded comb. To address this, we employ coherent detection, digital signal processing, and subspace tracking. Our experimental results show that the nonlinearly broadened comb preserves the standard phase noise model of the input electro-optic comb. In other words, the nonlinear processes neither introduce additional phase noise terms nor amplify the existing contributions from the seed laser and RF source. Hence, nonlinear broadening can be viewed as equivalent to driving the electro-optic comb with a much higher RF modulation power.
Self-heterodyne techniques are widely used for laser phase noise characterization due to their simple experimental setup and the removed need for a reference laser. However, when investigating low-noise lasers, optical delay paths shorter than the laser coherence length become necessary. This introduces interference patterns that distort the measured phase noise spectrum. To compensate for this distortion, we introduce a robust data-driven digital signal processing routine that integrates a kernel-based regression model into a phase noise power spectral density (PN-PSD) equalization framework. Unlike conventional compensation methods that rely on simplified phase noise models, our approach automatically adapts to arbitrary laser lineshapes by using Kernel Ridge Regression with automatic hyperparameter optimization. This approach effectively removes the interference artifacts and provides accurate PN-PSD estimates. We demonstrate the method's accuracy and effectiveness through simulations and via experimental measurements of two distinct low-noise lasers. The method's applicability to a broad range of lasers, minimal hardware requirements, and improved accuracy make this approach ideal for improving routine phase noise characterizations.
Technical limitations in pulse shaping lead to mode mismatch, which significantly reduces the secure key rate in CV-QKD systems. To address this, a machine learning approach is employed to optimize the transmitter pulse-shape, effectively minimizing mode mismatch and yielding substantial performance improvements.
The quantum limit is a fundamental lower bound on the uncertainty when estimating a parameter in a system dominated by the minimum amount of noise (quantum noise). For the first time, we derive and demonstrate a quantum limit for temperature-change estimation for coherent phase-OTDR sensing-systems.
Pairing coherent correlation OTDR with low-complexity analysis methods, we investigate the detection of fast temperature changes and vibrations in optical fibers. A localization accuracy of ~2 m and extraction of vibration amplitudes and frequencies is demonstrated.
This paper investigates the application of end-to-end (E2E) learning for joint optimization of pulse-shaper and receiver filter to reduce intersymbol interference (ISI) in bandwidth-limited communication systems. We investigate this in two numerical simulation models: 1) an additive white Gaussian noise (AWGN) channel with bandwidth limitation and 2) an intensity modulated direct detection (IM/DD) link employing an electro-absorption modulator. For both simulation models, we implement a wavelength division multiplexing (WDM) scheme to ensure that the learned filters adhere to the bandwidth constraints of the WDM channels. Our findings reveal that E2E learning greatly surpasses traditional single-sided transmitter pulse-shaper or receiver filter optimization methods, achieving significant performance gains in terms of symbol error rate with shorter filter lengths. These results suggest that E2E learning can decrease the complexity and enhance the performance of future high-speed optical communication systems.
We experimentally demonstrate the joint optimization of transmitter and receiver parameters in directly modulated laser systems, showing superior performance compared to nonlinear receiver-only equalization while using fewer memory taps, less bandwidth, and lower radiofrequency power.
Existing communication hardware is being exerted to its limits to accommodate for the ever increasing internet usage globally. This leads to non-linear distortion in the communication link that requires non-linear equalization techniques to operate the link at a reasonable bit error rate. This paper addresses the challenge of blind non-linear equalization using a variational autoencoder (VAE) with a second-order Volterra channel model. The VAE framework's costfunction, the evidence lower bound (ELBO), is derived for real-valued constellations and can be evaluated analytically without resorting to sampling techniques. We demonstrate the effectiveness of our approach through simulations on a synthetic Wiener-Hammerstein channel and a simulated intensity modulated direct detection (IM/DD) optical link. The results show significant improvements in equalization performance, compared to a VAE with linear channel assumptions, highlighting the importance of appropriate channel modeling in unsupervised VAE equalizer frameworks.
Nowadays, as the ever-increasing demand for more powerful computing resources continues, alternative advanced computing paradigms are under extensive investigation. Significant effort has been made to deviate from conventional Von Neumann architectures. In-memory computing has emerged in the field of electronics as a possible solution to the infamous bottleneck between memory and computing processors, which reduces the effective throughput of data. In photonics, novel schemes attempt to collocate the computing processor and memory in a single device. Photonics offers the flexibility of multiplexing streams of data not only spatially and in time, but also in frequency or, equivalently, in wavelength, which makes it highly suitable for parallel computing. Here, we numerically show the use of time and wavelength division multiplexing (WDM) to solve four independent tasks at the same time in a single photonic chip, serving as a proof of concept for our proposal. The system is a time-delay reservoir computing (TDRC) based on a microring resonator (MRR). The addressed tasks cover different applications: Time-series prediction, waveform signal classification, wireless channel equalization, and radar signal prediction. The system is also tested for simultaneous computing of up to 10 instances of the same task, exhibiting excellent performance. The footprint of the system is reduced by using time-division multiplexing of the nodes that act as the neurons of the studied neural network scheme. WDM is used for the parallelization of wavelength channels, each addressing a single task. By adjusting the input power and frequency of each optical channel, we can achieve levels of performance for each of the tasks that are comparable to those quoted in state-of-the-art reports focusing on single-task operation...