82 citations · 239 across the 26 of their papers we have counts for
26 papers
Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4
We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our m…
FPGA-Based Experimental Analysis of Fixed-Point Precision Impact on SOP Estimation in Coherent Communications Receivers
Geraldo Gomes, Rafael Vieira, Hani Kbashi +8
We experimentally evaluated the sensing-communication trade-off from the fixed-point precision MIMO equalizer using FPGA. At 7-bit, noise floor drops 100x and angular error 63%, bu…
Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4
We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventio…
From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysis
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo +5
Machine learning-based failure management in optical networks has gained significant attention in recent years. However, severe class imbalance, where normal instances vastly outnu…
Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo +5
We compare pre-, in-, and post-processing techniques for class imbalance mitigation in optical network failure detection. Threshold Adjustment achieves the highest F1 gain (15.3%),…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
FPGA Implementation of Low-Power Multiplierless Pre-Processing Free Chromatic Dispersion Equalizer
Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky +1
We present a novel time-domain chromatic dispersion equalizer, implemented on FPGA, eliminating pre-processing and multipliers, achieving up to 54.3% energy savings over 80-1280 km…
FPGA Implementation of Complex Value-based Clustering Filter for Chromatic Dispersion Compensation in Coherent Metro Links with Ultra-low Power Consumption
Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky +1
This paper introduces a new machine learning-assisted chromatic dispersion compensation filter, demonstrating its superior power efficiency compared to conventional FFT-based filte…
Geometric Clustering for Hardware-Efficient Implementation of Chromatic Dispersion Compensation
Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky +1
Power efficiency remains a significant challenge in modern optical fiber communication systems, driving efforts to reduce the computational complexity of digital signal processing,…
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
Pedro Freire, Egor Manuylovich, Jaroslaw E. Prilepsky +1
This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer scie…
Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam +6
For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to…
Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers
Sasipim Srivallapanondh, Pedro J. Freire, Antonio Napoli +2
To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approxi…
Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
Sasipim Srivallapanondh, Pedro J. Freire, Bernhard Spinnler +4
To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structur…
Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays
Pedro J. Freire, Sasipim Srivallapanondh, Michael Anderson +9
In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical t…
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation
Pedro J. Freire, Antonio Napoli, Diego Arguello Ron +7
In this paper, a new methodology is proposed that allows for the low-complexity development of neural network (NN) based equalizers for the mitigation of impairments in high-speed…
Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems
Pedro J. Freire, Michael Anderson, Bernhard Spinnler +8
For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to tha…
Computational Complexity Evaluation of Neural Network Applications in Signal Processing
Pedro Freire, Sasipim Srivallapanondh, Antonio Napoli +2
In this paper, we provide a systematic approach for assessing and comparing the computational complexity of neural network layers in digital signal processing. We provide and link…
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems
Pedro J. Freire, Bernhard Spinnler, Daniel Abode +7
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up…
Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications
Pedro J. Freire, Yevhenii Osadchuk, Antonio Napoli +5
Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of sever…
Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls
Pedro J. Freire, Antonio Napoli, Bernhard Spinnler +3
This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) nonlinear channel eq…
Deep Neural Network-aided Soft-Demapping in Optical Coherent Systems: Regression versus Classification
Pedro J. Freire, Jaroslaw E. Prilepsky, Yevhenii Osadchuk +2
We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in cohere…
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization
Diego R. Arguello, Pedro J. Freire, Jaroslaw E. Prilepsky +3
The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication sys…
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems
Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky +1
Transfer learning is proposed to adapt an NN-based nonlinear equalizer across different launch powers and modulation formats using a 450km TWC-fiber transmission. The result shows…
Experimental Study of Deep Neural Network Equalizers Performance in Optical Links
Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5
We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96 x WDM, DP-16QAM transmission over 450km…