15 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…
Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
Bilal Khalid, Pedro Freire, Sergei K. Turitsyn +1
Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications. However, their unique structure raises significant con…
DPD-KAN: Kolmogorov-Arnold Networks for Low Complexity Digital Predistortion in 5G Analog Radio-over-Fiber Systems
Bilal Khalid, Fabio Cavaliere, Luca Giorgi +3
We demonstrate the first KAN-based DPD model for 5G analog RoF fronthaul link, achieving a 24.2% lower EVM than multi-layer perceptron and 29.6% lower than Volterra-based GMP at eq…
Dual Line Coherent Detection
Nelson Castro, Yiming Li, Mohammed Patel +3
We experimentally demonstrate dual-line coherent detection using an optical frequency comb local oscillator, enabling large frequency offset tolerance with minimal additional signa…
Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules
Luca Nogueira Calçado, Sergei K. Turitsyn, Egor Manuylovich
Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical…
Low-complexity neural network equalization for long-haul coherent transmission with cascaded semiconductor optical amplifiers
S. Bogdanov, S. Sygletos, O. Sidelnikov +3
In this letter, we numerically investigate a long-haul coherent data transmission system with a cascade of semiconductor optical amplifiers (SOAs). We exploit low-complexity neural…