Feedforward and iterative phase noise compensation algorithms using expectation propagation achieve information rates close to phase-noise-free channels for 100 GBaud 64-QAM over 10,000 km fiber by applying PNC before CDC.
GluNet: A Deep Learning Framework for Accurate Glucose Forecasting
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
MetaboNet is a consolidated dataset of 3135 subjects with 1228 patient-years of CGM and insulin pump data for Type 1 Diabetes research.
Temporal Gaussian noise model reproduces burst-like SNR degradation from equalization-enhanced phase noise, enabling efficient simulation in coherent optical systems.
citing papers explorer
-
Feedforward and Iterative Phase Noise Compensation for Channels with Chromatic Dispersion
Feedforward and iterative phase noise compensation algorithms using expectation propagation achieve information rates close to phase-noise-free channels for 100 GBaud 64-QAM over 10,000 km fiber by applying PNC before CDC.
-
MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management
MetaboNet is a consolidated dataset of 3135 subjects with 1228 patient-years of CGM and insulin pump data for Type 1 Diabetes research.
-
Modeling and Mitigation of Equalization-Enhanced Phase Noise
Temporal Gaussian noise model reproduces burst-like SNR degradation from equalization-enhanced phase noise, enabling efficient simulation in coherent optical systems.