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Dynamic optical networking has promising potential to support the rapidly changing traffic demands in metro and long-haul networks. However, the improvement in dynamicity is hindered by wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) when channels change rapidly. We introduce a general approach that leverages machine learning (ML) to characterize and mitigate the power excursions of EDFA systems with different equipment and scales. An ML engine is developed and experimentally validated to show accurate predictions of the power dynamics in cascaded EDFAs. Recommended channel provisioning based on the ML predictions achieves within 1% error of the lowest possible power excursion over 94% of the time. We also showcase significant mitigation of EDFA power excursions in super-channel provisioning when compared to the first-fit wavelength assignment algorithm.

We demonstrate that direct sequence optical code- division multiple-access (DS-OCDMA) encoders and decoders using sampled fiber Bragg gratings (S-FBGs) behave as multipath interferometers. In that case, chip pulses of the prime sequence codes generated by spreading in time-coherent data pulses can result from multiple reflections in the interferometers that can superimpose within a chip time duration. We show that the autocorrelation function has to be considered as the sum of complex amplitudes of the combined chip as the laser source coherence time is much greater than the integration time of the photodetector. To reduce the sensitivity of the DS-OCDMA system to the coherence time of the laser source, we analyze the use of sparse and nonperiodic quadratic congruence and extended quadratic congruence codes.