Agastya Raj

Agastya Raj, Zehao Wang, Frank Slyne et al.

We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS testbeds show high-accuracy transfer-learning even when operated across different amplifier types.

Peyman Pahlevanzadeh, Venkata Virajit Garbhapu, Agastya Raj et al.

We propose a spectrum-configuration framework for open and disaggregated optical systems that maximizes throughput while guaranteeing the quality of transmission (QoT) margins. The framework jointly optimizes transceiver parameters, including modulation format, symbol rate, pulse-shaping roll-off factor, and wavelength-selective switch (WSS) bandwidth, under fixed spectral allocation constraints. The impact of roll-off factor optimization is first experimentally evaluated in the presence of cascaded WSS filtering, demonstrating measurable QoT gains for both single- and multi-channel transmission. Building on these observations, a knapsack-based optimization is applied in the context of Optical Spectrum as a Service (OSaaS) to select service configurations that maximize aggregate throughput within a fixed spectrum width and limited transceiver resources. Experimental validation on a metro-scale open testbed confirms the effectiveness of the proposed approach in achieving efficient spectrum utilization and adaptive throughput-margin trade-offs.

Agastya Raj, Zehao Wang, Frank Slyne et al.

We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios.

Agastya Raj, Zehao Wang, Tingjun Chen et al.

Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a Semi-Supervised Self-Normalizing Neural Network (SS-NN) that leverages internal EDFA features - such as VOA input or output power and attenuation, to improve gain spectrum prediction. Our SS-NN model employs a two-phase training strategy comprising unsupervised pre-training with noise-augmented measurements and supervised fine-tuning with a custom weighted MSE loss. Furthermore, we extend the framework with transfer learning (TL) techniques that enable both homogeneous (same-feature space) and heterogeneous (different-feature sets) model adaptation across booster, preamplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between source and target domains. Extensive experiments conducted across 26 EDFAs in the COSMOS and Open Ireland testbeds demonstrate that the proposed approach significantly reduces the number of measurements requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods.