12/19/2025
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Arash Rezaee on: "Cross-Layer Design and Modeling for Ultra-High-Capacity Elastic Optical Networks."

Candidate Name: Arash Rezaee
Date: Friday, December 19th, 2025
Time: 11 a.m.-12:30 p.m.
Location: Ball Hall 302

Committee:

  • Advisor: Vinod Vokkarane, Professor, Electrical and Computer Engineering, University of Massachusetts Lowell
  • Seung Woo Son, Associate Professor, Electrical and Computer Engineering, University of Massachusetts Lowell
  • Orlando Arias, Assistant Professor, Electrical and Computer Engineering, University of Massachusetts Lowell
  • Balagangadhar Bathula - Principal Member of Technical Staff, AT&T Research Labs

Abstract:
The rapid growth of bandwidth-intensive services driven by 5G and beyond, cloud computing, and large-scale IoT deployments is pushing optical transport networks toward unprecedented capacity and performance requirements. Elastic Optical Networks (EONs) have emerged as a key enabler for efficient spectrum utilization through flexible channel allocation and adaptive modulation. However, as networks evolve toward ultra-high-capacity architectures, including Space-Division Multiplexing EONs (SDM-EONs), Multi-Band EONs (MB-EONs), and combined Multi-Band over SDM (MBoSDM) systems, the complexity of resource provisioning increases substantially due to tightly coupled physical-layer impairments and cross-layer constraints.
This proposal investigates a unified, cross-layer framework for impairment-aware modeling and resource management in next-generation elastic optical networks. The research focuses on integrating linear and nonlinear physical-layer impairments, such as amplified spontaneous emission, nonlinear interference, inter-core crosstalk, and inter-channel stimulated Raman scattering, directly into dynamic routing, modulation, core, spectrum, and band allocation decisions. By embedding accurate, state-dependent impairment models into the control plane, the proposed approach enables routing and allocation strategies that adapt to real-time network conditions rather than relying on static candidate paths or post-hoc quality-of-transmission validation.

In addition to performance-driven optimization, this work incorporates techno-economic analysis to evaluate the cost–performance trade-offs of emerging optical network architectures under dynamic traffic. Comparative studies between flexible-grid and grooming-enhanced fixed-grid designs, as well as between spectral and spatial scaling strategies, are conducted to quantify blocking performance, transponder utilization, and capital efficiency. The key contributions of this research include the development of physically grounded, impairment-aware resource allocation algorithms across EONs, SDM-EONs, MB-EONs, and MBoSDMs architectures, along with a unified benchmarking framework that enables reproducible evaluation of both performance and economic viability in ultra-high-capacity optical networks.