NEC Laboratories America

Optical Communications & Computing

From forward-looking theoretical studies to cutting-edge experiments to world- and industry-first technology field trials, we deliver globally recognized innovation in optical communications and computing that looks into the future and translates it into present reality.

From the Internet backbone to the data center floor, optics and photonics form the foundation of modern communications and computing infrastructure. As global data demand accelerates, optical communications and computing technologies must evolve together to keep pace. Our department advances the science of high-capacity optical transmission alongside emerging photonic computing architectures, delivering research that spans theoretical modeling, laboratory experimentation, and real-world field trials.

The advent of AI-centric data networking combined with growing demand for globally connected, always-on networks is pushing optical communication systems toward new limits. Our researchers develop coherent optical transmission techniques, agile digital signal processing (DSP) approaches, and software-defined optical networking architectures, including optical line system telemetry, network digital twins, and automation through agentic AI. These innovations expand transmission capacity and extend network reach in traditional fiber links and satellite optical communications, while giving operators the flexibility to support dynamic networking environments such as AI-compute infrastructures and augmented reality (AR)/virtual reality (VR) applications.

Beyond transmission, we explore how photonic and optical computing principles can address the growing energy and latency demands of AI workloads. Our work in high-speed optical signal processing and RF spectral sensing implemented on photonic integrated chips, together with forward-looking research in photonic AI, extends the reach of optical technology from the network core into computing architectures themselves.

Optical communications and computing will play a pivotal role in shaping the future of connectivity, data processing, and AI infrastructure across industries.

Join Us

Are you interested in joining us? We are looking for the next generation of thought leaders in optical communications and computing to join our research team. If you are pursuing a career in this field, visit our careers page to learn more and apply for our available positions.

Summer 2026 Internship Applications are now closed.

Exciting internship opportunities for Summer 2027 will be available on our internship page this fall.  We are looking for students pursuing advanced degrees in Computer Science and Electrical Engineering. Internships are typically 3 months long during the summer. The benefits of working for us include the opportunity to quickly join a project team applying cutting-edge technology to industry-leading concepts.

To apply for a Summer Internship, visit our Internship page.

Learn More

Want to learn more about our optical communications and computing department? Meet our team of experts, check out our blog posts, or read our latest publications.

Featured Optical Communications & Computing Research Projects

Latest Optical Communications & Computing News

Field Verification of Fault Localization with Integrated Physical-Parameter-Aware Methodology

We report the first field verification of fault localization in an optical line system (OLS) by integrating digital longitudinal monitoring and OLS calibration, highlighting changes in physical metrics and parameters. Use cases shown are degradation of a fiber span loss and optical amplifier noise figure.

Optical orbital angular momentum analogy to the Stern-Gerlach experiment

Symmetry breaking has been shown to reveal interesting phenomena in physical systems. A notable example is the fundamental work of Otto Stern and Walther Gerlach [Stern and Zerlach, Z. Physik 9, 349 (1922)] nearly 100 years ago demonstrating a spin angular momentum (SAM) deflection that differed from

Accelerating Distributed Machine Learning with an Efficient AllReduce Routing Strategy

We propose an efficient routing strategy for AllReduce transfers, which compromise of the dominant traffic in machine learning-centric datacenters, to achieve fast parameter synchronization in distributed machine learning, improving the average training time by 9%.

Extension of the Local-Optimization Global-Optimization (LOGO) Launch Power Strategy to Multi-Band Optical Networks

We propose extending the LOGO strategy for launch power settings to multi-band scenarios, maintaining low complexity while addressing key inter-band nonlinear effects and accurate amplifier models. This methodology simplifies multi-band optical multiplex section control, providing an immediate, descriptive

First Field Demonstration of Hollow-Core Fibre Supporting Distributed Acoustic Sensing and DWDM Transmission

We demonstrate a method for measuring the backscatter coefficient of hollow-core fibre (HCF), and show the feasibility of distributed acoustic sensing (DAS) with simultaneous 9.6-Tb/s DWDM transmission over a 1.6-km field-deployed HCF cable.

Machine Learning Model for EDFA Predicting SHB Effects

Experiments show that machine learning model of an EDFA is capable of modelling spectral hole burning effects accurately. As a result, it significantly outperforms black-box models that neglect inhomogeneous effects. Model achieves a record average RMSE of 0.0165 dB between the model predictions and