AWS Public Sector Blog
Author: Alan Campbell
Alan Campbell is a principal space specialist solutions architect at Amazon Web Services (AWS). His focus is on empowering customers with innovative new cloud-based scalable solutions for the aerospace and satellite segment. Alan specializes in data analytics and machine learning (ML), enabling key insights in satellite communications, Earth observation, and IoT platforms.
Agile satellite communication ground systems with Amazon EC2 F2 FPGA solutions
In this post, we provide technical guidance to help satellite operators build, deploy, and analyze satcom waveforms on Amazon Web Services (AWS). Details are provided about the orchestration of multiple waveforms, including an example of one Amazon Field-Programmable Gate Array (FPGA) Image being swapped in for another. We analyze the FPGA utilization and metrics in Amazon CloudWatch and Amazon QuickSight, and validate network performance against system latency requirements. Finally, we recommend actions you can take to build an agile satcom strategy.
Maximizing satellite communications usage with Amazon Forecast
This walkthrough explores how to leverage Amazon Forecast to derive valuable business insights in satellite communications use-cases. Operations teams can quickly see accurate satellite capacity forecasts on a per beam basis. The benefits include lower cost via provisioning just the right amount of bandwidth, and a more streamlined customer experience since users will be less impacted by weather or surge events.
Creating satellite communications data analytics pipelines with AWS serverless technologies
Satellite communications (satcom) networks typically offer a rich set of performance metrics, such as signal-to-noise ratio (SNR) and bandwidth delivered by remote terminals on land, sea, or air. Customers can use performance metrics to detect network and terminal anomalies and identify trends to impact business outcomes. This walkthrough presents an approach using serverless resources from AWS to build satcom control plane analytics pipelines. The presented architecture transforms the data to extract key performance indicators (KPIs) of interest, renders them in business intelligence tools, and applies machine learning (ML) to flag unexpected SNR deviations.