Machine Learning Models for Optimisation of Satellite Laser Communication Terminals and Optical Space Networks

Aug 5, 2026

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Laser communication systems in optical SatCom often rely on point-to-point routing that performs poorly in transport and access satellite network contexts, while optical signal acquisition is further degraded by a dynamically changing satellite environment and atmospheric conditions. Craft Prospect and partners address this by applying machine learning to improve laser communication terminals and to simplify interconnectivity for networked laser SatCom systems across OPS-SAT VOLT and ESA HydRON.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Introduction

Spatial Acquisition Use Case

Acquisition, Pointing, Tracking (APT)

Sensor choices and conventional CoG detection

Low-SNR detection challenges

ML for Network Management

SSC24-IV-07 Machine Learning Models for Optimisation of Satellite Laser Communication Terminals and Optical Space Networks Georgia Harvey, Cameron Anderson, Murray Ireland Craft Prospect Ltd Suite 12 Fairfield, 1048 Govan Road, Glasgow, United Kingdom, G51 4XS; +44 (0) 7421 994 712 [georgia@craftprospect.com](georgia@craftprospect.com) Rob Hunter, Afonso Nunes, Rob Stansfield CGI IT UK Ltd 20 Fenchurch Street, 14th Floor, London, United Kingdom, EC3M 3BY, +44 (0) 2076 379 111 [rob.hunter@cgi.com](rob.hunter@cgi.com) ABSTRACT Laser communication systems in optical SatCom systems are mainly used in point-to-point networks, possibly deploying standard routing solutions which perform sub-optimally when deployed in transport and access satellite networks. Optical signal acquisition is also affected by the changing satellite environment and atmospheric conditions. In response, Craft Prospect and partners focused on upgrading optical systems by using machine learning (ML) methods to improve laser communication terminals and to simplify interconnectivity of networked laser SatCom systems. In this context we target the European OPS-SAT Versatile Optical Laboratory for Telecoms (VOLT) mission led by Craft Prospect and the European High Throughput Optical Network (HydRON) project which benefits from using machine learning systems. Based on initial research and development work on enhancement of satellite free space laser communication systems with machine learning, two use cases and their requirements were identified. Firstly, leveraging autonomic networking principles to develop distributed ML agents for space network nodes which monitor local network state and can autonomously make rerouting decisions on impaired links in a localised way to improve the average network throughput. This enables links between optical ground stations (OGS) and the space network segments to quickly switch in a smooth and responsive way without having to have multiple paths open; searches carried out for preset alternate paths in long/overloaded flow table lists; or an over-reliance on a software defined networking (SDN) controller. Secondly, for space segment laser terminals, development of a ML model to boost detection accuracy in coarse acquisition in reduced signal-to-noise ratio (SNR) cases with strong background light conditions and varying beacon intensity. This is relevant for the scenario where a LEO satellite receives an uplink signal from OGS with strong atmospheric fluctuations or background Earth reflections and the detector is saturated. Similarly, in intersatellite links (ISL) where satellites are undergoing relative movement and the signal intensity rate of change is rapid, the acquisition sensor quickly arrives in a region where SNR is low, or the sensor is saturated. In this contribution, we present the developed ML models for these use cases and describe training techniques and datasets. We discuss performance results from tests carried out with a network simulator with the results compared to an SDN controller solution to demonstrate the benefits. For small network topologies, the ML solution resulted in the throughput at the network endpoint being 16.7% higher following link degradation than the SDN controller solution. The spatial acquisition detection errors and accuracy under different SNR conditions using the ML solution was compared to a benchmark Centre of Gravity detection method typically used in laser terminal beacon acquisition. Results showed that the beacon detections from the ML solution were closer in distance to the true spot than the conventional method in low SNR conditions by over an order of magnitude, in addition to having an overall accuracy of 97.77% . With this we show how to enable autonomic routing in optical data, and laser terminals with improved acquisition rates in networks for OPS-SAT VOLT and for multitudes of future interconnected laser SatCom missions. INTRODUCTION Lase

FAQs

Why do traditional routing and acquisition methods underperform in satellite laser communication networks?

Conventional routing designed for point-to-point use can be sub-optimal in transport and access scenarios, and acquisition is impacted by changing satellite environments and atmospheric conditions that alter signal quality.

What are the two main machine learning use cases described in the document?

The contribution targets ML for spatial acquisition to improve detection in coarse acquisition under low SNR and strong background light, and ML for network management to enable autonomic routing decisions on impaired links.

How was performance evaluated and what benefits were reported?

Performance results were tested with a network simulator and compared against an SDN controller solution; for small topologies the ML approach increased throughput at the network endpoint by 16.7% after link degradation, and spatial acquisition accuracy reached 97.77% overall.

Aug 5, 2026

Upload by Liam