Deep Joint Source-Channel Coding for Small Satellite Applications
Olga Kondrateva, Grace Li Zhang, Julian Zobel, Bj\"orn Scheuermann, Stefan Dietzel

TL;DR
This paper develops an adaptive deep joint source-channel coding framework for small satellite communications, enabling efficient, robust data transmission across varying channel conditions with minimal model overhead.
Contribution
It introduces an adaptable neural network architecture with attention modules for satellite DJSCC, reducing storage needs and improving robustness over traditional methods.
Findings
Achieves comparable performance to multiple specialized models
Reduces model storage requirements significantly
Enhances robustness to channel estimation errors
Abstract
Small satellites used for Earth observation generate vast amounts of high-dimensional data, but their operation in low Earth orbit creates a significant communication bottleneck due to limited contact times and harsh, varying channel conditions. While deep joint source-channel coding (DJSCC) has emerged as a promising technique, its practical application to the complex satellite environment remains an open question. This paper presents a comprehensive DJSCC framework tailored for satellite communications. We first establish a basic system, DJSCC-SAT, and integrate a realistic, multi-state statistical channel model to guide its training and evaluation. To overcome the impracticality of using separate models for every channel condition, we then introduce an adaptable architecture, ADJSCC-SAT, which leverages attention modules to allow a single neural network to adjust to a wide range of…
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Taxonomy
TopicsWireless Signal Modulation Classification · Advanced Wireless Communication Techniques · Error Correcting Code Techniques
