Learning-Based Lossless Compression for Low-Latency Satellite Telemetry
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Solution Overview
Problem
Existing data compression methods for telemetry, tracking, and command (TT&C) subsystems in satellite systems face challenges in efficiently compressing large data sets with low latency while maintaining information integrity, particularly in lossless compression.
Innovation Solution
A learning-based lossless data compression system utilizing a computing device with neural networks, including an arithmetic encoder, long short-term memory system, and multilayer perceptron system, to achieve efficient and low-latency compression without losing information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional lossless compression algorithms are used, then information integrity is preserved, but compression speed and efficiency are insufficient for real-time satellite data transmission
Solution Approach 1:
The compression system is divided into multiple specialized neural network components: an embedding system for feature extraction, an LSTM system for temporal pattern recognition, and a PAQ arithmetic encoder for final compression. This segmentation allows each component to optimize for its specific function, achieving both high speed and high reliability simultaneously.
Solution Approach 2:
Traditional mechanical compression algorithms are replaced with a learning-based neural network system. The LSTM and multilayer perceptron systems learn optimal compression strategies from data patterns, substituting rigid algorithmic approaches with adaptive intelligent systems that achieve superior compression ratios while maintaining information integrity.
2Productivity
If higher compression ratios are achieved, then data transmission efficiency improves, but latency increases which is unacceptable for satellite communications
Solution Approach 1:
The embedding system performs preliminary feature extraction and representation learning on the input data before the main compression process. By pre-processing data into optimized embeddings, the subsequent LSTM and encoding stages operate more efficiently, achieving high compression ratios without proportionally increasing latency.
Solution Approach 2:
The LSTM system processes data in a continuous manner, maintaining temporal context across sequences without interrupting the compression flow. This continuous processing allows the system to achieve high compression ratios through learned patterns while minimizing idle time and reducing overall latency.
3Productivity
If complex neural network systems are integrated, then compression efficiency and ratios improve, but system complexity increases
Solution Approach 1:
The neural network compression system is designed as a universal architecture that can handle multiple data types and compression requirements through a single integrated framework. The embedding system, LSTM, and PAQ encoder work together as a multi-functional unit that adapts to different input characteristics, reducing the need for multiple specialized systems and thereby managing complexity.
Solution Approach 2:
The system incorporates self-optimization mechanisms where the neural networks learn from the data itself rather than requiring extensive manual configuration. The multilayer perceptron and LSTM automatically adapt their parameters and structures based on input patterns, reducing the operational complexity and making the system easier to deploy and maintain.
Data Source
AI summary
A system and method learning-based lossless data compression. The system and method proposed allow for fast and efficient lossless data compression of a large variety of data types. The system and method have a variety of real-world applications, including deep learning solutions for telemetry, tracking, and command subsystems for satellites. Satellites and their control centers are incredibly spaced apart which makes data compression an extremely important process to transmit large sets of information in a low-latency, high-efficiency environment. The proposed system and method utilize probability prediction driven arithmetic coding which provides faster encoding times and higher compression ratios when paired with a long short-term memory system for data compression.