Learning-Based Lossless Compression for Low-Latency Satellite TT&C
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Solution Overview
Problem
Current data compression methods, especially in telemetry, tracking, and command (TT&C) subsystems for satellites, face challenges in achieving low-latency and high-efficiency lossless compression without information loss.
Innovation Solution
A learning-based lossless data compression system utilizing a combination of neural networks, specifically a long short-term memory (LSTM) system and multilayer perceptron (MLP) with probability prediction driven arithmetic coding, to efficiently compress data types like text, audio, and video files, ensuring no information loss during compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional lossless compression algorithms are used, then data integrity is maintained, but compression ratio and processing speed are insufficient for satellite TT&C subsystems
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a neural network-based learning system. The neural network learns compression patterns from training data and applies learned transformations to compress satellite TT&C data losslessly while achieving higher compression ratios and processing speeds compared to conventional algorithms.
Solution Approach 2:
The patent transforms the compression problem into a parameter prediction problem where the neural network learns to predict future data values based on historical patterns. By changing from direct compression to parameter prediction, the system achieves improved compression efficiency while maintaining data integrity through reversible transformations.
2Productivity
If compression algorithms are optimized for higher compression ratios, then data transmission efficiency improves, but latency increases which is unacceptable for satellite communications
Solution Approach 1:
The patent performs preliminary training of the neural network compression model offline before actual satellite data compression. During online operation, the pre-trained model rapidly compresses incoming TT&C data without requiring real-time learning computations, thus achieving high compression ratios with minimal latency suitable for time-sensitive satellite communications.
3Productivity
If complex neural network architectures are used to improve compression performance, then compression ratio increases, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the compression task into distinct neural network components: an embedding system that transforms input data into latent representations, and a prediction system that generates compressed outputs. This modular segmentation allows each component to be optimized independently while maintaining overall compression performance and managing system complexity.
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.