Telemetry Data Compression Using Transformer-Based Lossless Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing telemetry data transmission and storage systems face challenges due to limited bandwidth, high latency, power constraints, and cost inefficiencies, particularly in satellite communication, necessitating more effective data compression methods.
Innovation Solution
Utilizing a transformer-based neural network with lossless compression techniques, including embedding layers, attention mechanisms, and various encoding methods, to efficiently compress and decompress telemetry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used for telemetry data, then device complexity is reduced, but compression ratio and efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a neural network-based system that uses embedding layers and attention mechanisms to achieve superior compression ratios. The neural network learns patterns in telemetry data to encode information more efficiently than conventional methods.
Solution Approach 2:
The system dynamically adjusts embedding dimensions and attention mechanism parameters to optimize compression performance for different telemetry data types and transmission requirements, allowing flexible trade-offs between compression ratio and computational complexity.
2Productivity
If machine learning models are trained for telemetry compression, then compression ratio improves, but training time and resources increase
Solution Approach 1:
The patent performs comprehensive model training and optimization in advance, creating pre-trained neural network models that can be deployed for real-time compression without requiring additional training time during operational phases. The model learns from extensive telemetry data samples beforehand.
Solution Approach 2:
The system uses copied and replicated training data to accelerate the training process, employing techniques like data augmentation and parallel training on multiple data copies to reduce overall training time while maintaining model quality.
3Loss of energy
If data is transmitted without compression, then transmission reliability is maintained, but bandwidth consumption increases
Solution Approach 1:
The patent replaces traditional error-prone compression methods with a neural network-based lossless compression system that maintains data integrity. The attention mechanism ensures accurate reconstruction of original telemetry data from compressed representations.
Solution Approach 2:
The system incorporates feedback mechanisms where the decoder verifies reconstructed data against expected patterns and requests retransmission or correction if errors are detected, maintaining transmission reliability while using compressed data.
Data Source
AI summary
A system and method are disclosed for compressing and restoring data. The system includes a computing device comprising at least a memory and a processor, and a telemetry encoding module comprising programming instructions stored in the memory and operable on the processor. The instructions cause the computing device to compress telemetry data to create compressed telemetry data and generate a bitstream of the compressed telemetry data. A telemetry decoding module includes programming instructions stored in the memory and operable on the processor that cause the computing device to receive the bitstream of compressed telemetry data and apply the bitstream of compressed telemetry data as input to the telemetry decoding module. The bitstream can be decompressed to generate a reconstructed version of the telemetry data to conserve important resources such as network bandwidth and storage.