Two-Stage Federated Compression Using Codewords for Low-Latency Telemetry
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Solution Overview
Problem
Existing data compression methods for satellite telemetry and command systems are inefficient and lack the ability to maintain low-latency, high-efficiency lossless compression without losing information.
Innovation Solution
A system and method for federated two-stage compression with federated joint learning, utilizing probability prediction driven arithmetic coding and a long short-term memory system, which includes processing data through a compression network on an edge server, converting data into codewords using a codebook, and training a lightweight codeword model to achieve efficient lossless compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data compression methods are used for satellite telemetry, then data transmission efficiency is improved, but information loss occurs and latency increases
Solution Approach 1:
The compression system is divided into two distinct stages: a first compression stage that handles initial data reduction, and a second compression stage that further compresses the already-compressed data. This segmentation allows each stage to optimize for different aspects of compression, achieving higher overall compression ratios while maintaining lossless compression to prevent information loss.
Solution Approach 2:
A codebook is introduced as an intermediary component that maps compressed data from the first stage to codewords, which are then processed by the second compression stage. This intermediary structure enables the system to achieve better compression ratios while preserving all original information through the reversible mapping process.
2Quantity of substance
If traditional data compression methods are used for satellite telemetry, then data size is reduced, but latency increases
Solution Approach 1:
The codebook is pre-trained and prepared in advance with optimal mappings between compressed data and codewords. This preliminary action allows the compression system to quickly encode data without requiring complex real-time computations, thereby reducing latency while achieving high compression ratios.
Solution Approach 2:
By dividing compression into two stages with different optimization goals, the system can perform aggressive compression in the first stage while the second stage optimizes for encoding speed using the pre-trained codebook, thus reducing overall latency.
3Productivity
If higher compression ratios are achieved, then data transmission efficiency is improved, but system complexity increases
Solution Approach 1:
The codebook serves as a simplified intermediary that bridges the two compression stages. Instead of using a single complex compression algorithm, the system uses the codebook as a lookup-based mediator that achieves high compression ratios through simple, fast operations, thereby reducing overall system complexity.
Solution Approach 2:
The codebook creates a simplified copy or representation of the data relationships, allowing the system to achieve complex compression goals through simple, repeatable lookup operations rather than complex real-time computations.
Data Source
AI summary
A system and method for federated two-stage compression with federated joint learning. 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.