Probabilistic Constellation Shaping for Data Transmission Entropy Control
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Solution Overview
Problem
Existing data transmission methods require a fixed information entropy per symbol, which is inadequate for long-distance transmission scenarios like submarine cables, necessitating more flexible information entropy management to ensure stability and efficiency.
Innovation Solution
The proposed method introduces a constellation diagram with a point of zero amplitude, allowing for adjustable probability values based on information entropy, encoding length, and the number of constellation points to dynamically control information entropy, enabling flexible information transmission by varying the occurrence probability of zero-amplitude constellation points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed information entropy per symbol is used in existing data transmission methods, then the transmission process is simple, but the transmission stability deteriorates in long-distance scenarios like submarine cables
Solution Approach 1:
The patent applies dynamics by making the information entropy adjustable rather than fixed. The encoding device can dynamically adjust the information entropy of transmitted data based on channel conditions, using a probabilistic constellation shaping algorithm that varies the probability distribution of constellation points. This allows the system to adapt to different transmission scenarios, improving reliability in long-distance transmission while maintaining manageable complexity through algorithmic optimization.
Solution Approach 2:
The patent changes the parameter of information entropy from a fixed value to a variable parameter. By modifying the probability distribution parameters in the constellation diagram and adjusting the information entropy level according to channel quality, the system can optimize transmission performance. The encoding device calculates appropriate information entropy values based on channel state information and adjusts the modulation scheme accordingly, resolving the contradiction between reliability and complexity.
2Quantity of substance
If larger information entropy is transmitted, then the data capacity increases, but the required signal-to-noise ratio increases, making transmission more difficult
Solution Approach 1:
The system dynamically adjusts information entropy based on channel conditions. When channel quality is good, higher information entropy can be transmitted to increase data capacity. When channel quality deteriorates, the system reduces information entropy to ensure reliable transmission. This dynamic adaptation allows the system to optimize the trade-off between data capacity and transmission feasibility in real-time.
Solution Approach 2:
The patent implements feedback mechanisms where the encoding device receives information about channel quality and adjusts the information entropy accordingly. Based on feedback regarding signal-to-noise ratio and transmission performance, the system modifies the probability distribution of constellation points and adjusts information entropy levels, enabling adaptive optimization of data capacity versus transmission reliability.
Data Source
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AI summary
This application provides an encoding method, a decoding method, and apparatuses, to implement more flexible information entropy in data transmission, especially to implement smaller information entropy to improve stability of long-distance data transmission. The encoding method includes: first, splitting obtained to-be-encoded data into phase data and amplitude data according to a preset rule; then, obtaining a constellation diagram corresponding to the to-be-encoded data, where the constellation diagram includes a plurality of constellation points, the plurality of constellation points include a constellation point with an amplitude value of 0, each constellation point has a corresponding probability value, and the probability value indicates an occurrence probability of the corresponding constellation point; then, performing probabilistic constellation shaping encoding on the amplitude data based on the constellation diagram and the probability value corresponding to each constellation point, to obtain at least one group of symbol sequences; and then combining the at least one group of symbol sequences and the phase data, and then performing encoding, to obtain output data.