Message-Passing Decoder Using Min-Sum Storage for Fast Convergence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional message-passing decoders in data storage and transmission systems require significant memory storage, which leads to slower convergence rates due to the need to store large amounts of data at each node.
Innovation Solution
Implementing a reduced-storage approach where check nodes store only the minimum and next-to-minimum values among incoming message magnitudes, their locations, and sign information, allowing for efficient computation and storage of return messages based on these values, thereby reducing memory requirements while maintaining fast convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a relatively large amount of data is stored at each node to maintain decoding accuracy, then the reliability of decoding is improved, but the convergence speed deteriorates due to increased processing time
Solution Approach 1:
The patent extracts only the essential information needed for decoding by storing only the minimum and next-to-minimum values among incoming message magnitudes, along with their locations and sign information. This selective extraction reduces the data volume at each node from storing all incoming messages to storing only the two smallest magnitude values, directly resolving the contradiction by maintaining decoding reliability with reduced storage and processing requirements.
2Quantity of substance
If the amount of stored data at each node is reduced, then the memory usage is improved, but the convergence speed deteriorates due to insufficient information for accurate decoding
Solution Approach 1:
The patent changes the parameters stored at each node from complete message data to specific statistical parameters (minimum magnitude, next-to-minimum magnitude, their locations, and sign information). This parameter transformation allows the system to use less memory while maintaining the essential information needed for accurate decoding and fast convergence, as these condensed parameters capture the critical information required for the belief propagation algorithm.
3Reliability
If all incoming message data is stored at check nodes, then the decoding accuracy is maintained, but the device complexity increases due to larger memory requirements
Solution Approach 1:
The patent extracts only the essential information needed for decoding by storing only the minimum and next-to-minimum values among incoming message magnitudes, along with their locations and sign information. This selective extraction reduces the data volume at each node from storing all incoming messages to storing only the two smallest magnitude values, directly resolving the contradiction by maintaining decoding reliability with reduced storage and processing requirements.
Solution Approach 2:
Instead of storing all incoming messages and then selecting the minimum values when needed, the patent inverts the approach by immediately identifying and storing only the minimum and next-to-minimum values as messages arrive. This inversion of the storage strategy eliminates the need for large memory buffers while maintaining decoding accuracy, as the essential information is captured and retained in condensed form throughout the decoding process.
Data Source
AI summary
A message-passing decoder operates by storing, at a check node, a minimum value, a next-to-minimum value, an edge location of the minimum value, and information regarding the signs of incoming messages. For an edge which is not the location of a previous minimum value, the minimum value and the next-to-minimum value, and the location of the minimum value, are set based on the magnitude of an incoming message. For an edge which is the location of the previous minimum value, the minimum value and the next-to-minimum value are set based on the magnitude of an incoming message, and when the magnitude of the incoming message is at most equal to the previous next-to-minimum value, the location of the minimum value is set to the respective edge, and when the magnitude of the incoming message is greater than the previous next-to-minimum value, the location of the minimum value is approximated.