FEC Decoding with Permanent Inactivation for Low-Overhead Recovery
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Solution Overview
Problem
Existing error-correcting codes for data transmission in communications systems are computationally intensive and inefficient in handling data erasures and errors, particularly in low-power devices with limited memory and computing power, and they do not effectively manage absolute and relative reception overhead.
Innovation Solution
The method involves generating intermediate symbols from source symbols using an invertible process, partitioning them into sets, and designating some as permanently inactivated symbols for decoding, allowing for reduced computational expense and overhead through techniques like Gaussian elimination and belief propagation decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error-correcting codes are used for data transmission, then data recovery reliability is improved, but computational complexity and energy consumption increase significantly
Solution Approach 1:
The encoding process is divided into two distinct stages: first, source symbols are transformed into intermediate symbols through an invertible process; second, encoded symbols are generated from these intermediate symbols. This segmentation allows the decoder to work backwards efficiently from received encoded symbols through the same staged process, reducing overall computational complexity while maintaining reliability
Solution Approach 2:
Intermediate symbols serve as a mediating layer between source symbols and encoded symbols. This intermediary representation enables more efficient decoding operations by providing a structured intermediate state that simplifies the recovery process, particularly when dealing with erasures and errors in resource-constrained devices
2Reliability
If traditional error-correcting codes are used for data transmission, then data recovery reliability is improved, but energy consumption increases
Solution Approach 1:
By segmenting the encoding into two stages with intermediate symbols, the decoding process can be optimized to require fewer computational operations. This reduction in computational steps directly translates to lower energy consumption in battery-powered and resource-constrained devices while preserving the ability to recover data reliably
Solution Approach 2:
The invention changes the structural parameters of the coding scheme by introducing intermediate symbols and a two-stage process. This parameter change optimizes the balance between reliability and energy consumption by creating a more efficient computational pathway for encoding and decoding operations
3Reliability
If more redundant data is transmitted to handle erasures and errors, then data recovery reliability is improved, but reception overhead increases
Solution Approach 1:
The invertible transformation of source symbols into intermediate symbols is performed in advance during encoding. This preliminary action creates a structured representation that enables more efficient decoding with less overhead, as the intermediate structure facilitates systematic recovery processes that require fewer additional redundant symbols
Data Source
AI summary
Encoding of a plurality of encoded symbols is provided wherein an encoded symbol is generated from a combination of a first symbol generated from a first set of intermediate symbols and a second symbol generated from a second set of intermediate symbols, each set having at least one different coding parameter, wherein the intermediate symbols are generated based on the set of source symbols. A method of decoding data is also provided, wherein a set of intermediate symbols is decoded from a set of received encoded symbols, the intermediate symbols organized into a first and second sets of symbols for decoding, wherein intermediate symbols in the second set are permanently inactivated for the purpose of scheduling the decoding process to recover the intermediate symbols from the encoded symbols, wherein at least some of the source symbols are recovered from the decoded set of intermediate symbols.