Segmented SDR Decoding for Faster Convolutional Error Correction
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Solution Overview
Problem
Existing software-defined radio decoding methods, particularly those using Viterbi algorithms for convolutional codes, are slow, memory-intensive, and lack flexibility in processing different waveform types, failing to effectively reduce errors in unreliable or noisy communication channels.
Innovation Solution
A method that divides encoded data blocks into segments for parallel decoding using estimated encoded sequences, merging these segments with an artificial neural network to perform a closest fit calculation, significantly increasing processing speed and enabling easier adaptation to various waveforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Viterbi algorithm is used for decoding convolutional codes, then error correction capability is improved, but processing speed deteriorates and memory consumption increases
Solution Approach 1:
The received encoded data block is divided into multiple segments, each of which is decoded independently using a simplified decoding approach. This segmentation allows parallel processing of multiple segments simultaneously, significantly increasing overall processing speed while maintaining acceptable error correction performance through subsequent merging of decoded sequences
Solution Approach 2:
Instead of using the computationally expensive Viterbi algorithm for all segments, the patent employs a simpler, faster decoding method for individual segments. The computational intensity is concentrated only in the final merging and selection stage, making the overall process more efficient while preserving reliability
2Reliability
If Viterbi algorithm is used for decoding convolutional codes, then error correction capability is improved, but memory consumption increases
Solution Approach 1:
Dividing the data block into segments allows each segment to be processed with reduced memory requirements. Only the necessary information for merging decoded sequences needs to be stored, significantly reducing peak memory consumption compared to storing entire Viterbi decoding states
Solution Approach 2:
The patent extracts only the essential decoded sequences from each segment that are needed for the final merging process, discarding intermediate computational data. This extraction approach minimizes memory usage while preserving the error correction capability through careful selection of candidate sequences
3Measurement precision
If traditional decoding method is used, then decoding accuracy is maintained, but flexibility in processing different waveform types deteriorates
Solution Approach 1:
The segmented decoding approach with merging and selection is a universal method that can be applied to different waveform types and modulation schemes. The core algorithm remains the same while adapting to various communication protocols, providing both accuracy and flexibility
Solution Approach 2:
The patent employs dynamic selection of decoding parameters and merging strategies based on the specific waveform type and channel conditions. This dynamic adaptation maintains decoding accuracy across different protocols while providing the flexibility needed for software-defined radio applications
4Measurement precision
If encoded data block is processed as a whole, then decoding accuracy is improved, but processing time increases
Solution Approach 1:
By dividing the encoded data block into multiple segments that can be decoded in parallel, the patent reduces overall processing time while maintaining decoding accuracy through the merging and selection of decoded sequences from all segments
Solution Approach 2:
The parallel processing of multiple segments allows useful computational action to continue simultaneously across all segments rather than sequentially. This continuous parallel processing significantly reduces total processing time while the final merging ensures decoding accuracy is maintained
Data Source
AI summary
A method of telecommunications includes the steps of receiving an encoded block having a plurality of values, dividing the received encoded block into a plurality of received segments, each received segment comprising at least two of the values, decoding each received segment by providing, for each received segment, a plurality of estimated encoded sequences, each estimated encoded sequence comprising at least two data units, merging estimated encoded sequences for consecutive segments to provide a plurality of candidate sequences, and selecting one of the plurality of candidate sequences by performing a closest fit calculation between the received encoded data block and each of the candidate sequences. The method is suitable for use in software-defined radios.