Tail-Biting Convolutional Decoding With Search Depth Correction
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Solution Overview
Problem
Current decoding methods for tail-biting convolutional codes in communication systems, such as those used in WiMAX and OFDMA, are too complex and consume excessive computational resources, making them unsuitable for real-time applications and efficient error protection in wireless communication systems.
Innovation Solution
A Search Depth Viterbi Algorithm (SDVA) is introduced, which reduces the complexity by running the Viterbi algorithm only one or two times over the received encoded block, initializing a correction depth, and selecting a starting state to minimize computational and memory usage, facilitating efficient decoding in systems like IEEE 802.16d/e.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Viterbi algorithm is used for decoding tail-biting convolutional codes, then error correction performance is improved, but computational complexity and memory usage increase excessively
Solution Approach 1:
The patent segments the decoding process into two distinct phases: a first Viterbi decoding pass that processes the entire received signal to generate initial decoded output, and a second Viterbi decoding pass that processes only a subset of symbols (determined by correction depth) using the initial output as input. This segmentation reduces the overall computational burden while maintaining error correction capability through iterative refinement.
Solution Approach 2:
The patent applies partial action by performing the computationally intensive Viterbi algorithm only on a portion of the symbols in the second pass, rather than processing the entire signal again. The correction depth parameter controls the subset size, allowing the system to achieve improved error correction with reduced computational complexity compared to traditional full-signal decoding.
2Reliability
If traditional Viterbi algorithm is used for decoding tail-biting convolutional codes, then error correction performance is improved, but memory usage increases excessively
Solution Approach 1:
The patent segments the decoding process into two passes, where the second pass operates on a reduced subset of symbols determined by the correction depth parameter. This segmentation reduces the memory requirements for storing path metrics and state information during the Viterbi decoding process, as the second pass handles fewer symbols than the traditional approach would require to be processed in a single comprehensive pass.
Solution Approach 2:
The patent applies partial action by performing the second Viterbi decoding pass on only a portion of the total symbols (controlled by correction depth), rather than processing the entire signal. This partial processing significantly reduces the memory usage required for the decoding operation while still achieving improved error correction performance through the iterative refinement approach.
3Reliability
If correction depth is increased in SDVA, then error protection performance is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by performing the second Viterbi decoding pass on only a portion of the total symbols (controlled by correction depth), rather than processing the entire signal. This partial processing significantly reduces the memory usage required for the decoding operation while still achieving improved error correction performance through the iterative refinement approach.
Data Source
AI summary
A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.