Tail-Biting Convolutional Code Decoding With Search-Depth Viterbi
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Solution Overview
Problem
Existing decoding methods for tail-biting convolutional codes in communication systems, such as those used in WiMAX and OFDMA, face high computational complexity and memory usage issues, 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 computational 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 path metrics, thereby facilitating efficient decoding with lower memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Viterbi algorithm is used for decoding tail-biting convolutional codes, then decoding accuracy is maintained, but computational complexity and memory usage increase significantly
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 bits and identify a candidate starting state, and a second Viterbi decoding pass that uses the identified starting state to perform refined decoding. This segmentation allows the system to maintain decoding accuracy while reducing overall computational complexity by focusing the second pass on a limited set of candidate states rather than all possible states.
Solution Approach 2:
The patent performs preliminary decoding actions in the first pass to identify candidate starting states and generate initial decoded bit sequences before performing the final decoding. By pre-identifying the most likely starting states based on path metrics from the first pass, the system prepares the necessary information in advance to significantly reduce the computational burden of the second decoding pass, thereby resolving the contradiction between accuracy and complexity.
2Reliability
If conventional Viterbi algorithm is used for decoding tail-biting convolutional codes, then error protection performance is maintained, but memory usage increases
Solution Approach 1:
The patent extracts and utilizes only the essential information from the first decoding pass—specifically the path metrics and candidate starting states—rather than storing and processing all intermediate decoding data. By extracting only the necessary elements needed for the second decoding pass, the system maintains error protection performance while significantly reducing memory requirements compared to conventional approaches that must store complete trellis diagrams and all possible state transitions.
3Measurement precision
If multiple Viterbi algorithm runs are performed to find optimal starting state, then decoding accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by performing the computationally intensive Viterbi algorithm twice rather than the potentially many times required by exhaustive search methods. The first pass provides sufficient information to identify candidate starting states, and the second pass using these limited candidates achieves near-optimal decoding accuracy without the excessive processing time that would result from evaluating all possible starting states through multiple full decoding runs.
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.