Tail-Biting Convolutional Decoding With Correction-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, are too complex and require significant 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 initializing a correction depth and performing the Viterbi algorithm only one or two times over the received encoded block, thereby facilitating efficient decoding with lower computational consumption and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Viterbi algorithm is used for decoding tail-biting convolutional codes, then decoding accuracy is improved, but computational complexity and memory usage increase significantly
Solution Approach 1:
The patent segments the decoding process into multiple passes, where each pass processes a portion of the codeword. Instead of processing the entire codeword in one exhaustive Viterbi algorithm execution, the method divides the decoding into sequential stages that can be executed with reduced computational resources at each step, thereby lowering peak complexity while maintaining overall decoding accuracy.
Solution Approach 2:
The patent performs preliminary actions by initializing the Viterbi algorithm with predetermined starting states based on the tail-biting structure. By pre-determining valid starting states and preparing initial path metrics before the main decoding process, the algorithm avoids exhaustive search through all possible states, reducing computational complexity while preserving decoding accuracy.
2Measurement precision
If traditional Viterbi algorithm is used for decoding tail-biting convolutional codes, then decoding accuracy is improved, but memory usage increases significantly
Solution Approach 1:
The patent segments the state space and path metric storage into manageable portions corresponding to different passes and stages of decoding. By processing the codeword in segments and storing only relevant path metrics for current and next states rather than maintaining full trellis memory, the method significantly reduces memory requirements while preserving decoding accuracy through systematic state tracking.
Solution Approach 2:
The patent performs preliminary initialization of state metrics and path history before the main decoding process. By pre-setting valid starting states and their associated metrics based on tail-biting constraints, the algorithm avoids the need to store and process all possible state combinations, thereby reducing memory usage while maintaining decoding accuracy.
3Reliability
If exhaustive decoding methods are used for tail-biting convolutional codes, then error protection performance is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent segments the exhaustive decoding process into multiple directed passes, where each pass processes the codeword in a specific direction (forward or backward) from predetermined starting states. This segmentation transforms an intractable exhaustive search into a series of manageable decoding passes that can be executed in real-time, maintaining error protection performance through systematic exploration of valid state transitions.
Solution Approach 2:
The patent performs preliminary identification of valid starting states and decoding directions before executing the main decoding process. By pre-determining which states can serve as starting points based on tail-biting constraints and channel conditions, the algorithm directs the decoding process along promising paths only, achieving real-time processing capability while preserving error protection performance through focused rather than blind exhaustive search.
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.