Viterbi Decoder Layout for Tail-Biting State Uncertainty
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Solution Overview
Problem
The existing Viterbi decoders for tail-biting convolutional encoding methods face complexity in determining initial and ending states, leading to potential errors in decoding and increased data rate due to the absence of zero-tail sequences.
Innovation Solution
A Viterbi decoder configuration including a receiving buffer, a bit sequence expanding unit, a Viterbi decoding unit, a central bit sequence selector, and a rearrange unit, which expands the encoding bit sequence, performs Viterbi decoding, selects central bits, and rearranges them to generate final decoded data, simplifying the decoding process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tail-biting convolutional encoding method is used, then data rate is improved, but decoding complexity and error rate increase due to difficulty in determining initial and ending states
Solution Approach 1:
The received bit sequence is divided into multiple segments (first, second, third sequences) and processed through separate decoding paths. The Viterbi decoder performs multiple decoding operations on different segments with different assumed initial states, then selects the most reliable decoded sequence. This segmentation approach breaks down the complex tail-biting decoding problem into manageable pieces while maintaining high data rate.
Solution Approach 2:
The system performs preliminary decoding operations by assuming multiple possible initial states (000 and 111) before knowing the actual initial state. Both decoding paths are executed in advance, and the results are compared to determine which path produced the correct decoded sequence. This preliminary action eliminates the need to accurately determine the initial state beforehand.
2Productivity
If tail-biting convolutional encoding method is used, then data rate is improved, but decoding accuracy decreases due to inability to reliably determine initial and ending states
Solution Approach 1:
The bit sequence is segmented into multiple parts that are decoded separately using different assumed initial states. By comparing results from different segments decoded with different initial state assumptions, the system can identify and select the most accurate decoded sequence, thereby maintaining high decoding accuracy while using tail-biting encoding.
Solution Approach 2:
The system implements a feedback mechanism where decoded sequences from multiple decoding paths are compared against each other. The comparison result feeds back into the selection process to determine which decoded sequence to output. This feedback-based selection ensures that the most accurate decoded sequence is chosen, compensating for the uncertainty in initial state determination.
3Device complexity
If zero-tail convolutional encoding method is used, then decoding simplicity is improved, but data rate decreases due to additional zero tail sequence
Solution Approach 1:
The Viterbi decoder is designed to be universal and can handle both tail-biting and zero-tail convolutional encoding methods using the same basic decoding structure. By making the decoder multi-functional, the system can achieve the decoding simplicity of zero-tail methods while also supporting the higher data rate of tail-biting methods, eliminating the need for separate decoding paths for different encoding types.
Data Source
AI summary
The present invention relates to a decoder for tail-biting convolution codes and a method thereof. The decoder receives an encoding bit sequence in a convolutional encoding method from a channel, generates an expanded encoding bit sequence, Viterbi decodes the expanded encoding bit sequence, and generates decoded data. In addition, the decoder selects a central bit sequence of the decoded data, rearranges the central bit sequence, and generates final decoded data. Accordingly, the decoder has a simplified configuration for decoding the bit sequence encoded in the tail biting convolutional encoding method, and the decoder also decodes a bit sequence encoded in a zero-tail convolutional encoding method.