TBCC Decoding With Trace-Back Convergence Early Termination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current TBCC decoding algorithms, such as the wrap-around Viterbi algorithm, require extensive iterations and trace-backs, leading to high computational complexity and memory usage, which is inefficient for decoding convolutional codes in wireless communication systems like LTE.
Innovation Solution
The proposed TBCC decoding algorithm implements an early termination condition based on a trace-back convergence check, reducing the number of iterations and trace-backs by sorting paths by their state metric values and terminating when a tail-biting path is found, thereby decreasing the computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the wrap-around Viterbi algorithm is used for TBCC decoding, then decoding accuracy is maintained, but computational complexity and memory usage increase significantly
Solution Approach 1:
The algorithm performs preliminary sorting of paths by their state metric values before conducting trace-back operations. This preliminary action identifies the most promising paths first, allowing the decoder to terminate early when a valid tail-biting path is found, rather than exhaustively processing all possible paths as in the conventional wrap-around Viterbi algorithm.
Solution Approach 2:
Instead of performing complete iterations through all possible paths, the algorithm performs partial action by terminating the trace-back process as soon as a valid tail-biting path is identified. This partial execution suffices to achieve the decoding objective while significantly reducing the number of operations required.
2Reliability
If extensive iterations and trace-backs are performed in TBCC decoding, then decoding reliability is improved, but time consumption increases
Solution Approach 1:
Paths are pre-sorted by state metric values before trace-back, enabling the algorithm to examine the most likely paths first. This preliminary arrangement allows early termination when a valid tail-biting path is found, reducing the average number of iterations required compared to exhaustive search methods.
Solution Approach 2:
The algorithm performs only the necessary iterations and trace-backs required to find a valid tail-biting path, terminating early rather than completing full iterations through all possible paths. This partial execution maintains reliability while reducing time consumption.
3Measurement precision
If multiple iterations and trace-backs are conducted, then decoding precision is maintained, but memory requirements increase
Solution Approach 1:
The algorithm pre-sorts paths by state metric values and identifies the most promising candidates before conducting trace-back operations. This preliminary action allows the decoder to work with a reduced set of high-probability paths, decreasing memory requirements while maintaining precision through focused examination of the most likely sequences.
Solution Approach 2:
By performing partial trace-backs and terminating early when a valid tail-biting path is found, the algorithm avoids storing and processing complete information for all possible paths. This partial execution reduces memory usage while preserving decoding precision for the identified valid sequence.
Data Source
AI summary
A user equipment (UE) comprising at least one component configured to decode a tail-biting convolution code (TBCC) by calculating a plurality of paths that correspond to a plurality of encoder starting states and trace back at least one of the calculated paths per at least one iteration until a trace-back convergence check (TCC) condition fails, wherein the TCC condition fails if a starting state of a first traced back path among the calculated paths is not equal to a starting state of a subsequent traced back path.