PAC Code Decoding With Dynamic List Viterbi Path Control
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Solution Overview
Problem
Existing decoding methods for polarization-adjusted convolutional (PAC) codes face challenges in achieving reliable error-correction performance while maintaining constant throughput, particularly due to path merging issues in list Viterbi algorithms (LVA) that increase the likelihood of discarding accurate paths, especially at varying signal-to-noise ratios (SNR).
Innovation Solution
A dynamic list size adjustment is integrated into the LVA decoding process, allowing the number of paths assigned to each state to be determined based on the number of active states, ensuring efficient decoding of PAC codes with reliable error-correction performance across varying SNR conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed list size is used in LVA decoding, then the decoding throughput remains constant, but the error-correction performance deteriorates due to path merging issues
Solution Approach 1:
The patent applies dynamics by making the list size variable rather than fixed. The list size is dynamically adjusted based on the number of active states in the trellis diagram during decoding. When the number of active states is large, a larger list size is used to maintain more paths; when active states are fewer, a smaller list size is used. This dynamic adaptation resolves the contradiction by allowing the system to maintain both high reliability (through sufficient path tracking) and high productivity (through efficient resource utilization).
Solution Approach 2:
The patent changes the parameter of list size from a static value to a variable that adapts to decoding conditions. By modifying the list size parameter based on the number of active states, the system optimizes the balance between error-correction performance and decoding throughput. This parameter change allows the decoder to allocate computational resources efficiently while maintaining accurate path tracking throughout the decoding process.
2Reliability
If the number of paths assigned to each state is increased, then the likelihood of discarding accurate paths decreases, but the decoding complexity increases
Solution Approach 1:
The patent uses dynamics to adjust the number of paths assigned to each state based on the actual number of active states in the trellis. Instead of uniformly increasing paths for all states (which would increase complexity unnecessarily), the system dynamically determines the appropriate number of paths for each state. This approach maintains high path retention accuracy for states that need it while avoiding unnecessary complexity in states with fewer active transitions.
Solution Approach 2:
The patent applies local quality by allowing different numbers of paths to be assigned to different states based on their specific characteristics. States with more active states receive more paths to maintain accuracy, while states with fewer active states receive fewer paths. This localized adaptation resolves the contradiction by optimizing path assignment individually for each state rather than applying a uniform approach system-wide.
Data Source
AI summary
The disclosure relates to fifth generation (5G) or sixth generation (6G) communication systems to support higher data rates. A receiver of a communication system includes a transceiver configured to receive a signal encoded based on polarization-adjusted convolutional (PAC) coding from a transmitter, and a controller configured to calculate branch metrics of branches defined in a trellis associated with the PAC, calculate a path metric based on an accumulated sum of the branch metrics, determine a path assigned to each state based on the path metric, and estimate a codeword based on the determined path, wherein a number of paths assigned to each state is dynamically determined according to the number of active states, and wherein the active state includes at least one branch.


