Polar Decoder Dynamic List Sizing for Faster SCL Decoding
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Solution Overview
Problem
Polar decoding with Successive Cancellation List (SCL) schemes faces high complexity and longer running times, which are not efficiently addressed by existing adaptive decoders that use brute-force methods to determine the minimum list size.
Innovation Solution
A method for dynamically adjusting the SCL size at each level based on the statistical distribution of path metrics, allowing for reduced complexity and running time without degrading decoding performance, by setting the SCL size larger when path metrics are close, smaller when they are far apart, and maintaining the current size otherwise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Successive Cancellation List (SCL) decoding is used to improve decoding performance, then Block Error Rate performance is improved, but decoding complexity and running time increase
Solution Approach 1:
The patent applies dynamics by making the list size L adaptive rather than fixed. The list size dynamically adjusts at each decoding level based on the statistical distribution of path metrics. When path metrics are closely distributed, the list size increases to maintain performance; when path metrics are widely distributed, the list size decreases to reduce complexity. This dynamic adaptation resolves the contradiction between maintaining high decoding performance and reducing decoding complexity.
Solution Approach 2:
The patent changes the parameter L (list size) based on the statistical distribution characteristics of path metrics. By monitoring the distribution of path metrics and adapting L accordingly, the system optimizes the balance between decoding performance and complexity. This parameter change approach allows the decoder to use larger list sizes only when necessary for performance while using smaller list sizes when performance requirements are met with lower complexity.
2Reliability
If larger SCL list size is used to maintain decoding performance, then Block Error Rate performance is maintained, but running time increases
Solution Approach 1:
The adaptive list size mechanism dynamically adjusts the computational effort at each decoding level based on the actual difficulty of the decoding task. When path metrics are closely distributed (indicating higher decoding difficulty), the list size increases to maintain performance. When path metrics are widely distributed (indicating easier decoding), the list size decreases to reduce running time. This dynamic adjustment ensures performance is maintained only when necessary, reducing overall running time.
3Adaptability or versatility
If brute-force method is used to determine minimum list size, then adaptive decoding is achieved, but complexity and running time increase in worst case
Solution Approach 1:
The patent implements self-service by having the decoder automatically determine the appropriate list size based on the statistical distribution of path metrics without requiring external control or brute-force searching. The decoder monitors its own decoding state and autonomously adjusts the list size parameter, achieving adaptability through self-regulation rather than complex external control mechanisms.
Data Source
AI summary
It provides a method (300) for polar decoding a received signal into a number, N, of bits with Successive Cancellation List, SCL. The method (300) includes: at the i-th level of a binary tree for decoding the i-th bit of the N bits, where 1≤i≤N: when the i-th bit is an information bit, calculating (310) a path metric for each of 2*Li-1 candidate paths at the i-th level, where Li-1 is an SCL size at the (i−1)-th level and L0=1; setting (320) an SCL size at the i-th level, Li, based on Li-1 and a statistical distribution of the path metrics calculated for the 2*Li-1 candidate paths; and selecting (330) Li surviving paths from the 2*Li-1 candidate paths based on their respective path metrics.