Flexible Polar Encoding Architecture for Faster Parallel Decoding
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Solution Overview
Problem
Polar codes face performance issues at short to moderate lengths compared to LDPC codes and have low decoding throughput due to their serial nature, limiting their effectiveness in communication systems.
Innovation Solution
A non-systematic polar encoder architecture that uses multiplexers to extract and modify polar codes, allowing for direct decoding of constituent codes without recursion, and implements flexible encoding and decoding algorithms to enhance error correction performance and adapt to varying communication conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If successive-cancellation decoding is used for polar codes, then decoding complexity is reduced, but decoding throughput becomes low due to serial processing
Solution Approach 1:
The polar code is divided into multiple constituent codes of length N/2, which are decoded in parallel rather than sequentially. This segmentation transforms the serial successive-cancellation decoding into parallel processing, thereby increasing throughput while maintaining the low complexity advantage of SC decoding.
Solution Approach 2:
The decoding process transitions from a single-dimensional serial sequence to a multi-dimensional parallel structure by processing multiple constituent codes simultaneously. This dimensional change enables throughput improvement without increasing the fundamental complexity of the decoding algorithm.
2Reliability
If polar code length is increased to improve error-correction performance, then performance approaches channel capacity, but decoding latency increases due to serial processing
Solution Approach 1:
Long polar codes are segmented into multiple shorter constituent codes that can be decoded in parallel. This reduces the effective processing depth while maintaining the overall code length needed for high reliability, thereby reducing decoding latency without sacrificing error-correction performance.
Solution Approach 2:
The encoder prepares the code structure in advance by creating a hierarchical decomposition into constituent codes, enabling the decoder to process multiple segments simultaneously from the outset, thus reducing overall decoding latency.
3Reliability
If systematic encoding is used, then error-correction performance improves, but device complexity increases
Solution Approach 1:
The systematic encoding process is divided into multiple stages corresponding to different constituent codes. Each stage processes a portion of the data independently, allowing for modular implementation that improves error-correction performance while managing complexity through structured decomposition.
Data Source
AI summary
Methods and systems for encoding data are described herein. The method comprises inputting data to a first pipeline of a non-systematic polar encoder capable of encoding a polar code of length nmax, extracting, via at least one first multiplexer of size log nmax×1, a first polar code of length n<nmax at a first location along the first pipeline to generate a first encoded output, modifying the first encoded output to set frozen bits to a known value to obtain a modified first encoded output, inputting the modified first encoded output to a second pipeline of the non-systematic polar encoder, and extracting, via at least one second multiplexer of size log nmax×1, a second polar code of length n<nmax at a second location along the second pipeline to generate a second encoded output, the second encoded output corresponding to a systematically encoded polar code of length n.