Improved Successive Cancellation Decoding of Polar Codes

TL;DR

This paper introduces a new hybrid decoding algorithm for polar codes that combines existing methods to improve performance and reduce complexity, achieving near-ML decoding performance with efficient resource use.

Contribution

A novel successive cancellation hybrid (SCH) decoding algorithm is proposed, combining SCL and SCS ideas for better complexity-performance trade-offs, along with a pruning technique to further reduce complexity.

Findings

ISC algorithms approach ML performance with acceptable complexity.

Pruning technique significantly reduces ISC decoding complexity.

SCH decoder offers a better complexity-performance balance.

Abstract

As improved versions of successive cancellation (SC) decoding algorithm, successive cancellation list (SCL) decoding and successive cancellation stack (SCS) decoding are used to improve the finite-length performance of polar codes. Unified descriptions of SC, SCL and SCS decoding algorithms are given as path searching procedures on the code tree of polar codes. Combining the ideas of SCL and SCS, a new decoding algorithm named successive cancellation hybrid (SCH) is proposed, which can achieve a better trade-off between computational complexity and space complexity. Further, to reduce the complexity, a pruning technique is proposed to avoid unnecessary path searching operations. Performance and complexity analysis based on simulations show that, with proper configurations, all the three improved successive cancellation (ISC) decoding algorithms can have a performance very close to that of maximum-likelihood (ML) decoding with acceptable complexity. Moreover, with the help of the proposed pruning technique, the complexities of ISC decoders can be very close to that of SC decoder in the moderate and high signal-to-noise ratio (SNR) regime.