Problem
Research questions and friction points this paper is trying to address.
MPAC Codes
Fano Decoding
Successive Cancellation
Minimum Weight Codewords
Performance-Complexity Tradeoff
Construction and Design of MPAC Codes
🤖 AI Summary
本文提出改进的极化调整卷积（MPAC）码及其混合解码方法，通过部分信息位进行卷积变换，并结合Fano和连续取消解码，以优化性能与复杂度之间的平衡。
📝 Abstract
This paper proposes modified polarization-adjusted convolutional (MPAC) codes and their hybrid decoding that achieves an improved performance-complexity tradeoff. For MPAC codes, only a subset of the information bits undergo the convolutional transform. The output is then combined with the remaining information bits for the inner polar transform. Correspondingly, the convolutionally transformed bits are recovered by Fano decoding, while the remaining information bits are recovered by the successive cancellation (SC) decoding, constituting the hybrid Fano-successive cancellation (HFSC) decoding. The MPAC codes are further designed by the coset-wise analysis that characterizes the number of minimum weight codewords (MWCs). It is discovered that a partially convolutional transform can improve the codeword through utilizing the row combinations of the frozen set efficiently. This property enables the MPAC codes to outperform their prototype polarization-adjusted convolutional (PAC) codes and cyclic redundancy check (CRC)-polar codes. Furthermore, MPAC codes can be optimized by reducing the number of MWCs. Our numerical results demonstrate that, with a similar decoding complexity budget, the MPAC codes offer competent decoding performance when compared with PAC codes using Fano decoding and CRC-polar codes using SC list (SCL) decoding.
Innovation
Methods, ideas, or system contributions that make the work stand out.
MPAC codes
hybrid decoding
Fano decoding
successive cancellation (SC) decoding
minimum weight codewords (MWCs)
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