A Monte-Carlo based construction of polarization-adjusted convolutional (PAC) codes

Abstract

This paper proposes a rate-profile construction method for polarization-adjusted convolutional (PAC) codes of any code length and rate, which is capable of preserving the trade-off between the error-correction performance and decoding complexity of PAC codes. The proposed method can improve the error-correction performance of PAC codes while guaranteeing a low mean sequential decoding complexity for signal-to-noise ratio (SNR) values beyond a target SNR value.