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This paper proposes a rate-profile construction method forpolarization-adjusted convolutional (PAC) codes of any code length and rate,which is capable of maintaining trade-off between the error-correctionperformance and decoding complexity of PAC code. The proposed method canimprove the error-correction performance of PAC codes while guaranteeing a lowmean sequential decoding complexity for signal-to-noise ratio (SNR) valuesbeyond a target SNR value.

A Monte-Carlo Based Construction of Polarization-Adjusted Convolutional (PAC) Codes