Application of Guessing to Sequential Decoding of Polarization-Adjusted Convolutional (PAC) Codes

Despite the extreme error-correction performance, amount of computation sequential decoding polarization-adjusted convolutional (PAC) codes is random. In codes, cutoff rate denotes region between rates whose average computational complexity finite and those which infinite. this paper, by benefiting from polarization guessing techniques, we prove that required in pre-transformed polar polarizes, determines set bit positions within profile may result high complexity. Based on this, propose a technique for taming Reed-Muller (RM) rate-profile construction, performance results demonstrate PAC can achieve theoretical bounds using tamed-RM construction requires significantly lower than RM construction.