  Polarization-adjusted convolutional (PAC) codes are special concatenated
codes in which we employ a one-to-one convolutional transform as a pre-coding
step before the polar transform. In this scheme, the polar transform (as a
mapper) and the successive cancellation process (as a demapper) present a
synthetic vector channel to the convolutional transformation. The numerical
results show that this concatenation improves the Hamming distance properties
of polar codes. In this work, we implement the parallel list Viterbi algorithm
(LVA) and show how the error correction performance moves from the poor
performance of the Viterbi algorithm (VA) to the superior performance of list
decoding by changing the constraint length, list size, and the sorting strategy
(local sorting and global sorting) in the LVA. Also, we analyze the latency of
the local sorting of the paths in LVA relative to the global sorting in the
list decoding and the trade-off between the sorting latency and the error
correction performance.
