Abstract
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.