A Path Metric Based Construction of Polarization-Adjusted Convolutional Codes

The talk will be on Zoom.

Abstract: We propose an approach to understand and exploit Polarization-Adjusted Convolutional (PAC) Codes that is directly tied to decoders with memory, specifically Successive Cancellation List (SCL). The crux of our approach is to use a modified Density Evolution Gaussian Approximation (DEGA) to account for errors in the decoding path and more accurately track the path metrics (PMs) likely to incur decoding errors. Our approach not only explains the benefits provided by the use of the rate one precoding, but also provides new insight into why certain information sets perform better under PAC than polar.

Short Bio: Tyler Kann is originally from New Mexico and is a 3rd year PhD student at Georgia Tech, advised by Prof. Matthieu Bloch. His work focuses on coding theory, currently looking at codes for extremely short block lengths.