DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep
  Learning
S Ashwin Hebbar Sravan Kumar Ankireddy Hyeji Kim Sewoong Oh Pramod Viswanath
Polar codes, developed on the foundation of Arikan's polarization kernel,
represent a breakthrough in coding theory and have emerged as the
state-of-the-art error-correction-code in short-to-medium block length regimes.
Importantly, recent research has indicated that the reliability of polar codes
can be further enhanced by substituting Arikan's kernel with a larger one,
leading to a faster polarization. However, for short-to-medium block length
regimes, the development of polar codes that effectively employ large kernel
sizes has not yet been realized. In this paper, we explore a novel, non-linear
generalization of polar codes with an expanded kernel size, which we call
DeepPolar codes. Our results show that DeepPolar codes effectively utilize the
benefits of larger kernel size, resulting in enhanced reliability compared to
both the existing neural codes and conventional polar codes.