Binary Polarization Kernels from Code Decompositions
  In this paper, code decompositions (a.k.a. code nestings) are used to design binary polarization kernels. The proposed kernels are in general non-linear. They provide a better polarization exponent than the previously known kernels of the same dimensions. In particular, non-linear kernels of dimensions 14, 15, and 16 are constructed and are shown to have optimal asymptotic error-correction performance. The optimality is proved by showing that the exponents of these kernels achieve a new upper bound that is developed in this paper. 
Noam Presman, Ofer Shapira, Simon Litsyn, Tuvi Etzion, Alexander Vardy
cs.IT math.IT