  In this paper, we present an adaptive reweighted sparse belief propagation
(AR-SBP) decoder for polar codes. The AR-SBP technique is inspired by decoders
that employ the sum-product algorithm for low-density parity-check codes. In
particular, the AR-SBP decoding strategy introduces reweighting of the
exchanged log-likelihood-ratio in order to refine the message passing,
improving the performance of the decoder and reducing the number of required
iterations. An analysis of the convergence of AR-SBP is carried out along with
a study of the complexity of the analyzed decoders. Numerical examples show
that the AR-SBP decoder outperforms existing decoding algorithms for a reduced
number of iterations, enabling low-latency applications.
