We investigate the performance-complexity tradeoffs of Raptor codes over Gaussian channels. Two different implementations of the belief-propagation (BP) decoding algorithm are considered, which we respectively refer to as "message-reset decoding" and "incremental decoding". We show that incremental decoding offers great advantages over message-reset decoding in terms of this tradeoff
['Ketai Hu', 'Jeff Castura', 'Yongyi Mao']
Performance-Complexity Tradeoffs of Raptor Codes over Gaussian Channels