Low-Complexity Decoding of Repeat-Accumulate Codes over Quasi-Static Fading Channels

Abstract

We consider iterative decoding of repeat-accumulate (RA) codes over frequency-flat, quasi-static fading channels. A soft-input, soft-output decoder is proposed for the inner convolutional decoding, which fuses the decoding approach of the soft-output Viterbi algorithm and the estimation approach of the maximum-likelihood sequence detector. The decoder deploys trellis search algorithm based on the generalized likelihood ratio test, whereby the channel state information is acquired implicitly using both the pilot and data signals during the decoding process. Through simulations, we show that the RA decoding with the proposed decoder has much better error performance than standard RA decoding with pilot-symbol-assisted channel estimation, while having approximately the same computational complexity. Compared with the conventional scheme of iterative channel estimation and decoding, the proposed decoder has much simpler structure and requires significantly less computational power, although it incurs some loss in error performance.