Abstract
Decoding of polar codes, a class of capacity-achieving channel codes, typically requires the perfect knowledge of channel parameter in advance. This paper aims to investigate how to decode polar codes when channel parameter is unknown. Specifically, we study a generalized Gilbert-Elliott channel model, which assumes that the channel switches between a finite number of states. On the platform of Soft CANcellation (SCAN), which is a low-complexity iterative decoding algorithm of polar codes superior to the widely-used Successive Cancellation (SC) decoder, we propose three adaptive algorithms, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Sliding-Window SCAN (SWSCAN), Weighted-Window SCAN (W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SCAN), and Linear-Weighting SCAN (LWSCAN). These adaptive SCAN decoders are seeded with a coarse estimate of channel state, and after each SCAN iteration, the decoders progressively refine the estimate of channel state. Experimental results demonstrate that the proposed adaptive SCAN decoders outperform the original SCAN decoder and other competitors.
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