Low-Latency Adaptive Ordered Statistic Decoding of Polar Codes

Abstract

Deploying polar codes in ultra-reliable low-latency communication (URLLC) is of critical importance and is currently receiving tremendous attention in both academia and industry. However, most of the state of the art polar codes decoders like progressive bit-flipping decoder (PBF) and successive cancellation list (SCL) decoder, involve strong data dependencies and suffer from huge decoding delay. This contradicts the low-latency requirement in URLLC. To address such issue, this paper appeals to the parallel computing and proposes an adaptive ordered statistic decoder (OSD). In particular, we first propose a novel codeword searching metric which proves to be hardware-friendly, and an adaptive OSD algorithm is then developed to adaptively rule out the unpromising codewords, thus significantly reducing the latency. Secondly, to further reduce the computational complexity of the proposed algorithm, we decompose the current code sequence into several independent subcodes, and by handling these subcodes with concatenated adaptive OSDs, a good trade-off between decoding latency and complexity can be achieved. Finally, numerical results show that the proposed adaptive OSD outperforms the conventional decoders in terms of block error rate (BLER) and decoding latency.