Adaptive Ordered Statistic Decoding of Polar Codes for URLLC Systems

Deploying polar codes in ultra-reliable low-latency communication (URLLC) has received tremendous attention in both academia and industry. However, the state of the art polar codes decoders, e.g., successive cancellation (SC) 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 this issue, this paper appeals to parallel computing and proposes an adaptive ordered statistic decoder (OSD). Capitalizing on a novel codeword searching metric, the proposed decoder proves to be hardware-friendly and can adaptively rule out the unpromising codewords, thus reducing the latency significantly. Numerical results show that the adaptive OSD outperforms the conventional decoders in terms of block error rate (BLER) and decoding latency.