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The de-facto standard decoding algorithm for polar codes, successive cancellation list (SCL) decoding, is a breadth-first search algorithm. By keeping a list of candidate codewords, SCL decoding improves the performance as the list size L increases. However, generating this list requires storing L copies of internal log-likelihood ratios (LLRs). On the contrary, near-maximum likelihood (near-ML) decoding does not require internal LLRs. This algorithm returns the ML solution of the coded bits in a finite search space and shows superior error-rate performance for high code rates. In this paper, we propose a novel near-ML decoding algorithm based on the breadth-first search, a list ordered statistics decoding (List-OSD) algorithm, which estimates the coded bits starting from the most reliable ones. Simulation results show that the List-OSD exhibits 0.25 dB gain compared with SCL decoding at a frame error rate of 10(-3) for (128, 105) polar code with much less memory consumption for LLRs. Moreover, we design the pipelined hardware architecture of the proposed algorithm based on SMIC 65 nm technology, delivering a 353.14 Mbps throughput and 0.7 mm(2) area when L = 32. To the best of our knowledge, this is the first work that presents ASIC implementation results for OSD-based decoders.
Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yifei Shen, Yuqing Ren, Leyu Zhang, Chuan Zhang
Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yifei Shen, Yuqing Ren, Chuan Zhang