Polar codes have emerged as the most favorable channel codes for their unique capacity-achieving property. To date, numerous approaches for efficient decoding of polar codes have been reported. However, these prior efforts focused on design of polar decoders via deterministic computation, while the behavior of stochastic polar decoder, which can have potential advantages such as low complexity and strong error-resilience, has not been studied in existing literatures. This paper, for the first time, investigates polar decoding using stochastic logic. Specifically, the commonly-used successive cancellation (SC) algorithm is reformulated into the stochastic form. Several methods that can potentially improve decoding performance are discussed and analyzed. Simulation results show that a stochastic SC decoder can achieve similar error-correcting performance as its deterministic counterpart. This work can pave the way for future hardware design of stochastic polar codes decoders.
The utilization of 5G and beyond wireless communication systems in autonomous driving and remote healthcare has necessitated the development of channel codes with high reliability and low latency. The multi-bit successive cancellation list (MSCL) parallel decoding of polar codes has shown great potential in the decrease of decoding latency while maintaining a high level of reliability, in comparison to the conventional successive cancellation list (SCL) decoding. However, the complexity of decoding path sorting increases exponentially with the increase of parallelism, which poses significant limitations to the improvement of parallelism. To address this issue, this paper proposes a critical set aided multi-bit successive cancellation list (CS-MSCL) decoding algorithm. Critical sets are introduced to the MSCL decoding, which prunes less reliable candidate paths to guarantee high decoding performance. Meanwhile, dynamic configuration is allowed according to the application scenario to make a good balance in decoding latency, error correction performance, and algorithmic complexity. Simulation results demonstrate that the algorithmic complexity decreased by 1099.04% compared to MSCL decoding for (1024, 512) polar codes when the degree of parallelism is 8 and the list size is 16.
Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5th generation wireless standards (5G). However, there remains room for the design of polar decoders that are both efficient and reliable in the short blocklength regime. Motivated by recent successes of data-driven channel decoders, we introduce a novel
Cur
RIculum based
Sequential neural decoder for
Polar codes (CRISP). We design a principled curriculum, guided by information-theoretic insights, to train CRISP and show that it outperforms the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(32,16) and Polar(64,22) codes. The choice of the proposed curriculum is critical in achieving the accuracy gains of CRISP, as we show by comparing against other curricula. More notably, CRISP can be readily extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC decoders are significantly less reliable. To the best of our knowledge, CRISP constructs the first data-driven decoder for PAC codes and attains near-optimal performance on the PAC(32,16) code.