Abstract
In this research, we study soft-output decoding of polar codes. Two representative soft-output decoding algorithms are belief propagation (BP) and soft cancellation (SCAN). The BP algorithm has low latency but suffers from high computational complexity. On the other hand, the SCAN algorithm, which is proposed for reduced complexity of soft-output decoding, achieves good decoding performance but suffers from long latency. These two algorithms are suitable only for two extreme cases that need very low latency (but with high complexity) or very low complexity (but with high latency). However, many practical systems may need to work for the moderate cases (i.e., not too high latency and not too high complexity) rather than two extremes. To adapt to the various needs of the systems, we propose a very flexible soft-output decoding framework of polar codes. Depending on which system requirement is most crucial, the proposed scheme can adapt to the systems by controlling the level of parallelism. Numerical results demonstrate that the proposed scheme can effectively adapt to various system requirements by changing the level of parallelism.
Highlights
The development of polar codes by E
The convergence is declared if each IAPP(j), which is the mutual information between the a posteriori likelihood ratio (LLR) evaluated by a Variable node (VN) and an associated codeword bit xj, reaches 1 as the iteration number increases
6 Conclusion In this work, we propose the framework of soft-output polar decoding adaptable to system requirements
Summary
The development of polar codes by E. To increase the throughput of SC decoders, estimating simultaneously certain redundant decoding steps in SC decoding, called simplified successive cancellation [7], and several works based on that approach [8, 9] were proposed Another interesting decoding method of polar codes is a belief propagation (BP) decoding which was originally introduced in [1]. In soft-output decoding of polar codes, the SCAN and BP algorithms can be considered as two extreme cases With these two algorithms, the system can work well only for two cases: (i) when very low latency is demanded at the expense of very high complexity, or (ii) when very low complexity is demanded at the expense of very high latency. It is shown that the expense of the decreased latency is the increased complexity
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