论文标题
RM代码的递归投影聚集解码的多因素修剪
Multi-Factor Pruning for Recursive Projection-Aggregation Decoding of RM Codes
论文作者
论文摘要
最近引入的Reed-Muller（RM）代码的递归投影聚合（RPA）解码方法可以达到接近最大的可能性（ML）解码性能。但是，其较高的计算复杂性使其实施对时间和资源关键应用程序具有挑战性。在这项工作中，我们提出了一种称为多因素修剪的复杂性降低技术，可大大降低RPA的计算复杂性。我们的仿真结果表明，提出的具有适当选择因素的修剪方法可以将RPA的复杂性降低到$ 92 \％$，对于$ \ text {rm}（8,3）$，同时保持可比较的错误校正校正性能。
The recently introduced recursive projection aggregation (RPA) decoding method for Reed-Muller (RM) codes can achieve near-maximum likelihood (ML) decoding performance. However, its high computational complexity makes its implementation challenging for time- and resource-critical applications. In this work, we present a complexity reduction technique called multi-factor pruning that reduces the computational complexity of RPA significantly. Our simulation results show that the proposed pruning approach with appropriately selected factors can reduce the complexity of RPA by up to $92\%$ for $\text{RM}(8,3)$ while keeping the comparable error-correcting performance.
