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.

翻译：最近引入的Reed-Muller(RM)代码的循环预测汇总解码法(RPA)解码法(RPA)可以实现几乎最大的可能性解码性(ML)性能。然而,它的计算复杂性很高,使得其实施对时间和资源至关重要的应用程序具有挑战性。在这项工作中,我们提出了一个叫做多因素的减少复杂性技术,这大大降低了RPA的计算复杂性。我们的模拟结果表明,拟议的调整法与适当选择的因素可以将RPA的复杂程度降低到92美元($/text{RM}(8,3美元),同时保持类似的错误纠正性能。