论文标题
半确定性的子空间选择，用于稀疏递归投影聚集解码的芦苇 - 毛刺代码
Semi-Deterministic Subspace Selection for Sparse Recursive Projection-Aggregation Decoding of Reed-Muller Codes
论文作者
论文摘要
[1]中介绍的递归投影聚集（RPA）是一种新型解码算法，该算法接近短长的Reed-Muller代码的最大似然解码器。最近，已经提出了RPA解码的扩展名，称为稀疏多码编码器RPA（SRPA）[2]。与RPA解码相比，SRPA方法利用多个修剪的RPA解码器来降低计算量，同时保持性能损失较小。但是，使用多个稀疏解码器再次增加了计算负担。因此，重点是优化稀疏的单码RPA解码以使复杂性保持较小。在本文中，提出了一种新方法，以选择SRPA解码的投影和聚合步骤中使用的子集的子集，以降低AWGN通道上的解码误差概率。所提出的方法基于评估每个子空间的性能的功绩图，用半决选择方法代替了子空间子集的随机选择。我们的仿真结果表明，与SRPA相比，半决赛子空间选择将解码性能提高到$ 0.2 \，\ text {db} $。同时，与SRPA相比，SRPA解码的复杂性$ r \ geq 3 $的复杂性最多可降低81％。
Recursive projection aggregation (RPA) decoding as introduced in [1] is a novel decoding algorithm which performs close to the maximum likelihood decoder for short-length Reed-Muller codes. Recently, an extension to RPA decoding, called sparse multi-decoder RPA (SRPA), has been proposed [2]. The SRPA approach makes use of multiple pruned RPA decoders to lower the amount of computations while keeping the performance loss small compared to RPA decoding. However, the use of multiple sparse decoders again increases the computational burden. Therefore, the focus is on the optimization of sparse single-decoder RPA decoding to keep the complexity small. In this paper, a novel method is proposed, to select subsets of subspaces used in the projection and aggregation step of SRPA decoding in order to decrease the decoding error probability on AWGN channels. The proposed method replaces the random selection of subspace subsets with a semi-deterministic selection method based on a figure of merit that evaluates the performance of each subspace. Our simulation results show that the semi-deterministic subspace selection improves the decoding performance up to $0.2\,\text{dB}$ compared to SRPA. At the same time, the complexity of SRPA decoding for RM codes of order $r\geq 3$ is reduced by up to 81% compared to SRPA.
