论文标题
学习软输出线性mimo demappers的扰动
Learning Perturbations for Soft-Output Linear MIMO Demappers
论文作者
论文摘要
用于多输入多输出（MIMO）检测的基于树的Demapper，例如球体解码器，可以实现近乎最佳的性能，但由于其顺序性质而产生的高计算成本。在本文中，我们提出了扰动的线性Demapper（PLM），该线性Demapper（PLM）是一个新型的数据驱动模型，用于并行计算软输出。为了实现这一目标，PLM使用端到端的贝叶斯优化学习以初始线性估计和对数可能性比率剪辑参数为中心的分配。此外，我们表明还可以自然合并到PLM管道中，从而可以将计算成本与编码块误差率降低进行交易。我们发现，优化的PLM可以在瑞利通道中实现接近最大样品（ML）性能，从而使其成为基于树的Demappers的有效替代品。
Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to their sequential nature. In this paper, we propose the perturbed linear demapper (PLM), which is a novel data-driven model for computing soft outputs in parallel. To achieve this, the PLM learns a distribution centered on an initial linear estimate and a log-likelihood ratio clipping parameter using end-to-end Bayesian optimization. Furthermore, we show that lattice-reduction can be naturally incorporated into the PLM pipeline, which allows to trade off computational cost against coded block error rate reduction. We find that the optimized PLM can achieve near maximum-likelihood (ML) performance in Rayleigh channels, making it an efficient alternative to tree-based demappers.
