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.
翻译:用于多输出多输出检测(MIIMO)的基于树的映射器,如球形解码器可以实现接近最佳的性能,但由于其相继性质,计算成本很高。 在本文中,我们提议了受干扰的线性映射器(PLM),这是同时计算软输出的新数据驱动模型。为了实现这一点,PLM学习了以初步线性估计为核心的分布器,以及使用端到端Bayesian优化的日志-类似剪切比参数。此外,我们表明,可以自然地将 Lattice 纳入PLM 管道,这样可以将计算成本与编码块错误率的减少进行交换。我们发现,优化的PLM能够在Rayleigh 频道实现接近最大相似性能,从而成为以树为主的映射器的有效替代品。