论文标题
基于退火失败的Langevin动力学加速大规模的MIMO探测器
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
论文作者
论文摘要
我们基于\ emph {不足的} langevin（随机）动态的退火版本提出了一个多输入多输出（MIMO）检测器。我们的探测器以符号错误率（SER）的方面达到了最先进的性能，同时保持计算复杂性。实际上，我们的方法可以轻松调整以在当前的应用程序要求的情况下达到计算复杂性和性能之间的正确平衡。通过调整控制模拟Langevin动态长度的超参数来实现这种平衡。通过数值实验，我们证明，与先前提出的基于Langevin的MIMO检测器相比，与竞争方法（包括基于学习的方法）相比，我们的检测器的运行时间（包括基于学习的方法）低于竞争方法（包括基于学习的方法）。
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed \emph{overdamped} Langevin-based MIMO detector.
