论文标题
通过抽样检测：基于Langevin动力学的大量MIMO检测器
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
论文作者
论文摘要
多输入多输出（MIMO）系统中的最佳符号检测已知是NP硬性问题。因此，任何实践相关性检测器的目的都是使其合理地接近最佳解决方案，同时检查计算复杂性。在这项工作中，我们根据Langevin（随机）动力学的退火版本提出了一个MIMO检测器。更确切地说，我们定义了一个随机动力学过程，其固定分布与符号的后部分布相吻合，鉴于我们的观察结果。从本质上讲，这使我们能够通过从提出的langevin动力学中采样传输符号的最大后验估计器。此外，我们通过逐渐添加一系列噪声，并减少轨迹，从而确保估计的符号属于预先指定的离散星座，从而逐步添加一系列噪声，仔细地制作了这种随机动态。通过数值实验，我们表明我们提出的检测器会产生最先进的符号错误率性能。
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Through numerical experiments, we show that our proposed detector yields state-of-the-art symbol error rate performance.
