论文标题
通过增强学习的数据辅助通道估计器
Data-Aided Channel Estimator for MIMO Systems via Reinforcement Learning
论文作者
论文摘要
本文提出了一个数据辅助的通道估计器，该估计器可减少用于多输入多输出通信系统的常规线性最小值 - 均方根（LMMSE）方法的通道估计误差（LMMSE）方法。基本思想是选择性利用从数据检测获得的其他试点信号获得的检测到的符号向量。为了优化检测到的符号向量的选择，定义了Markov决策过程（MDP），该过程找到了最佳选择，以最大程度地减少通道估计的均方越（MSE）。然后开发了一种增强学习算法来以计算有效的方式解决该MDP。仿真结果表明，与常规LMMSE方法相比，所提供的通道估计值大大降低了通道估计值的MSE，因此提高了系统的块错误率。
This paper presents a data-aided channel estimator that reduces the channel estimation error of the conventional linear minimum-mean-squared-error (LMMSE) method for multiple-input multiple-output communication systems. The basic idea is to selectively exploit detected symbol vectors obtained from data detection as additional pilot signals. To optimize the selection of the detected symbol vectors, a Markov decision process (MDP) is defined which finds the best selection to minimize the mean-squared-error (MSE) of the channel estimate. Then a reinforcement learning algorithm is developed to solve this MDP in a computationally efficient manner. Simulation results demonstrate that the presented channel estimator significantly reduces the MSE of the channel estimate and therefore improves the block error rate of the system, compared to the conventional LMMSE method.
