论文标题
部分可观测时空混沌系统的无模型预测
Sensing-aided Uplink Channel Estimation for Joint Communication and Sensing
论文作者
论文摘要
联合通信和传感（JCAS）技术通过使用相同的传输信号来进行通信和传感，引起了人们的高度关注。利用上行链路（UL）通道与感应结果之间的相关性，我们提出了一个感应的Kalman滤波器（SAKF）基于UL JCAS的基于基于的通道状态信息（CSI）估计方法，该方法利用了Ar-Argy Arger-Argy Argy-Arconival（AOA）估计，以提高CSI估计精度。提出了一种基于Kalman滤波器（KF）的CSI增强方法，以通过将估计的AOA作为先验信息来完善最小二乘CSI的估计。仿真结果表明，使用拟议的基于SAKF的CSI估计方法方法的ul通信的位错误率（BER）使用最小平方误差（MMSE）方法的方法，同时显着降低了复杂性。
The joint communication and sensing (JCAS) technique has drawn great attention due to its high spectrum efficiency by using the same transmit signal for both communication and sensing. Exploiting the correlation between the uplink (UL) channel and the sensing results, we propose a sensing-aided Kalman filter (SAKF)-based channel state information (CSI) estimation method for UL JCAS, which exploits the angle-of-arrival (AoA) estimation to improve the CSI estimation accuracy. A Kalman filter (KF)-based CSI enhancement method is proposed to refine the least-square CSI estimation by exploiting the estimated AoA as the prior information. Simulation results show that the bit error rates (BER) of UL communication using the proposed SAKF-based CSI estimation method approach those using the minimum mean square error (MMSE) method, while at significantly reduced complexity.
