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.
翻译:联合通信和遥感技术因其高频谱效率而引起极大注意,因为它在通信和遥感方面使用相同的传输信号。我们利用上链(UL)频道与遥感结果之间的关联,提议为UL JCAS采用基于遥感辅助的卡尔曼过滤器(SAKF)频道状态信息估算法,该方法利用抵达角度估计来提高CSI估计的准确性。建议以Kalman过滤器(KF)为基础的CSI强化方法,通过将估计的AoA作为先前的信息来完善最小的 CSI估计。模拟结果显示,使用拟议的SAKF CSI频道信息估算法的UL通信比特差率(BER)接近使用最低平均平方差法的方法,同时大大降低复杂性。