Uplink sensing in perceptive mobile networks (PMNs), which uses uplink communication signals for sensing the environment around a base station, faces challenging issues of clock asynchronism and the requirement of a line-of-sight (LOS) path between transmitters and receivers. The channel state information (CSI) ratio has been applied to resolve these issues, however, current research on the CSI ratio is limited to Doppler estimation in a single dynamic path. This paper proposes an advanced parameter estimation scheme that can extract multiple dynamic parameters, including Doppler frequency, angle-of-arrival (AoA), and delay, in a communication uplink channel and completes the localization of multiple moving targets. Our scheme is based on the multi-element Taylor series of the CSI ratio that converts a nonlinear function of sensing parameters to linear forms and enables the applications of traditional sensing algorithms. Using the truncated Taylor series, we develop novel multiple-signal-classification grid searching algorithms for estimating Doppler frequencies and AoAs and use the least-square method to obtain delays. Both experimental and simulation results are provided, demonstrating that our proposed scheme can achieve good performances for sensing both single and multiple dynamic paths, without requiring the presence of a LOS path.
翻译:感知性移动网络(PMNs)使用上链通信信号对基站周围的环境进行感知性移动网络(PMNs)的上传感测,面临时钟无同步问题和发射机和接收机之间对视线路径的要求等挑战性问题。但是,对频道状态信息(CSI)比率的当前研究被用于解决这些问题,但目前对CSI比率的研究仅限于单一动态路径中的多普勒估计。本文件提出一个先进的参数估计方案,它可以提取多个动态参数,包括多普勒频率、抵达角度(AoA)和延迟,在通信上链路频道中,并完成多个移动目标的本地化。我们的计划以CSI的多元素泰勒比例为基础,将遥感参数的非线性功能转换成线性形式,使传统遥感算法得以应用。我们利用调高的泰勒系列,开发了新的多信号级电算法,用于估算多普勒频率和AoAs,并使用最小的平方方法获取多移动目标。我们的实验和模拟路径方法都提供了一种需要实现单一感测和模拟结果的多动路径。