Orthogonal time frequency space (OTFS) modulation has the potential to enable robust communications in highly-mobile scenarios. Estimating the channels for OTFS systems, however, is associated with high pilot signaling overhead that scales with the maximum delay and Doppler spreads. This becomes particularly challenging for massive MIMO systems where the overhead also scales with the number of antennas. An important observation however is that the delay, Doppler, and angle of departure/arrival information are directly related to the distance, velocity, and direction information of the mobile user and the various scatterers in the environment. With this motivation, we propose to leverage radar sensing to obtain this information about the mobile users and scatterers in the environment and leverage it to aid the OTFS channel estimation in massive MIMO systems. As one approach to realize our vision, this paper formulates the OTFS channel estimation problem in massive MIMO systems as a sparse recovery problem and utilizes the radar sensing information to determine the support (locations of the non-zero delay-Doppler taps). The proposed radar sensing aided sparse recovery algorithm is evaluated based on an accurate 3D ray-tracing framework with co-existing radar and communication data. The results show that the developed sensing-aided solution consistently outperforms the standard sparse recovery algorithms (that do not leverage radar sensing data) and leads to a significant reduction in the pilot overhead, which highlights a promising direction for OTFS based massive MIMO systems.
翻译:测量 OTFS 系统的频道,但利用这一动机,我们提议利用雷达遥感手段获得关于环境中移动用户和散散射者移动方向的这一信息,并利用它协助OTFS频道在大型MIMO系统中进行估估测。对于大型MIMO系统来说,这特别具有挑战性,因为大型MIMO系统,其间接费用也以天天线数量为尺度。然而,一个重要的观察是,延迟、多普勒和离开/抵达角度/抵达信息角度与移动用户和各种环境散射者在环境中的距离、速度和方向信息直接相关,以便能够在高流动性假设情景下进行强的通信。但是,根据这一动机,我们提议利用雷达感测来获取关于环境中移动用户和散射者移动系统在最大范围内的高级信号,并以此为大规模IMIMIMIM系统中的大规模扩展。作为实现我们愿景的一种方法,将大型IMO系统OTFS 频道估算问题作为一个稀薄的恢复问题,并利用雷达遥感信息来确定支持(基于非零延迟延迟-多普雷尔探点的定位) 和各种环境撒散射线的大规模信息。 拟议的雷达帮助稀释后恢复算法根据准确的3DRDADRDRDRDRDRDRD RDRDF FD FDFDRDF 的回收框架进行了评估。