In this work, the uplink channel estimation problem is considered for a millimeter wave (mmWave) multi-input multi-output (MIMO) system. It is well known that pilot overhead and computation complexity in estimating the channel increases with the number of antennas and the bandwidth. To overcome this, the proposed approach allows the channel estimation at the base station to be aided by the sensing information. The sensing information contains an estimate of scatterers locations in an environment. A simultaneous weighting orthogonal matching pursuit (SWOMP) - sparse Bayesian learning (SBL) algorithm is proposed that efficiently incorporates this sensing information in the communication channel estimation procedure. The proposed framework can cope with scenarios where a) scatterers present in the sensing information are not associated with the communication channel and b) imperfections in the scatterers' location. Simulation results show that the proposed sensing aided channel estimation algorithm can obtain good wideband performance only at the cost of fractional pilot overhead. Finally, the Cramer-Rao Bound (CRB) for the angle estimation and multipath channel gains in the SBL is derived, providing valuable insights into the local identifiability of the proposed algorithms.

翻译：本文针对毫米波（mmWave）多输入多输出（MIMO）系统的上行链路信道估计问题展开研究。众所周知，信道估计中的导频开销和计算复杂度会随天线数量和带宽的增加而上升。为克服这一难题，所提方法允许通过感知信息辅助基站进行信道估计。感知信息包含对环境中散射体位置的估计。本文提出一种同步加权正交匹配追踪（SWOMP）-稀疏贝叶斯学习（SBL）算法，该算法能高效地将感知信息融入通信信道估计流程。所提框架可处理以下场景：a）感知信息中的散射体与通信信道无关；b）散射体位置存在误差。仿真结果表明，所提感知辅助信道估计算法仅需部分导频开销即可获得良好的宽带性能。最后，推导了SBL中角度估计和多径信道增益的克拉美罗界（CRB），为所提算法的局部可辨识性提供了重要理论洞察。