论文标题
智能反射表面启用传感：Cramér-rao下限优化
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization
论文作者
论文摘要
本文研究了智能反射表面（IRS）启用了非线视线（NLOS）无线传感，其中部署了IRS以帮助接入点（AP）感知其NLOS地区的目标。假定AP配备了多个天线，IRS配备了均匀的线性阵列。 AP的目的是根据来自AP-IRS-TARGET-target-target-irs-ap链接的回声信号来估计目标相对于IRS的排序方向（DOA）。在此设置下，我们共同设计在AP处的发射光束形成，并在IRS处设计反射仪，以最大程度地减少Cramér-Rao下限（CRLB）在估计误差上。为此，我们首先获得CRLB表达式以估算封闭形式的DOA。接下来，我们通过交替的优化，半明确放松和连续的凸近近似来优化关节波束形成设计，以最大程度地减少CRLB。数值结果表明，与传统的传统方案相比，基于CRLB最小化的拟议设计可以提高感应性能，从而提高了感应性能，而信噪比最大化和单独的波束成式。
This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming.
