In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy and training overhead. Furthermore, VI-SBL with semi-passive elements is shown to be more spectral- and energy-efficient than the baselines with passive reflecting elements.

翻译：本文提出一种面向智能反射面辅助（IRS辅助）毫米波（mmWave）大规模多输入多输出（MIMO）系统的贝叶斯信道估计器，该系统采用半无源元件，可在主动感知模式下接收信号。我们的根本目标是利用基站的接收信号及从IRS少量有源传感器获取的额外信息，最小化信道估计误差。与近期利用半无源元件进行信道估计的研究不同（这些研究需依赖上行和下行训练信号来估计UE-IRS与IRS-BS链路），本文仅使用上行训练信号完成所有链路的估计。为计算所有链路的最小均方误差（MMSE）估计值，我们提出一种新颖的变分推理-稀疏贝叶斯学习（VI-SBL）信道估计器，该估计器在SBL框架下利用变分推理结合平均场近似对信道进行近似后验推理。仿真结果表明，在采用无源反射元件的IRS系统中，VI-SBL在信道估计精度与训练开销方面均优于现有最先进基线方法。此外，采用半无源元件的VI-SBL在频谱效率和能量效率上均优于采用无源反射元件的基线方法。