In this paper, we investigate a channel estimation problem in a downlink millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, which suffers from impulsive interference caused by hardware non-idealities or external disruptions. Specifically, impulsive interference presents a significant challenge to channel estimation due to its sporadic, unpredictable, and high-power nature. To tackle this issue, we develop a Bayesian channel estimation technique based on variational inference (VI) that leverages the sparsity of the mmWave channel in the angular domain and the intermittent nature of impulsive interference to minimize channel estimation errors. The proposed technique employs mean-field approximation to approximate posterior inference and integrates VI into the sparse Bayesian learning (SBL) framework. Simulation results demonstrate that the proposed technique outperforms baselines in terms of channel estimation accuracy.

翻译：本文研究了下行链路毫米波多输入多输出系统中的信道估计问题，该系统受到由硬件非理想性或外部干扰引起的脉冲干扰影响。具体而言，脉冲干扰因其突发性、不可预测性和高功率特性，对信道估计构成了显著挑战。为解决这一问题，我们提出了一种基于变分推断的贝叶斯信道估计方法，该方法利用毫米波信道在角度域的稀疏性以及脉冲干扰的间歇性特性，以最小化信道估计误差。所提方法采用平均场近似进行后验推断近似，并将变分推断融入稀疏贝叶斯学习框架中。仿真结果表明，所提方法在信道估计精度方面优于基线方法。