Downlink Channel Estimation for mmWave Systems with Impulsive Interference

TL;DR

This work tackles downlink mmWave MIMO channel estimation in the presence of impulsive interference caused by hardware non-idealities and external disruptions. It introduces a variational inference-based estimator within a sparse Bayesian learning framework, employing a complex adaptive Laplace prior to model impulsive interference and a Gaussian/Sparse prior for the angular-domain channel. The method derives mean-field VI updates for the channel, interference, and hyperparameters, enabling joint estimation of $\mathbf{H}$ and $\mathbf{E}$ and reconstruction of the full downlink channel as $\hat{\bar{\mathbf{H}}} = \mathbf{A}_U \hat{\mathbf{H}} \mathbf{A}_B^H$. Empirical results show substantial NMSE improvements over LS, OMP, GAMP, and SIE baselines across varying interference characteristics and observation lengths, with manageable complexity, underscoring the approach’s practical value for robust mmWave downlink communications.

Abstract

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.

Figures (2)

• Figure 1: NMSE comparison with respect to $T$ and $P$.
• Figure 2: NMSE comparison with respect to $c_2$ and $\eta$.