Downlink Channel Estimation for mmWave Systems With Impulsive Interference

2025-11-04

IEEE Wireless Communications Letters

AI Summary

In millimeter-wave (mmWave) MIMO downlink systems, sporadic, high-power impulsive interference—arising from hardware imperfections or external sources—severely degrades conventional channel estimation performance. To address this, we propose a novel variational inference framework jointly modeling the channel and impulsive interference via sparse Bayesian learning (SBL). Leveraging angular-domain channel sparsity and the intermittent nature of impulsive interference, our method performs coupled Bayesian inference of channel parameters and interference statistics under a mean-field approximation, enabling effective interference separation and enhanced estimation robustness. Simulation results demonstrate that the proposed approach consistently outperforms existing baselines across interference intensities, reducing average channel estimation error by 35%–52%. Notably, it maintains high-accuracy channel recovery even under strong interference. This work establishes an interpretable, scalable statistical inference paradigm for robust mmWave communications.

Technology Category

• Machine Learning: Bayesian Learning
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

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.

Problem

• Estimating downlink mmWave MIMO channels under impulsive interference conditions
• Addressing sporadic high-power interference from hardware issues or disruptions
• Developing Bayesian variational inference method for sparse channel estimation

Innovation

• Bayesian channel estimation using variational inference
• Leveraging sparsity in angular domain and interference
• Integrating VI into sparse Bayesian learning framework