Adaptive Beam Tracking with the Unscented Kalman Filter for Millimeter Wave Communication

Abstract

Millimeter wave (mmWave) communication links for 5G cellular technology require high beamforming gain to overcome channel impairments and achieve high throughput. While much work has focused on estimating mmWave channels and designing beamforming schemes, the time dynamic nature of mmWave channels quickly renders estimates stale and increases sounding overhead. We model the underlying time dynamic state space of mmWave channels and design sounding beamformers suitable for tracking in a Kalman filtering framework. Given an initial channel estimate, filtering efficiently leads to refined estimates and allows forward prediction for higher sustained beamforming gain during data transmission. From tracked prior channel estimates, adaptively chosen optimal and constrained suboptimal beams reduce sounding overhead while minimizing estimation error.

arXiv commentSubmitted to IEEE Signal Processing Letters

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• Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion
• GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication