Dual-Band Super-Resolution Channel Prediction in High-Mobility MIMO Systems

Abstract

For multiple-input multiple-output systems, channel prediction is crucial for mitigating channel aging in mobile scenarios. The existing channel prediction schemes typically require strictly equal sampling intervals of historical and predicted channel sequences, which imposes enormous pilot overhead in high-mobility scenarios with frequent channel estimation. To tackle this problem, we investigate the super-resolution channel prediction, where the future channel sequence is predicted at a finer temporal resolution without additional channel estimation. Specifically, we theoretically analyze the physics process underlying super-resolution channel prediction to show that the measurement of Doppler phase rotation faces the challenging issue of phase ambiguity in high-mobility and high-frequency scenarios. To address this issue, a deep learning-based dualband fusion approach is proposed to adaptively integrate the lowfrequency information for accurate Doppler phase measurement. To realize accurate channel prediction at a finer temporal resolution, we propose the physics feature-inspired neural ordinary differential equation with modulated-periodic-based multi-layer perceptron for effectively learning the dynamics of fast timevarying channels. Simulation results verify that our proposed scheme outperforms existing channel prediction schemes and it maintains robust performance in high-mobility scenarios.

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