CSI Transfer From Sub-6G to mmWave: Reduced-Overhead Multi-User Hybrid Beamforming
arxiv(2024)
摘要
Hybrid beamforming is vital in modern wireless systems, especially for
massive MIMO and millimeter-wave deployments, offering efficient directional
transmission with reduced hardware complexity. However, effective beamforming
in multi-user scenarios relies heavily on accurate channel state information,
the acquisition of which often incurs excessive pilot overhead, degrading
system performance. To address this and inspired by the spatial congruence
between sub-6GHz (sub-6G) and mmWave channels, we propose a Sub-6G information
Aided Multi-User Hybrid Beamforming (SA-MUHBF) framework, avoiding excessive
use of pilots. SA-MUHBF employs a convolutional neural network to predict
mmWave beamspace from sub-6G channel estimate, followed by a novel multi-layer
graph neural network for analog beam selection and a linear minimum mean-square
error algorithm for digital beamforming. Numerical results demonstrate that
SA-MUHBF efficiently predicts the mmWave beamspace representation and achieves
superior spectrum efficiency over state-of-the-art benchmarks. Moreover,
SA-MUHBF demonstrates robust performance across varied sub-6G system
configurations and exhibits strong generalization to unseen scenarios.
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