3 accepted papers
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context, infrastructure-mounted cameras can capture rich environmental information that can facilitate beam tracking design. In t…
Environmental sensing can significantly enhance mmWave communications by assisting beam training, yet its benefits must be balanced against the associated sensing costs. To this end, we propose a unified machine learning framework that dynamically determines when to sense and leverages sensory data…
Beamforming in millimeter-wave (mmWave) high-mobility environments typically incurs substantial training overhead. While prior studies suggest that sub-6 GHz channels can be exploited to predict optimal mmWave beams, existing methods depend on large deep learning (DL) models with prohibitive computa…