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eess.SP2026
Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays
Ibrahim Kilinc, Robert W. Heath
AI/ML-based beam selection methods coupled with location information effectively reduce beam training overhead. Unfortunately, heterogeneous antenna hardware with varying dimension…
eess.SP2024★ 1 cited
Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
Ibrahim Kilinc, Ryan M. Dreifuerst, Junghoon Kim +1
Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though,…