MmWave Beam Prediction Under Hand Blockage

Abstract

To address the significant overhead associated with millimeter-wave (mmWave) beam management, deep learning (DL) techniques can be leveraged. However, existing DL-based beam prediction methods usually overlook the effects of hand blockage at the user equipment (UE) side, which may seriously impact the quality of beams. This letter aims to address the challenge of optimal beam prediction in the presence of hand blockage. Especially, we deploy a specific DL model at the UE side to handle the discovery of hand blockage and the corresponding beam prediction. Considering the computing resource constraints at the UE side, we propose a low-complexity shift transformer. It is similar to the traditional transformer structure, except that it replaces the attention operation with the shift operation, which could reduce the computational complexity while maintaining prediction accuracy. Furthermore, we introduce a novel beam recovery scheme under hand blockage. Simulation results show that the proposed method could efficiently predict hand blockage and the optimal beam.