Improved Beam Training with Finite Slots in Millimeter Wave Wireless Communications

Abstract

Beamforming (BF) is usually adopted in millimeter wave wireless systems to compensate high path loss. To increase the received power at user equipment (UE), beam training (BT) is performed to align the beam direction between base station (BS) and UE, where good trade-off between BT overhead and BF gain is preferred. In this letter, two memory-less statistical BT algorithms with finite slots are proposed to reduce the overhead and failure probability of BT, including minimum BT overhead (MO-BT) algorithm and minimum failure probability of BT (MFP-BT) algorithm. Both algorithms make the assumption that memory-less BS randomly select a beam in each slot, based on the prior probability of UE preference, and UE feeds back the beam index to BS to inform which beam should be adopted. By carefully adjusting the probability mass function of beam scanning at BS, MFP-BT can reduce the failure probability of BT, and MO-BT can reduce BT time consumption when compared to their traditional BT counterpart. Numerical results demonstrate the superior performance of our proposed algorithms.