Opportunistic beam training with hybrid analog/digital codebooks for mmWave systems

Abstract

Millimeter wave (mmWave) communication is one solution to provide more spectrum than available at lower carrier frequencies. To provide sufficient link budget, mmWave systems will use beamforming with large antenna arrays at both the transmitter and receiver. Training these large arrays using conventional approaches taken at lower carrier frequencies, however, results in high overhead. In this paper, we propose a beam training algorithm that efficiently designs the beamforming vectors with low training overhead. Exploiting mmWave channel reciprocity, the proposed algorithm relaxes the need for an explicit feedback channel, and opportunistically terminates the training process when a desired quality of service is achieved. To construct the training beamforming vectors, a new multi-resolution codebook is developed for hybrid analog/digital architectures. Simulation results show that the proposed algorithm achieves a comparable rate to that obtained by exhaustive search solutions while requiring lower training overhead when compared to prior work.