Beam Training Optimization for Millimeter Wave Communication

Abstract

Millimeter wave (mmWave) technology has emerged as a promising solution to the spectrum demands of 5G networks. To achieve the necessary link budget for high throughput data transfer, mmWave links employ beamforming using antenna arrays with a large number of elements. However, this introduces the need for beam training, the process of finding beam alignments that can support a robust data link. Due to the dynamic nature of 5G environments, beam training must be conducted quickly since devices move in and out of communication range over short time scales. In this thesis, the optimization of beam training is explored within this context. Specifically, we derive a complexity bound for beam training in the limited scope of an idealized Boolean model, and a class of algorithms which achieve the bound is presented. The performance of these algorithms in non-ideal contexts are evaluated through simulation, and improvements over the existing IEEE 802.11ad standard are demonstrated.