Beam Alignment and Tracking for Millimeter Wave Communications via Bandit Learning

Abstract

Millimeter wave (mmwave) communications have attracted increasing attention thanks to the abundant spectrum resource. The short wave-length of mmwave signals facilitates exploiting large antenna arrays to achieve large array gains and combat the large path-loss. However, the use of large antenna arrays along with narrow beams leads to a large overhead in beam training for obtaining channel state information, especially in dynamic environments. To reduce the overhead of beam training, in this paper we formulate the problem of beam alignment and tracking (BA/T) as a stochastic bandit problem. In particular, to sense the change of the environments, the actions are designed based on the offset of successive beam indexes (i.e., beam index difference), which measures the rate of change of the envir-onments. Then, we propose two efficient BA/T algorithms based on the stochastic bandit learning. To reveal useful insights, the performance of effective achievable rate is further analyzed for the proposed BA/T algorithms. The analytical results show that the algorithms can sense the change of the environments and adjust beam training strategies intelligently. In addition, they do not require any priori knowledge of dynamic channel modeling, and thus are applicable to a variety of complicated scenarios. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.