High-Resolution Hierarchical Beam Alignment with Segmented Beams

We propose a high-resolution hierarchical beam alignment algorithm that learns millimeter wave channel space using segmented beams obtained from feasible phase shifted sum of given hierarchical basis. To be specific, at each stage, the segmented beam is designed using a linear combination of given training beam basis instead of just choosing a beam among them. Then, the designed beam is used to determine the subset of higher resolution training beam candidates for the next stage which is referred to as the over-complete dictionary embracing even partially overlapped beams as elements. At the next stage, the proposed beam alignment can focus only on the most promising directions associated with finer grids. The simulation results demonstrate that the proposed beam alignment framework can remedy the limitations of the existing grid-of-beams method and achieve favorable beam alignment performance with reduced training time.