Beam selection for performance-complexity optimization in high-dimensional MIMO systems

Abstract

Millimeter-wave (mm-wave) communications systems offer a promising solution to meeting the increasing data demands on wireless networks. Not only do mm-wave systems allow orders of magnitude larger bandwidths, they also create a high-dimensional spatial signal space due to the small wavelengths, which can be exploited for beamforming and multiplexing gains. However, the complexity of digitally processing the entire high-dimensional signal is prohibitive. By exploiting the inherent channel sparsity in beamspace due to highly directional propagation at mm-wave, it is possible to design near-optimal transceivers with dramatically lower complexity. In such beamspace MIMO systems, it is first necessary to determine the set of beams which define the low-dimensional communication subspace. In this paper, we address this beam selection problem and introduce a simple power-based classifier for determining the beamspace sparsity pattern that characterizes the communication subspace. We first introduce a physical model for a small cell which will serve as the setting for our analysis. We then develop a classifier for the physical model, and show its optimality for a class of ideal signals. Finally, we present illustrative numerical results and show the feasibility of the classifier in mobile settings.