Hybrid-beamforming-based millimeter-wave cellular network optimization

TLDR: This paper develops a joint hybrid beamforming and congestion control algorithmic framework for mmWave network utility maximization and reveals a pseudoconvexity structure in the hybrid beamform scheduling problem, which leads to simplified analog beamforming protocol design.

Abstract: Massive MIMO and millimeter-wave communication (mmWave) have recently emerged as two key technologies for building 5G wireless networks and beyond. To reconcile the conflict between the large antenna arrays and the limited amount of radio-frequency (RF) chains in mmWave systems, the so-called hybrid beamforming becomes a promising solution and has received a great deal of attention in recent years. However, existing research on hybrid beamforming focused mostly on the physical layer or signal processing aspects. So far, there is a lack of theoretical understanding on how hybrid beamforming could affect mmWave network optimization. In this paper, we consider the impacts of hybrid beamforming on utility-optimality and queueing delay in mmWave cellular network optimization. Our contributions in this paper are three-fold: i) we develop a joint hybrid beamforming and congestion control algorithmic framework for mmWave network utility maximization; ii) we reveal a pseudoconvexity structure in the hybrid beamforming scheduling problem, which leads to simplified analog beamforming protocol design; and iii) we theoretically characterize the scalings of utility-optimality and delay with respect to channel state information (CSI) accuracy in digital beamforming.