CSI acquisition for sleeping cells in hyper cellular networks based on channel learning

Abstract

Channel state information (CSI) plays an important role in next-generation cellular systems with massive multiple-input multiple-output (MIMO) technology as the indicator of wireless channels. In hypercellular networks (HCNs), traffic base stations (TBSs) improve energy efficiency by dynamical sleeping. However, conventional pilot-based CSI acquisition methods cannot be applied to sleeping cells. We propose a novel CSI scheme based on channel learning to address this problem. Unlike location-aided CSI acquisition schemes, the proposed method utilizes CSI at the control base station (CBS) as input to avoid errors caused by positioning. We validate our scheme in an HCN generated by the geometry-based stochastic channel model (GSCM). The prediction accuracy of the proposed scheme is better than the K-nearest neighbor (KNN) method and close to the location-aided CSI acquisition scheme, which requires the knowledge on user position.