Clustering-based Adaptive Beam Footprint Design for 5G Urban Macro-Cell

In a dense 5G urban-eMBB environment, the user density and traffic loads follow a spatiotemporal variability. To meet high traffic demands, the 5G base stations exploit spatial multiplexing by means of Active Antenna Systems (AAS) and beamforming. However, pedestrian and vehicular users are highly mobile, rendering non-dynamic beamforming designs totally inefficient in terms of meeting the users' demand requests. In particular, the latter results in either overload or underutilized beams in a cell. Hence, a practical approach to meet such spatio-temporal heterogeneous demand is to consider dynamic and adaptive beam footprint design that takes into account both the actual users' position as well as the traffic loads. In this paper, we first study and evaluate the state-of-the-art fixed-cell beamforming (based on ITU-R M.2412-0) in a test environment and highlight its drawbacks. Next, we propose a adaptive macro-cell beam footprint design where the beams are dynamically shaped based on the spatial users distribution and their demand requests. Numerical simulations demonstrate the high system performance achieved by the proposed methodology.