Scalable, resource and locality-aware selection of active scatterers in Geometry-based stochastic channel models

Abstract

In this paper we adopt and modify a well-known locality-aware hashing scheme to the problem of active stochastic scatterer selection in vehicular non-stationary geometry-based stochastic channel models (GSCM). We show, how under relaxed assumptions on the query set an efficient selection of active stochastic scatterers during simulation is computationally feasible. The proposed approach enables real-time simulation and emulation of large-scale GSCMs by restricting the active stochastic scatterer set to meet given resource constraints. We showcase our approach by introducing a GSCM that is boot-strapped via OpenStreetMap data. The stochastic scatterers are placed automatically along buildings, traffic signs and vegetation. We validate and investigate the impact of the proposed approach on the accuracy of a GSCM by means of second order statistics of the time- and frequency-varying fading process. For validation and performance evaluation we parameterize our GSCM using a vehicular wireless channel measurement campaign conducted in the inner city of Vienna. The impact of selecting only a subset of scatterers is then evaluated using the calibrated GSCM.