1 research outputs found
Efficient Computation and Covariance Analysis of Geometry-Based Stochastic Channel Models
In this work, we study a family of wireless channel simulation models called
geometry-based stochastic channel models (GBSCMs). Compared to more complex
ray-tracing simulation models, GBSCMs do not require an extensive
characterization of the propagation environment to provide wireless channel
realizations with adequate spatial and temporal statistics. The trade-off they
achieve between the quality of the simulated channels and the computational
complexity makes them popular in standardization bodies. Using the generic
formulation of the GBSCMs, we identify a matrix structure that can be used to
improve the performance of their implementations. Furthermore, this matrix
structure allows us to analyze the spatial covariance of the channel
realizations. We provide a way to efficiently compute the spatial covariance
matrix in most implementations of GBSCMs. In accordance to wide-sense
stationary and uncorrelated scattering hypotheses, this covariance is static in
frequency and does not evolve with user movement