Ray-Based Versus Statistical Channel Models

Ray-based channel (RBC) models are physically linked to both array topology and the propagation environment. As a result, they form the basis of many standardized channel models and are commonly used in research. Nevertheless, their complexity means that many initial or analytical studies are still based on simpler statistical channel models. Since RBCs commonly assume a reasonable number of rays, the central limit theorem suggests that these channels may be approximately Gaussian. Hence, Rayleigh and Ricean approximations are obvious possibilities. Here, we explore these possibilities and present novel results demonstrating that simple statistical models can be used to closely approximate the signal-to-interference-and-noise ratio (SINR) performance of linear receivers with RBCs and highlighting the importance of user-specific correlation models.

Automated summary

Ray-based channel models are closely related to antenna topology and propagation environment, serving as the foundation for many standardized channel models. While their complexity leads to the use of simpler statistical channel models in initial or analytical studies, this paper explores how these simple models can closely approximate the signal-to-interference-and-noise ratio (SINR) performance of linear receivers with ray-based channels. The importance of user-specific correlation models is also highlighted.