PyLayers is an open source project aiming to build a pure Python indoor radio propagation simulator for mobile communications research. This project, intends to deliver a set of specialized tools for advanced applications in heterogeneous mobile radio networks. Currently, the very focus is on obtaining realistic location-dependent parameters (LDPs) to be exploited in indoor localization applications dedicated to complement GPS in indoor environments.
The indoor radio propagation channel is synthesized by using a graph-based ray tracing method which relies heavily on python module NetworkX. The electromagnetic field is calculated by using vectorized Numpy high level operators as broadcasting and the powerful np.einsum method applied on multi-dimensional ndarrays. The mobility of the agents is handled thanks to the SimPy module.
The proposed poster will: - present an overview of the PyLayers project context (FP7-WHERE2) - emphasis on using NetworkX data structures in the indoor propagation modeling context emphasis on using Numpy broadcasting and multi dimensional arrays for fast implementation of intensive computations. - exemplify how SimPy is utilized for handling agent mobility.