  Recently, Differentiable Ray Tracing has been successfully applied in the
field of wireless communications for learning radio materials or optimizing the
transmitter orientation. However, in the frame of gradient based optimization,
obstruction of the rays by objects can cause sudden variations in the related
objective functions or create entire regions where the gradient is zero. As
these issues can dramatically impact convergence, this paper presents a novel
Ray Tracing framework that is fully differentiable with respect to any scene
parameter, but also provides a loss function continuous everywhere, thanks to
specific local smoothing techniques. Previously non-continuous functions are
replaced by a smoothing function, that can be exchanged with any function
having similar properties. This function is also configurable via a parameter
that determines how smooth the approximation should be. The present method is
applied on a basic one-transmitter-multi-receiver scenario, and shows that it
can successfully find the optimal solution. As a complementary resource, a 2D
Python library, DiffeRT2d, is provided in Open Access, with examples and a
comprehensive documentation.
