How Many Rays Do I Need for Monte Carlo Optimization? KN%Xp/lkX
While it is important to ensure that a sufficient number of rays are traced to 3 wr ~ P
distinguish the merit function value from the noise floor, it is often not necessary to O<+C$J|
trace as many rays during optimization as you might to obtain a given level of VRx Bi!d
accuracy for analysis purposes. What matters during optimization is that the C ]#R7G
changes the optimizer makes to the model affect the merit function in the same way W 9u (
that the overall performance is affected. It is possible to define the merit function so c k Tnb
that it has less accuracy and/or coarser mesh resolution than meshes used for beq)Frn^
analysis and yet produce improvements during optimization, especially in the early doe[f_\
stages of a design. O|OPdD
A rule of thumb for the first Monte Carlo run on a system is to have an average of at }q~A( u
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays E`'+1
on the receiver to achieve uniform distribution. It is likely that you will need to
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. \Q3m?)X=Gd
When using simplified meshes as merit functions, you should check the before and >#V8l@IH
after performance of a design to verify that the changes correlate to the changes of 7tyn?t0 n
the merit function during optimization. As a design reaches its final performance Gd'^vqo<
level, you will have to add rays to the simulation to reduce the noise floor so that (K2 p3M^
sufficient accuracy and mesh resolution are available for the optimizer to find the sd=i!r)ya
best solution.
