How Many Rays Do I Need for Monte Carlo Optimization? q$yTG!q*
While it is important to ensure that a sufficient number of rays are traced to Ww{bh -nyq
distinguish the merit function value from the noise floor, it is often not necessary to p[!&D}&6h
trace as many rays during optimization as you might to obtain a given level of %|I~8>m
accuracy for analysis purposes. What matters during optimization is that the YiTiJ9jf
changes the optimizer makes to the model affect the merit function in the same way X"z ^4?Aj+
that the overall performance is affected. It is possible to define the merit function so ?<k s^2 D
that it has less accuracy and/or coarser mesh resolution than meshes used for Q;*TnVbJ
analysis and yet produce improvements during optimization, especially in the early ||;V5iR:
stages of a design. 2y>~<S
A rule of thumb for the first Monte Carlo run on a system is to have an average of at
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays k-Hy>5;
on the receiver to achieve uniform distribution. It is likely that you will need to -lQ8
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. CCN rjaA
When using simplified meshes as merit functions, you should check the before and H{x'I@+
after performance of a design to verify that the changes correlate to the changes of bX Q*d_]WT
the merit function during optimization. As a design reaches its final performance <~X4&E]rT_
level, you will have to add rays to the simulation to reduce the noise floor so that ]u?|3y^ (
sufficient accuracy and mesh resolution are available for the optimizer to find the - ,)&? S
best solution.
