How Many Rays Do I Need for Monte Carlo Optimization? X&,a=#C^
While it is important to ensure that a sufficient number of rays are traced to Chtls;Ph[
distinguish the merit function value from the noise floor, it is often not necessary to L(BL_
trace as many rays during optimization as you might to obtain a given level of T7*p! 0
accuracy for analysis purposes. What matters during optimization is that the u6*mHkM
changes the optimizer makes to the model affect the merit function in the same way g+=f=5I3
that the overall performance is affected. It is possible to define the merit function so h'A
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that it has less accuracy and/or coarser mesh resolution than meshes used for wodf f_l
analysis and yet produce improvements during optimization, especially in the early 'z Qp64]F
stages of a design. (M%ZSF V
A rule of thumb for the first Monte Carlo run on a system is to have an average of at cA`X(Am6]g
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays wQX%*GbL2
on the receiver to achieve uniform distribution. It is likely that you will need to 1VG7[#Zy
define more rays than 800 in a simulation in order to get 800 rays on the receiver. UujKgL4
When using simplified meshes as merit functions, you should check the before and *)i+ c{~
after performance of a design to verify that the changes correlate to the changes of C6:;
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the merit function during optimization. As a design reaches its final performance " R-P e\W
level, you will have to add rays to the simulation to reduce the noise floor so that w5mSoK b
sufficient accuracy and mesh resolution are available for the optimizer to find the xQ8?"K;iX
best solution.
