| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? 5Rs?CVVb
While it is important to ensure that a sufficient number of rays are traced to (3 fPt;U
distinguish the merit function value from the noise floor, it is often not necessary to /=M.-MU2
trace as many rays during optimization as you might to obtain a given level of 3wNN<R
accuracy for analysis purposes. What matters during optimization is that the qJMp1D C
changes the optimizer makes to the model affect the merit function in the same way @3 "DBJ
that the overall performance is affected. It is possible to define the merit function so @,vv\M0)p
that it has less accuracy and/or coarser mesh resolution than meshes used for &7F&}7*c
analysis and yet produce improvements during optimization, especially in the early Mf7E72{D
stages of a design. 6D^%'[4t
A rule of thumb for the first Monte Carlo run on a system is to have an average of at -A@U0=o
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays LW?2}`+
on the receiver to achieve uniform distribution. It is likely that you will need to vs* I7 <
define more rays than 800 in a simulation in order to get 800 rays on the receiver. mh8nlB
When using simplified meshes as merit functions, you should check the before and r %xB8e9
after performance of a design to verify that the changes correlate to the changes of Ph\F'xROe
the merit function during optimization. As a design reaches its final performance m t .,4
level, you will have to add rays to the simulation to reduce the noise floor so that p;ZDpR
sufficient accuracy and mesh resolution are available for the optimizer to find the q_5 8 L w
best solution.
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