| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? [ `1` E1X
While it is important to ensure that a sufficient number of rays are traced to ` L 1+j
distinguish the merit function value from the noise floor, it is often not necessary to Wu<;QY($5
trace as many rays during optimization as you might to obtain a given level of $M4Z_zle)
accuracy for analysis purposes. What matters during optimization is that the P_0[spmFU
changes the optimizer makes to the model affect the merit function in the same way JFO,Q
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that the overall performance is affected. It is possible to define the merit function so D!OY <?
that it has less accuracy and/or coarser mesh resolution than meshes used for 1$.svR
analysis and yet produce improvements during optimization, especially in the early n*ShYsc
stages of a design. ?< ^8,H
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Dn J `]r
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays u-y?i`
on the receiver to achieve uniform distribution. It is likely that you will need to ~E((n
define more rays than 800 in a simulation in order to get 800 rays on the receiver. [e
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When using simplified meshes as merit functions, you should check the before and kppi>!6
after performance of a design to verify that the changes correlate to the changes of ~XP |dn}
the merit function during optimization. As a design reaches its final performance !QvmzuK
level, you will have to add rays to the simulation to reduce the noise floor so that 52j3[in
sufficient accuracy and mesh resolution are available for the optimizer to find the 62,dFM7
best solution.
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