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Table 4 Collected data

From: Cognitive radio engine parametric optimization utilizing Taguchi analysis

ID A B C D run-1 run-2 run-3 run-4 run-5 mean SNRTag d
1 1 1 1 1 0.6488 0.6465 0.6504 0.6535 0.6613 0.6521 -3.7145 0.0762
2 1 2 2 2 0.7546 0.7574 0.7586 0.7590 0.7557 0.7571 -2.4175 0.5035
3 1 3 3 3 0.7284 0.7282 0.7308 0.7330 0.7285 0.7298 -2.7363 0.3922
4 2 1 2 3 0.8433 0.8436 0.8451 0.8483 0.8438 0.8448 -1.4648 0.8746
5 2 2 3 1 0.6700 0.6691 0.6760 0.6820 0.6753 0.6745 -3.4212 0.1690
6 2 3 1 2 0.8393 0.8180 0.8418 0.8428 0.8451 0.8374 -1.5432 0.8430
7 3 1 3 2 0.8603 0.8604 0.8606 0.8612 0.8630 0.8611 -1.2990 0.9423
8 3 2 1 3 0.7736 0.7750 0.7757 0.7749 0.7749 0.7748 -2.2160 0.5777
9 3 3 2 1 0.7346 0.7229 0.7753 0.7382 0.7423 0.7427 -2.5913 0.4432
  1. The tabulated results from L9 Testing Matrix are shown here. Each row is a specific test denoted by the ID and the unique assignment of the configuration parameters. Five repetitions of each test were performed. The mean, Taguchi-SNR, and desirability are listed.