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EURASIP Journal on Wireless Communications and Networking

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.
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