Gain and noise figure values were obtained by simulating all TP-TDFA designs separately. These values are shown in two separate graphs as gain and noise figures for easy comparison.
In the first case, the input signal at 1469 nm wavelength, pumps at 1050 nm wavelength with 1000 mW power, 5 m length, and 20e+ 24 ion concentration for each TDF were applied to all designs. Each design was simulated separately by feeding at different input powers from 0 dBm to -40 dBm. Figure 3a shows the spectrum of the gain according to the input power, and Fig. 3b shows the spectrum of the noise figure according to the input power. According to Fig. 3a, -30 dBm was determined to be the most efficient input power. While the noise figure values of Types 1 and 2 are close to each other, Type 3 is half of the others.
In the second case, using − 30 dBm, which is determined in the first case, each design was separately simulated by increasing 250 mW each time at pump powers between 250 and 3500 mW. Figure 4a shows the spectrum of the gain according to the wavelength, and Fig. 4b shows the spectrum of the noise figure according to the wavelength. Figure 4a shows that the most efficient pump power was 1750 mW for Types 1 and 2, while it was 2750 mW for Type 3. Since the pump power is divided into three, it does not reach saturation until higher values.
When the noise figure spectra in Fig. 4b are considered, it is seen that the noise figures of Type 2 are higher. This increase is attributed to the higher counterpropagating ASE at the input part of the amplifier.
In case 3, using 1750 mW, which was determined as the optimum pump power for Type 1 and Type 2 in the second case, TDF 1 of Type 1 and Type 2 were optimized separately by using the software, and it was 5.2 m for both of them. Then, using TDF 1: 5.2 m, Type 1 and Type 2 were simulated by increasing 0.5 m each time at TDF 2 lengths between 3 and 7 m. Figure 5a shows the spectrum of the gain according to the TDF 2 length, and Fig. 5b shows the spectrum of the noise figure according to the TDF 2 length. Figure 5a shows that 5.5 m is the optimum value of TDF 2 length for Type 1 and 5 m is the optimum value of TDF 2 for Type 2. These values continued to be used in the next case. In addition, it is seen in Fig. 5b that the noise figure values of these types are close to each other.
Then, again, using 2750 mW, which was determined as the optimum pump power for Type 3 in the second case, TDF 1 of Type 3 was optimized by using the software, and it was 6.3 m. Using TDF 1: 6.3 m, it was simulated by increasing 0.5 m each time at TDF 2 length between 3 and 7 m. Figure 6a shows the spectrum of the gain according to the TDF length, and Fig. 6b shows the spectrum of the noise figure according to the TDF length. As shown in Fig. 6a, the most efficient TDF 2 length is 6 m. Then, using the optimum TDF 1: 6.3 m and TDF 2: 6 m lengths, it was simulated by increasing 0.5 m each time at TDF 3 length between 3 and 7 m. Finally, the obtained values were added in Fig. 6. The most efficient TDF 3 length is 3.5 m. Additionally, since the third TDF is close to saturation, the noise figure values are lower.
In case 4, using TDF 1: 5.2 m and TDF 2: 5.5 m, which were determined to be the optimum lengths for Type 1 and Type 2 in the third case, ion concentration 1 of Type 1 and Type 2 was optimized separately and was 18e+ 24 m− 3 for both. Then, using ion concentration 1: 18e+ 24 m− 3, Type 1 and Type 2 were simulated by increasing 2.5e+ 24 m− 3 each time at ion concentration 2 between 5e+ 24 and 30e+ 24 m− 3. Figure 7a and 7b shows the gain and noise figure spectra according to ion concentration 2. Figure 7a shows that 20e+ 24 m− 3 is the optimum value of ion concentration 2 for Type 1 and 22.5e+ 24 m− 3 is the optimum value of ion concentration 2 for Type 2. These values continued to be used in the next case.
Then, again, using TDF1: 6.3 m, TDF 2: 6 m, and TDF 3: 3.5 m, which were determined to be the optimum TDF lengths for Type 3 in the third case, ion concentration 1 of Type 3 was optimized by using the software and was 16e+ 24 m-3. Using ion concentration 1: 16e+ 24 m-3, it was simulated by increasing 2.5e+ 24 m-3 each time at ion concentration 2 between 5e+ 24 and 30e+ 24 m-3.
Gain and noise figure graphs in Fig. 8 were created using the obtained values. As shown in Fig. 8a, 22.5e+ 24 m-3 is the optimum value of ion concentration 2 for this type. Then, using the optimum ion concentration 1: 16e+ 24 m-3 and ion concentration 2: 22.5e+ 24 m-3, it was simulated by increasing 2.5e+ 24 m-3 each time at ion concentration 3 between 5e+ 24 and 30e+ 24 m-3. Finally, the obtained values were added in Fig. 8. The most efficient ion concentration 3 is 17.5e+ 24 m-3.
Additionally, since the third TDF is close to saturation, it is seen that the noise figure values are slightly lower. The noise figure is 3.45 dB at the 17.5e+ 24 m-3 ion concentration. When noise figures of other ion concentrations are taken into account, this value is an average value.
All optimization studies thus far have been made for the input signal at 1469 nm wavelength, where the highest gain is obtained in the S-band. The values obtained for this signal listed in Table 3 were applied to the input signals in the 1444–1499 nm wavelength range (S-band).
Table 3. Optimized values were determined in the all cases.
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)
The pump power values of Type 1 and Type 2 are the same, and the TDF lengths and ion concentrations are quite close to each other. TDF 2 of Type 1 is slightly longer, while the ion concentration 2 of Type 2 is slightly higher. In Type 3, the optimum fiber lengths are longer. For easier comparison, the obtained gain and noise graphs of all types are shown in Fig. 9.
As shown in Fig. 9a, the gain values of the models optimized for the signal in the 1469 nm wavelength are 44.64 dB for Type 1, 45.67 dB for Type 2 and 43.95 dB for Type 3. While the highest gain and noise figure values belong to Type 2, the lowest gain and noise figure values belong to Type 3. The gain values of Type 2 are higher than those of Type 1. As a result, the gain value of the signal amplified with a single pass after a double pass is higher compared to the gain of a double-pass amplified signal used after a single pass.
The highest noise figure values occurred in the Type-2 design. This increase is attributed to the higher counterpropagating ASE at the input part of the amplifier. This reduces the population inversion at the input part of TDF and afterward increases the noise figure [29].