Digital Back Propagation Optimization Algorithm Based on Step-Size Optimization and Power Compensation Factor

Lifu Zhang; Tao Zhang; Mengyan Li; Heming Deng; Fengguang Luo; Liu Yang

doi:10.3788/AOS240725

Acta Optica Sinica, Volume. 44, Issue 16, 1619002(2024)

Digital Back Propagation Optimization Algorithm Based on Step-Size Optimization and Power Compensation Factor

Lifu Zhang¹, Tao Zhang¹, Mengyan Li¹, Heming Deng^1、*, Fengguang Luo², and Liu Yang^1、**

¹Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, School of Microelectronics, Hubei University, Wuhan 430062, Hubei , China

²National Engineering Research Center for Next Generation Internet Access-system, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, Hubei , China

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Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. Principle of DBP

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Fig. 2. Power comparisons between real fiber and virtual fiber with different steps

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Fig. 3. Comparisons of power propagation of CS-DBP, LS-DBP and EP-DBP with the same step size

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Fig. 4. Fitting comparisons of different power compensation factors

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Fig. 5. Optimization process of power compensation factor using genetic algorithm

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Fig. 6. Simulation model for long-distance coherent optical communication

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Fig. 7. Relationships between Q factor and launch power of EDC, CS-DBP, LS-DBP, EP-DBP algorithms in single-channel Nyquist 32 Gbaud system, respectively. (a) 4QPSK-2400 km; (b) 16QAM-1500 km; (c) 64QAM-1000 km

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Fig. 8. Comparisons of bit error rates and OSNR of EDC, 10 step CS-DBP, 5 step LS-DBP, 5 step EP-DBP algorithms in single-channel Nyquist 32 Gbaud system, respectively. (a) 4QPSK-2000 km; (b) 16QAM-1200 km

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Fig. 9. Relationships between Q factor of the EDC, CS-DBP, LS-DBP and EP-DBP algorithms versus step number per span in 32 Gbaud Nyquist system, respectively

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Table 1. Pseudo-code of genetic algorithm

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Table 1. Pseudo-code of genetic algorithm

Step	Algorithm
1	Initialization： $λ_{P, i, j} = 1 + {x_{b i a s}}_{, r a n d o m}$
2	Iteration： for n=1， 2， …， maximum generation do
3	Calculate fitness： ${x_{F i t n e s s}}_{, i} = f_{F u n} (λ_{P, i, 1}, \dots, λ_{P, i, j})$
4	Select： $P_{s, k} = \sum_{1}^{m} {x_{F i t n e s s}}_{, i} - (\sum_{1}^{m} {x_{F i t n e s s}}_{, i} - {x_{F i t n e s s}}_{, k})$
5	Cross： $λ_{P, i, n} = λ_{P, i, n} \cdot P_{c} + λ_{P, l, n} \cdot (1 - P_{c})$
6	Mutation： $λ_{P, i, n} = λ_{P, i, n} (1 + {x_{b i a s}}_{, n}) P_{m} +$ $λ_{P, i, n} (1 - {x_{b i a s}}_{, n}) (1 - P_{m})$
7	Loop end

Table 2. System setting

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Table 2. System setting

Parameter	Value
Fiber length /km	$100$
Attenuation coefficient $α$ /(dB/m)	$0.2 \times 10^{- 3}$
Dispersion coefficient $β$ /(s/m²)	$16 \times 10^{- 16}$
Nonlinear refractivity coefficient $γ$ /(m²/W)	$2.6 \times 10^{- 20}$
EDFA gain /dB	$20$
EDFA noise figure /dB	$4$

Table 3. Step lengths for different step design methods

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Table 3. Step lengths for different step design methods

Step-size	First step /km	Second step /km	Third step /km	Fourth step /km	Fifth step /km
Constant step-size	20.00	20.00	20.00	20.00	20.00
Logarithmic step-size	68.34	13.53	8.00	5.70	4.43
Equal power step-size	44.72	21.56	14.34	10.76	8.62

Table 4. Comparison of complexity and cerformance
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Table 4. Comparison of complexity and cerformance
Algorithm Pre-optimized RMPB Q factor /dB
CS-10 step None $(l n L + 5) \cdot 10 + 6$ 8.92
LS-5 step LS $(l n L + 5) \cdot 10 + 6$ 8.96
EP-5 step EP $(l n L + 5) \cdot 10 + 6$ 9.27
EP-5 step-GA EP+GA $(l n L + 5) \cdot 10 + 6$ 9.46

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Lifu Zhang, Tao Zhang, Mengyan Li, Heming Deng, Fengguang Luo, Liu Yang. Digital Back Propagation Optimization Algorithm Based on Step-Size Optimization and Power Compensation Factor[J]. Acta Optica Sinica, 2024, 44(16): 1619002

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