Academic Journal of Engineering and Technology Science, 2022, 5(12); doi: 10.25236/AJETS.2022.051204.

## Crosstalk prediction and statistical properties of transmission line

Author(s)

Zhou Chen1, Jiafei Ding1, Mingyuan He1, Weichen Gu2, Wu Zhang1

Corresponding Author:
Weichen Gu
Affiliation(s)

1Nanjing Normal University, Nanjing, Jiangsu, 210023, China

2Guoneng Jiangsu New Energy Technology Development Co., Ltd, Nanjing, 210000, China

### Abstract

In this paper, for the crosstalk error problem of multi-core stranded wires caused by non-uniform pitch, the Monte Carlo method is used to establish the probability distribution model of non-uniform pitch of stranded wires. For stranded wire crosstalk prediction, a transmission line pa-rameter prediction model is constructed using the BSO-BP neural network algorithm, and the effec-tiveness of the algorithm in this paper is verified by simulation. The final calculation gives the statis-tical characteristics of crosstalk on multicore stranded wires.

### Keywords

Crosstalk, Neural networks, EMI, Finite time domain difference method

### Cite This Paper

Zhou Chen, Jiafei Ding, Mingyuan He, Weichen Gu, Wu Zhang. Crosstalk prediction and statistical properties of transmission line. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 12: 26-33. https://doi.org/10.25236/AJETS.2022.051204.

### References

[1] O. Gassab, S. Bouguerra, and L. Zhou. Stochastic analysis of multitwisted cables with random parameters excited by random plane-wave fields[J]. IEEE Transactions on Electromagnetic Compat-ibility, 2020, 62(5):2084-2095.

[2] ZHANG Zhao, WANG Shishan, ZHAO Liang, et al. Prediction of probability distribution of cross-talk in multi-conductor wiring harness [J]. Journal of Electrotechnical Technology, 2017, 32(7):204-214.

[3] S. Sun, G. Liu, J. L. Drewniak, et al. Hand-assembled cable bundle modeling for crosstalk and common-mode radiation prediction[J]. IEEE Transactions on Electromagnetic Compatibility, 2007, 49(3):708-718.

[4] M. Sung, W. Ryu, H. Kim, et al. An efficient crosstalk parameter extraction method for high-speed interconnection fines [J]. IEEE Transactions on Advanced Packaging, 2000, 23(2):148-155.

[5] C. J. Wang. Leaky Coaxial cable with circular polarization property [J]. IEEE Transactions on Antennas&Propagation, 2011, 59(2):682-685.

[6] P. Manfredi, F. G. Canavero. Numerical calculation of polynomial chaos coefficients for stochas-tic per-unit-length parameters of circular conductors [J]. IEEE Transactions on Magnetics, 2014, 50(3): 74-82.

[7] Chengpan Yang, et al., Analysis on RLCG parameter matrix extraction for multi-core twisted ca-ble based on back propagation neural network algorithm [J]. IEEE Access., vol. 2, no. 1, pp. 16-19, Aug. 2019.

[8] Mohamad Hassoun, Fundamentals of Artificial Neural Networks, Cambridge, USA: Bradford Book, 2003.

[9] Huang C, Zhao Y, Yan W, et al. A New Method for Predicting Crosstalk of Random Cable Bundle Based on BAS-BP Neural Network Algorithm [J]. IEEE Access, 2020, 8:1-1.

[10] G. Spadacini, F. Grassi, S. A. Pignari. Field-to-wire coupling model for the common mode in random bundles of twisted-wire pairs [J]. IEEE Transactions on Electromagnetic Compatibility, 2015, 57(5): 1246-1254.

[11] A. Tatematsu, F. Rachidi, M. Rubinstein. A technique for calculating voltages induced on twist-ed-wire pairs using the FDTD method [J]. IEEE Transactions on Electromagnetic Compatibility, 2017, 59(1): 301-304.