Chao Huang

This paper proposes a novel crosstalk prediction method between the triple-twisted strand (uniform and non-uniform) and the signal wire, that is, using back-propagation neural network optimized by the beetle antennae search algorithm based on chaotic disturbance mechanism (CDBAS-BPNN) to extract the per unit length (p.u.l) parameter matrix, and combined with the chain parameter method to obtain crosstalk. Firstly, the geometric model and cross-sectional model between the uniform triple-twisted strand and the signal wire are established, and the corresponding model between the non-uniform triple-twisted strand and the signal wire is obtained by the Monte Carlo (MC) method. Then, the beetle antennae search algorithm based on chaotic disturbance mechanism (CDBAS) and backpropagation neural network (BPNN) are combined to construct a new extraction network of the p.u.l parameter matrix, and the chain parameter method is combined to predict crosstalk. Finally, in the verification and analysis part of the numerical experiments, comparing the crosstalk results of CDBAS-BPNN, BAS-BPNN and Transmission Line Matrix (TLM) algorithms, it is verified that the proposed method has better accuracy for the prediction of the model.

In this paper, a twisted-wire pairs (TWP) with random non-uniform twisting is established. It is divided into a complete pitch segment and a noncomplete pitch segment by the ratio between the pitch and the length. The randomness of the actual TWP cable is accurately simulated by the following methods: 1) random combination of complete pitch segments; 2) random combination of non-complete pitch segments; 3) random combination between 1) and 2). Based on the TWP model, an equivalent multi-conductor transmission lines (MTLs) model can be obtained. The neural network algorithm is introduced to describe the complex relationship between the arbitrary position of the TWP and the per-unit-length (p.u.l) parameter matrix. In addition, the crosstalk and the common-mode (CM) and differential-mode (DM) noise under field-to-wire coupling are predicted. The numerical results show that crosstalk and CM/DM noise in TWP cable are susceptible to the twisted pitch at high frequencies. Compared with full-wave simulation, the accuracy of the proposed method is proved.

This letter presents an algorithm for canceling crosstalk in multiconductor transmission lines (MTL). At present, to reduce the crosstalk, multiconductor systems usually use differential mode transport, in which the number of transmitted signals is half the number of conductors. A method is proposed to cancel crosstalk in MTL, through the singular value decomposition (SVD) method in matrix theory, in which the transmission line matrix is converted into an identity matrix. That is, the off-diagonal elements in the transmission matrix are zero, which corresponds to crosstalk cancellation. In this letter, this method is applied to the previous literature models to verify its effectiveness firstly. Then, the method is applied to a triple-twisted strand. In the strand, different rotation degrees correspond to different per unit length (p.u.l) parameter matrix. A set of parameters are extracted, the rest of are predicted by the beetle antennae search-back propagation neural network (BAS-BPNN) proposed in a previous work. The simulation results show that the near and far end crosstalk can be canceled effectively.

Based on the research of Monte Carlo (MC) method and adaptive beetle antennae search (ABAS) algorithm, a new crosstalk estimation method for non-uniform pitch twisted pair is proposed in this paper. First, the model of non-uniform pitch twisted pair is established based on the principle of twisted pair production. Then, the MC method and ABAS-BPNN (back propagation neural network) algorithm are used to construct a parasitic parameter mean extraction network for non-uniform pitch twisted pairs. Finally, the network is combined with the finite difference time domain (FDTD) algorithm to predict crosstalk. In the verification and analysis part of the numerical experiments, on the one hand, the ABAS-BPNN algorithm model is compared with the basic BAS-BPNN algorithm model, the BPNN algorithm model and the GA (genetic algorithm) -BPNN algorithm model, verifying the accuracy and efficiency of the improved BAS-BPNN algorithm. On the other hand, the validity and applicability of the proposed method in crosstalk prediction for non-uniform pitch twisted pair are verified by comparison with the results of the transmission line matrix (TLM) algorithm.