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<p>To better allow accurate identification of non-smooth signals in two-way relay networks. In this paper, an algorithm based on the two-way relay network system model, the RLS algorithm, is proposed. The algorithm introduces the recursive least squares principle algorithm to the basic cost function of nonsmooth signal identification, optimizes the signal identification model by exponential weighting, and uses the time-varying variance matrix and the optimal identification data separation matrix to make real-time estimates of the new cost function for obtaining nonsmooth signals. And a new index, the PI index, is introduced to define whether the non-stationary signal is stable and reliable. Through two sets of comparison tests, we can learn that the RLS algorithm based on the two-way relay network system is better than other algorithms in the identification of non-stationary signals. It shows that the RLS algorithm can better identify and manage the information flow in a two-way relay network, make the non-stationary signal more stable, and further improve the convergence and convergence speed of the non-stationary signal to provide a better information interaction experience for both sides of electronic communication.</p>
</abstract>

To better allow accurate identification of non-smooth signals in two-way relay networks. In this paper, an algorithm based on the two-way relay network system model, the RLS algorithm, is proposed. The algorithm introduces the recursive least squares principle algorithm to the basic cost function of nonsmooth signal identification, optimizes the signal identification model by exponential weighting, and uses the time-varying variance matrix and the optimal identification data separation matrix to make real-time estimates of the new cost function for obtaining nonsmooth signals. And a new index, the PI index, is introduced to define whether the non-stationary signal is stable and reliable. Through two sets of comparison tests, we can learn that the RLS algorithm based on the two-way relay network system is better than other algorithms in the identification of non-stationary signals. It shows that the RLS algorithm can better identify and manage the information flow in a two-way relay network, make the non-stationary signal more stable, and further improve the convergence and convergence speed of the non-stationary signal to provide a better information interaction experience for both sides of electronic communication.

Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To better allow accurate identification of non-smooth signals in two-way relay networks. In this paper, an algorithm based on the two-way relay network system...

Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Publicado en línea: 22 may 2023

Páginas: -

Recibido: 07 ago 2022

Aceptado: 15 nov 2022

DOI: https://doi.org/10.2478/amns.2023.1.00274

Palabras clave
Two-way relay network, Non-smooth signal identification algorithm, Optimal identification matrix, Time-varying variance matrix, RLS algorithm

© 2023 Jie Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Publicado en línea: 22 may 2023

Páginas: -

Recibido: 07 ago 2022

Aceptado: 15 nov 2022

DOI: https://doi.org/10.2478/amns.2023.1.00274

Palabras claveTwo-way relay network, Non-smooth signal identification algorithm, Optimal identification matrix, Time-varying variance matrix, RLS algorithm

© 2023 Jie Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Two-way relay network, Non-smooth signal identification algorithm, Optimal identification matrix, Time-varying variance matrix, RLS algorithm