Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets

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Odesanya Ituabhor 1,* Joseph Isabona 1 Jangfa T. zhimwang 1 Ikechi Risi 2

1. Department of Physics, Federal University Lokoja, Lokoja, Kogi State, Nigeria

2. Department of Physics, River State University Port Harcourt, Nigeria

* Corresponding author.


Received: 25 Dec. 2021 / Revised: 20 Jan. 2022 / Accepted: 6 Feb. 2022 / Published: 8 Jun. 2022

Index Terms

Stochastic phenomenon, Neural networks, Adaptive modelling, Adaptive learning, Practical SINR


In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded forward neural network model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.

Cite This Paper

Odesanya Ituabhor, Joseph Isabona, Jangfa T. zhimwang, Ikechi Risi, "Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets", International Journal of Computer Network and Information Security(IJCNIS), Vol.14, No.3, pp.63-74, 2022. DOI:10.5815/ijcnis.2022.03.05


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