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Causal Convolution Employing Almeida–Pineda Recurrent Backpropagation for Mobile Network Design


Affiliations
1 Department of Computer Science and Engineering, Sona College of Technology, India
2 School of Engineering, Ajeenkya DY Patil University, India
3 Department of Information Technology, University of Technology and Applied Sciences - Salalah, Oman
4 Department of Electronics and Telecommunication Engineering, Siddhant College of Engineering, India
     

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Designing efficient mobile networks is crucial for meeting the growing demand for high-speed, reliable communication. However, existing convolutional neural network (CNN) architectures face challenges in capturing temporal dependencies, hindering their performance in mobile network design. The introduction highlights the increasing importance of mobile networks and identifies the limitations of current CNN architectures in capturing temporal dynamics. The problem statement emphasizes the need for an enhanced model that can effectively address temporal dependencies in mobile network design. This research addresses this problem by proposing a novel approach: Causal Convolution employing Almeida–Pineda Recurrent Backpropagation (CC-APRB). The causal convolution captures temporal dependencies by considering only past and present inputs, while the recurrent backpropagation optimizes the model parameters based on sequential data. The integration of these techniques aims to enhance the model ability to capture temporal features in mobile network data. The results indicate significant improvements in the performance of the CC-APRB model compared to traditional CNN architectures. The model demonstrates enhanced accuracy and efficiency in capturing temporal dependencies, making it well-suited for mobile network design applications.

Keywords

Causal Convolution, Almeida–Pineda Recurrent Backpropagation, Mobile Network Design, Temporal Dependencies, Deep Learning.
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  • Causal Convolution Employing Almeida–Pineda Recurrent Backpropagation for Mobile Network Design

Abstract Views: 171  |  PDF Views: 1

Authors

Vidyabharathi Dakshinamurthi
Department of Computer Science and Engineering, Sona College of Technology, India
Syed Ibad Ali
School of Engineering, Ajeenkya DY Patil University, India
T. Karthikeyan
Department of Information Technology, University of Technology and Applied Sciences - Salalah, Oman
Nanda Satish Kulkarni
Department of Electronics and Telecommunication Engineering, Siddhant College of Engineering, India

Abstract


Designing efficient mobile networks is crucial for meeting the growing demand for high-speed, reliable communication. However, existing convolutional neural network (CNN) architectures face challenges in capturing temporal dependencies, hindering their performance in mobile network design. The introduction highlights the increasing importance of mobile networks and identifies the limitations of current CNN architectures in capturing temporal dynamics. The problem statement emphasizes the need for an enhanced model that can effectively address temporal dependencies in mobile network design. This research addresses this problem by proposing a novel approach: Causal Convolution employing Almeida–Pineda Recurrent Backpropagation (CC-APRB). The causal convolution captures temporal dependencies by considering only past and present inputs, while the recurrent backpropagation optimizes the model parameters based on sequential data. The integration of these techniques aims to enhance the model ability to capture temporal features in mobile network data. The results indicate significant improvements in the performance of the CC-APRB model compared to traditional CNN architectures. The model demonstrates enhanced accuracy and efficiency in capturing temporal dependencies, making it well-suited for mobile network design applications.

Keywords


Causal Convolution, Almeida–Pineda Recurrent Backpropagation, Mobile Network Design, Temporal Dependencies, Deep Learning.

References