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.
Vidyabharathi Dakshinamurthi1, Syed Ibad Ali2, T. Karthikeyan3, Nanda Satish Kulkarni4
Sona College of Technology, India1, Ajeenkya DY Patil University, India2, University of Technology and Applied Sciences - Salalah, Sultanate of Oman3, Siddhant College of Engineering, India4
Causal Convolution, Almeida–Pineda Recurrent Backpropagation, Mobile Network Design, Temporal Dependencies, Deep Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 4 , Pages: 3091 - 3096 )
Date of Publication :
December 2023
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