ICTACT Journal on Communication Technology

Syed Ibad Ali ¹, Jagannath Jadhav ², R. Arunkumar ³, N. Kanagavalli ³

Affiliations
1 Department of Electronics and Telecommunication Engineering, GH Raisoni Institute of Engineering and Technology, IN
2 Department of Electronic and Communication Engineering, K.L.E. College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, IN

Abstract

The 5G technology will become a truly integrated technology. It’s becoming the most famous technology because of its optimal system computing complex and transforming a group of individual network components. Adequate resources must be provided for the various devices that are typically on the same network. The various functions in the data system also require resource allocation to suit its needs. This will ensure that the various devices on that network perform different types of work effectively. It also represents the various device functionalites to ensure the estimated resources onetime manner. Only then will it be convenient to carry out a variety of jobs depending on their speed and selection. And its various data requirements vary according to the functionality of the various devices featured in this series segment dynamic configuration. In this paper a smart resource utilization scheme was proposed. Its main purpose is to better manage the off-the-shelf resources available here. And provide it where it is needed and streamline data delivery to users on the network. This ensures that all data goes to the users in the correct manner. The proposed method getting 49% energy consumption, 90% resource utilization, 92% resource reservation and 91% Quality of services.

Keywords

5G Technology, Resource Utilization, Communication Networks, Complex System, Data Streamline

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Vidyabharathi Dakshinamurthi ¹, Syed Ibad Ali ², T. Karthikeyan ³, Nanda Satish Kulkarni ⁴

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

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.

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