Open Access

ARTICLE

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

A. Jameer Basha^1,*, M. Ramya Devi², S. Lokesh¹, P. Sivaranjani³, D. Mansoor Hussain⁴, Venkat Padhy⁵

1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, 641032, India
2 Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, 641032, India
3 Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, 638060, India
4 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
5 School of Computing Science & Engineering, VIT Bhopal University, Bhopal, 466114, India

* Corresponding Author: A. Jameer Basha. Email:

Computer Systems Science and Engineering 2023, 45(2), 1483-1493. https://doi.org/10.32604/csse.2023.021540

Received 06 July 2021; Accepted 08 October 2021; Issue published 03 November 2022

Abstract

Data transmission through a wireless network has faced various signal problems in the past decades. The orthogonal frequency division multiplexing (OFDM) technique is widely accepted in multiple data transfer patterns at various frequency bands. A recent wireless communication network uses OFDM in long-term evolution (LTE) and 5G, among others. The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network. This transmission loss is called peak-to-average power ratio (PAPR). This wireless signal distortion can be reduced using various techniques. This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication. Partial transmit sequence (PTS) helps in the fast transfer of data in wireless LTE. PTS is merged with deep belief neural network (DBNet) for the efficient processing of signals in wireless 5G networks. Result indicates that the proposed system outperforms other existing techniques. Therefore, PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization. Hence, the specified design supports in improving the proposed PAPR reduction architecture.

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