PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network
作者机构：Department of Computer Science and EngineeringHindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and EngineeringHindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication EngineeringKongu Engineering CollegeErode638060India Department of Computer Science and EngineeringSri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&EngineeringVIT Bhopal UniversityBhopal466114India
出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)
年 卷 期：2023年第45卷第5期
页 面：1483-1493页
核心收录：
学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network
摘 要：Data transmission through a wireless network has faced various signal problems in the past *** 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 longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.
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