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性别: 男

毕业院校: 北京航空航天大学

学位: 博士

所在单位: 信息与通信工程学院

学科: 通信与信息系统. 信号与信息处理. 电路与系统

办公地点: 创新园大厦A520

联系方式: Tel: 86-0411-84707719 实验室网址: http://wican.dlut.edu.cn

电子邮箱: mljin@dlut.edu.cn

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A Novel Tone Reservation Scheme Based on Deep Learning for PAPR Reduction in OFDM Systems

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论文类型: 期刊论文

发表时间: 2020-06-01

发表刊物: IEEE COMMUNICATIONS LETTERS

收录刊物: SCIE

卷号: 24

期号: 6

页面范围: 1271-1274

ISSN号: 1089-7798

关键字: Peak to average power ratio; Training; Biological neural networks; Neurons; Bandwidth; Feedforward neural network; orthogonal frequency division multiplexing; peak-to-average power ratio; tone reservation

摘要: A major defect of orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR). In this letter, a novel scheme based on deep leaning, called tone reservation network (TRNet), is proposed for OFDM systems to improve the performance of the tone reservation (TR) technique. More specifically, TRNet reserves a part of tones to generate a peak-canceling signal. The feedforward neural network is used to adaptively generate a peak-canceling signal according to the characteristics of the input signal. Computer simulation results show that the proposed scheme provides a better PAPR reduction performance with fewer reserved tones, which is also beneficial to improve the bandwidth efficiency.

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