A Model-Driven DL Algorithm for PAPR Reduction in OFDM System

被引:20
|
作者
Wang, Xin [1 ]
Jin, Ningde [1 ]
Wei, Jidong [1 ]
机构
[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金;
关键词
Peak to average power ratio; Training; Time-domain analysis; Kernel; Computational modeling; Neural networks; Deep learning; Orthogonal frequency division multiplexing (OFDM); peak-to-average power ratio (PAPR); deep learning (DL); model-driven; tone reservation (TR); TONE RESERVATION SCHEME; MIMO;
D O I
10.1109/LCOMM.2021.3076605
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Deep learning (DL) has dramatically improved the peak-to-average power ratio (PAPR) performance. However, the high computational complexity and excessive training data constitute a significant hurdle. In this letter, a model-driven deep learning algorithm is proposed for PAPR reduction in orthogonal frequency division multiplexing (OFDM) system. Precisely, an iterative peak-canceling signal generation scheme is unfolded as a layer structure of the DL network. The scheme falls into the category of tone reservation technique. A set of trainable parameters, which optimizes the clipping threshold and weights time-domain kernel function, has been designed and introduced into the iterative scheme. Compared with the existing approaches, the simulation results demonstrate that the proposed algorithm achieves comparable PAPR performance with low complexity and training costs.
引用
收藏
页码:2270 / 2274
页数:5
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