Low-Complexity PAPR Reduction Method for OFDM Systems Based on Real-Valued Neural Networks
Zhijun Liu, Xin Hu, Kang Han, Sun Zhang, Linlin Sun, Lexi Xu, Weidong Wang, Fadhel M. Ghannouchi
Abstract
High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems is one of the major drawbacks in wireless transmitters. In this letter, a novel low complexity PAPR reduction method based on the real-valued neural network (NN) is proposed. This method first builds the PAPR reduction module using the real-valued NN to achieve the reduction of PAPR. To reconstruct the transmission signal, the PAPR decompression module is introduced into the receiving end to recover the signal in order to minimize the bit error rate (BER) of the systems. The PAPR reduction module and the PAPR decompression module can be jointly trained offline, therefore, the reduction of PAPR and the minimization of BER can be achieved simultaneously. To reduce the process rate of the PAPR reduction, the trained model uses multiple parallel processing links to achieve the reduction of the PAPR. The extensive simulations indicate that the proposed method outperforms previously available methods in terms of PAPR and BER, while having low complexity.