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.

中文翻译：

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基于实值神经网络的OFDM系统低复杂度PAPR降低方法

正交频分复用 (OFDM) 系统中的高峰均功率比 (PAPR) 是无线发射机的主要缺点之一。在这封信中，提出了一种基于实值神经网络 (NN) 的新型低复杂度 PAPR 降低方法。该方法首先使用实值神经网络构建PAPR降低模块，实现PAPR的降低。为了重建传输信号，在接收端引入PAPR解压缩模块来恢复信号，以最小化系统的误码率（BER）。PAPR降低模块和PAPR解压模块可以离线联合训练，因此可以同时实现PAPR的降低和BER的最小化。为了降低 PAPR 降低的过程速率，训练好的模型使用多个并行处理环节来实现PAPR的降低。广泛的模拟表明，所提出的方法在 PAPR 和 BER 方面优于以前可用的方法，同时具有低复杂性。