Orthogonal frequency division multiplexing (OFDM) is extensively applied in the downlink of narrowband Internet of Things (NB-IoT). However, the high peak-to-average power ratio (PAPR) of OFDM systems leads to a decrease in transmitter efficiency. Therefore, the researchers proposed the artificial neural network (ANN) based PAPR reduction schemes. However, these schemes have the disadvantages of high complexity or cannot overcome the defects of traditional schemes. In this paper, a novel PAPR reduction scheme based on neural networks (NNs) is proposed for OFDM systems. This scheme establishes a PAPR reduction module based on NN, which is trained using the low PAPR data obtained by the simplified clipping and filtering (SCF) method. To overcome the defect of poor BER performance of the SCF scheme, a recovery module is introduced at the receiver, to recover the distorted signal. To realize the improvement of BER performance and the reduction of PAPR simultaneously, the two modules are jointly trained based on multiobjective optimization. Experimental results based on a 100 MHz OFDM signal show that this scheme can reduce PAPR by 4.5 dB. Meanwhile, the BER of this scheme can be reduced to 0.001 times that of the SCF scheme.

中文翻译：

---

基于神经网络的OFDM系统PAPR抑制新方案

正交频分复用（OFDM）广泛应用于窄带物联网（NB-IoT）的下行链路中。然而，OFDM系统的高峰均功率比（PAPR）导致发射机效率的降低。因此，研究人员提出了基于人工神经网络（ANN）的PAPR降低方案。然而，这些方案具有高复杂度的缺点或无法克服传统方案的缺陷。在本文中，提出了一种基于神经网络的新的PAPR降低方案，用于OFDM系统。该方案建立了一个基于NN的PAPR降低模块，该模块使用通过简化限幅和滤波（SCF）方法获得的低PAPR数据进行训练。为了克服SCF方案BER性能差的缺陷，在接收器处引入了恢复模块，恢复失真的信号。为了同时实现BER性能的改善和PAPR的降低，两个模块在多目标优化的基础上进行了联合训练。基于100 MHz OFDM信号的实验结果表明，该方案可以将PAPR降低4.5 dB。同时，该方案的BER可以降低到SCF方案的0.001倍。