论文标题
GOLAY层：限制基于OFDM的自动编码器的峰值与平均功率比率
Golay Layer: Limiting Peak-to-Average Power Ratio for OFDM-based Autoencoders
论文作者
论文摘要
在这项研究中，我们为OFDM基自动编码器（OFDM-AES）提出了一个可区分的层，以避免高瞬时功率，而无需正规化培训过程中使用的成本函数。所提出的方法依赖于对通过深神经网络（DNN）产生互补序列（CSS）的一组函数参数的操纵。我们保证每个OFDM-AE符号的峰值功率比（PAPR）小于或等于3 dB。我们还展示了如何通过使用PAPR来使用功能来标准化平均功率。引入的层允许辅助参数，该参数允许一个人控制频域中的幅度和相位偏差。数值结果表明，发射器和接收器处的DNN可以在此保护层下实现可靠的通信，而牺牲了复杂性。
In this study, we propose a differentiable layer for OFDM-based autoencoders (OFDM-AEs) to avoid high instantaneous power without regularizing the cost function used during the training. The proposed approach relies on the manipulation of the parameters of a set of functions that yield complementary sequences (CSs) through a deep neural network (DNN). We guarantee the peak-to-average-power ratio (PAPR) of each OFDM-AE symbol to be less than or equal to 3 dB. We also show how to normalize the mean power by using the functions in addition to PAPR. The introduced layer admits auxiliary parameters that allow one to control the amplitude and phase deviations in the frequency domain. Numerical results show that DNNs at the transmitter and receiver can achieve reliable communications under this protection layer at the expense of complexity.
