基于卷积自动编码器的OFDM SYSTEM低PAPR波形设计
Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder
摘要：
介绍了一种用于正交频分复用（OFDM）系统的卷积自编码器（CAE）的结构，该CAE用于降低峰均功率比（PAPR）和波形设计。该架构集成了一个峰均比降低模块和一个非线性高功率放大器（HPA）模型。我们将渐进损失学习应用于多目标优化。通过对该模型的误比特率、峰均比和频谱响应的分析，并与常用的峰均比降低算法进行了比较。
英文摘要：
This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) systems. The proposed architecture integrates a PAPR reduction block and a non-linear high power amplifier (HPA) model. We apply gradual loss learning for multi-objective optimization. We analyze the models performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.
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