论文标题
基于卷积自动编码器的OFDM系统的低PAPR波形设计
Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder
论文作者
论文摘要
本文介绍了卷积自动编码器（CAE）的架构，用于峰值与平均功率比（PAPR）降低和波形设计，用于正交频施加多路复用（OFDM）系统。所提出的体系结构集成了PAPR还原块和非线性高功率放大器（HPA）模型。我们将逐渐的损失学习用于多目标优化。我们通过检查位错误率（BER），PAPR和光谱响应，并将其与常见的PAPR还原算法进行比较来分析模型性能。
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.
