通过非线性信道增强快于奈奎斯特信号的模型驱动法

Tongzhou Yu;Baoming Bai;Ruimin Yuan;Chao Chen
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引用次数: 0

摘要

为了提高未来卫星通信系统的容量,人们越来越多地考虑采用比奈奎斯特还快的信令(FTN)。现有的高功率放大器(HPA)非线性补偿方法需要完美的 HPA 模型知识。为解决这一问题,我们分析了 FTN 符号分布,并提出了一种复值深度神经网络(CVDNN)辅助的 HPA 非线性补偿方案,该方案不需要完全了解 HPA 模型,并能在训练过程中学习 HPA 非线性。为进一步提高性能,还提出了一种模型驱动的非线性补偿网络。此外,还设计了两个基于 FTN 符号分布的训练集来训练网络。大量仿真表明,高斯分布是 FTN 符号分布的良好近似。通过使用高斯分布来训练近似 FTN 信号的拟议模型驱动网络,与接收器处不使用 HPA 参数的现有方法相比,可实现 0.5 dB 的性能增益。所提出的神经网络也适用于其他系统的非线性补偿,包括正交频分复用(OFDM)系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model-Driven Approach to Enhance Faster-than-Nyquist Signaling over Nonlinear Channels
In order to increase the capacity of future satellite communication systems, faster-than-Nyquist (FTN) signaling is increasingly considered. Existing methods for compensating for the high power amplifier (HPA) nonlinearity require perfect knowledge of the HPA model. To address this issue, we analyze the FTN symbol distribution and propose a complex-valued deep neural network (CVDNN) aided compensation scheme for the HPA nonlinearity, which does not require perfect knowledge of the HPA model and can learn the HPA nonlinearity during the training process. A model-driven network for nonlinearity compensation is proposed to further enhance the performance. Furthermore, two training sets based on the FTN symbol distribution are designed for training the network. Extensive simulations show that the Gaussian distribution is a good approximation of the FTN symbol distribution. The proposed model-driven network trained by employing a Gaussian distribution to approximate an FTN signaling can achieve a performance gain of 0.5 dB compared with existing methods without HPA's parameters at the receiver. The proposed neural network is also applicable for non-linear compensation in other systems, including orthogonal frequency-division multiplexing (OFDM).
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