论文标题
使用条件gan的一位多源Mimo的通道估计
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
论文作者
论文摘要
通道估计是一项具有挑战性的任务，尤其是在具有一位类似物到数字转换器（ADC）的大量多输入多输出（MIMO）系统中。传统的深度学习方法（DL）方法是学习从输入到真实渠道的映射，在估算准确的渠道方面存在很大的困难，因为它们的损失功能的设计和研究不佳。在本文中，开发了有条件的生成对抗网络（CGAN），以通过对抗训练两个DL网络来预测更现实的渠道。 CGAN不仅学习从量化的观测值到真实渠道的映射，而且还学习适应性损失功能以正确训练网络。数值结果表明，所提出的基于CGAN的方法的表现优于现有的DL方法，并且在大型MIMO系统中实现了很高的鲁棒性。
Channel estimation is a challenging task, especially in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep learning (DL) methods, that learn the mapping from inputs to real channels, have significant difficulties in estimating accurate channels because their loss functions are not well designed and investigated. In this paper, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks. cGANs not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks. Numerical results show that the proposed cGAN based approach outperforms existing DL methods and achieves high robustness in massive MIMO systems.
