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Channel Estimation Enhancement With Generative Adversarial Networks

机译:具有生成对抗网络的信道估计增强

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Improving the accuracy of channel estimation is a significant topic in the context of wireless communications. For training-based channel estimations, increasing the length of a training sequence may improve the accuracy of channel estimation but causes a higher overhead. Nevertheless, this paper shows that benefiting from generative adversarial networks (GANs), which is an emerging deep learning framework, the accuracy of channel estimation can be improved without transmitting a longer training sequence. To this end, this paper proposes a GAN-based channel estimation enhancement algorithm, where GANs are trained online with receive sequence so as to obtain a longer mimic sequence and enhance channel estimation. In order to address the problem of improving the training stability and the learning ability of GANs, this paper proposes a novel framework by integrating a conditional GAN with an improved Wasserstein GAN. Furthermore, a strategy based on a lookup table is proposed to alleviate overfitting that may occur during the training of GANs. Simulation results indicate that the proposed GAN-based channel estimation enhancement algorithm can benefit the conventional training-based channel estimation, yielding lower relative error performance, especially in the low SNR regions.
机译:提高信道估计的准确性是无线通信的上下文中的重要主题。对于基于训练的信道估计,增加训练序列的长度可以提高信道估计的准确性,但导致更高的开销。然而,本文显示从生成的对抗性网络(GAN)受益于新兴的深度学习框架,可以提高信道估计的准确性而不发出更长的训练序列。为此,本文提出了一种基于GaN的信道估计增强算法,其中GAN在线接收序列训练,以获得更长的模拟序列并增强信道估计。为了解决提高培训稳定性的问题和GANS的学习能力,本文通过将条件GaN与改进的Wassersein GaN集成,提出了一种新的框架。此外,提出了一种基于查找表的策略,以减轻在培训GAN期间可能发生的过度装备。仿真结果表明,所提出的基于GaN的信道估计增强算法可以使基于培训的信道估计有益,产生更低的相对误差性能,尤其是在低SNR区域中。

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