Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future.

中文翻译：

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稳态和非稳态衰落环境的统一信道估计框架


信道估计对于现代无线系统至关重要，并且随着 6 GHz 以下多输入多输出配置中用户吞吐量的增长，信道估计变得越来越具有挑战性。大量文献在提高估计精度方面投入了大量精力，而忽视了插值方案。为了应对这一挑战，我们利用超分辨率图像恢复方案来模拟非线性插值机制。此外，为了将估计方案扩展到在即将到来的6G中特别有吸引力的非平稳环境，我们利用循环网络结构来近似非线性信道统计相关性，以对难以预测的非平稳行为进行建模。以理论的方式完成。为了使其更加实用，我们根据统计信道模型离线生成数值信道系数来训练神经网络并直接将其应用在不同的环境中。如本文所示，所提出的基于统一超分辨率的信道估计方案在静态和非静态场景中都可以优于传统方法，我们相信这可以在不久的将来显着改变当前的信道估计方法。