论文标题
大型智能表面辅助误差通信的渠道估计：从LMMSE到深度学习解决方案
Channel Estimation for Large Intelligent Surface Aided MISO Communications: From LMMSE to Deep Learning Solutions
论文作者
论文摘要
我们考虑了由大型智能表面（LIS）提供帮助的多端纳无线系统。 LIS提出了一种新的物理层技术，可通过智能控制传播环境来提高覆盖范围和能源效率。但是，实际上，实现LIS的预期收益需要准确的渠道估计。最近解决此问题的尝试考虑了最小二乘方法（LS）方法，这很简单，但也是最佳的。基于最低于点（MMSE）标准的最佳通道估计器，由于在接收器处看到的有效通道的非高斯性，因此获得了挑战，并且是非线性的。在这里，我们提出了近似最佳MMSE通道估计器的方法。作为第一种方法，我们在分析上开发了最佳的线性估计器LMMSE，以及相应的基于大型化最小化的算法，旨在优化训练阶段的LIS相移矩阵。证明该估计器通过利用无线通道的二阶统计特性和噪声来提高LS进近的精度。为了进一步提高性能并更好地近似全球最佳的MMSE通道估计器，我们建议基于深度学习的数据驱动的非线性解决方案。具体而言，通过将MMSE通道估计问题提出作为图像降解问题，我们提出了两种基于卷积神经网络（CNN）的方法，以执行并近似最佳的MMSE MMSE通道估计解决方案。我们的数值结果表明，与线性估计方法相比，这些基于CNN的估计值具有出色的性能。它们的计算复杂性要求也很低，从而激发了它们在未来的LIS辅助无线通信系统中的潜在用途。
We consider multi-antenna wireless systems aided by large intelligent surfaces (LIS). LIS presents a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice however, achieving the anticipated gains of LIS requires accurate channel estimation. Recent attempts to solve this problem have considered the least-squares (LS) approach, which is simple but also sub-optimal. The optimal channel estimator, based on the minimum mean-squared-error (MMSE) criterion, is challenging to obtain and is non-linear due to the non-Gaussianity of the effective channel seen at the receiver. Here we present approaches to approximate the optimal MMSE channel estimator. As a first approach, we analytically develop the best linear estimator, the LMMSE, together with a corresponding majorization-minimization based algorithm designed to optimize the LIS phase shift matrix during the training phase. This estimator is shown to yield improved accuracy over the LS approach by exploiting second-order statistical properties of the wireless channel and the noise. To further improve performance and better approximate the globally-optimal MMSE channel estimator, we propose data-driven non-linear solutions based on deep learning. Specifically, by posing the MMSE channel estimation problem as an image denoising problem, we propose two convolutional neural network (CNN) based methods to perform the denoising and approximate the optimal MMSE channel estimation solution. Our numerical results show that these CNN-based estimators give superior performance compared with linear estimation approaches. They also have low computational complexity requirements, thereby motivating their potential use in future LIS-aided wireless communication systems.
