Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.

中文翻译：

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毫米波智能反射面的深度去噪神经网络辅助压缩信道估计

将大型智能反射面 (IRS) 集成到毫米波 (mmWave) 大规模多输入多输出 (MIMO) 中已成为提高覆盖范围和吞吐量的有前途的方法。大多数现有工作都假设理想的信道估计，由于高维级联 MIMO 信道和无源反射元件，这可能具有挑战性。因此，本文提出了一种用于毫米波 IRS 系统的深度去噪神经网络辅助压缩信道估计，以减少训练开销。具体来说，我们首先介绍一种混合被动/主动 IRS 架构，其中使用很少的接收链来估计上行链路用户到 IRS 信道。在通道训练阶段，只有一小部分元素会被连续激活，使部分通道发声。而且，完整的信道矩阵可以从基于压缩感知的有限测量中重建，从而利用不同子载波之间角域 mmWave MIMO 信道的公共稀疏性来提高精度。此外，进一步提出了复值去噪卷积神经网络（CV-DnCNN）以提高性能。仿真结果证明了所提出的解决方案优于最先进的解决方案。