Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System.
Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System.
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DOI：
10.1155/2022/8234709
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发表时间：
2022
影响因子：
--
通讯作者：
Jiang, Fengyuan
中科院分区：
文献类型：
--
作者：
Chen, Yong;Jiang, Fengyuan
将大型智能反射表面（IRS）集成到毫米波（mmWave）大规模多输入多输出（MIMO）技术中已经成为利用信道状态信息（CSI）来提高无线通信系统性能的有前途的方法。现有的大多数信道估计方法都假设可以获得理想的信道估计，但高维级联MIMO信道和无源反射器的提出给这些方法带来了很大的挑战。针对上述问题，本文提出了一种新的方法来减少训练开销的IRS与部分ON/OFF模型和导频设计方法的优化策略。大大降低了大规模天线阵列的能量消耗和信号传输训练阶段的导频开销。此外，我们提出了一种改进的深度残差收缩去噪网络，它具有更好的去噪性能与软阈值模型。信道数据可以通过深度学习方法去噪，大大提高了信道估计的准确性。仿真结果表明，该网络优于以往的解决方案。
Integrating large intelligent reflecting surfaces (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) technique has been a promising approach to enhance the performance of the wireless communication system with the channel state information (CSI). Most existing work assume that ideal channel estimation can be obtained, but the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a great challenge to these methods. To address the abovementioned problems, we proposed a new method for the reduction of training overhead in IRS with a partial ON/OFF model and an optimizing strategy for pilot design approach. The energy consumption of large-scale antenna arrays and the pilot overhead in the training phase of signal transmission are greatly reduced. Besides, we proposed an improved deep residual shrinkage denoising network, which possesses better denoising performance with a soft thresholding model. The channel data can be denoised by deep learning methods, which greatly improve the accuracy of channel estimation. Simulation results demonstrate that the superiority of the proposed network over prior solutions.
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