论文标题
王子：一种修剪的AMP集成深CNN方法，用于有效的毫米波和Terahertz超质量MIMO系统的通道估计
PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel Estimation of Millimeter-wave and Terahertz Ultra-Massive MIMO Systems
论文作者
论文摘要
毫米波（mmwave）和Terahertz（THZ） - 频带通信利用丰富的带宽来满足6G无线通信的数据速率需求不断提高。为了通过降低硬件成本来补偿高繁殖损失，具有混合波束成形结构的超质量多输入多输出（UM-MIMO）是MMWave和THZ频段中的一项有希望的技术。但是，对于混合UM-MIMO系统，通道估计（CE）具有挑战性，这需要从严重的通道观测中恢复高维通道。在本文中，首先提出了一个综合的近似消息传递（AMP）综合深卷积神经网络（DCNN）CE（Prince）方法，该方法提出了通过附加DCNN网络来提高AMP方法的估计准确性。此外，通过截断DCNN网络卷积层中微不足道的特征图，开发了一种修剪方法，包括进行正则化，修剪和精炼程序的培训，以减少网络规模。仿真结果表明，王子在CE准确性和显着较低的复杂性之间取得了良好的权衡，在消除$ 80 \％$ $ $ $ $ 80 \％$ $ 80 \％的功能映射后，标准均方平方英尺（NMSE）为$ -10 $ db为$ -10 $ dB。
Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the abundant bandwidth to fulfill the increasing data rate demands of 6G wireless communications. To compensate for the high propagation loss with reduced hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) with a hybrid beamforming structure is a promising technology in the mmWave and THz bands. However, channel estimation (CE) is challenging for hybrid UM-MIMO systems, which requires recovering the high-dimensional channels from severely few channel observations. In this paper, a Pruned Approximate Message Passing (AMP) Integrated Deep Convolutional-neural-network (DCNN) CE (PRINCE) method is firstly proposed, which enhances the estimation accuracy of the AMP method by appending a DCNN network. Moreover, by truncating the insignificant feature maps in the convolutional layers of the DCNN network, a pruning method including training with regularization, pruning and refining procedures is developed to reduce the network scale. Simulation results show that the PRINCE achieves a good trade-off between the CE accuracy and significantly low complexity, with normalized-mean-square-error (NMSE) of $-10$ dB at signal-to-noise-ratio (SNR) as $10$ dB after eliminating $80\%$ feature maps.
