期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 11, 期 4, 页码 846-850出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2022.3147590
关键词
Channel estimation; Azimuth; Quantization (signal); Estimation; Sensors; Compressed sensing; Chaos; Reconfigurable intelligent surface; channel estimation; deep compressed sensing; encoder-decoder
资金
- Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province [SKLACSS-202115]
- Natural Science Foundation of Hunan Province [2021JJ40228, 2020JJ4341]
- Key Projects of Hunan Provincial Department of Education Department [21A0408]
- Outstanding Youth Project of Hunan Provincial Education Department [20B267, 20B269]
In this paper, a deep compressed sensing-based channel estimation scheme is proposed using U-Net and ResU-Net structures to recover the high-dimensional cascaded channel matrix from limited pilot overhead. The results show that ResU-Net achieves more accurate channel estimation compared to conventional algorithms and other network models, while maintaining good generalization and robustness.
To reduce the pilot overhead of cascaded channel estimation for RIS-aided Massive MIMO communication system, we proposed a deep compressed sensing-based channel estimation scheme, where U-shaped network (U-Net), an encoder-decoder with skip connection, is used to recover the high-dimensional cascaded channel matrix from limited pilot overhead. The skip connections between encoder and decoder can fuse features of different scales and semantic by concatenating the feature map, which enhance the reconstruction performance of cascaded channel. To further improve the feature extraction ability of U-Net, we design a ResU-Net architecture with stacked residual units to increase the depth of network. Simulation results show the channel estimation of ResU-Net is more accurate than conventional algorithm and other network model. Meanwhile, ResU-Net has good generalization and robustness for different pilot lengths and phase quantization errors.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据