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- Title
基于两步噪声学习网络的波束域毫米 波大规模 MIMO 信道估计.
- Authors
杨 静; 王朋朋; 陶华伟
- Abstract
For beamspace millimeter-wave(mmWave) massive multiple-input and multiple-output(MIMO) system,a new two-step noise learning network ( TNLNet) is proposed. Firstly,the noise characteristic is extracted from the received signals through the convolution and the pooling. Then, by utilizing similar characteristics of adjacent elements caused by sparse characteristics of beamspace mmWave massive MIMO,down-sampling is implemented. Finally,four sub-matrices are reconstructed in the channel matrix, so as to improve the training and the testing efficiency. The results show that TNLNet achieves better normalized mean squared error (NMSE) performance than Least Square,Minimum Mean Square Error, Fully Convolutional Denoising Approximate Message Passing ( FCDAMP ) and Learned Denoising-based Approximate Message Passing ( LDAMP),with lower complexity compared with FCDAMP and LDAMP. Specially,although the complexity of TNLNet is slightly higher than that of Fast and Flexible Denoising Convolutional Neural Network ( FFDNet), TNLNet has better NMSE performance. Especially, TNLNet is more practical than FFDNet in a single training model.
- Subjects
CONVOLUTIONAL neural networks; SIGNAL convolution; MESSAGE passing (Computer science); CHANNEL estimation; NOISE
- Publication
Telecommunication Engineering, 2023, Vol 63, Issue 3, p390
- ISSN
1001-893X
- Publication type
Article
- DOI
10.20079/j.issn.1001-893x.220108001