Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231000191-6.doi: 10.11896/jsjkx.231000191

• Network & Communication • Previous Articles     Next Articles

Method of Outdoor CSI Feedback for Massive MIMO Systems Based on Deep Autoencoder

CHEN Meng, QIAN Rongrong, ZHU Yujia, HUANG Zhenguo   

  1. School of Information,Yunnan University,Kunming 650500,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:CHEN Meng,born in 1998,postgra-duate.His main research interests include deep learning and channel state information feedback.
    QIAN Rongrong,born in 1985,Ph.D.Her main research interests include wireless communication,signal proces-sing and so on.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(61701433) and Yunnan Provincial Department of Science and Technology General Project(2018FB099).

PDF (PC)

142

Abstract: In outdoor scenarios with high compression,aiming at the problems of low accuracy and high complexity of reconstruction of most existing channel state information(CSI)feedback methods in massive multiple-input multiple-output(MIMO)systems,a deep autoencoder-based CSI compression feedback method is proposed.The method firstly uses a convolutional neural network in the encoder to extract the feature information of the original CSI,and then uses a fully connected network to compress it into a low-dimensional codeword for feedback to the decoder.Considering that the spatial pattern of CSI in outdoor environments is more complicated,and the loss of information is more at high compression,the decoder employs parallel multi-resolution convolutional networks and fully connected networks in a residual structure to reconstruct the received feature codewords.This design enhances the reconstruction and generalization capabilities of the proposed method.Experimental results show that the reconstruction quality of the proposed method is significantly improved at different compression ratios.

Key words: Massive MIMO, CSI feedback, Deep autoencoder, Outdoor scenario, High compression

CLC Number: 

  • TN925
[1]ANDREWS J G,BUZZI S,CHOI W,et al.What will 5G be? [J].IEEE Journal on Selected Areas in Communications,2014,32(6):1065-1082.
[2]METZLER C A,MALEKI A,BARANIUK R G.From denoi-sing to compressed sensing[J].IEEE Transactions on Information Theory,2016,62(9):5117-5144.
[3]DAUBECHIES I,DEFRISE M,MOL C D.An iterative thres-holding algorithm for linear inverse problems with a sparsity constraint[J].Communications on Pure and Applied Mathematics,2004,57(11):1413-1457.
[4]WEN C K,SHIH W T,JIN S.Deep learning for massive MIMOCSI feedback[J].IEEE Wireless Communications Letters,2018,7(5):748-751.
[5]GUO J J,WEN C K,JIN S,et al.Convolutional neural network based multiple-rate compressive sensing for massive MIMO CSI feedback:Design,simulation,and analysis[J].IEEE Transactions on Wireless Communications,2020,19(4):2827-2840.
[6]LU Z L,WANG J T,SONG J.Multi-resolution CSI feedbackwith deep learning in massive MIMO system[C]// IEEE International Conference on Communications.IEEE,2020:1-6.
[7]JI S,LI M.CLNet:Complex input lightweight neural network designed for massive MIMO CSI feedback[J].IEEE Wireless Communications Letters,2021,10(10):2318-2322.
[8]WANG T,WEN C K,JIN S,et al.Deeplearning-based CSI feedback approach for time-varying massive MIMO channels[J].IEEE Wireless Communications Letters,2019,8(2):416-419.
[9]CAO B,YANG Y,RAN P,et al.ACCsiNet:Asymmetric convolution-based autoencoder framework for massive MIMO CSI feedback[J].IEEE Communications Letters,2021,25(12):3873-3877.
[10]CAO Z,SHIH W,GUO J J,et al.Lightweight convolutionalneural networks for CSI feedback in massive MIMO[J].IEEE Communications Letters,2021,25(8):2624-2628.
[11]YE H Y,GAO F F,QIAN J,et al.Deep learning-based denoise network for CSI feedback in FDD massive MIMO systems[J].IEEE Communications Letters,2020,24(8):1742-1746.
[12]CAI Q,DONG C,NIU K.Attention model for massive MIMOCSI compression feedback and recovery[C]//IEEE Wireless Communications and Networking Conference.IEEE,2019:1-5.
[13]LIU W B,YAN B,SHEN L,et al.CLPNet:CSI feedback network for massive MIMO based on deeplearning[J].Radio Engineering,2022,52(9):1660-1665.
[14]ZHANG Y,ZHANG X,LIU Y.Deep learning-based CSI compression and quantization with high compression ratios in FDD massive MIMO systems[J].IEEE Wireless Communications Letters,2021,10(10):2101-2105.
[15]GUO J J,WEN C K,JIN S,et al.Overview of deep learning-based CSI feedback in massive MIMO systems[J].IEEE Tran-sactions on Communications,2022,70(12):8017-8045.
[16]LU Z,ZHANG X,HE H,et al.Binarized aggregated networkwith quantization:Flexible deep learning deployment for CSI feedback in massive MIMO systems[J].IEEE Transactions on Wireless Communications,2022,21(7):5514-5525.
[17]LIU L F,OESTGES C,POUTANEN J,et al.The COST2100 MIMO channel model[J].IEEE Wireless Communications,2012,19(6):92-99.
[18]KOLMONEN V M,ALMERS P,SALMI J,et al.A dynamicdual-link wideband MIMO channel sounder for 5.3 GHz[J].IEEE Transactions on Instrumentation and Measurement,2010,59(4):873-883.
[19]ZHU M,ERIKSSON G,TUFVESSON F.The COST 2100channel model:Parameterization and validation based on outdoor MIMO measurements at 300 MHz[J].IEEE Transactions on Wireless Communications,2013,12(2):888-897.
[20]HU Z Y,GUO J H,LIU G Z,et al.MRFNet:A deep learning-based CSI feedback approach of massive MIMOsystems[J].IEEE Communications Letters,2021,25(10):3310-3314.
[21]HE K M,CHEN X L,XIE S N,et al.Maskedautoencoders arescalable vision learners[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.New Orleans,LA,USA,2022:15979-15988.
[22]CHEN M H,GUO J J,LI X,et al.An overview of the CSI feedback based on deep learning for massive MIMO systems[J].Internet Things,2020,4(10):33-44.
[1] XUE Jianbin, WANG Jiahao. Design of Adaptive Hybrid Precoder in mmWave MU-MIMO Systems [J]. Computer Science, 2023, 50(11A): 221200047-5.
[2] QING Chao-jin, DU Yan-hong, YE Qing, YANG Na, ZHANG Min-tao. Enhanced ELM-based Superimposed CSI Feedback Method with CSI Estimation Errors [J]. Computer Science, 2022, 49(6A): 632-638.
[3] LI Hao,CUI Xin-kai,GAO Xiang-chuan. Maximum Likelihood Blind Detection Algorithm Based on Piecewise Gaussian Approximation for Massive MIMO Outdoor Wireless Optical Communication Systems [J]. Computer Science, 2020, 47(3): 255-260.
[4] WANG Peng-fei, ZHANG Hang. Sub-sampling Signal Reconstruction Based on Principal Component Under Underdetermined Conditions [J]. Computer Science, 2019, 46(10): 103-108.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.