Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11): 298-305.doi: 10.11896/jsjkx.241000004

• Computer Network • Previous Articles     Next Articles

Joint Optimization of Pilot Design and Channel Estimation Based on Deep Learning

WANG Anyi, LI Ruoman, LI Xinyu, LI Mingzhu   

  1. College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710600,China
  • Received:2024-10-08 Revised:2024-12-18 Online:2025-11-15 Published:2025-11-06
  • About author:WANG Anyi,born in 1968,professor,Ph.D supervisor.His main research interests include key technologies in broadband digital mobile communications,intelligent information processing technologies,and coal mine intelligence.
    LI Ruoman,born in 1999,master.Her main research interests include wireless communication and channel estimation.
  • Supported by:
    National Natural Science Foundation of China(62471384).

PDF (PC)

17

Abstract: With the continuous innovation and development of mobile communication technology,higher requirements have emerged communication reliability and data transmission performance.Accurate and efficient acquisition of CSI is a key prerequisite for fully harnessing the technical potential of wireless communication systems.In response to the challenges of high pilot overhead and low channel estimation accuracy in MIMO-OFDM systems,a joint optimization scheme of pilot design and channel estimation based on Deep Learning(AE-DRSN) is designed.The scheme initially utilizes Concrete AE to identify and select the pilot positions with maximum information content to achieve pilot optimization.The optimized pilot positions are then input into the DRSN to achieve more accurate CSI and further complete the accurate estimation of the channel.Experimental results demonstrate that compared with traditional channel estimation methods,the joint optimization scheme based on AE-DRSN can still achieve high-precision channel estimation with minimal pilot overhead,fully verifying the effectiveness of the scheme.

Key words: MIMO-OFDM, Pilot design, Channel estimation, Autoencoder, Deep residual shrinkage network

CLC Number: 

  • TN929.5
[1]BJÖRNSONE,ELDAR Y C,LARSSON E G,et al.Twenty-five years of signal processing advances for multiantenna communications:From theory to mainstream technology[J].IEEE Signal Processing Magazine,2023,40(4):107-117.
[2]WANGX,HOU Y,SHEN X.Narrowband interference cancellation technology of OFDM system based on artificial neural network[C]//IEEE 3rd International Conference on Electronics Technology(ICET).IEEE,2020:675-678.
[3]WANG Y C,ZHOU P,HUANG J.Research status of channel estimation and signal detection techniques for orthogonal time frequency space modulation[J].Journal on Communications,2024,45(9):229-243.
[4]KARTHIKA J,THENMOZHI G,RAJKUMAR M.PAPR re-duction of MIMO-OFDM system with reduced computational complexity SLM scheme[J].Materials Today:Proceedings,2021,37:2563-2566.
[5]ZHONGC,LOU M T,GU C R,et al.Performance optimization and parameters estimation for MIMO-OFDM dual-functional communication -radar systems[J].Digital Communications and Networks,2023,11(2):387-400.
[6]MEI K,LIU J,ZHANG X,et al.A low complexity learning-based channel estimation for OFDM systems with online training[J].IEEE Transactions on Communications,2021,69(10):6722-6733.
[7]JEONW G,PAIK K H,CHO Y S.Two-dimensional MMSEchannel estimation for OFDM systems with transmitter diversity[C]//IEEE 54th Vehicular Technology Conference/ VTC Fall 2001.IEEE,2001:1682-1685.
[8]SCHAFHUBERD,RUPP M,MATZ G,et al.Adaptive identification and tracking of doubly selective fading channels for wireless MIMO-OFDM systems[C]//2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications-SPAWC 2003.IEEE,2003:417-421.
[9]GAOC,ZHAO M,ZHOU S,et al.Blind channel estimation algorithm for MIMO-OFDM systems[J].Electronics Letters,2003,39(19):1420-1422.
[10]BAEKM S,KIM M J,YOU Y H,et al.Semi-blind channel estimation and PAR reduction for MIMO-OFDM system with multiple antennas [J].IEEE Transactions on Broadcasting,2004,50(4):414-424.
[11]QIN Q,GUI L,GONG B,et al.Sparse channel estimation formassive MIMO-OFDM systems over time-varying channels[J].IEEE Access,2018,6:33740-33751.
[12]UWAECHIAA N,SUHAIMI N S M,MAHYUDDIN N M,et al.Beamspace channel estimation in wideband lens antenna array-based mmWave mMIMO-OFDM systems under beam squint[J].Physical Communication,2022,50:101512.
[13]JEYAR,AMUTHA B.Optimized semiblind sparse channel estimation algorithm for MU-MIMO OFDM system[J].Computer Communications,2019,146:103-109.
[14]MEENALAKSHMI M,CHATURVEDI S,DWIVEDI V K.Enhancing channel estimation accuracy in polar-coded MIMO-OFDM systems via CNN with 5G channel models[J].AEU-International Journal of Electronics and Communications,2024,173:155016.
[15]SING H H,BANSAL S.Channel estimation with ISFLA based pilot pattern optimization for MIMO OFDM system[J].AEU-International Journal of Electronics and Communications,2017,81:143-149.
[16]ZHOUY C,HE X Y,LIANG Y.Pilot Design Schemes for Compressed Sensing-Based MIMO-OFDM Channel Estimation[J].Journal of Data Acquisition and Processing,2019,34(4):673-681.
[17]DHANASEKARAN S,RAMALINGAM S,KARTHICK P V,et al.An improved pilot pattern design-based channel estimation in wireless communication using distribution ant colony optimization[J].Simulation Modelling Practice and Theory,2023,129:102820.
[18]LI T,ZHOU F,MA L,et al.Non-orthogonal pilot pattern design and sparse channel estimation for underwater acoustic MIMO-OFDM systems[J].Applied Acoustics,2024,220:109933.
[19]BALIN M F,ABID A,ZOU J.Concrete autoencoders:Differentiable feature selection and reconstruction[C]//International Conference on Machine Learning.PMLR,2019:444-453.
[20]MADDISONC J,MNIH A,TEH Y W.The concrete distribution:A continuous relaxation of discrete random variables[J].arXiv:1611.00712,2016.
[21]HEK M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:770-778.
[22]ZHAO M,ZHONG S,FU X,et al.Deep residual shrinkage networks for fault diagnosis[J].IEEE Transactions on Industrial Informatics,2019,16(7):4681-4690.
[1] YIN Wencui, XIE Ping, YE Chengxu, HAN Jiaxin, XIA Xing. Anomaly Detection of Multi-variable Time Series Data Based on Variational Graph Auto-encoders [J]. Computer Science, 2025, 52(6A): 240700124-8.
[2] XIA Zhuoqun, ZHOU Zihao, DENG Bin, KANG Chen. Security Situation Assessment Method for Intelligent Water Resources Network Based on ImprovedD-S Evidence [J]. Computer Science, 2025, 52(6A): 240600051-6.
[3] AN Rui, LU Jin, YANG Jingjing. Deep Clustering Method Based on Dual-branch Wavelet Convolutional Autoencoder and DataAugmentation [J]. Computer Science, 2025, 52(4): 129-137.
[4] YAN Zhilin, NIE Rencan. Infrared and Visible Image Fusion Cross-modality Contrastive Representation Network Based on Rolling MLP Feature Extraction [J]. Computer Science, 2025, 52(11): 123-130.
[5] LI Zhe, LIU Yiyang, WANG Ke, YANG Jie, LI Yafei, XU Mingliang. Real-time Prediction Model of Carrier Aircraft Landing Trajectory Based on Stagewise Autoencoders and Attention Mechanism [J]. Computer Science, 2024, 51(9): 273-282.
[6] XU Bei, LIU Tong. Semi-supervised Emotional Music Generation Method Based on Improved Gaussian Mixture Variational Autoencoders [J]. Computer Science, 2024, 51(8): 281-296.
[7] CHEN Jie, JIN Linjiang, ZHENG Hongbo, QIN Xujia. Deep Feature Learning and Feature Clustering of Streamlines in 3D Flow Fields [J]. Computer Science, 2024, 51(7): 221-228.
[8] YUAN Zhen, LIU Jinfeng. Denoising Autoencoders Based on Lossy Compress Coding [J]. Computer Science, 2024, 51(6A): 230400172-7.
[9] ZHANG Jie, LU Miaoxin, LI Jiakang, XU Dayong, HUANG Wenxiao, SHI Xiaoping. Residual Dense Convolutional Autoencoder for High Noise Image Denoising [J]. Computer Science, 2024, 51(6A): 230400073-7.
[10] ZHAO Ziqi, YANG Bin, ZHANG Yuanguang. Hierarchical Traffic Flow Prediction Model Based on Graph Autoencoder and GRU Network [J]. Computer Science, 2024, 51(6A): 230400148-6.
[11] PENG Bo, LI Yaodong, GONG Xianfu. Improved K-means Photovoltaic Energy Data Cleaning Method Based on Autoencoder [J]. Computer Science, 2024, 51(6A): 230700070-5.
[12] WU Huinan, XING Hongjie, LI Gang. Deep Multiple-sphere Support Vector Data Description Based on Variational Autoencoder with Mixture-of-Gaussians Prior [J]. Computer Science, 2024, 51(6): 135-143.
[13] LI Shasha, XING Hongjie. Robust Anomaly Detection Based on Adversarial Samples and AutoEncoder [J]. Computer Science, 2024, 51(5): 363-373.
[14] GE Yinchi, ZHANG Hui, SUN Haohang. Differential Privacy Data Synthesis Method Based on Latent Diffusion Model [J]. Computer Science, 2024, 51(3): 30-38.
[15] CHEN Meng, QIAN Rongrong, ZHU Yujia, HUANG Zhenguo. Method of Outdoor CSI Feedback for Massive MIMO Systems Based on Deep Autoencoder [J]. Computer Science, 2024, 51(11A): 231000191-6.
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.