计算机科学 ›› 2025, Vol. 52 ›› Issue (11): 298-305.doi: 10.11896/jsjkx.241000004
王安义, 李婼嫚, 李新宇, 李明珠
WANG Anyi, LI Ruoman, LI Xinyu, LI Mingzhu
摘要： 随着移动通信技术不断创新与发展,对通信的可靠性和数据传输性能提出了更高要求。准确高效地获取信道状态信息(Channel State Information,CSI)是充分发挥无线通信系统各项技术潜能的关键前提。针对多输入多输出-正交频分复用(MIMO-OFDM)系统中导频开销大及信道估计准确性低的问题,设计了一种基于深度学习的导频设计和信道估计联合优化方案(AE-DRSN)。该方案首先利用Concrete自编码器来识别和选择具有最大信息量的导频位置,从而实现导频优化。然后,将优化后的导频位置输入深度残差收缩网络获取更精确的CSI,进一步完成信道的精确估计。实验结果表明,与传统的信道估计方法相比,基于AE-DRSN的联合优化方案在少量的导频开销下仍能实现高精度的信道估计,充分验证了该方案的有效性。
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| [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. |
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