Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition

doi:10.13190/j.jbupt.2020-188

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (4): 89-94.doi: 10.13190/j.jbupt.2020-188

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Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition

ZHU Jun¹, TANG Bao-yu¹, LI Kai²

1. School of Electronics and Information Engineering, Anhui University, Hefei 230601, China;
2. School of Creativity and Art, Shanghai Tech University, Shanghai 201210, China

Received:2020-09-28 Published:2021-10-13

Abstract

Abstract: To predict the channel capacity of mobile users and appropriately allocate user resources in multiple-input-multiple-output systems, a channel capacity prediction method based on dynamic mode decomposition (DMD) is proposed. Meanwhile, a selective normalized dynamic mode decomposition method based on empirical mode decomposition (ESN-DMD)is proposed to optimize the system.. The simulation results show that the DMD algorithm is only suitable for the prediction of user signals at low moving speed and low complex, while the ESN-DMD algorithm can adapt to the prediction of channel capacity of users with different moving speeds.

Key words: multiple input multiple output, dynamic mode decomposition, empirical mode decomposition, selective normalization, channel capacity prediction

CLC Number:

TN929.5

ZHU Jun, TANG Bao-yu, LI Kai. Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 89-94.

References

[1] 梁晓林, 赵雄文, 李树, 等. 移动端到移动端3D MIMO宽带信道建模与仿真[J]. 北京邮电大学学报, 2017, 40(4):91-97. Liang Xiaolin, Zhao Xiongwen, Li Shu, et al. Modeling and simulation for 3D wideband MIMO mobile-to-mobile radio channels[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(4):91-97.
[2] Xie Hongxiang, Gao Feifei, Jin Shi. An overview of low-rank channel estimation for massive MIMO systems[J]. IEEE Access, 2016, 4:7313-7321.
[3] Baddour K E, Beaulieu N C. Autoregressive modeling for fading channel simulation[J]. IEEE Transactions on Wireless Communications, 2005, 4(4):1650-1662.
[4] Fan Lixing, Wang Qi, Huang Yongming, et al. Performance analysis of low-complexity channel prediction for uplink massive MIMO[J]. IET Communications, 2016, 10(14):1744-1751.
[5] Héas P, Herzet C. Optimal low-rank dynamic mode decomposition[C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New Orleans:IEEE Press, 2017:4456-4460.
[6] le Ngo A C, See J, Phan R C W. Sparsity in dynamics of spontaneous subtle emotions:analysis and application[J]. IEEE Transactions on Affective Computing, 2017, 8(3):396-411.
[7] Proctor J L, Eckhoff P A. Discovering dynamic patterns from infectious disease data using dynamic mode decomposition[J]. International Health, 2015, 7(2):139-145.
[8] Brunton B W, Johnson L A, Ojemann J G, et al. Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition[J]. Journal of Neuroscience Methods, 2016, 258:1-15.
[9] Nayak I, Teixeira F L. Dynamic mode decomposition for prediction of kinetic plasma behavior[C]//2020 International Applied Computational Electromagnetics Society Symposium(ACES). Monterey:IEEE Press, 2020:1-2.
[10] Kuttichira D P, Gopalakrishnan E A, Menon V K, et al. Stock price prediction using dynamic mode decomposition[C]//2017 International Conference on Advances in Computing, Communications and Informatics(ICACCI). Udupi, India:IEEE Press, 2017:55-60.
[11] Schmid P J. Dynamic mode decomposition of numerical and experimental data[J]. Journal of Fluid Mechanics, 2010, 656:5-28.
[12] 周杰, 袁梅, 唐登洪. Von-Mises分布下椭圆散射信道建模[J]. 北京邮电大学学报, 2017, 40(2):36-42. Zhou Jie, Yuan Mei, Tang Denghong. The elliptical scattering channel modeling based on Von-Mises distribution[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):36-42.
[13] Flandrin P, Rilling G, Concalves P. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letters, 2004, 11(2):112-114.
[14] Kopsinis Y, McLaughlin S. Development of EMD-based denoising methods inspired by wavelet thresholding[C]//IEEE Transactions on Signal Processing. Manchester:IEEE Press, 2014:1351-1362.
[15] Yang Zhijing, Jiang Jianmin, Yang Lihua, et al. A new bidimensional EMD algorithmand its applications[C]//2014 9th International Symposium on Communication Systems, Networks & Digital Sign (CSNDSP). Manchester:IEEE Press, 2014:152-155.

Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition

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