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Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

In Time-Division-Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to variation of spatial signature vectors in vehicular scenarios. To mitigate this, downlink beams must be adjusted according to changing propagation dynamics. This can be achieved by modeling spatial signature vectors in the uplink period and then predicting them for new mobile position in the downlink period. This paper examines time delay feedforward neural network (TDFN), adaptive linear neuron (ADALINE) network and autoregressive (AR) filter to predict spatial signature vectors. We show that predictions of spatial signatures using these models provide certain level of performance improvement compared to conventional beamforming method under varying mobile speed and filter (delay) order conditions. We observe that TDFN outperforms ADALINE and AR modeling for downlink SNR improvement and relative error improvement with high mobile speed and higher filter order/delay conditions in fixed Doppler case in multipaths.

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Author information

Authors and Affiliations

  1. Dept. of Electronics and Computer Ed., Kocaeli University, 41300, Izmit, Kocaeli, Turkey

    Halil Yigit

  2. Dept. of Computer Engineering, Kocaeli University, 41040, Izmit, Kocaeli, Turkey

    Adnan Kavak

  3. Dept. of Mechatronics Engineering, Kocaeli University, 41040, Izmit, Kocaeli, Turkey

    Metin Ertunc

Authors
  1. Halil Yigit
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  2. Adnan Kavak
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  3. Metin Ertunc
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Yigit, H., Kavak, A., Ertunc, M. (2004). Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_40

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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