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Leveraging Deep Learning for MmWave Channel Impulse Response Prediction

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Advances on Intelligent Computing and Data Science (ICACIn 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 179))

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Abstract

In communication systems research, wireless channel estimation is a challenging problem due to the dual requirements of real-time implementation and high estimation accuracy. This work presents a Long-Short Term Memory (LSTM) based deep learning (DL) model for the prediction of mmWave channel response for real-time and real-world non-stationary channel scenarios. The DL model trains on the predefined history of channel impulse response (CIR) data along with two other features viz. root-mean-square delay spread values and transmitter-receiver update distance, which are also varying in time with the CIR. The objective is to generate an estimate of CIRs using prediction through the DL model. For training the model, a sample dataset is generated through the open-source channel simulation software NYUSIM which produces samples of CIRs using measurement-based channel models based on various multipath channel parameters. From the DL model test results, it is observed that the proposed approach provides a viable lightweight solution for root-mean-square delay spread values channel prediction.

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Notes

  1. 1.

    NYUSIM Version 3.1 available at: https://wireless.engineering.nyu.edu/nyusim/

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Author information

Authors and Affiliations

  1. Aligarh Muslim University, Aligarh, India

    Mohd. Sharique & Chirag Gangal

  2. University of Chester, Chester, UK

    Mohammad Samar Ansari

Authors
  1. Mohd. Sharique
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  2. Mohammad Samar Ansari
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  3. Chirag Gangal
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Corresponding author

Correspondence to Mohammad Samar Ansari .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. Information Systems Department, School of Computing, Universiti Utara Malaysia (UUM), Sintok, Malaysia

    Fathey Mohammed

  3. Faculty of Sciences, University of Hassan II Casablanca, Casablanca, Morocco

    Errais Mohammed

  4. Department of Computing and Technology, Nottingham Trent University, Nottingham, UK

    Tawfik Al-Hadhrami

  5. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem

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Sharique, M., Ansari, M.S., Gangal, C. (2023). Leveraging Deep Learning for MmWave Channel Impulse Response Prediction. In: Saeed, F., Mohammed, F., Mohammed, E., Al-Hadhrami, T., Al-Sarem, M. (eds) Advances on Intelligent Computing and Data Science. ICACIn 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-031-36258-3_28

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