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A deep learning-based antenna selection approach in MIMO system

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Abstract

Multiple Input Multiple Output (MIMO) systems offer interesting advantages in terms of throughput and reliability thanks to the diversity and spatial multiplexing technique. However, the system complexity and cost, caused by the RF elements, are still an ongoing challenge. To remedy this, antenna selection technique arises as an alternative to reduce the requirement on RF chains by selecting a subset of the available transmit antennas. This will decrease the overall cost without a significant degradation of the performance. The exhaustive search method, considered as optimal, scans all possible antennas subsets to select the optimal one. It involves a high computational complexity even more with the increase in the number of the antennas. Hence, transferring this huge computation to offline system seems to be the only way to benefit from this technique. In this paper, we propose to analyse the performance of the deep neural network to construct a deep learning decision server to assist the MIMO system for making intelligent decision for antenna selection that maximize either the capacity or the energy efficiency. Extensive simulations show that the designed network can suitably resolve the antennas selection problem with high accuracy and robustness against imperfect channel estimation while leading to low online computational time compared to different optimization-driven decision making method.

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References

  1. Gorokhov, A., Gore, D. A., & Paulraj, A. J. (2003). Receive antenna selection for MIMO spatial multiplexing: Theory and algorithms. IEEE Transactions on Signal Processing, 51(11), 2796–2807.

    Article  Google Scholar 

  2. Molisch, A. F., & Win, M. Z. (2004). MIMO systems with antenna selection. IEEE Microwave Magazine, 5(1), 46–56.

    Article  Google Scholar 

  3. Sanayei, S., & Nosratinia, A. (2004). Antenna selection in MIMO systems. IEEE Communications Magazine, 42(10), 68–73.

    Article  Google Scholar 

  4. Molisch, A. F., Win, M. Z., Choi, Y.-S., & Winters, J. H. (2005). Capacity of MIMO systems with antenna selection. IEEE Transactions on Wireless Communications, 4(4), 1759–1772.

    Article  Google Scholar 

  5. Choi, Y.-S., Molisch, A.F., Win, M.Z., Winters, J.H. (2003). Fast algorithms for antenna selection in MIMO systems. In: IEEE 58th Vehicular Technology Conference, vol. 3, pp. 1733–1737. IEEE.

  6. Bouchibane, F., Bensebti, M. (2017). Parameter tuning of artificial bee colony algorithm for energy efficiency optimization in massive MIMO systems. In: 2017 Seminar on Detection Systems Architectures and Technologies (DAT). pp. 1–5. https://doi.org/10.1109/DAT.2017.7889188

  7. Bouchibane, F. Z., & Bensebti, M. (2018). Artificial bee colony algorithm for energy efficiency optimisation in massive MIMO system. International Journal of Wireless and Mobile Computing, 15(2), 97–104.

    Article  Google Scholar 

  8. Bouchibane, F., Tayakout, H., Ziane, N., Siahmed, F., Hebib, S. (2023). Upgraded-abc algorithm for antenna selection in energy efficient massive MIMO system. In: 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS), pp. 1–5. https://doi.org/10.1109/ICAECCS56710.2023.10105114

  9. Singh, C., Kishoreraja, P. (2021). An energy efficient resource allocation and transmit antenna selection scheme in mm-wave using massive MIMO technology.

  10. El-Khamy, S., Moussa, K., & El-Sherif, A. (2017). A smart multi-user massive MIMO system for next g wireless communications using evolutionary optimized antenna selection. Telecommunication Systems, 65(2), 309–317.

  11. Wen, C.-K., Shih, W.-T., & Jin, S. (2018). Deep learning for massive MIMO CSI feedback. IEEE Wireless Communications Letters, 7(5), 748–751.

    Article  Google Scholar 

  12. He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018). Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wireless Communications Letters, 7(5), 852–855.

    Article  Google Scholar 

  13. Nasir, Y. S., & Guo, D. (2019). Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks. IEEE Journal on Selected Areas in Communications, 37(10), 2239–2250.

    Article  Google Scholar 

  14. Peken, T., Adiga, S., Tandon, R., & Bose, T. (2020). Deep learning for SVD and hybrid beamforming. IEEE Transactions on Wireless Communications, 19(10), 6621–6642.

    Article  Google Scholar 

  15. Joung, J. (2016). Machine learning-based antenna selection in wireless communications. IEEE Communications Letters, 20(11), 2241–2244.

    Article  Google Scholar 

  16. Yang, P., Zhu, J., Xiao, Y., & Chen, Z. (2019). Antenna selection for MIMO system based on pattern recognition. Digital Communications and Networks, 5(1), 34–39.

    Article  Google Scholar 

  17. Cai, J.-X., Zhong, R., & Li, Y. (2019). Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks. PLoS ONE, 14(5), 1–16.

    Article  Google Scholar 

  18. An, W., Zhang, P., Xu, J., Luo, H., Huang, L., & Zhong, S. (2020). A novel machine learning aided antenna selection scheme for MIMO internet of things. Sensors, 20(8), 2250.

    Article  Google Scholar 

  19. Gao, X., Dai, L., Han, S., Chih-Lin, I., & Heath, R. W. (2016). Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE Journal on Selected Areas in Communications, 34(4), 998–1009. https://doi.org/10.1109/JSAC.2016.2549418

    Article  Google Scholar 

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Authors and Affiliations

  1. Telecom Division, Center of Development of Advanced Technologies (CDTA), 16000, Baba Hassan, Algeria

    Fatima Zohra Bouchibane & Hakim Tayakout

  2. Institute of Electrical Engineering and Electronics, University Mhamed Bougara, 35000, Boumerdes, Algeria

    Elhocine Boutellaa

Authors
  1. Fatima Zohra Bouchibane
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  2. Hakim Tayakout
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  3. Elhocine Boutellaa
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Correspondence to Fatima Zohra Bouchibane.

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Bouchibane, F.Z., Tayakout, H. & Boutellaa, E. A deep learning-based antenna selection approach in MIMO system. Telecommun Syst 84, 69–76 (2023). https://doi.org/10.1007/s11235-023-01036-y

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