IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v65y2017i2d10.1007_s11235-016-0232-9.html
   My bibliography  Save this article

A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection

Author

Listed:
  • Said El-Khamy

    (Alexandria University)

  • Karim Moussa

    (Alexandria Institute of Engineering and Technology)

  • Amr El-Sherif

    (Alexandria University)

Abstract

Massive multi-user multiple input multiple output is a very promising technique for next generation communication. It can provide further improvement to the wireless communication link performance due to relatively large number of transmitting antennas equipped at the base station. This large number has the potential to improve the performance but these systems suffer from high cost, complexity and large size. The transmit antenna selection (TAS) can be employed to solve these problems and with the objective of maximizing the achievable ergodic capacity. In this paper, The TAS problem is solved using a modified evolutionary algorithm, in particular, the chaotic binary particle swarm optimization algorithm is utilized for maximization of the total achievable capacities with reduced system complexity and minimized hardware cost. The multi-user is supported using the zero-forcing baseband beamforming. The convergence of the proposed evolutionary algorithm is proved and its performance is evaluated using numerical analysis. The presented results proved that the proposed evolutionary algorithm can achieve competitive ergodic capacities while utilizing small number of radio frequency chains. In addition, the proposed technique is better than random and maximum norm TAS. It can achieve near optimal performance that can be achieved by exhaustive search TAS but with reduced computational complexity.

Suggested Citation

  • Said El-Khamy & Karim Moussa & Amr El-Sherif, 2017. "A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(2), pages 309-317, June.
    • Handle: RePEc:spr:telsys:v:65:y:2017:i:2:d:10.1007_s11235-016-0232-9
    DOI: 10.1007/s11235-016-0232-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-016-0232-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-016-0232-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Minjoong Rim & Seungyeob Chae & Chung G. Kang, 2019. "MIMO receivers considering preamble collisions for grant-free random access in machine type communication systems," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(2), pages 185-191, February.
    2. Ghasan Ali Hussain & Lukman Audah, 2020. "A novel BCS code in a downlink LTE system over an LTE-MIMO channel," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(4), pages 467-476, August.
    3. Hilary I. Okagbue & Muminu O. Adamu & Timothy A. Anake & Ashiribo S. Wusu, 2019. "Nature inspired quantile estimates of the Nakagami distribution," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(4), pages 517-541, December.
    4. Fatima Zohra Bouchibane & Hakim Tayakout & Elhocine Boutellaa, 2023. "A deep learning-based antenna selection approach in MIMO system," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 84(1), pages 69-76, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:telsys:v:65:y:2017:i:2:d:10.1007_s11235-016-0232-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.