Antenna Selection on Spatial Modulation: A Machine Learning Approach


Gecgel S., Gortepe C., Karabulut Kurt G. Z.

27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, 24 - 26 April 2019 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2019.8806300
  • City: Sivas
  • Country: Turkey

Abstract

In 5G and beyond wireless communication systems, energy and spectral efficiency requirements should be satisfied while improving the error performance. Massive multiple input multiple output spatial modulation (MIMO-SM) systems are considered to be one of the candidate technologies for next-generation communication systems in terms of providing energy and spectral efficiency requirements. Error performance of massive MIMO-SM systems can be improved with Euclidean distance based antenna selection (EDAS), which strengthens this idea. In this paper, massive MIMO-SM systems are implemented for the first time in a real-time environment. In order to improve the error performance of the system, a machine learning based approach for transmitter antenna selection that has lower complexity than the optimal method. The designed system was on simulation and real-time environments. As a result of the study, in real-time systems nearest neighborhood (k-NN) algorithm's practicality has been demonstrated.