Antenna Selection on Spatial Modulation: A Machine Learning Approach

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 MIMOSM 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.