Transmit Antenna Selection for Large-Scale MIMO GSM With Machine Learning

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

IEEE WIRELESS COMMUNICATIONS LETTERS, vol.9, no.1, pp.113-116, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1109/lwc.2019.2944179
  • Journal Name: IEEE WIRELESS COMMUNICATIONS LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.113-116
  • Istanbul Technical University Affiliated: Yes

Abstract

A dynamic and flexible generalized spatial modulation (GSM) framework is proposed for large-scale MIMO systems. Our framework is leveraged on the utilization of machine learning methods for GSM in order to improve the error performance in the presence of time-correlated channels and channel estimation errors. The decision tree and multi-layer perceptron algorithms are adopted as transmit antenna selection approaches. Simulation results indicate that in the presence of real-life impairments, machine learning based approaches provide a superior performance when compared to the classical Euclidean distance based approach. The observations are validated through measurement results over the designed 16 x 4 MIMO test-bed using software defined radio nodes.