# Transmit Antenna Selection for Massive MIMO-GSM with Machine Learning

## TL;DR

This paper introduces a machine learning-based transmit antenna selection method for massive MIMO-GSM systems, improving error performance under real-world impairments through decision trees and neural networks validated by simulations and measurements.

## Contribution

It presents a novel GSM framework utilizing machine learning for antenna selection, enhancing robustness against correlated channels and estimation errors.

## Key findings

- ML approaches outperform classical methods under impairments
- Decision trees and neural networks improve error rates
- Validation through real-world measurements

## Abstract

A dynamic and flexible generalized spatial modulation (GSM) framework is proposed for massive MIMO systems. Our framework is leveraged on the utilization of machine learning methods for GSM in order to improve the error performance in presence of correlated channels and channel estimation errors. Both decision tree and multi-layer perceptrons approaches are adopted for the GSM transmitter. Simulation results indicate that in 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 $16\times 4$ MIMO test-bed using software defined radio nodes.