Performance Enhancement of mmWave MIMO Systems Using Machine Learning

Abstract

For future wireless communication, millimeter wave (mmWave) coupled with the massive multiple-input multiple-output (MIMO) are key technologies to overcome the huge data rate requirements. Although massive MIMO greatly improves the spectral efficiency (SE) of the system, the use of large antenna arrays not only increases the computational complexity it may also decrease the energy efficiency. Focusing on improvement in energy efficiency, we propose a low-complexity solution for joint transmit antenna selection and hybrid precoder design for multi-user mmWave Massive MIMO communication systems. Particularly, considering a partially connected hybrid architecture, binary particle swarm optimization and deep neural network (DNN) algorithms are employed for transmit antenna selection and analog precoder design, respectively. Results show that the proposed solution performs very close, in terms of spectral efficiency, to the optimal exhaustive search based antenna selection and singular value decomposition based precoder design with lower computational complexity. It is also shown that the proposed solution also improves the energy efficiency of the system. Finally, the proposed solution is not very sensitive to channel imperfections.

Keywords

• Spectral efficiency
• deep learning
• antenna selection
• hybrid precoding
• energy efficiency