Performance Enhancement of mmWave MIMO Systems Using Machine Learning

Fawad Ahmad, Waqas Bin Abbas, Salman Khalid, Farhan Khalid, Ibrar Khan, Fahad Aldosari

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number9828035
Pages (from-to)73068-73078
Number of pages11
JournalIEEE Access
Early online date13 Jul 2022
Publication statusPublished - 18 Jul 2022


Dive into the research topics of 'Performance Enhancement of mmWave MIMO Systems Using Machine Learning'. Together they form a unique fingerprint.

Cite this