Research Article
Optimal Transmit Antenna Selection for Massive MIMO Systems
@INPROCEEDINGS{10.1007/978-3-030-93709-6_9, author={Shenko Chura Aredo and Yalemzewd Negash and Yihenew Wondie and Feyisa Debo and Rajaveerappa Devadas and Abreham Fikadu}, title={Optimal Transmit Antenna Selection for Massive MIMO Systems}, proceedings={Advances of Science and Technology. 9th EAI International Conference, ICAST 2021, Hybrid Event, Bahir Dar, Ethiopia, August 27--29, 2021, Proceedings, Part I}, proceedings_a={ICAST}, year={2022}, month={1}, keywords={Antenna selection Energy efficiency Massive MIMO mmWave Precoding}, doi={10.1007/978-3-030-93709-6_9} }- Shenko Chura Aredo
Yalemzewd Negash
Yihenew Wondie
Feyisa Debo
Rajaveerappa Devadas
Abreham Fikadu
Year: 2022
Optimal Transmit Antenna Selection for Massive MIMO Systems
ICAST
Springer
DOI: 10.1007/978-3-030-93709-6_9
Abstract
Antenna selection in Multiple input Multiple Output (MIMO) is a signal processing method in which the elements of Radio Frequency (RF) chain are switched to their corresponding subset of antennas. Due to the large number of RF transceivers, antenna selection resolves the complexity and power consumption. In this paper, a sub-optimal antenna selection algorithm that combines two selection techniques is proposed. The algorithm leverages the use of minimum signal to noise ratio (SNR) at the cell edge and dynamic channel condition due to mobility. To apply fractional transmit power re-allocation at sub 6 GHz and mmWave frequencies, the same number of RF components are set to be active and the rest to sleep mode after adaptive selection. As a result, the branch in the array with the best signal quality is chosen and applied in iteration until the desired value is reached however re-selection boosts EE at the expense of total rate. In comparison to complete array consumption and random selection, the results show that the algorithm outperforms random selection and achieves higher energy efficiency. Furthermore, capacity loss due to selection is offset by using nonlinear precoding at the expense of complexity.
