TY - JOUR
T1 - Antenna Selection for Improving Energy Efficiency in XL-MIMO Systems
AU - Marinello, José Carlos
AU - Abrão, Taufik
AU - Amiri, Abolfazl
AU - De Carvalho, Elisabeth
AU - Popovski, Petar
PY - 2020/11
Y1 - 2020/11
N2 - We consider the recently proposed extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, with some hundreds of antennas serving a smaller number of users. Since the array length is of the same order as the distance to the users, the long-term fading coefficients of a given user vary with the different antennas at the base station (BS). Thus, the signal transmitted by some antennas might reach the user with much more power than that transmitted by some others. From a green perspective, it is not effective to simultaneously activate hundreds or even thousands of antennas, since the power-hungry radio frequency (RF) chains of the active antennas increase significantly the total energy consumption. Besides, a larger number of selected antennas increases the power required by linear processing, such as precoding matrix computation, and short-term channel estimation. In this paper, we propose four antenna selection (AS) approaches to be deployed in XL-MIMO systems aiming at maximizing the total energy efficiency (EE). Besides, employing some simplifying assumptions, we derive a closed-form analytical expression for the EE of the XL-MIMO system, and propose a straightforward iterative method to determine the optimal number of selected antennas able to maximize it. The proposed AS schemes are based solely on long-term fading parameters, thus, the selected antennas set remains valid for a relatively large time/frequency intervals. Comparing the results, we find that the genetic-algorithm based AS scheme usually achieves the best EE performance, although our proposed highest normalized received power AS scheme also achieves very promising EE performance in a simple and straightforward way.
AB - We consider the recently proposed extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, with some hundreds of antennas serving a smaller number of users. Since the array length is of the same order as the distance to the users, the long-term fading coefficients of a given user vary with the different antennas at the base station (BS). Thus, the signal transmitted by some antennas might reach the user with much more power than that transmitted by some others. From a green perspective, it is not effective to simultaneously activate hundreds or even thousands of antennas, since the power-hungry radio frequency (RF) chains of the active antennas increase significantly the total energy consumption. Besides, a larger number of selected antennas increases the power required by linear processing, such as precoding matrix computation, and short-term channel estimation. In this paper, we propose four antenna selection (AS) approaches to be deployed in XL-MIMO systems aiming at maximizing the total energy efficiency (EE). Besides, employing some simplifying assumptions, we derive a closed-form analytical expression for the EE of the XL-MIMO system, and propose a straightforward iterative method to determine the optimal number of selected antennas able to maximize it. The proposed AS schemes are based solely on long-term fading parameters, thus, the selected antennas set remains valid for a relatively large time/frequency intervals. Comparing the results, we find that the genetic-algorithm based AS scheme usually achieves the best EE performance, although our proposed highest normalized received power AS scheme also achieves very promising EE performance in a simple and straightforward way.
KW - Extra large-scale MIMO
KW - antenna selection
KW - energy efficiency
KW - near-field
KW - non-stationary
KW - spectral efficiency
KW - visibility region (VR)
UR - http://www.scopus.com/inward/record.url?scp=85096239624&partnerID=8YFLogxK
U2 - 10.1109/TVT.2020.3022708
DO - 10.1109/TVT.2020.3022708
M3 - Journal article
VL - 69
SP - 13305
EP - 13318
JO - I E E E Transactions on Vehicular Technology
JF - I E E E Transactions on Vehicular Technology
SN - 0018-9545
IS - 11
M1 - 9187980
ER -