  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.
