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.

中文翻译：

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用于提高 XL-MIMO 系统能效的天线选择

我们考虑最近提出的超大规模大规模多输入多输出 (XL-MIMO) 系统，其中有数百个天线为较少数量的用户提供服务。由于阵列长度与到用户的距离在同一数量级，给定用户的长期衰落系数随基站（BS）的不同天线而变化。因此，一些天线发射的信号可能以比其他一些天线发射的信号大得多的功率到达用户。从绿色的角度来看，同时激活数百甚至数千个天线是无效的，因为有源天线的耗电射频 (RF) 链会显着增加总能耗。此外，更多的选定天线增加了线性处理所需的功率，例如预编码矩阵计算，和短期信道估计。在本文中，我们建议在 XL-MIMO 系统中部署四种天线选择 (AS) 方法，旨在最大限度地提高总能效 (EE)。此外，采用一些简化的假设，我们推导出 XL-MIMO 系统的 EE 的封闭形式解析表达式，并提出了一种直接的迭代方法来确定能够使其最大化的所选天线的最佳数量。所提出的 AS 方案仅基于长期衰落参数，因此，所选天线组在相对较大的时间/频率间隔内保持有效。比较结果，我们发现基于遗传算法的 AS 方案通常可以实现最佳的 EE 性能，