In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.

中文翻译：

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子阵列交换XL-MIMO系统中频谱效率最大化的准分布式天线选择

在本文中，我们考虑了迫零（ZF）预编码的超大规模大规模MIMO（XL-MIMO）系统的下行链路（DL）。由于与全数字实现相关的高成本，功耗和互连带宽，基站（BS）与有限数量的射频（RF）收发器一起工作。用子阵列交换架构实现的BS选择每个子阵列内的有源天线组以发送DL信号。这项工作提出了有效的资源分配（RA）程序，以执行联合天线选择（AS）和功率分配（PA），以最大化在不同负载设置下运行的XL-MIMO系统的DL频谱效率（SE）。评估并比较了两种基于遗传算法（GA）的元启发式RA程序，协调数据大小和计算复杂性。一种算法基于准分布式方法，而另一种算法则基于常规的集中处理。数值结果表明，基于准分布式GA的过程可在性能，复杂性和交换的协调数据之间进行适当的权衡。同时，通过适当的系统操作设置，它的性能优于集中式过程。