In line-of-sight massive MIMO, there are use cases where the channel vectors of some users become highly correlated. Highly correlated users lead to a large reduction in the sum-rate of linear and nonlinear precoders with max-min power control. To alleviate the loss in the sum-rate, some users can be dropped and rescheduled. The optimal dropping strategy can be found by an exhaustive search. In this letter, a successive filter-based dropping algorithm (SFDA) is proposed, which improves upon the existing dropping algorithms in the literature. At each step, the user with the highest filter norm is dropped. By comparing the sum-rate of all the steps, the best set of dropped users is found. In contrast to previous threshold-based algorithms in the literature, SFDA does not require a predefined threshold for the spatial correlation of users. Compared to an exhaustive search, the complexity of SFDA is reduced. Simulations results show when a 100 antennas base station serves 10 users, SFDA improves the 5th percentile sum-rate compared to previous algorithms in the literature up to 6 bits/channel use.

中文翻译：

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一种改进的基于连续滤波器的大规模 MIMO 丢弃算法，具有最大-最小功率控制


在视距大规模 MIMO 中，存在一些用户的信道向量变得高度相关的用例。高度相关的用户导致具有最大最小功率控制的线性和非线性预编码器的总速率大大降低。为了减轻总速率的损失，可以删除一些用户并重新安排。可以通过穷举搜索找到最优的丢弃策略。在这封信中，提出了一种基于连续过滤器的丢弃算法（SFDA），该算法改进了文献中现有的丢弃算法。在每一步中，过滤器范数最高的用户都会被删除。通过比较所有步骤的总速率，找到最佳的掉线用户集。与文献中之前基于阈值的算法相比，SFDA 不需要为用户的空间相关性预先定义阈值。与穷举搜索相比，SFDA 的复杂性降低了。仿真结果显示，当 100 个天线基站为 10 个用户提供服务时，与文献中之前的算法相比，SFDA 将总速率提高了第 5 个百分点，最高可达 6 位/信道使用。