Multi-user MIMO (MU-MIMO) is a technique that improves spectral efficiency by allowing concurrent communication between one access point (AP) and multiple clients. In practice, the expected gain is not always achieved and is sometimes even negative. We experimentally demonstrate that the downlink MU-MIMO performance in a practical network not only depends on the client’s channel but is also influenced by factors that are not captured by conventional models, such as client motion and device type. We propose a data-driven algorithm with a low computational complexity that determines whether a client should operate in MU mode and the MU-MIMO group for clients in MU mode. Such a mode and group selection algorithm is based on a sequence of channel state information (CSI), SNR, and client device type. The algorithm can automatically adapt to the motion and characteristics of individual clients. Experimental results using implementation on a commodity 802.11ac AP show that the proposed data-driven mode and group selection algorithm can improve network throughput by up to 35% over existing algorithms based on conventional models. We also show that the proposed data-driven algorithm has limited sensitivity to environmental changes and can be deployed into new environments without retraining.

中文翻译：

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在商品802.11ac网络中实现的下行MU-MIMO数据驱动模式和组选择

多用户MIMO（MU-MIMO）是一种通过允许一个接入点（AP）与多个客户端之间并发通信来提高频谱效率的技术。实际上，并非总是能达到预期的收益，有时甚至是负数。我们通过实验证明，实际网络中的下行链路MU-MIMO性能不仅取决于客户端的信道，而且还受到传统模型无法捕获的因素（例如客户端运动和设备类型）的影响。我们提出了一种具有低计算复杂度的数据驱动算法，该算法确定客户端是否应以MU模式运行，并确定客户端是否应以MU模式运行。这种模式和组选择算法基于信道状态信息（CSI），SNR和客户端设备类型的序列。该算法可以自动适应单个客户的运动和特征。使用商用802.11ac AP实施的实验结果表明，与基于传统模型的现有算法相比，所提出的数据驱动模式和组选择算法可以将网络吞吐量提高多达35％。我们还表明，提出的数据驱动算法对环境变化的敏感性有限，可以在不进行重新训练的情况下部署到新环境中。