Electrical Engineering Unit, Tampere University, 33014 Tampere, Finland.
Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón, Spain.
Sensors (Basel). 2020 Dec 11;20(24):7124. doi: 10.3390/s20247124.
The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.
在 5G 新无线电 (NR) 中使用具备波束赋形功能的基站进行部署需要一个高效的移动性管理系统，以最小的努力和中断可靠运行。在这项工作中，我们提出了两种人工神经网络模型来优化小区级和波束级的移动性管理。这两个模型都由卷积和密集层块组成。基于当前和过去的接收功率测量值以及定位信息，它们分别选择最佳的服务小区和服务波束。所得结果表明，与基准解决方案相比，当接收到的信号强度测量值引入不确定性（表示阴影、干扰等）时，所提出的小区级移动性模型能够维持较强的服务小区，并将切换次数减少多达 94.4%。所提出的波束级移动性管理模型能够主动选择并维持最强的服务波束，即使在对测量值引入高不确定性的情况下也是如此。
