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Mobility Prediction For Aerial Base Stations for a Coverage Extension in 5G Networks

机译：5G网络中覆盖扩展的空中基站的移动性预测

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摘要

A promising potential of Unmanned Aerial Vehicles (UAV) in 5G networks is to act as Aerial Base Stations (ABSs) that dynamically extend terrestrial base stations coverage without overloading the infrastructure. However, coverage extension faces crucial challenges such as user mobility and determining the best coordinates for new base station deployment. In this paper, we address this problem based on the prediction of users' spatial distribution that allows Aerial base stations (ABS) to adjust their position accordingly. We first analyze the performance of two machine learning schemes (Long Short Term Memory (LSTM)-based encoder-decoder and self-attention-based Transformer) for user mobility prediction based on a real DataSet. Then, we use these schemes to enhance the ABS deployment algorithm. Numerical results reveal significant gains when applying the proposed mobility prediction models over traditional deployment algorithms. In four hours of the day, both the Transformer and LSTM based models show, respectively, more than 31% and 22% gain in coverage rates compared to regular deployment schemes.
机译：5G网络中无人驾驶飞行器（UAV）的有希望的潜力是充当空中基站（ABS），其动态地延长地面基站覆盖范围而不重载基础设施。但是，覆盖扩展面临着诸如用户移动性等至关重要的挑战，并确定新的基站部署的最佳坐标。在本文中，我们基于对允许空中基站（ABS）相应地调节其位置的用户空间分布来解决这个问题。我们首先分析两台机器学习方案的性能（用于基于真实数据集的用户移动性预测的两个机器学习方案（长期内存（LSTM）的编码器和自我关注的变压器）。然后，我们使用这些方案来增强ABS部署算法。数值结果显示在传统部署算法上应用所提出的移动预测模型时显着提升。在一天中的四个小时内，与常规部署方案相比，变压器和基于LSTM的模型分别显示出超过31％和22％的覆盖率增益。

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