论文标题
机器学习，用于预测具有多个访问的无人机部署
Machine Learning for Predictive Deployment of UAVs with Multiple Access
论文作者
论文摘要
在本文中，研究了一个基于机器学习的部署框架，该框架的无人机（UAV）进行了研究。在考虑的模型中，将无人机作为飞行基站（BS）部署，以卸载地面BSS的繁重流量。由于时变流量分布，引入了基于短期记忆（LSTM）的长期记忆（LSTM）算法来预测未来的蜂窝流量。为了预测用户服务分布，提出了基于高斯混合模型（GMM）的关节K-均值和期望最大化（EM）算法的KEG算法，以确定每个无人机的服务区域。根据预测的流量，得出了最佳的无人机位置，并比较了三种多访问技术，以最大程度地减少总发射功率。仿真结果表明，与没有流量预测的常规方法相比，所提出的方法可以降低总功耗的24％。此外，与频域多访问（FDMA）和时域多次访问（TDMA）相比，多个访问率（RSMA）具有较低所需的传输功率。
In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future cellular traffic. To predict the user service distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining the service area of each UAV. Based on the predicted traffic, the optimal UAV positions are derived and three multi-access techniques are compared so as to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24\% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA).
