利用机器学习进行基于几何特征的毫米波无人机路径损耗预测,以实现 5G 及更高标准

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of the Communications Society Pub Date : 2024-08-26 DOI:10.1109/OJCOMS.2024.3450089
Sajjad Hussain;Syed Faraz Naeem Bacha;Adnan Ahmad Cheema;Berk Canberk;Trung Q. Duong
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引用次数: 0

摘要

无人飞行器(UAVs)在现代电信和无线传感器网络中发挥着举足轻重的作用,可在各种环境中为通信和数据收集提供无与伦比的灵活性和机动性。本文对无人机辅助毫米波(mmWave)无线电网络中用于路径损耗(PL)预测的有监督机器学习(ML)模型的性能进行了全面研究。利用一组独特的可解释几何特征,使用典型城市环境中大量光线跟踪(RT)模拟生成的海量数据集,对线性回归(LR)、支持向量回归器(SVR)、K 近邻(KNN)、随机森林(RF)、极梯度提升(XGBoost)和深度神经网络(DNN)六种不同的 ML 模型进行了严格评估。结果表明,射频算法优于其他模型,在测试数据集上显示出卓越的预测性能,均方根误差 (RMSE) 为 2.38 dB。与 3GPP 和 ITU-R 模型相比,所提出的 ML 模型在毫米波无线网络方面表现出更高的准确性。这项研究深入探讨了这些模型对未知环境的适应性,并研究了用稀疏数据集训练这些模型以提高准确性的可行性。研究评估了使用 ML 模型代替大量 RT 计算稀疏训练数据集所减少的计算时间,并提出了训练此类模型的高效算法。此外,我们还分析了 ML 模型对噪声输入特征的敏感性。我们还评估了几何特征的重要性以及依次增加这些特征的数量对模型性能的影响。结果强调了所提出的几何特征的重要性,并证明了 ML 模型在不同城市环境中提供计算效率高且相对准确的 PL 预测的潜力。
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Geometrical Features Based-mmWave UAV Path Loss Prediction Using Machine Learning for 5G and Beyond
Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern telecommunication and wireless sensor networks, offering unparalleled flexibility and mobility for communication and data collection in diverse environments. This paper presents a comprehensive investigation into the performance of supervised machine learning (ML) models for path loss (PL) prediction in UAV-assisted millimeter-wave (mmWave) radio networks. Leveraging a unique set of interpretable geometrical features, six distinct ML models–linear regression (LR), support vector regressor (SVR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN)–are rigorously evaluated using a massive dataset generated from extensive raytracing (RT) simulations in a typical urban environment. Our results demonstrate that the RF algorithm outperforms other models showcasing superior predictive performance for the test dataset with a root mean square error (RMSE) of 2.38 dB. The proposed ML models demonstrate superior accuracy compared to 3GPP and ITU-R models for mmWave radio networks. This study thoroughly investigates the adaptability of these models to unseen environments and examines the feasibility of training them with sparse datasets to improve accuracy. The reduction in computation time achieved by using ML models instead of extensive RT computations for sparse training datasets is evaluated, and an efficient algorithm for training such models is proposed. Additionally, the sensitivity of ML models to noisy input features is analyzed. We also assess the importance of geometrical features and the impact of sequentially increasing the number of these features on model performance. The results emphasize the significance of the proposed geometrical features and demonstrate the potential of ML models to provide computationally efficient and relatively accurate PL predictions in diverse urban environments.
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来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
期刊介绍: The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.
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