Machine Learning-Based Urban Canyon Path Loss Prediction Using 28 GHz Manhattan Measurements
Machine Learning-Based Urban Canyon Path Loss Prediction Using 28 GHz Manhattan Measurements
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DOI：
10.1109/tap.2022.3152776
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发表时间：
2022-06-01
影响因子：
5.7
通讯作者：
Sellathurai, Mathini
中科院分区：
文献类型：
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作者：
Gupta, Ankit;Du, Jinfeng;Sellathurai, Mathini
毫米波（mm波）的大带宽对于第五代（5G）及以后至关重要，但高路径损耗（PL）需要高度准确的PL预测用于网络规划和优化。与斜率截距拟合的统计模型在捕捉城市峡谷中看到的大的变化不足，而射线追踪，能够表征特定地点的功能，在描述树叶和街道杂波和相关的反射/衍射射线计算面临的挑战。机器学习（ML）在PL预测中很有前途，但面临三个关键挑战：1）测量数据不足; 2）缺乏对新街道的外推; 3）极其复杂的特征/模型。我们提出了一个ML为基础的城市峡谷PL预测模型的基础上广泛的28 GHz测量从曼哈顿街道杂波建模通过光探测和测距（LiDAR）点云数据集和建筑物的网格建筑数据集。我们从点云中提取专家知识驱动的街道杂乱特征，并使用卷积自动编码器积极压缩3-D建筑信息。使用新的逐街训练和测试程序来提高泛化能力，所提出的模型使用杂波和建筑物特征实现了4.8 +/- 1.1 dB的预测误差[均方根误差（RMSE）]，而3GPP视线（LOS）和斜率-截距预测分别为10.6 +/- 4.4和6.5 +/- 2.0 dB，其中标准差表示逐街变化。通过只使用四个最有影响力的杂波特征，达到了5.5 +/- 1.1 dB的RMSE。
Large bandwidth at millimeter wave (mm-wave) is crucial for fifth generation (5G) and beyond, but the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen in urban canyons, whereas ray tracing, capable of characterizing site-specific features, faces challenges in describing foliage and street clutter and associated reflection/diffraction ray calculation. Machine learning (ML) is promising but faces three key challenges in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to new streets; 3) overwhelmingly complex features/models. We propose an ML-based urban canyon PL prediction model based on extensive 28 GHz measurements from Manhattan where street clutters are modeled via a light detection and ranging (LiDAR) point cloud dataset and buildings by a mesh-grid building dataset. We extract expert knowledge-driven street clutter features from the point cloud and aggressively compress the 3-D building information using a convolutional autoencoder. Using a new street-by-street training and testing procedure to improve generalizability, the proposed model using both clutter and building features achieves a prediction error [root-mean-square error (RMSE)] of 4.8 +/- 1.1 dB compared to 10.6 +/- 4.4 and 6.5 +/- 2.0 dB for 3GPP line of sight (LOS) and slope-intercept prediction, respectively, where the standard deviation indicates street-by-street variation. By only using four most influential clutter features, the RMSE of 5.5 +/- 1.1 dB is achieved.