研究目的
To analyze how digital geographical data and radio propagation models may be enhanced and tailored to answer new mmWave simulation requirements, comparing three different propagation methods in an urban 60 GHz scenario.
研究成果
The study demonstrates the importance of fine trees representation in mmWave urban backhaul scenarios. The use of Lidar point cloud data significantly improves the accuracy of propagation models. Indirect antenna alignment can reduce NLoS obstruction losses, offering significant path-loss reduction in deep shadowing cases.
研究不足
The study lacks backhaul measurements to validate the models. The outcomes depend on tree density, type of foliage, season, HR data accuracy, and considered antenna heights.
1：Experimental Design and Method Selection:
The study compares three propagation methods based on different geographical data (OSM buildings, high-resolution vectors, and Lidar point cloud) and a fourth method enabling multi-paths with Lidar data.
2：Sample Selection and Data Sources:
The geographical data models were produced on the same 1 km2 area in a North-american city.
3：List of Experimental Equipment and Materials:
The study relies on Siradel’s Volcano technology for propagation modeling.
4：Experimental Procedures and Operational Workflow:
The propagation models are assessed in the context of a mesh mmWave backhaul network design, testing connectivity on lamppost-to-lamppost pairs.
5：Data Analysis Methods:
The analysis focuses on the predicted excess path-loss (EPL) and the impact of indirect paths on connectivity.
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