Dataset for 3D radio (RSSI) map under urban scenario (1.25kmX1.25km)
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The radio map, radio environment map (REM), or RSSI map, can visualize the information of invisible electromagnetic spectrum, and is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. It is useful for the abnormal spectral activity detection, radiation source localization, spectrum resource management, etc. The performance of different 3D REM construction methods should be compared based on the data under realistic scenarios. However, 3D RSSI data collecting by a spectrum sensing system is quite different and high costing. Moreover, it's unrepeatable and uncontrolable. So we obtained the RSSI by the RT-based calculation method under urban scenario . It includes two datasets as 1) dynamic scenario (radiation sources are moving for 300 seconds): Collecting data at the height of 2m and 80m 2) static scenario (radiation sources are fixed) : Collecting data at the height of 2m, 10m, 20m, 30m, 40m, 50m, 80m. The dataset has been applied and validated in the following references. [1]. J. Wang, Q. Zhu, Z. Lin, J. Chen, G. Ding, Q. Wu, G. Gu, Q. Gao. “Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing,” IEEE Transactions on Wireless Communications, early access, 2024, doi: 10.1109/TWC.2024.3416447. [2]. Y. Zhao, Q. Zhu, Z. Lin, L. Guo, Q. Wu, J. Wang, W. Zhong. “Temporal prediction for spectrum environment maps with moving radiation sources,” IET Communications, vol. 17, no. 5, pp. 538–548, 2023. [3]. Q. Gao, Q. Zhu, Z. Lin, Y. Zhao, J. Wang, W. Zhong, Y. Huang, Q. Wu. “Spatial Sensor Layout Optimization for Radio Environment Map Construction,” 2024 IEEE Globecom Workshops, 2024, for publication More details and instrucitons can be found in the guidemanual.pdf.
无线电地图、无线电环境地图（REM）或信号强度地图，能够直观呈现无形电磁频谱的信息，对于认知无线电（CR）网络中频谱资源的监控、管理及安全保障至关重要。该地图在异常频谱活动检测、辐射源定位、频谱资源管理等方面具有显著应用价值。针对不同三维REM构建方法的性能比较，应基于实际场景下的数据进行。然而，采用频谱感知系统收集三维RSSI数据的过程既复杂又昂贵，且具有不可重复性和不可控性。因此，本研究在都市场景下，通过基于RT的计算方法获取了RSSI数据。该数据集包括两个部分：1）动态场景（辐射源移动300秒）：在2米和80米高度收集数据；2）静态场景（辐射源固定）：在2米、10米、20米、30米、40米、50米、80米高度收集数据。该数据集已在以下文献中得到应用和验证。[1]. W. Wang, Q. Zhu, Z. Lin, J. Chen, G. Ding, Q. Wu, G. Gu, Q. Gao. “基于稀疏贝叶斯学习的高层三维无线电环境地图构建方法研究，” IEEE Transactions on Wireless Communications，early access，2024，doi: 10.1109/TWC.2024.3416447。[2]. Y. Zhao, Q. Zhu, Z. Lin, L. Guo, Q. Wu, J. Wang, W. Zhong. “具有移动辐射源的频谱环境地图的时序预测，” IET Communications，vol. 17，no. 5，pp. 538–548，2023。[3]. Q. Gao, Q. Zhu, Z. Lin, Y. Zhao, J. Wang, W. Zhong, Y. Huang, Q. Wu. “无线电环境地图构建中的空间传感器布局优化，” 2024 IEEE Globecom Workshops，2024，待发表。更多细节和指导说明请参阅 guidemanual.pdf。
