RT-based dataset for 3D radio map under dynamic built-up scenario (1.25kmX1.25km)

The radio map, spectrum environment map (SEM), 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 SEM 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 600 seconds): Collecting data at the height of 2m, 25m, 50m 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, Q. Wu, Y. Huang, X. Cai, et al., “Sparse Bayesian Learning-Based 3D Radio Environment Map Construction—Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery,” IEEE Transactions on Cognitive Communications and Networking, vol.10, pp.80-93, Feb. 2024.
[2]. 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, vol.23, no.10, pp.14560-14574, Oct. 2024.
[3]. Q. Gao, Q. Zhu, Z. Lin et al., "Time-variant radio map reconstruction with optimized distributed sensors in dynamic spectrum environments,", IEEE Internet of Things Journal, early access, Feb. 2025, doi: 10.1109/JIOT.2025.3545542.
[4]. 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.

More details and instrucitons can be found in the guidemanual.pdf.

无线电地图、频谱环境图（Spectrum Environment Map, SEM）或接收信号强度指示（Received Signal Strength Indicator, RSSI）地图，可将不可见的电磁频谱信息可视化，对于认知无线电（Cognitive Radio, CR）网络中的频谱资源监测、管理与安全保障至关重要。其可应用于异常频谱活动检测、辐射源定位、频谱资源管理等场景。

不同三维频谱环境图构建方法的性能，需基于真实场景下的数据进行对比评估。然而，通过频谱感知系统采集三维RSSI数据的难度极大且成本高昂，且此类数据具备不可复现性与不可控性。为此，本数据集采用基于射线追踪（Ray Tracing, RT）的计算方法，在城市场景下获取RSSI数据。该数据集包含两类场景：
1) 动态场景（辐射源持续移动600秒）：在2m、25m、50m、80m四个高度采集数据；
2) 静态场景（辐射源固定不动）：在2m、10m、20m、30m、40m、50m、80m七个高度采集数据。

本数据集已在以下参考文献中得到应用与验证：
[1] J. Wang, Q. Zhu, Z. Lin, Q. Wu, Y. Huang, X. Cai, 等. “基于稀疏贝叶斯学习的三维无线电环境地图构建——采样优化、场景相关字典构建与稀疏恢复”，《IEEE认知通信与网络汇刊》，第10卷，第80-93页，2024年2月。
[2] J. Wang, Q. Zhu, Z. Lin, J. Chen, G. Ding, Q. Wu, G. Gu, Q. Gao. “结合信道遮蔽效应的基于稀疏贝叶斯学习的三维无线电环境地图分层构建方法”，《IEEE无线通信汇刊》，第23卷，第10期，第14560-14574页，2024年10月。
[3] Q. Gao, Q. Zhu, Z. Lin 等. “动态频谱环境中基于优化分布式传感器的时变无线电地图重构”，《IEEE物联网期刊》，提前出版，2025年2月，DOI: 10.1109/JIOT.2025.3545542。
[4] Y. Zhao, Q. Zhu, Z. Lin, L. Guo, Q. Wu, J. Wang, W. Zhong. “面向移动辐射源的频谱环境地图时间预测”，《IET通信》，第17卷，第5期，第538-548页，2023年。

更多细节与使用说明请参阅guidemanual.pdf。