Measured dataset for radio map under campus scenario (117mX97m)

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 REM construction methods should be compared based on the data under realistic scenarios. So we measured the signal strength under campus scenario by a spectrum sensing system. This project includes two datasets as
1) Raw received signal strength: Collecting RSSI data at the sampled positions in the ROI (117mX97m).
2) Constructed REM data: Recovery RSSI data at the unsampled positions and obtain a whole REM

The dataset has been applied and validated in the following references.
[1]. Q. Zhu et al., DEMO Abstract: An UAV-based 3D Spectrum Real-time Mapping System, 2022 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), New York, NY, USA, 2022, pp. 1-2.
[2]. Y. Zhao, et al. Temporal prediction for spectrum environment maps with moving radiation sources, IET Communications, vol. 17, no. 5, pp. 538–548, 2023.
[3] J. Wang, 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.
[4]. J. Wang, ea al. Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing, IEEE Transactions on Wireless Communications, IEEE Transactions on Wireless Communications, 2024, vol.23, no.10, pp.14560-14574, Oct. 2024.
[5] Yang Huang, et al. Space-Based Electromagnetic Spectrum Sensing and Situation Awareness. Space Sci Technol. 2024;4:0109. DOI:10.34133/space.0109
[6]. Q. Gao, et al. 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_measuredCompus_117m_97m.pdf.

无线电地图、无线电环境地图（Radio Environment Map, REM）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。

不同无线电环境地图构建方法的性能，需基于真实场景下的数据开展对比研究。为此，本研究通过频谱感知系统在校园场景中完成了信号强度测量。本项目包含两类数据集：
1. 原始接收信号强度数据集：在感兴趣区域（Region of Interest, ROI，117m×97m）内的采样点位采集RSSI数据；
2. 构建型无线电环境地图数据集：对未采样点位的RSSI数据进行恢复，以此得到完整的无线电环境地图。

本数据集已在以下参考文献中得到应用与验证：
[1] Q. Zhu等. 演示摘要：基于无人机的三维频谱实时测绘系统[C]//2022年IEEE计算机通信研讨会（INFOCOM WKSHPS），美国纽约，2022：1-2.
[2] Y. Zhao等. 面向移动辐射源的频谱环境地图时序预测[J]. IET Communications，2023，17(5)：538–548.
[3] J. Wang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[J]. IEEE认知通信与网络汇刊，2024，10：80-93，2月.
[4] J. Wang等. 融合信道遮蔽的三维无线电环境地图分层构建：基于稀疏贝叶斯学习[J]. IEEE无线通信汇刊，2024，23(10)：14560-14574，10月.
[5] Yang Huang等. 天基电磁频谱感知与态势感知[J]. 空间科学与技术，2024，4：0109. DOI:10.34133/space.0109
[6] Q. Gao等. 面向无线电环境地图构建的空间传感器布局优化[C]//2024年IEEE全球通信研讨会，2024，（待出版）.

更多详细信息与操作说明可参阅guidemanual_measuredCompus_117m_97m.pdf。