Measurement dataset for radio (RSSI) 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.

无线电图谱、无线电环境图谱（REM）或接收信号强度（RSSI）图谱，能够直观地展示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、管理和安全保障至关重要。该图谱在异常频谱活动检测、辐射源定位、频谱资源管理等方面发挥着重要作用。为了对比不同REM构建方法在现实场景下的性能，本研究通过频谱感知系统在校园场景下测量了信号强度。项目包括以下两个数据集：1）原始接收信号强度：在ROI（117mX97m）内采样位置收集RSSI数据；2）构建的REM数据：在非采样位置恢复RSSI数据，从而获得完整的REM。该数据集已在以下参考文献中得到应用和验证。[1]. Q. Zhu等，DEMO摘要：基于无人机3D频谱实时映射系统，2022年IEEE计算机通信研讨会（INFOCOM WKSHPS），纽约，NY，美国，2022年，第1-2页。[2]. Y. Zhao等，移动辐射源频谱环境地图的时间预测，IET通信，第17卷，第5期，第538-548页，2023年。[3] J. Wang等，“基于稀疏贝叶斯学习的3D无线电环境图谱构建——采样优化、场景相关字典构建和稀疏恢复”，IEEE认知通信与网络transactions，第10卷，第80-93页，2024年2月。[4]. J. Wang等，基于稀疏贝叶斯学习的3D无线电环境图谱分层构建，融合信道阴影，IEEE无线通信transactions，2024年，第23卷，第10期，第14560-14574页，2024年10月。[5] Yang Huang等，基于空间电磁频谱感知与态势感知，空间科学技术，第4卷，0109号，DOI：10.34133/space.0109。[6]. Q. Gao等，无线电环境图谱构建的空域传感器布局优化，2024年IEEE全球通信研讨会，2024年，待发表。更详细的信息和说明请参阅guidemanual_measuredCompus_117m_97m.pdf。