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

Description

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.

Steps to reproduce

The RT-based simulation data files include two scenarios, dynamic scenario (radiation sources are moving) data at the height of 2m, 25m, 50m and 80m, and static scenario (radiation sources are fixed) data at the height of 2m, 10m, 20m, 30m, 40m, 50m, 80m. The received signal strength data in the dynamic scenario is a .mat file in the format of a 250*250*600 tensor, where x, y represents the spatial grid dimension, z represents the time dimension, and the numerical value represents the signal strength in dBm. The data file sizes for the heights of 2m, 25m, 50m, 80m are 225MB, 259MB, 263MB, and 263MB, respectively. The received signal strength data in the static scenario is a .mat file in the format of a 250*250 matrix, where x, y represents the spatial grid dimension, and the numerical value represents the signal strength in dBm. The data file sizes for the heights of 10m, 20m, 30m, 40m, and 50m are 363KB, 385KB, 400KB, 406KB, and 405KB, respectively.

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