3D Spectrum Mapping and Reconstruction under Multi-Radiation Source Scenarios

no code implementations • 13 Mar 2024 • Wang Jie, Lin Zhipeng, Zhu Qiuming, Wu Qihui, Lan Tianxu, Zhao Yi, Bai Yunpeng, Zhong Weizhi

Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data.

Management

Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing

no code implementations • 13 Mar 2024 • Wang Jie, Zhu Qiuming, Lin Zhipeng, Chen Junting, Ding Guoru, Wu Qihui, Gu Guochen, Gao Qianhao

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recovery the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed.

Computational Efficiency