Sparse Bayesian Learning-Based 3-D Radio Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction, and Sparse Recovery

Sparse Bayesian Learning-Based 3-D Radio Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction, and Sparse Recovery

The radio environment map (REM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. In view of a limited number of spectrum sensors and constrained sampling time, this pa...

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Veröffentlicht in:IEEE transactions on cognitive communications and networking 2024-02, Vol.10 (1), p.80-93
Hauptverfasser: Wang, Jie, Zhu, Qiuming, Lin, Zhipeng, Wu, Qihui, Huang, Yang, Cai, Xuezhao, Zhong, Weizhi, Zhao, Yi
Format: Artikel
Sprache:eng
Schlagworte:
3D radio environment map
Algorithms
Bayesian analysis
channel propagation model
Clustering
clustering algorithm
Cognitive radio
Data models
Dictionaries
Machine learning
mutual information
Optimization
Recovery
Sampling
Sensors
sparse Bayesian learning
Sparse matrices
Three-dimensional displays
Transmitters
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