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The spectrum environment map (SEM), which can visualize the information ofinvisible electromagnetic spectrum, is vital for monitoring, management, andsecurity of spectrum resources in cognitive radio (CR) networks. In view of alimited number of spectrum sensors and constrained sampling time, this paperpresents a new three-dimensional (3D) SEM construction scheme based on sparseBayesian learning (SBL). Firstly, we construct a scenario-dependent channeldictionary matrix by considering the propagation characteristic of theinterested scenario. To improve sampling efficiency, a maximum mutualinformation (MMI)-based optimization algorithm is developed for the layout ofsampling sensors. Then, a maximum and minimum distance (MMD) clustering-basedSBL algorithm is proposed to recover the spectrum data at the unsampledpositions and construct the whole 3D SEM. We finally use the simulation data ofthe campus scenario to construct the 3D SEMs and compare the proposed methodwith the state-of-the-art. The recovery performance and the impact of differentsparsity on the constructed SEMs are also analyzed. Numerical results show thatthe proposed scheme can reduce the required spectrum sensor number and hashigher accuracy under the low sampling rate.

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