Deep Generative Model Learning For Blind Spectrum Cartography with NMF-Based Radio Map Disaggregation
Deep Generative Model Learning For Blind Spectrum Cartography with NMF-Based Radio Map Disaggregation
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DOI：
10.1109/icassp39728.2021.9413382
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
S. Shrestha;Xiao Fu;Min-Fong Hong
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文献类型：
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作者：
S. Shrestha;Xiao Fu;Min-Fong Hong
频谱制图（SC）旨在估计多方面（例如，空间、频率和时间）干扰水平。早期的SC方法依赖于关于无线电地图的模型假设，例如，稀疏性和平滑性，在关键场景下可能严重违反，例如，在严重的阴影下。最近的数据驱动方法训练深度生成网络来提取复杂场景的简约表示，以提高SC的性能。挑战在于，这种学习问题的状态空间非常大-由关键问题成分的不同组合引起，例如，发射器的数量、发射器的载波频率和发射器位置。在如此巨大的空间上学习，在样本复杂性和训练时间方面可能代价高昂;它也经常导致泛化问题。我们的方法集成了模型和数据驱动方法的优点，大大“缩小”了状态空间。具体而言，所提出的学习范例仅需要学习单个发射器的无线电地图的生成模型（与多个发射器的众多组合相反），利用基于非负矩阵分解（NMF）的发射器分解过程。数值证据表明，所提出的方法优于最先进的纯模型驱动和纯数据驱动的方法。
Spectrum cartography (SC) aims at estimating the multi-aspect (e.g., space, frequency, and time) interference level caused by multiple emitters from limited measurements. Early SC approaches rely on model assumptions about the radio map, e.g., sparsity and smoothness, which may be grossly violated under critical scenarios, e.g., in the presence of severe shadowing. More recent data-driven methods train deep generative networks to distill parsimonious representations of complex scenarios, in order to enhance performance of SC. The challenge is that the state space of this learning problem is extremely large—induced by different combinations of key problem constituents, e.g., the number of emitters, the emitters’ carrier frequencies, and the emitter locations. Learning over such a huge space can be costly in terms of sample complexity and training time; it also frequently leads to generalization problems. Our method integrates the favorable traits of model and data-driven approaches, which substantially ‘shrinks’ the state space. Specifically, the proposed learning paradigm only needs to learn a generative model for the radio map of a single emitter (as opposed to numerous combinations of multiple emitters), leveraging a nonnegative matrix factorization (NMF)-based emitter disaggregation process. Numerical evidence shows that the proposed method outperforms state-of-the-art purely model-driven and purely data-driven approaches.