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Spectrum cartography (SC) techniques craft multi-domain (e.g., space and frequency) radio maps from limited measurements, which is an ill-posed inverse problem. Recent works used low-dimensional priors such as a low tensor rank structure and a deep generative model to assist radio map estimation---with provable guarantees. However, a premise of these approaches is that the sensors are able to send real-valued feedback to a fusion center for SC---yet practical communication systems often use (heavy) quantization for signaling. This work puts forth a limited feedback-based SC framework. Similar to a prior work, a generative adversarial network (GAN)-based deep prior is used in our framework for fending against heavy shadowing. However, instead of using real-valued feedback, a random quantization strategy is adopted and a maximum likelihood estimation (MLE) criterion is proposed. Analysis shows that the MLE provably recovers the radio map, under reasonable conditions. Simulations are conducted to showcase the effectiveness of the proposed approach.
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Deep Generative Model Learning For Blind Spectrum Cartography with NMF-Based Radio Map Disaggregation
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
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- PAR ID:
- 10287810
- Date Published:
- Journal Name:
- IEEE ICASSP 2021
- Page Range / eLocation ID:
- 4920 to 4924
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP39728.2021.9413382
