Deep generative model learning for blind spectrum cartography with NMF-based radio map disaggregation

Sagar Shrestha, Xiao Fu, Mingyi Hong

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)4920-4924
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: Jun 6 2021Jun 11 2021

Bibliographical note

Funding Information:
† The work of S. Shrestha and X. Fu is supported in part by the NSF-Intel MLWiNS Program CNS-2003082, and in part by NSF ECCS-2024058. ∗ The work of M. Hong is supported in part by the NSF-Intel MLWiNS Program CNS-2003033, and in part by ARO W911NF-19-1-0247.

Publisher Copyright:
© 2021 IEEE

Keywords

  • Cognitive radio
  • Deep generative model
  • Nonnegative matrix factorization
  • Radio map
  • Spectrum cartography

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