Spectrum Cartography via Coupled Block-Term Tensor Decomposition

Guoyong Zhang, Xiao Fu, Jun Wang, Xi Le Zhao, Mingyi Hong

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map) - from limited samples taken sparsely over the region. Classic cartography methods are mostly concerned with recovering the aggregate radio frequency (RF) information while ignoring the constituents of the radio map - but fine-grained emitter-level RF information is of great interest. In addition, many existing cartography methods explicitly or implicitly assume random spatial sampling schemes that may be difficult to implement, due to legal/privacy/security issues. The theoretical aspects (e.g., identifiability of the radio map) of many existing methods are also unclear. In this work, we propose a joint radio map recovery and disaggregation method that is based on coupled block-term tensor decomposition. Our method guarantees identifiability of the individual radio map of each emitter (thereby that of the aggregate radio map as well), under realistic conditions. The identifiability result holds under a large variety of geographical sampling patterns, including a number of pragmatic systematic sampling strategies. We also propose effective optimization algorithms to carry out the formulated radio map disaggregation problems. Extensive simulations are employed to showcase the effectiveness of the proposed approach.

Original languageEnglish (US)
Article number9093220
Pages (from-to)3660-3675
Number of pages16
JournalIEEE Transactions on Signal Processing
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 1991-2012 IEEE.


  • Coupled tensor decomposition
  • block term decomposition
  • fiber sampling
  • radio map
  • slab sampling
  • spectrum cartography
  • tensor completion


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