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 language||English (US)|
|Number of pages||16|
|Journal||IEEE Transactions on Signal Processing|
|State||Published - 2020|
Bibliographical noteFunding Information:
Manuscript received November 27, 2019; revised April 2, 2020; accepted April 27, 2020. Date of publication May 14, 2020; date of current version June 26, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Remy Boyer. This work was developed during Guoyong Zhang’s visit to Oregon State University. The work of Xiao Fu was supported by NSF under Grants CNS-2003082, ECCS-1808159, ECCS-1608961, and III-1910118. The work of Mingyi Hong was supported by NSF Grants CNS-2003033, CMMI-172775, CIF-1910385 and by ARO under Grant 73202-CS. Xiao Fu and Mingyi Hong were jointly supported by ARO under Grant W911NF-19-1-0247. The work of Guoyong Zhang and Jun Wang was supported in part by the National Natural Science Foundation of China (NSFC) under Grant U19B2014, in part by the National Research Program of China under Grant 9020302, in part by the Foundation of National Key Laboratory of Science and Technology on Communications, and in part by the Innovation Fund of NCL (IFN) under IFN2019102. (Corresponding author: Xiao Fu.) Guoyong Zhang and Jun Wang are with the National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 610054, China (e-mail: guoyong. firstname.lastname@example.org; email@example.com).
© 1991-2012 IEEE.
- Coupled tensor decomposition
- block term decomposition
- fiber sampling
- radio map
- slab sampling
- spectrum cartography
- tensor completion