Learning power spectrum maps from quantized power measurements

Daniel Romero, Seung Jun Kim, Georgios B. Giannakis, Roberto Lopez-Valcarce

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Power spectral density (PSD) maps providing the distribution of RF power across space and frequency are constructed using power measurements collected by a network of low-cost sensors. By introducing linear compression and quantization to a small number of bits, sensor measurements can be communicated to the fusion center with minimal bandwidth requirements. Strengths of data- and model-driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric formulations are investigated. It is shown that PSD maps can be obtained using support vector machine-type solvers. In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms.

Original languageEnglish (US)
Article number7849214
Pages (from-to)2547-2560
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume65
Issue number10
DOIs
StatePublished - May 15 2017

Bibliographical note

Funding Information:
The work of D. Romero and G. B. Giannakis was supported by the Army Research Office under Grant W911NF-15-1-0492 and National Science Foundation under Grant 1343248. The work of S.-J. Kim was supported by the National Science Foundation under Grant 1547347.

Keywords

  • Wireless networks
  • cognitive radio
  • quantization
  • support vector machines

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