A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction

Mengyun Tang, Guoru Ding, Qihui Wu, Zhen Xue, Theodoros A. Tsiftsis

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Spectrum data, which are usually characterized by many dimensions, such as location, frequency, time, and signal strength, present formidable challenges in terms of acquisition, processing, and visualization. In practice, a portion of spectrum data entries may be unavailable due to the interference during the acquisition process or compression during the sensing process. Nevertheless, the completion work in multi-dimensional spectrum data has drawn few attention to the researchers working in the field. In this paper, we first put forward the concept of spectrum tensor to depict the multi-dimensional spectrum data. Then, we develop a joint tensor completion and prediction scheme, which combines an improved tensor completion algorithm with prediction models to retrieve the incomplete measurements. Moreover, we build an experimental platform using Universal Software Radio Peripheral to collect real-world spectrum tensor data. Experimental results demonstrate that the effectiveness of the proposed joint tensor processing scheme is superior than relying on the completion or prediction scheme only.

Original languageEnglish
Article number7742926
Pages (from-to)8044-8052
Number of pages9
JournalIEEE Access
Publication statusPublished - 2016


  • Spectrum tensor
  • cognitive radio
  • tensor completion
  • tensor prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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