SpecKriging: GNN-based Secure Cooperative Spectrum Sensing

Yan Zhang, Ang Li, Jiawei Li, Dianqi Han, Tao Li, Rui Zhang, Yanchao Zhang

Research output: Contribution to journalArticlepeer-review


Cooperative spectrum sensing (CSS) adopted by spectrum-sensing providers (SSPs) plays a key role for dynamic spectrum access and is essential for avoiding interference with licensed primary users (PUs). A typical SSP system consists of geographically distributed spectrum sensors which can be compromised to submit fake spectrum-sensing reports. In this paper, we propose SpecKriging, a new spatial-interpolation technique based on Inductive Graph Neural Network Kriging (IGNNK) for secure CSS. In SpecKriging, we first pretrain a graphical neural network (GNN) model with the historical sensing records of a few trusted anchor sensors. During system runtime, we use the trained model to evaluate the trustworthiness of non-anchor sensors’ data and also use them along with anchor sensors’ new data to retrain the model. SpecKriging outputs trustworthy sensor reports for spectrum-occupancy detection. To the best of our knowledge, SpecKriging is the first work that explores GNNs for trustworthy CSS and also incorporates the hardware heterogeneity of spectrum sensors. Extensive experiments confirm the high efficacy and efficiency of SpecKriging for trustworthy spectrum-occupancy detection even when malicious spectrum sensors constitute the majority.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Wireless Communications
StateAccepted/In press - 2022


  • Computational modeling
  • cooperative spectrum sensing
  • Data models
  • GNN
  • Interpolation
  • Sensor systems
  • Sensors
  • Spatiotemporal phenomena
  • Wireless communication
  • Wireless security

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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