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Margin-Based Active Online Learning Techniques for Cooperative Spectrum Sharing in CR Networks

  • K. Praveen KumarEmail author
  • Eva Lagunas
  • Shree Krishna Sharma
  • Satyanarayana Vuppala
  • Symeon Chatzinotas
  • Björn Ottersten
Conference paper
  • 325 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 291)

Abstract

In this paper, we consider a problem of acquiring accurate spectrum availability information in the Cooperative Spectrum Sensing (CSS) based Cognitive Radio Networks (CRNs), where a fusion center collects the sensing information from all the sensing nodes within the network, analyzes the information and determines the spectrum availability. Although Machine Learning (ML) techniques have been recently applied to enhance the cooperative sensing performance in CRNs, they are mostly supervised learning based techniques and need a significant amount of labeled data, which is difficult to acquire in practice. Towards relaxing this requirement of large labeled data of supervised learning, we focus on Active Learning (AL), where the fusion center can query the label of the most uncertain cooperative sensing measurements. This is particularly relevant in CRN environments where primary user behavior changes in a quick manner. In this regard, we briefly review the existing AL techniques and adapt them to the considered CSS based CRNs. More importantly, we propose a novel margin based active on-line learning algorithm that selects the instance to be queried and updates the classifier by using the Stochastic Gradient Descent (SGD) technique. In this approach, whenever an unlabeled instance is presented, the proposed AL algorithm compares the margin of instance with a threshold to decide whether it should query a label or not. Supporting results based on numerical simulations show that the proposed method has significant advantages on classification and detection performances, and time-complexity as compared to state-of-the-art techniques.

Keywords

Active learning Cooperative spectrum sensing Cognitive radio network 

Notes

Acknowledgment

This work has received partial funding from the European Research Council (ERC) under the European Union’s Horizon H2020 research and innovation programme (grant agreement No 742648), and from the Luxembourg National Research Fund (FNR) in the framework of the AFR research grant entitled “Learning-Assisted Cross-Layer Optimization of Cognitive Communication Networks”.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • K. Praveen Kumar
    • 1
    Email author
  • Eva Lagunas
    • 1
  • Shree Krishna Sharma
    • 1
  • Satyanarayana Vuppala
    • 1
  • Symeon Chatzinotas
    • 1
  • Björn Ottersten
    • 1
  1. 1.Interdisciplinary Centre for SecurityReliability and Trust University of LuxembourgLuxembourg CityLuxembourg

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