Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio

: The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal’s spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is e ff ective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods.

• Publication year

  2019

• Venue

  SICE Journal of Control Measurement and System Integration

• Publication date

  2019-07-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.9746/JCMSI.12.134
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Normalization method for online learning on radio access technology identification in cognitive radio

  2017cited by this paper
• Online Deep Learning: Learning Deep Neural Networks on the Fly

  2017cited by this paper
• Privacy-Preserving Location Proof for Securing Large-Scale Database-Driven Cognitive Radio Networks

  2016cited by this paper
• Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks

  2016cited by this paper
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

  2015cited by this paper
• Wireless Standard Classification in Cognitive Radio Networks Using Self-Organizing Maps

  2013influential reference
• Blind wireless standard identification for green radio communications

  2013cited by this paper
• Radio access technology classification for cognitive radio networks

  2013influential reference
• Feature Quantity for Universal Receivers to Identify Protocols of Radio Services and Devices

  2013cited by this paper
• Capacity of cellular networks deployed in TV White Space

  2012cited by this paper
• Novel cognitive radio technique for using white space in public cellular networks

  2012cited by this paper
• Smart meters for power grid — Challenges, issues, advantages and status

  2011cited by this paper
• Scikit-learn: Machine Learning in Python

  2011cited by this paper
• Confidence-weighted linear classification

  2008cited by this paper
• Cognitive radio: brain-empowered wireless communications

  2005cited by this paper
• Online Passive-Aggressive Algorithms

  2003cited by this paper
• Cognitive radio: making software radios more personal

  1999cited by this paper
• Nonlinear neural networks: Principles, mechanisms, and architectures

  1988cited by this paper

• No citing papers are available for this paper.