ws 16(7): e3

Research Article

Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning

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  • @ARTICLE{10.4108/eai.19-1-2016.150999,
        author={V. Balaji and S. Anand and C.R. Hota and G. Raghurama},
        title={Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning},
        journal={EAI Endorsed Transactions on Wireless Spectrum},
        keywords={Cognitive radio, Dynamic Spectrum Access, Cooperative Sensing, TV white space, Machine Learning},
  • V. Balaji
    S. Anand
    C.R. Hota
    G. Raghurama
    Year: 2016
    Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning
    DOI: 10.4108/eai.19-1-2016.150999
V. Balaji1,*, S. Anand1, C.R. Hota1, G. Raghurama2
  • 1: Department of Computer science and Information Systems, BITS Pilani, Hyderabad Campus, 500078, India
  • 2: Department of Electrical and Electronics Engineering, BITS Pilani, Goa Campus, 403726, India
*Contact email:


In this paper we present a Cooperative Spectrum Sensing (CSS) algorithm for Cognitive Radios (CR) based on IEEE 802.22Wireless Regional Area Network (WRAN) standard. The core objective is to improve cooperative sensing efficiency which specifies how fast a decision can be reached in each round of cooperation (iteration) to sense an appropriate number of channels/bands (i.e. 86 channels of 7MHz bandwidth as per IEEE 802.22) within a time constraint (channel sensing time). To meet this objective, we have developed CSS algorithm using unsupervised K-means clustering classification approach. The received energy level of each Secondary User (SU) is considered as the parameter for determining channel availability. The performance of proposed algorithm is quantified in terms of detection accuracy, training and classification delay time. Further, the detection accuracy of our proposed scheme meets the requirement of IEEE 802.22 WRAN with the target probability of falsealrm as 0.1. All the simulations are carried out using Matlab tool.