Y. Zeng and Y. C. Liang, “Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances,” IEEE Transactions on Vehicular Technology, Vol. 58, No. 4, 2009, pp. 1804-1815. doi:10.1109/TVT.2008.2005267

has been cited by the following article:

• TITLE: Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

  AUTHORS: Sandhya Pattanayak, Palanaindavar Venkateswaran, Rabindranath Nandi

  KEYWORDS: ANN; Cognitive Radio; Spectrum Sensing; Cognitive Engine

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.6 No.3, March 27, 2013

  ABSTRACT: The recent phenomena of tremendous growth in wireless communication application urge increasing need of radio spectrum, albeit it being a precious but natural resource. The recent technology under development to overview the situation is the concept of Cognitive Radio (CR). Recently the Artificial Intelligence (AI) tools are being considered for the topic. AI is the core of the cognitive engine that examines the external and internal environment parameters that leads to some postulations for QoS improvement. In this article, we propose a new Artificial Neural Network (ANN) model for detection of a spectrum hole. The model is trained with some pertinent features over a channel like SNR, channel capacity, bandwidth efficiency etc. The channel capacity status could be identified in a quantized index form . Some simulation results are presented.