ISSN 2996-8223
International Journal of Computer Science and Technology | Vol. 8, No. 10, September 2006 | pp. 73–80
DOI: 10.46882/2006/IJCST/081025
Research Article
Title: Dynamic Channel Profiling for Cognitive Radio Networks Using Hidden Markov Models
Names of Authors: Elena Rostova¹, Khalid Al-Mansoor²
Authors’ Affiliations: Faculty of Computer Science, Saint Petersburg State University, Saint Petersburg, Russia; ²Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Abstract:
Spectrum scarcity requires the deployment of secondary cognitive radio systems that can sense and utilize idle licensed channels without disrupting primary users. This paper presents a dynamic channel profiling technique that uses a continuous-time Hidden Markov Model (HMM) to predict channel occupancy patterns. The algorithm estimates the state transition probabilities of primary operators by processing signal energy metrics gathered during brief sensing windows. Simulation experiments across a 24-channel multi-user spectrum topology demonstrated a primary user detection accuracy rate of 97.8% under a signal-to-noise ratio of SNR = -15 dB. The secondary network spectrum utilization efficiency improved by 31.4% compared to standard energy detectors. The predictive processing pipeline completed state estimation loops within a latency boundary of t = 12.5 ms, enabling real-time opportunistic channel handovers.
Keywords: Cognitive Radio, Spectrum Sensing, Hidden Markov Models, Channel Profiling, Wireless Communications
Manuscript Timeline: Received June 02, 2006; Revised July 15, 2006; Accepted August 11, 2006; Published September 22, 2006
Citation: Rostova, E., & Al-Mansoor, K. (2006). Dynamic Channel Profiling for Cognitive Radio Networks Using Hidden Markov Models. International Journal of Computer Science and Technology, 8(10), 73–80.
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