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http://dx.doi.org/10.12673/jkoni.2013.17.4.435

A Predictive Connection Admission Control Using Neural Networks for Multiclass Cognitive Users Radio Networks  

Lee, Jin-Yi (Dept. of Electronic Engineering, Chungwoon University)
Publication Information
Journal of Advanced Navigation Technology / v.17, no.4, 2013 , pp. 435-441 More about this Journal
Abstract
This paper proposes a neural net based-predictive connection admission control (CAC) scheme for multiclass users in wireless cognitive radio networks. We classifies cognitive users(cu) into real and non real time services, and then permit only real time services to reserve the demanded resource for spectrum handoff in guard channel for provisioning the desired QoS. Neural net is employed to predict primary user's arrival on time and demanded channels. Resource scheduling scheme is based on $C_IA$(cognitive user I complete access) shown in this paper. For keeping primary users from interference, the CAC is performed on only cognitive user not primary user. Simulation results show that our schemes can guarantee the desired QoS by cognitive real time services.
Keywords
Real and nonreal-time cognitive users; Predictive connection admission control; $C_IA$; resource scheduling scheme; Neural network-based resource prediction; Guard channel;
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Times Cited By KSCI : 2  (Citation Analysis)
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