Adaptive resource allocation with traffic peak duration prediction and admission control for cognitive Wi-Fi networks

摘要: Abstract Cognitive radio network (CRN) architecture can be efficiently utilized to support different QoS requirements under variable traffic and channel conditions. Generally, deterministic resource allocation algorithms could significantly increase the utilization as well QoS. In this paper, we propose an advanced cognitive algorithm for IEEE 802.11 Wi-Fi networks. By making use of status transmission channels conditions, proposed effectively allocates secondary resources improve overall CSMA/CA-based To accuracy efficiency algorithm, a Markov chain model based technique that estimates achievable throughput is employed. Furthermore, autoregressive moving average (ARMA) used predict peaks when allocating channels. OMNeT++ simulation models are then developed analyze performance algorithm. It shown our predictive offers higher compared existing techniques.