An Adaptive Resource Allocation Technique with Admission Control for Cognitive Wi-Fi Networks

Radio resource allocation techniques play a key role in determining the performance of wireless networks and quality-of-service (QoS) offered to various applications supported by the networks. The cognitive radio network (CRN) architecture can be efficiently utilized when advanced resource allocation techniques are employed to assign secondary channels to support varying traffic and channel conditions. In a cognitive network, deterministic radio resource allocation algorithms could significantly increase the secondary channel utilization as well as the network QoS. In this paper, we propose an advanced cognitive network resource allocation algorithm for IEEE802.11 cognitive Wi-Fi networks. The proposed algorithm utilizes information about transmission channels and traffic conditions to effectively allocate secondary radio resources to improve the radio resource utilization and the QoS of carrier sense multiple access with collision avoidance (CSMA/ CA) based networks. This paper also adopts a Markov chain model that estimates the achievable network throughput to allocate secondary radio resources to contending Wi-Fi networks. OMNeT++ based simulation models are developed to analyze the performance of the proposed algorithm. Simulation results show that our predictive resource allocation technique offers higher throughput and QoS compared to the traffic measurement-based resource allocation techniques in IEEE 802.11 networks.