Geolocation database-assisted QoS-aware cognitive network architecture

Cognitive networks (CN) introduce an adaptable network architecture which enables efficient sharing of transmission resources among heterogeneous networks. Traditional CN architecture requires each network devices to independently employ channel sensing mechanisms to discover free (or white) spectrum that can be temporarily borrowed for its own use. However, the distributed physical layer-based channel borrowing techniques cannot guarantee the quality-of-service (QoS) requirements of primary and secondary users in a CN due to the lack of channel availability information. On the other hand, the FCC regulations introduced in 2010 allowed the use of Geo-Location Database (GDB) as the primary means of determining white space availability. Using GDB-based CN network architecture could offer significant QoS advantages. In this paper, we propose a new GDB-based QoS-aware CN architecture and secondary channel allocation technique that guarantees predictable and stable QoS for the secondary users. The proposed channel allocation technique uses statistical QoS estimation to predict the radio resource requirements of secondary users. We also propose to deploy a centralized channel manager that uses both proactive and reactive resource allocation techniques to allocate the white spectrum among secondary users. An OMNeT++ based CN simulation model has been developed to evaluate the proposed secondary channel allocation technique for an IEEE802.11 based cognitive WiFi network. Simulation results show that the proposed secondary channel allocation technique offers significant QoS advantages and stability to secondary users compared to the conventional distributed channel sensing technique.