Cross Layer Aware Adaptive MAC based on Knowledge Based Reasoning for Cognitive Radio Computer Networks

In this paper we are proposing a new concept in MAC layer protocol design for Cognitive radio by combining information held by physical layer and MAC layer with analytical engine based on knowledge based reasoning approach. In the proposed system a cross layer information regarding signal to interference and noise ratio (SINR) and received power are analyzed with help of knowledge based reasoning system to determine minimum power to transmit and size of contention window, to minimize backoff, collision, save power and drop packets. The performance analysis of the proposed protocol indicates improvement in power saving, lowering backoff and significant decrease in number of drop packets. The simulation environment was implement using OMNET++ discrete simulation tool with Mobilty framework and MiXiM simulation library.Comment: 8 page

Intelligent Approaches for Routing Protocols In Cognitive Ad-Hoc Networks

This dissertation describes the CogNet architecture and five cognitive routing protocols designed to function within this architecture. In this document, I first provide detailed modeling and analysis of CogNet architecture and then provide the detailed approach, mathematical analysis, and simulation results for each of the developed cognitive routing protocols. The fundamental idea for these cognitive routing protocols is that a proper and adaptive network topology should be constructed from network nodes based on predictions using cognitive functions and past experience. The nodes in the cognitive radio network employ machine learning techniques to use past experience and make wise decisions by predicting future network conditions. The cognitive protocol architecture is a cross-layer optimized construct where the lower layer knowledge of the wireless medium is shared with the network layer. This dissertation investigates several intelligent approaches for cognitive routing protocols, such as the multi-channel optimized approach, the scalability optimized cognitive approach, the multi-path optimized approach, and the mobility optimized approach. Analytical and simulation results demonstrate that network performance can be increased significantly by applying cognitive routing protocols