Modeling and performance analysis of cognitive radio networks

Abstract

Cognitive radio heralds the next step in the evolution of wireless communications. Cognitive radio networks are inherently priority based and preemptive. Hence, keeping track of each resource in each cell is mandatory. In this thesis, an analytical model for infrastructure based cognitive radio systems is proposed, and its performance is evaluated under even and uneven traffic scenarios in a multiple cell environment. Performance metrics like probabilities of dropping and blocking for primary and secondary users as well as forced termination and forced frequency handoff for secondary users are investigated, and the analytical model is verified with simulations. In addition to the analytical model, a new capacity assignment method is proposed to compensate for uneven traffic load distribution. The proposed method considers offered traffic, hop count to the heavily loaded cell, and velocity of mobile users during capacity assignment and performs better in terms of probability of blocking, dropping, and forced termination.