Throughput Modeling, Analysis, and Optimization in Cognitive Wireless Networks

In cognitive wireless networks, secondary nodes are required to sense the spectrum and to be cognizant of their interference on the primary receiving nodes. In this chapter, the authors show that the throughputs in the primary and the secondary networks have an entangled relationship and need to be considered jointly for optimization. Assuming that the primary and secondary networks use ALOHA as their medium access control protocol, the chapter shows that the throughputs in primary and secondary networks are influenced by the choices of transmission attempt probabilities in both of these networks. Authors show that the interference constraint limits the feasible space of transmission probabilities over which these networks can optimize their throughputs. They propose a progressive transmission probability optimizations algorithm and show through simulation that the algorithm converges to the optimum values of throughput in few time slots. Future research directions and the application of the constrained game theory to the throughput optimization problem are also discussed.