Throughput Optimization in Cognitive Random Wireless Ad hoc Networks

We consider coexistence of overlapping but distinct primary and secondary random ad hoc networks. A secondary node has node-level and network-level obligations to avoid interference on primary receiving nodes. At the node level, it is allowed to transmit only if it does not detect data reception from its primary neighbors. At the network level, it needs to make sure that the aggregate secondary interference on an arbitrary primary receiving node satisfies an interference constraint. In this paper, we show that both of these obligations are non-trivially related to transmission attempt probabilities in primary and secondary networks and our aim is the optimization of throughput in these networks. We show that the interference constraint limits the feasible space for transmission probabilities of both systems. Looking at the secondary network, as a network plugged into the primary, we propose a progressive transmission probability optimization algorithm and show through simulation that it converges to the optimum throughput in a few time slots. Results show that at the optimum transmission probabilities, while the primary network deviates slightly from its optimal throughput (obtained when secondary network is not present), a considerable throughput can be gained by the secondary network. Results also show that the sum throughput also increases as a result of this optimization.