Maximizing the Route Capacity in Cognitive Radio Networks

The problem of choosing the route providing the best communication opportunities among the available routes is particularly challenging in self-organizing Cognitive Radio networks, since the communication opportunities are deeply affected by the primary-user (PU) activity. Furthermore, whenever the route selection exploits proactively acquired information on the PU activity, routing update packets need to be periodically exchanged among the nodes. The time interval between these exchanges, i.e., the routing update period, deeply affects the overall communication opportunities provided by a route, regardless of the adopted routing protocol. Hence, in this paper, we analytically derive the optimal route priority rule in the sense of maximizing the average capacity, by accounting for both the PU activity and the routing update period. The theoretical analysis is conducted by adopting two different widely-adopted PU activity models to confer generality to the analysis. Finally, the analytical results are validated through numerical evaluations.