Stochastic Optimization of Cognitive Networks

In this paper we aim to propose, upon a statistical modeling of the spectrum sensing energy, a stochastic joint optimization method that allows the minimization of the energy consumption of the spectrum sensing of a multi-hop secondary network subject to constraints on the detection performance and the number of network hops, in a trade-off between the overall probability of missed detection and false alarm, and the energy consumption. The optimal closed-form solution of the optimization problem is computed by means of two approaches: worst case and stochastic approach. Both theoretical analysis and numerical results show that the proposed method allows reducing the energy consumption, by showing its effectiveness with different data fusion rules. Particularly, the optimal solution outperforms the existing ones in terms of computational complexity, and of energy consumption specially for a number of hops greater than 4. The proposed technique has been finally proven in several environments that characterize different primary operative scenarios, such as wireless metropolitan area networks and satellite communications in the presence of interference with very low signal-to-noise ratio.