Energy-Efficient Cooperative Spectrum Sensing Using Two Hard Decision Rules

In this paper, we propose a sensing nodes selection scheme to achieve energy efficiency in cooperative spectrum sensing for cognitive sensor networks. In cooperative spectrum sensing, nodes send their results to a fusion center (FC) to make the final decision using a fusion rule. While minimize the energy consumption, we also consider improving the detection performance in our approach by maximizing the global probability of detection and minimizing the global probability of false alarm. Moreover, we simplify the resulted NP-complete problem by mapping the assignment indices from integer to the real domain. A closed-form equation is obtained to determine the priority of nodes for sensing and a solution is provided based on convex optimization techniques. The proposed algorithm is independent of the distribution of the global probability of detection and the type of selecting fusion rule. Simulation results is investigated for AND and OR fusion rules and show that our algorithm is very effective in saving energy in different scenarios.