Joint Energy-Efficient Cooperative Spectrum Sensing and Power Allocation in Cognitive Machine-to-Machine Communications

Abstract

In battery-powered Cognitive Machine-to-Machine Communications (CM2M), the energy consumption, opportunis- tic data access capacity and interference to the licensed system need to be optimized simultaneously. We consider this as joint cooperative spectrum sensing and power allocation, and model this as a constraint multiobjective optimization problem of three objectives. Our model helps to find a Pareto optimal variable set of sensing duration, detection threshold and transmission power for each individual sensor in cooperative spectrum sensing. The evaluation of our model shows that energy consumption, opportunistic data capacity and interference are optimized simultaneously while keeping the total cooperative spectrum sensing error lower than a predefined threshold. Pareto optimal results show that better energy efficiency [bits/joule] makes lower harmful interference to the primary system.