Optimal-Stopping Spectrum Sensing in Energy Harvesting Cognitive Radio Systems

In this paper, a cognitive radio system in which the secondary user (SU) is powered by energy harvested exclusively from environment is considered. The SU operates in a timeslotted mode and uses a timeslot in turn for energy harvesting, spectrum sensing and data transmission. In order to optimize the SU's expected achievable throughput, strategy for energy harvesting and spectrum sensing should be carefully designed to tackle the tradeoff among the three. Such a problem leads to a parametrized optimal stopping problem, i.e., a mash-up of static and dynamic optimization problems in which save-ratio for energy harvesting is fixed (as a static variable parameter) while spectrum sensing runs in a channel-by-channel manner based on sensing results (as an optimal stopping problem). We propose an efficient algorithm to derive the optimal save-ratio and spectrum sensing rule, which is significantly faster than conventional simulated annealing algorithm. To further reduce the computational complexity, we also propose a suboptimal solution with an alternative optimization problem, where save-ratio and number of channels to be sensed are both static variables to be optimized. The alternative problem is formulated as a mixed-integer non-linear programming (MINLP) problem and closed-form solution is derived with in-depth analysis. We show that the proposed suboptimal solution is close in performance to the optimal one and outperforms a baseline strategy, which decouples optimization for energy harvesting and spectrum sensing by combining two existing techniques.