Hybrid Energy Harvesting-Based Cooperative Spectrum Sensing and Access in Heterogeneous Cognitive Radio Networks

In order to design energy efficient and energy harvesting (EEH) cooperative spectrum sensing (EEH-CSS), four fundamental constraints must be considered: 1) collision constraint to protect primary users from the collision with secondary users (SUs); 2) energy-causality constraint which states that the energy harvested by a time instant must be greater than or equal to the consumed energy until that time instant; 3) energy half-duplex (EHD) constraint which prevents the batteries from charging and discharging at the same time; and 4) correlation constraint which limits the information about the primary channel (PC) state of next time slot can be extracted from the current PC state. In this regard, we consider a hybrid energy harvesting SU (EH-SU) model which can harvest energy from both renewable sources, e.g., solar and ambient radio frequency signals. A heterogeneous EEH-CSS scheme is first proposed to handle EH-SUs with nonidentical harvesting, sensing, and reporting characteristics by permitting them to sense and report at different sensing accuracy. Formulating the energy state evolution of EH-SUs with and without EHD constraint, we analyze the asymptotic activity behavior of a single EH-SU by deriving the theoretical upper bound for the chance of being active to sense and transmit. Thereafter, we develop a convex framework to find maximum achievable total throughput by optimizing the asymptotic active probability, sensing duration, and detection threshold of each SU subject to above constraints. Given a potential set of SUs, determining the optimal subset of cooperating EH-SUs is of the essence to achieve maximum achievable total throughput. Since EH-SU selection is inherently a combinatorial problem, a fast yet high performance solution is proposed based on SUs' energy harvesting, sensing, and reporting attributes. Finally, a myopic access procedure is developed to determine the active set of EH-SUs given the best subset of SUs.