Joint Optimization of Spectrum Sensing and Transmit Power in Energy Harvesting Cognitive Radio Sensor Networks

In this research, we consider the resource allocation of spectrum sensing and transmit power for a cognitive sensor node with energy harvesting capability, operating in time-slotted fashion with causal knowledge of the channel state and the energy harvesting state. Taking into account the status of primary channel occupation and the sensing imperfection, we formulate this resource allocation problem as an infinite-horizon discrete-time Markov decision process (MDP) in which the cognitive sensor node aims at maximizing the long-term expected throughput. An optimal sensing-transmission (OST) policy which specifies the time duration allocated for spectrum sensing as well as the power level to be used upon the transmission is proposed. A structural property pertaining to the OST policy is revealed, that is the optimal long-term expected throughput is non-decreasing with the level of the battery available energy. Moreover, we study a special case with sufficiently high signal-to-noise (SNR) power ratio of the primary signal. We demonstrate that the optimal transmit power has a monotonic structure with respect to the battery energy states. Based on this monotonic structure, an efficient sensing-transmission algorithm with low-complexity is developed. The simulation results are presented to confirm the theoretical analysis and the predominance of our proposed policies.