Optimal Spectrum Access for Energy Harvesting Cognitive Radio Networks

We consider energy harvesting cognitive radio networks in which a secondary transmitter harvests energy from ambient sources or wireless power transfer systems while opportunistically accessing the spectrum licensed to the primary network. The primary traffic is modeled as a time-homogeneous discrete Markov process, and the secondary transmitter may not be able to operate continuously due to sporadic and unstable energy sources. At the beginning of each time slot, the secondary transmitter thus needs to determine whether to remain idle so as to conserve energy, or to execute spectrum sensing to acquire knowledge of the current spectrum occupancy state. It also needs to configure the spectrum sensor detection threshold to achieve an effective tradeoff between false alarms and misdetections. This sequential decision-making, done to maximize the expected total throughput, requires the joint design of a spectrum sensing policy and a detection threshold under the energy causality and collision constraints. We formulate this stochastic optimization problem as a constrained partially observable Markov decision process (POMDP), and then convert it to a computationally tractable unconstrained POMDP. Numerical results show that the proposed approach enables efficient usage of the harvested energy by exploiting the temporal correlation of the primary traffic.