Residual Energy Maximization in Cognitive Radio Networks With Q-Routing

The present work explores a multihop cognitive radio network model that adopts energy harvesting (EH) and reinforcement learning based Q-routing. An optimization problem is formulated that maximizes the total residual energy (RE) in the secondary network under the constraints of primary user (PU) cooperation rate, secondary outage probability, and interference to the primary receiver. Closed-form expressions of EH duration, equivalent transmit power, and its consequent allocation factor on the secondary data transmission and the PU cooperation are found. A large set of simulation results show a gain by ~4.45%, ~21.80%, ~12.09%, and 7.937 times in the RE, secondary outage probability, network lifetime, and path set-up time, respectively, over the existing algorithms.