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For an RF-powered cognitive radio network with ambient backscatteringcapability, while the primary channel is busy, the RF-powered secondary user(RSU) can either backscatter the primary signal to transmit its own data orharvest energy from the primary signal (and store in its battery). Theharvested energy then can be used to transmit data when the primary channelbecomes idle. To maximize the throughput for the secondary system, it iscritical for the RSU to decide when to backscatter and when to harvest energy.This optimal decision has to account for the dynamics of the primary channel,energy storage capability, and data to be sent. To tackle that problem, wepropose a Markov decision process (MDP)-based framework to optimize RSU'sdecisions based on its current states, e.g., energy, data as well as theprimary channel state. As the state information may not be readily available atthe RSU, we then design a low-complexity online reinforcement learningalgorithm that guides the RSU to find the optimal solution without requiringprior- and complete-information from the environment. The extensive simulationresults then clearly show that the proposed solution achieves higherthroughputs, i.e., up to 50%, than that of conventional methods.

Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter