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Ambient backscatter has been introduced with a wide range of applications forlow power wireless communications. In this article, we propose an optimal andlow-complexity dynamic spectrum access framework for RF-powered ambientbackscatter system. In this system, the secondary transmitter not only harvestsenergy from ambient signals (from incumbent users), but also backscatters thesesignals to its receiver for data transmission. Under the dynamics of theambient signals, we first adopt the Markov decision process (MDP) framework toobtain the optimal policy for the secondary transmitter, aiming to maximize thesystem throughput. However, the MDP-based optimization requires completeknowledge of environment parameters, e.g., the probability of a channel to beidle and the probability of a successful packet transmission, that may not bepractical to obtain. To cope with such incomplete knowledge of the environment,we develop a low-complexity online reinforcement learning algorithm that allowsthe secondary transmitter to "learn" from its decisions and then attain theoptimal policy. Simulation results show that the proposed learning algorithmnot only efficiently deals with the dynamics of the environment, but alsoimproves the average throughput up to 50% and reduces the blocking probabilityand delay up to 80% compared with conventional methods.

Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning