  Ambient backscatter (AB) communication is an emerging wireless communication
technology that enables wireless devices (WDs) to communicate without requiring
active radio transmission. In an AB communication system, a WD switches between
communication and energy harvesting modes. The harvested energy is used to
power the devices operations, e.g., circuit power consumption and sensing
operation. In this paper, we focus on maximizing the throughput performance of
AB communication system by adaptively selecting the operating mode under fading
channel environment. We model the problem as an infinite-horizon Markov
Decision Process (MDP) and accordingly obtain the optimal mode switching policy
by the value iteration algorithm given the channel distributions. Meanwhile,
when the knowledge of channel distribution is absent, a Q-learning (QL) method
is applied to explore a suboptimal strategy through device repeated interaction
with the environment. Finally, our simulations show that the proposed QL method
can achieve close-to-optimal throughput performance and significantly
outperforms the other than representative benchmark methods.
