n [87] for short-range information sharing. A Q-learning-based power allocation method was designed to minimize the interference while improving the received SINR. It was shown that the received signal levels could be significantly improved by their proposed scheme. By considering the strict latency requirements, the authors in [88] employed DQN to solve the communication rate maximization problem for wireless powered ambient backscat- ter tags. A Q-learning model for ambient backscatter scenarios was developed first, and an algorithm was then proposed that used DNNs to approximate the complex Q-network table. 10.2.4.3 AmBC-CR Methods Several works combined AmBC with CRN. An RF-powered backscatter CRN enables the secondary transmitter not only to harvest energy from primary signals, but also to backscat- ter these signals to the secondary receiver for data transmission [72]. Such a combination can provide SUs with potential connection options instead of simply waiting for access opportunities. When the primary channels in RF-powered CRNs that employ AmBC are mostly busy, instead restricting their activity to harvesting energy, the secondary trans- mitters can use a fraction of the wait time to transmit data by modulating and backscat- tering the received signals through the AmBC Thus, AmBC enables secondary systems to maximize their performance by simultaneously optimize spectrum usage and energy harvesting. In an AmBC-assisted CRN network, the mode selection between signal backscatter and energy harvest is critical to achieving high RF-powered SU (RSU) throughput. The dynam- ics of the primary channel, energy storage capability, and data to be sent all need to be considered when making decision. An MDP-based framework was developed in [84] to determine optimal decisions with consideration to states such as energy, data, and primary channel. It was then expanded to include a scenario in which the state information was unavailable at the RSU. A low complexity online RL algorithm was proposed to enable the RSU to f