er then can decode and obtain useful information from the transmitter. By separating the carrier emitter and the backscatter receiver, the number of RF components is minimized at backscatter devices and the devices can operate actively, i.e. backscatter transmitters can transmit data without initiation from receivers when they harvest sufficient energy from the RF source [188]. Therefore, AmBC systems can share spectrum with existing systems and achieve better spectral efficiency than that of RFID systems. The existing ML-based works for AmBC systems are mainly focused on the information extraction and mode selections. 10.2.4.1 Information Extraction Since ambient backscatter uses uncontrollable RF signals that already have information encoded in them, it needs a different mechanism to extract the backscattered information. Several existing works have proposed different ML-based methods to help extract information. Figure 10.8 AmBC network. Source: [188] Backscattered signals (© [2018] IEEE). Ambient RF source Legacy receiver Transreceiver B Transreceiver A 10 Secure Spectrum Sharing with Machine Learning: An Overview To solve the individual channel estimation in the AmBC system, the authors in [120] designed a communication protocol for the reader and the tag to obtain all the channel parameters. Based on the protocol, they proposed an ML-aided semi-blind estimator which utilizes an Expectation-Maximization (EM) algorithm and a few pilots from RF resources together with some superimposed pilots at the reader. The maximum likelihood estimator was applied to obtain the uplink channel between the reader and the tags, while the super- imposed pilots from the reader were used to estimate the downlink channel. Finally, the Cramer-Rao Bounds (CRB) of the proposed channel estimators were derived. In an AmBC system, the readers receive the backscattered signal from the backscatter device (BD) and the Direct-Link Interference (DLI) from the RF source simultaneously. Due to the randomness of ambient RF sources, it is chall