enging to distinguish backscatter sym- bols from DLI. Furthermore, the existence of DLI can further cause the conventional Energy Detector (ED) to fall into severe error-floor problems. To tackle this issue, the authors in [67] developed a novel error-floor free detector by using multiple receive antennas at the reader side. They first considered the perfect CSI case and used beamforming-assisted ED and likelihood ratio-based detector to decode the backscatter symbol. Based on this, a novel statistical clustering framework was designed for joint CSI feature learning and backscatter symbol detection. It was verified that their method can achieve comparable performance with perfect CSI and significantly outperformed the conventional ED. An ML-assisted AmBC information extraction method was proposed in [200]. The information was modulated on top of the unknown Gaussian-distributed ambient RF signals. The binary phase-shift keying backscatter signals encoded by Hadamard codes can be decoded by the proposed method. By eliminating the direct path signal and correlating the residual signal with the coarse estimate of the ambient signal, the proposed method first extracted the learnable features for the tag signal. k-nearest neighbors' classification algorithm was then employed to recover the tag signals. Finally, a Hadamard decoder was used to retrieve the original information bits from the recovered signals. The energy detector or Minimum Mean Square Error (MMSE) detector utilized in exist- ing AmBC systems to detect tag signals suffers from a high BER. To overcome this chal- lenge, Support Vector Machine (SVM) and random forest methods were proposed in [80] for detecting the tag signals in an AmBC system by changing the detection problem into a clas- sification problem. To minimize the BER, the proposed method could classify the received signals into different groups based on their energy features. 10.2.4.2 Operating Mode Selection and User Coordination Due to their passive nature, Backscatter Devices (BDs) in AmBC sy