# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 2 Related Works  
[PERSON] et al. [12] proposed a signal detection method for a backscatter communication system in a supervised learning manner, where the label signal of AmBC is detected by transforming the detection problem into a classification problem. Particularly, support vector machine and random forest are used to decode label symbols. Simultaneously, efficient features are extracted to minimize the bit error rate of the AmBC system. Experimental results show that with different extended gains, the proposed detector has a lower bit error rate and higher data rate than the traditional minimum mean squared error (MMSE) detectors at low signal-to-noise ratio. [PERSON] et al. [13] proposed an AmBC signal detection system in the binary phase shift keying (BPSK) modulation based on machine learning. Particularly, BPSK-based backscattering signal encoded by Hadamard code can be decoded with this method. Firstly, the direct path signal is eliminated, the residual signal is associated with the rough environment signal estimation to extract the tag signal's learnable features. Then, the \(k\)-nearest neighbor classification algorithm is used to recover the tag signal. The recovered signal is decoded by Hadamard to obtain the original information bits. Finally, the effectiveness of the method is verified by experimental simulation. In reference [14], the AmBC signal detection problem is transformed into a clustering problem, and two kinds of known labels are transmitted from the tags as prior knowledge to assist cluster initialization and signal detection. By directly using the received signals, two clustering-based detection methods are developed, i.e., one is the clustering of labeled signals (CLS), and the other one is the clustering of labeled and unlabeled signals (CLUS). Both two methods are developed based on the proposed modulation constrained Gaussian mixture model (GMM). Compared with the optimal detection method with perfect correlation channel state information, the proposed method has only a small gap. An AmBC signal detection method is constructed in reference [15] in an unsupervised learning manner. The characteristics of the received signals are directly utilized, which are clustered with an unsupervised learning means. In addition, the tag bits are transmitted for cluster bit mapping to assist signal detection without the estimation of channel coefficients and noise power. For the spread spectrum gain \(N>1\) and \(N=1\), two detection methods are proposed, in which the features follow different mixed distributions. The detection threshold is derived with the learned parameters to optimize the detection performance. Finally, a large number of simulation results are given to verify the performance of the proposed scheme. In reference [16], the tag signal detection problem for AmBC systems were studied by adopting the deep transfer learning (DTL) technology. Firstly, a universal DTL-basedtag signal detection framework is designed, which uses a deep neural network (DNN) to implicitly extract the features of communication channels and directly recover the tag symbols. Based on the established pre-trained DNN and a few pilots, a DTL-based likelihood ratio test (DTL-LRT) was obtained through transfer learning. Moreover, exploiting the advantages of the convolutional neural networks' powerful capability in exploring features of data in a matrix form, a covariance matrix-aware neural network (CMNet) for the sample covariance matrix is designed, and a CMNet-based detection algorithm is proposed. Finally, the simulation shows that the proposed CMNet method can achieve a close-to-optimal performance without explicitly obtaining the channel state information. There are some differences between our proposed method and [12; 13; 14; 15; 16]; the main differences are as follows. First, supervised learning is used in references [12; 13; 14; 16], manual intervention is required during learning, and the operation is complex and time-consuming.