# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.2.2 Review of Selected PLA Technique-based Channel Information  
Multiple studies considered communication channel characteristics when building their authentication schemes. Channel characteristics generally provide richer information, although this requires more complexity to obtain a precise channel estimation. To address this, **(S19)** developed an approach for physical layer authentication by examining the most effective method for extracting channel differences from the channel matrix and employing several learning algorithms. In particular, the proposed approach achieved an average authentication accuracy of 77% across all the positions; when MIMO was used, performance was greatly improved across all positions in relation to SISO. Furthermore, **(S24)** deployed physical layer authentication approaches in cooperative communications, in which some trustworthy relay nodes assist a receiver in accurately authenticating a legitimate transmitter based on wireless channel characteristics. Furthermore, the authors proposed two feature selection models that can be used with statistical and ML-based classification techniques; they also considered equal gain combining (EGC) and all feature selection (AFS). When employing EGC, the statistical approach has a clear benefit, whereas AFS is best served by ML classification. This means that ML approaches benefit from more features than statistical approaches. In **(S25)**, researchers also used an ML algorithm to enhance physical layer security using channel features. The unique features of the physical layer channel are used to realize physical layer authentication. Mainly considered one-way authentication, the network authenticates the terminal and the terminal does network authentication, whereas mutual authentication is the combination of the two one-way authentications. In that study, the authors focused on 4G-LTE wireless communication technology like OFDM multi-carrier technology and MIMO multi-antenna transmission technology, taking the judgment condition of 4G mobile terminal cell reselection/cell handover into account. Multiple studies proposed approaches for multiuser authentication when the traditional method cannot discriminate between multiusers simultaneously. For example, in **(S17)**, a multiuser authentication model was presented, distinguishing between multiple legitimate transmitters and one spoofer in the MIMO-OFDM system. They employed CNN and used the CSI of multiple transmitters as input and the corresponding tags as output to implement their model. As a result, the authentication rate was 90.5% when the number of iterations was eight; when the number of iterations was thirteen, the authentication rate was around 100%. As the number of iterations increased, the authentication rates of the test data also increased proportionally. In **(S22)**, the authors proposed an approach combining DNNs with data augmentation techniques for multiuser authentication in open area test sites (OATS). Three data augmentation techniques were proposed and tested to enhance physical layer authentication datasets and effectively solve the problem of insufficient data volume. Furthermore, they tested the proposed authentication approach in two common industry IoT environments: OATS and an automotive factory. They also investigated how channel differences between these two representative environments influence the performance of the proposed approach. They concluded that the proposed approach provided faster and lighter-weight authentication and improved the authentication success rate. A new lightweight multiuser physical layer authentication mechanism was proposed in **(S27)**. The purpose was to prevent illegal nodes from imitating legitimate nodes to gain access to industrial wireless networks. The effectiveness of the proposed mechanism was verified by nodes in a real dynamic industrial scenario using the dataset collected by the National Institute of Standards and Technology (NIST).