# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.3.2 Open Issues and Future Trends  
This section addresses key issues that have not yet been extensively investigated and future trend solutions. **Datasets Issues:** Most studies' experiments faced a lack of sufficient data, which affected the results of the proposed classifiers. When **(S14)** faced this issue, they implemented their experiment with a small number of wireless smart utility devices. In **(S22)**, the authors used the data augmentation technique to regenerate datasets from existing datasets by computing operations, which is an efficient way to expand limited training datasets to build an accurate authentication model for ML and DL algorithms. However, deep learning approaches require many training samples to achieve high accuracy. Recently, few studies have tried to utilize a generative model to overcome the lack of training samples. Both **(S2)** and **(S15)** used the GAN framework as a generative model, which allows realistic generation of samples from a particular distribution that can then be used to train a discriminator to distinguish real samples from those created by the generator. Figure 9 represents the percentage of dataset types in the reviewed papers. **Environment Issues:** The shape of the RF signal can change according to variations in wireless channel conditions, which can affect tag identification. Furthermore, a channel-based PLA and the transmitter's location may also significantly influence authentication performance because the channel depends on a party of transceivers. Consequently, extra effort should be exerted for PLA under a dynamic channel environment. To solve the problem of varying channels dynamically, RF signal data must be collected under different channel conditions so that ML and DL models can cope with unstable channel conditions. On this topic **(S29)** and **(S31)** concluded that as the number of transmitters increases, the authentication error rate increases even if distances decline. In addition, noise, attenuation in the communication medium, interference, Doppler shift, and fading affect dynamic channel variation in short-range communication. Because of time and space constraints, most reviewed papers only discussed recognition methods based on specific channel conditions. As a result, the real wireless channel effect is not entirely reflected. Therefore, future studies should use a functional model to describe the unique physical layer differences of devices. Furthermore, the channel influence must be separated from the function model so that this technology can be used in scenarios in which the channels are dynamic. In the same context, considering the mobility of devices in the IoT and the characteristics of dense access networks of terminal devices, **(S16)**, **(S25)**, and **(S27)** pointed out that PLA models for moving devices have been a more challenging issue than authenticating static devices. In the future, further studies can be conducted on physical layer authentication in advanced wireless communication technology terminals and IoT devices with mobility and high-speed environment scenarios. **ML/DL Approache Issues:** Machine learning has recently received attention in the communication domain because of successful deep learning approaches in computer vision, automatic speech recognition, and natural language processing. However, unlike computer vision, in which the dataset is generally represented as pixel values, communication system design is based on real-world channel conditions and RF signals [101], which are unpredictable and varied. Moreover, we have noticed that current authentication DL models are mainly trained offline. Therefore, it is imperative to design models for specific or general scenarios that dynamically adapt to varying channels. For example, in **(S9)**, the authors referred to CNN as a robust technique in a static environment, but if the receiver changed orientation or had to be moved from one place to another, the system would have to be retrained.