# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 4 Security and Privacy in 5G
Any unauthorized users can view public messages, and it is easy for them to create security breaches. Therefore, both confidential and public messages are accessed by insiders. To avoid such issues, encoding techniques should be restructured and modified. In this way, the superimposition of both messages results in their transmission without facing any interference issue. * Coding schemes: PHY-SI performs a superposition coding technique at the transmission side; on the other end, the receiving side performs interference cancellation to obtain desired outcomes. Depending on the service type, PHY-SI uses different codebook formats for transmitting waveforms. Different PHY-SI models are presented in [336] to attain a high capacity region. Artificial noise (AN) and eigen transmission were also discussed to reduce the interference in the MIMO system. In the AN scheme, simulated noise is sent by the transmitter to create interference for the eavesdropper. Thus, this helps to protect the entire network from unauthorized access, crosstalk, noise, reflections, and jitter responses. Besides calculating the number and availability of eavesdroppers, it is also located after statistical analysis, and it optimizes the channel by transmitting AN noise through it. It generates a balance between the intervention provided to both authorized and unauthorized customers [337]. The authors analyzed a multi-core fiber (MCF) in the paper to investigate the impairments and detriments of inter-core crosstalk. The attack-aware routing and core assignments are taken into consideration to evade vulnerable threats to the bottom layer, and efforts were devoted to overcoming these effects. The integer linear programming model (ILP) increases the channel efficiency and simultaneously reduces inter-core crosstalk levels. For static network planning, a heuristic algorithm is proposed to analyze the issue and improve network efficiency in terms of time. Similarly, for dynamic provision networks, the same algorithm was proposed to adjust the tradeoff between blocking probability and the impact of crosstalk [338]. Another paper [339] discussed the vulnerabilities caused by crosstalk in elastic optical networks (EON). The author proposed differentiating RSA schemes for inter and intra-domain requests while considering security measures. The author viewed the vulnerabilities createdby both trusted and untrusted sources to achieve differentiation. Different RSA schemes were examined according to various physical layer attacks. The ILP model resolved the crosstalk issue, and simulation analyses were performed to determine the benefits of the heuristic algorithm. Crosstalk issues for EON (elastic optical networks) were discussed, and corresponding solutions were proposed in [340]. The crosstalk-aware routing spectrum and core assignment (CA-RSCA) algorithms for space-division multiplexing (SDM-EONs) were presented in [341]. The author investigated the RSCA issue and evaluated the band status in these networks by designing the CA spectrum compactness metric. First-fit (FF) and random-fit (RF), which are the two essential classifications of crosstalk-aware spectrum defragmentation (CASD) algorithms, were proposed to obtain high performance in contrast to baseline algorithms while considering PB (blocking probability) and frequency utilization [342]. In [343], the author determined the performance of the CASD algorithm with multiple spectrum compactness (SC) thresholds, and the improved results were generated at the SC threshold. By following the same approach, researchers investigated RSCA issues in SDM-EON from the network point of view. The number of cores present in the multi-core fiber (MCF) is larger than in the single-core fiber (SCF), but the presence of additional modes also increases the cost of MCF [344]. Currently, 5G and Beyond-5G (B5G) wireless network architectures have been revolutionized due to IoT applications, and the number of user devices and amount of data created by these devices is increasing exponentially [345; 346].