## 1 Introduction  
Currently, 5G wireless networks are fully commercialized worldwide and B5G networks are in the process of development and are supposed to be deployed within the next few years. With the fast adoption of 5G technology, the number of users who utilize wireless mobile networks has increased exponentially over the last few years. It is expected that this rapid growth will continue to increase enormously due to the deployment of more smart applications and new enabling technologies within the upcoming B5G networks. Future wireless networks will not only provide much higher spectral efficiency/data rates and lower latency but will also provide new services and technologies that can be applied in various vertical industries [1]. The presence of ubiquitous connections and the growing number of devices that are connected to the internet are expected to cause a challenge for efficient and reliable resource management systems [2, 3, 4]. Moreover, the rapid growth of the massive number of new devices that are connected to the internet and the Internet of Things (IoT) is expected to cause a serious risk to network security if not manipulated properly [5]. Therefore, when considering all of these capabilities, it can be seen that there is a need for robust security mechanisms across all segments of 5G and B5G networks. Conventionally, high-layer cryptography-based techniques have been widely adopted to deal with any discrepancies that are associated with information confidentiality, which include data authentication, secret key establishment and secret dissemination [6]. However, with the advancement in the computing capacities of eavesdropping devices, the above-mentioned techniques may not be sufficient or may even become unsuitable when an extra secure channel is required for secret key generation [7]. Recently, physical layer security (PLS) has emerged as a promising means of addressing the eavesdropping computational capability of secure transmission problems [8; 9; 10; 11; 12]. Compared to complex cryptography techniques, PLS does not depend on the computational capacity of the eavesdropping devices and, therefore, it has the benefit of reducing computational costs and resource consumption. From the perspective of information-theoretic fundamentals, it has been found that PLS can achieve secure and reliable communication even when network intruders have very strong computing capabilities [13]. This approach to information security is especially effective since it does not rely on underlying computational capabilities, but rather on the characteristics of the transmission media, such as noise, fading and interference, and it provides security guarantees that are independent from the computing power of the eavesdropper. In general, the PLS approach presents distinct advantages and is well suited for distributed processing systems and dynamic network configurations. Therefore, the PLS approach can be used as an alternative supplement for computationally demanding high-layer technologies to further ensure data security. Although the PLS can be precisely evaluated using popular performance metrics, such as secrecy capacity, secrecy rate, secrecy throughput, etc., which are discussed in detail in the literature, security performance is quantified by maximizing the performance difference between legitimate channels and wiretap channels [14; 15; 16; 17]. This is intuitive since PLS aims to enhance the received signal quality at the intended receiver or reduce the performance of the wiretap channel relative to the legitimate channel. In this circumstance, there is a need to allocate transmission power based on the states of the legitimate and eavesdropper channels in order to improve the PLS, as transmission power affects the signal quality at the intended receiver and eavesdropper. However, the allocation of power in PLS is a challenging task. It relies heavily on the prior information that the transmitter has on the channel state information (CSI) of the intended receiver and the eavesdropper.