### Contributions
The aim of edge computing is to bring computing and storage capacities closer to the users, which will have a significant impact on decreasing network latency. The authors of [11; 12] have conducted studies discussing recent trends in intelligent edge computing for AI training and AI model interfacing, with the latter focusing on future 6G wireless networks. The authors of [13] explored the key components of edge ML and theoretical concepts behind designing neural network architectures and their training, while attempting to answer research questions regarding latency, security, and reliability under hardware and privacy restrictions. The authors of [14] provided insights into integrating multi-access edge computing with open radio access networks for beyond 5G (or 6G) networks. Deep learning (DL), a sub-field of machine learning, has recently been gaining attention as it brings improvements over the conventional ML techniques by reducing the need for hand-crafted feature sets used for training [15] while improving accuracy. Many attempts have been made to facilitate the applications of wireless communication by employingdeep learning models. Recently, [PERSON] et al. presented a comprehensive study that investigates DL applications from the perspective of four major concepts for 6G wireless communication systems specific to the physical layer (PHY), including massive MIMO systems, advanced multicarrier waveforms, RIS-empowered systems, and PHY security. [PERSON] et al. [16] presented a deep learning-based solution for intelligent and automated network slicing in 5G Open RAN (ORAN) deployment. They introduced a framework for dynamic RAN slicing using AI to meet multi-service SLAs. The proposed framework included modules for traffic load prediction and radio resource planning, demonstrating significant improvements in network resource management and service quality. [PERSON] et al. investigated deep learning-based wireless communication techniques specific to applications in PHY layers such as multiple access, channel estimation, and performance optimization [17]. In [18], a review of deep learning applications in wireless communication systems is presented, where the authors present the applications in PHY, MAC, and routing layers while also discussing future research trends. Ref. [19] presented a survey of different works that tailor deep learning models to mobile networking tasks. The authors in [20] discussed the roles of deep learning in intelligent edge applications to provide higher throughput and resource allocation. The utilization of deep learning models specific to certain wireless communication scenarios has also been presented by the authors of [21, 22]. In [21], the authors present a review of deep neural network models and investigate how they can benefit different IoT sectors, and, in [22], the authors present an overview of the deep learning architectures and algorithms relevant to the network traffic control systems. In [23], an analysis of the literature on deep learning for wireless communication networks is provided; the authors considered several case studies and showed that deep learning proves extremely useful for network design. The authors of [14] presented a review of existing DL-based radio access networks (RANs) as well as their (DL) integration with open-RANs (O-RANs). In their work, they also present two case studies on DL deployment in O-RAN while discussing open research problems in O-RAN architectures and how DL techniques can be used to tackle those problems. The authors of [24] present a literature review on applications of deep reinforcement learning, an emerging technique in AI, to address issues related to network access, security, wireless caching, data offloading, etc., specific to modern communication networks. Building on the previous discussion, integrating machine learning and AI into wireless communication systems (WCSs) indeed offers significant potential, but it also presents various challenges.