 and beyond involving network planning, diagnostics, optimization, and control. Meanwhile, the ITU focus group on ML for 5G (FG-ML5G) [188] has classified the UCs for ML into five categories as follows: 1. Network slicing and other network services-related use cases including cognitive heterogeneous networks and ML-based self-organized networks (SON). 2. User plane-related UCs such as traffic classification and emergency services based on ML. 3. Application-related UCs for ML data-driven architecture at the edge. 4. Signaling or management-related UCs involving ML-based mobility pattern prediction, ML-based QoE optimization, ML-based E2E network management, and SO on. 5. Security-related UCs. 1.4.4.1 Research Works on Network Slicing with AI/ML Strategies In this section, AI/ML research works are addressed to remedy challenges facing network slicing in 5G RAN. Various challenging issues were highlighted in [188] for network slicing such as optimization of RAN slicing, the prediction of allocation trajectory per network slice and cell, optimal radio resource allocation per slice, and SO on. Table 1.8 presents recent AI/ML research works that were proposed in the literature to alleviate several network slicing challenges in terms of resource allocation and management. In [189], a Q-Learning algorithm to maximize the M. Alfaqawi et al. Table 1.8 Research works adopting AI/ML for network slicing ML algorithm Challenge Outcome [189] Q-Learning Slice Optimize the network infrastructure revenue while admission guarantee the QoS to third-party tenants. policy An adaptive admission algorithm based on optimization Q-Learning framework was proposed to achieve high substantial gains for the network infrastructure provider compared to a smart heuristic method [190] Convolutional Slice Provide an optimal balance between resource neuron admission over-provisioning and insufficient resource network control and allocation to minimize the economic cost for the cross-slice infrastructure provider. resource A deep neural network str