# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 5 Challenges and Future Directions
We need to find a way to manage all underlying slices and the E2E orchestration of all available resources while keeping the fact in view that all network slices must meet their service; e.g., services and experience level agreements (ELAs/SLAs). 5. Mobility management in 5G networks: Fifth-generation networks will face mobility management issues due to the numbers of smart devices increasing exponentially, heterogeneous networks, ultra-dense small cell networks, fast-moving vehicles, and concerns about the truthfulness of information in vehicle-to-vehicle communications [357]. While due to the fixed position of devices in an industrial area, there is no need for mobility management, as they do not need to relocate, a number of researchers have proposed frameworks/solutions to handle mobility management in 5G networks. Automated driving services have different criteria than mobile broadband management; i.e., high-speed trains (e.g., 600 km/h) may trigger multiple handovers for railway communication [358]. Maintaining high priority for real-time services and seamless mobility support is crucial, so the requirements for automated driving services are different. Therefore, ML-based optimized and efficient methodologies are required that depend upon use cases, main tain service-aware QoE/QoS control in 5G systems, and enable users to maneuver between all SDN controllers in a 5G heterogeneous environment. * 6. Network sharing and slicing in 5G: Software-based platforms have the potential to make support for multi-tenancy more accessible using SDN/NFV based infrastructure in 5G systems. Therefore, multiple services and applications may be entertained successfully. This network sharing paradigm allows many virtual network functions to be set up on a similar 5G NFV platform and introduces various management challenges [359]. Vast amounts of research are required correlated to the isolation between slices, inter-domain services slicing, network functions placement within a slice, dynamic slice creation, and understanding the slicing concept's performance in 5G networks. Besides other issues, QoS/QoE performance must also be ensured on every slice, neglecting network congestion and other slices' performance levels. * 7. Security and privacy challenges in 5G networks: Providing various services, multiple network slices, and resource sharing for different verticals can introduce different levels of security concerns and privacy policy requirements in 5G networks [360]. Hence, complicated research challenges are raised and addressed considering the impact of one slice on another, efficient coordination mechanisms, and the impact of entire network systems, particularly in multi-domain infrastructures. Intelligent ML-based algorithms can meet these challenges and ensure network performance. * 8. Network reconstruction: Fifth-generation technology is envisioned to increase capacity, the density of connections, and energy efficiency with reliability while decreasing latency. At the network edge, 5G can also transmit touch-perception-type real-time communication; i.e., robotics and haptics equipment. In this respect, wide-ranging changes are required in network architecture, including the core and radio access network (RAN). Fifth-generation heterogeneous wireless networks are required to reconstruct RAN and CN architecture to support E2E to achieve an end-to-end latency of 1 ms in network slicing with the help of optimized ML-based methodologies. The cooperation of multiple RATs and macrocells with ultra-dense small cells in complex heterogeneous networks may confront these slicing demands [361]. * 9. Fifth-generation technologies collaboration: Future 5G architecture demands the coexistence and cooperation of all conventional and recent technologies--i.e., broadband transmission, LTE/LTE-A systems, C-RAN, mmWave, massive MIMO, SDN, NFV, network slicing, and mobile cloud engineering (MCE)--to support all use cases of 5G [362].