# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 5 Challenges and Future Directions  
Fifth-generation networks require robust architectures and ML-based solutions on account of the heterogenous behavior of communication networks [351]. Hence, we mention the crucial security and privacy challenges and their potential solutions for 5G networks. Possible technologies--i.e., mmWave and terahertz band, RAN, NFV, network slicing, wireless SDN, MEC, and fog/cloud computing--are paving the way to revamp the upcoming 5G network architecture [352]. Besides this revolution, various challenges to complete 5G implementation lie ahead. In the following, we discuss the leading challenges and future research directions enabled by emerging technologies that demand to be addressed for the success of 5G. 1. Business model and economic challenges for 5G network: Before 5G technology, telecommunication operators were providing services using integrated services (IntServ) and differentiated services (DiffServ) models, while 5G technology introduces eMBB, URLLC, and mMTC. Therefore, 5G is expected to meet the requirements (bandwidth and latency) of various vertical applications and servicesaccordingly. Hence, the future network should be capable of new business models based on heterogeneously oriented services and provide the services in all use case scenarios. Business models for the network could be business to business (B2B), business to consumers (B2C), and business to business to consumer (B2B2C) [353]. Furthermore, we need to conduct detailed and comprehensive research to find out the real-time problems for all 5G use cases and embedded ML-based optimized solutions for the upcoming era. 2. Collaboration of OTT and ISP for 5G service management: The quality of experience in vertical heterogeneous networks is one of the significant challenges. This can be achieved using QoE monitoring and QoE management theories. A collaboration between over-the-top providers (OTT) and internet service providers (ISP) needs to be established for QoE/QoS monitoring and measurement factors. Researchers have already proposed monitoring probes (passive) with OTT applications at UE to exchange information for desired QoS [354]. We need to find ML-based standardized interfaces, ML-based optimized level frequency, and ML-based tradeoffs between QoE and latency in network operations. This will have a high impact, and optimized ML-based algorithms can enhance network performance. Besides these issues, the scalability and effectiveness of QoE also need to be addressed. 3. RAN virtualization in 5G network: RAN slicing, an integral part of virtualized 5G systems, is yet to be addressed because it is in the nascent phase. Docker and VM-based solutions do not address radio resource problems to an acceptable degree in terms of shared and multiple RATs in 5G networks. Hence, another challenge for RAN virtualization is RAN as a service (RaaS), where beyond physical infrastructure, radio resource sharing is crucial [355]. Furthermore, ML-based solutions are greatly needed for mobility management and the scheduling of radio resources as virtualized control functions to be implemented. The optimized RaaS will improve network performance and cost-effectiveness for the complex environment. At the same time, ML is needed to address system integration, achieving widespread adoption, technology support difficulties, and security risks. 4. End-to-end slice orchestration and management: With the introduction of SDN and NFV in 5G networks, it is necessary to change the deployment, operation, and management of networks and find intelligent methods for how resources are to be orchestrated [356]. Recently, many projects have shown promise in this context; i.e., AT&T's ECOMP project, OSM project, ETSI MANO framework, and ONAP project implementation. However, several challenges remain with these advancements, such as moving towards a concrete network slice from a high-level service description. Scalability and resilience are core services supporting multi-vendor case scenarios and entertaining upcoming 5G network elements.