## 8 Challenges, Future Directions, and Lessons Learned
### Future Directions
The performance aspect of interference, mobility, and spectrum management for 5G technology may be improved with more studies focused on this area. e. Spectrum management mechanism: Modern wireless applications have significantly raised the demand for spectrum resources. To overcome the issue of spectrum scarcity, achieve high data rates, and ensure good quality of service, spectrum sharing is seen as a crucial method for 5G networks (QoS). Even though various studies have been conducted on coming up with a suitable plan for 5G spectrum management [198; 199], more work needs to be done to obtain cutting-edge technology that will satisfy future spectrum demands. f. Introduction of Reconfigurable Intelligent Surfaces (RIS): An RIS is a specifically designed human-made surface of electromagnetic (EM) material that is electronically controlled with integrated electronics and has unnatural wireless communication capabilities. In the most commonly considered case, the large number of small-sized, low-cost, and almost passive elements that comprise a RIS can simply modify the incident signal over the air to improve the signal coverage and quality [200; 201]. RISs offers a potential solution to achieve a software-configurable smart radio environment. The most distinguishable property of RISs is their inexpensive, nearly passive panel of unit cells. Even though some works [200; 201; 202] have been conducted in this area, further research efforts are still necessary, especially on its application to solving the 5G millimeter wave propagation challenges, such as blockage and absorption experienced at higher frequencies. g. Application of the emerging graph-based deep learning methods: Graph Neural Networks (GNNs) are forms of artificial intelligence techniques that use the graph-based deep learning method to predict the nodes, edges, and graph-related tasks after training the graph-based data sets. They are used for graph and node classification, link predictions, graph clustering, and generating, as well as image and text classification [203]. Traditional challenges in 5G systems, such as routing, load balancing, power control and resource allocation, and emerging ones, such as virtual network embedding in Software Defined Networking, can be solved when deep learning models, such as the GNN, are applied. Researchers use GNN to mine deep information hidden in graph-structured data to further improve the abilities to learn and simulate interactions between network nodes [204]. These Deep Learning techniques are being applied to wireless communications to combat problems such as resource allocations, physical layer design, and networking [205]. Further research efforts on applying the GNN deep learning approach to 5G technology will undoubtedly pose solutions to routing, power control, and QoS issues. h. Introduction of Semantic Communication (SemCom): The introduction of very advanced artificially intelligent approaches needs to be investigated to achieve the high transmission rate needed for 5G communication systems that go beyond the requirements of the traditional enabling technologies. Semantic communication integration is one of these strategies, as it integrates all aspects of the network, including users, their specific service needs, and the meaning of the information being transmitted into data processing and transmission [206; 207], by leveraging AI technology to communicate the most pertinent information to the receiver. Unlike traditional data-oriented transmission, SemCom recognizes and uses the meaning of information during the process of communication over the Internet, thereby reducing network load. Because SemCom strives to convey the meaning of the data and not only replicate the actual transmitted data, other undesirable phenomena, such as obstruction and air absorption observed at higher frequencies, will also be avoided [208; 209]. The 5G system will successfully communicate the meaning of transmitted information while utilizing a significantly reduced amount of bandwidth, thanks to SemCom [207].