# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 2 Background  
### Network Intelligence  
Traditional network development and infrastructure management present significant challenges due to their complexity and the intensive knowledge and labor required. These tasks demand substantial expertise and manual effort, as they involve continuous adaptation to evolving technical standards and protocols. The shift from 5G to 6G, for instance, necessitates updating numerous protocols, posing a significant burden on network engineers. Additionally, critical tasks such as network performance monitoring and fault diagnosis are formidable technical obstacles. In complex network environments, effectively monitoring performance, as well as detecting and locating faults promptly, is a substantial challenge, requiring sophisticated tools and strategies. To overcome these issues, network intelligence leverages advanced computational techniques to enhance the management and optimization of network systems. Network intelligence refers to the application of intelligent methodologies to automate and improve various aspects of network operations [65; 66; 67]. Common advanced technologies include deep learning [68; 69], machine learning [70; 71], transfer learning [72; 73], and reinforcement learning [74; 75]. Each of these models can be tailored to specific environments and tasks. For example, adaptive routing algorithms can improve quality of service (QoS), edge intelligence can optimize high-performance computing [76; 77], automated configuration synthesis modules can reduce manual errors, and intelligent assistants can enhance customer service. These intelligence-driven approaches enable more efficient, responsive, and resilient network management, paving the way for smarter and more adaptable networking solutions. In the realm of network engineering, there are primarily four stages: network design and planning, network implementation, network analytics, and network management. As illustrated in Figure 1, these stages encompass a comprehensive lifecycle of network systems, from initial design to ongoing management. Network intelligence offers transformative potential in addressing the limitations of traditional approaches across these stages in network engineering. We provide an overview below of how network intelligence can be incorporated to enhance network engineering:  
* _Network Design and Planning_: Efficient network design and planning are essential for ensuring optimal network performance and resource utilization. This process involves developing the network architecture, such as capacity planning, resource scheduling, and load balancing. Typically, these tasks are labor-intensive, demanding considerable expertise and manual intervention, and they require ongoing adaptation to emerging standards and protocols. AI algorithms can enhance these tasks by automating the design process, increasing precision, and minimizing the dependence on manual expertise. * _Network Implementation_: Successful network implementation is essential for the deployment of robust and efficient network infrastructures. This task involves the deployment, configuration, and documentation of network infrastructure. Manual implementation can be error-prone and time-consuming, requiring extensive coordination and detailed configuration. AI-driven tools can streamline deployment and configuration processes, ensuring precise execution and significantly reducing setup time and effort. * _Network Analytics_: Continuous network analytics are vital for maintaining network health and performance. This process focuses on monitoring and analyzing network performance through traffic analysis, log analysis, and behavior analysis. Effective monitoring and fault diagnosis are challenging due to the complexity of modern networks, leading to potential delays in issue detection and resolution. Advanced AI models can continuously analyze network data, predict potential issues, and provide proactive maintenance insights, enhancing reliability and efficiency.