# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Technological Aspect**} \\ \cline{3-5}  & & **AI** & **LLMs** & **Application Field** \\ \hline [PERSON] et al. [29] & 2018 & ✓ & ✗ & Optical Networks \\ [PERSON] et al. [30] & 2018 & ✓ & ✗ & Next-Generation Wireless Networks \\ [PERSON] et al. [31] & 2018 & ✓ & ✗ & Software-Defined Networking \\ [PERSON] et al. [32] & 2018 & ✓ & ✗ & 5G Mobile and Wireless Networks \\ [PERSON] et al. [33] & 2019 & ✓ & ✗ & Software-Defined Networking \\ [PERSON] et al. [34] & 2019 & ✓ & ✗ & Future Wireless Networks \\ [PERSON] et al. [35] & 2019 & ✓ & ✗ & Edge Computing \\ [PERSON] et al. [36] & 2019 & ✓ & ✗ & Mobile and Wireless Networking \\ [PERSON] et al. [37] & 2019 & ✓ & ✗ & Wireless Networks \\ [PERSON] et al. [38] & 2020 & ✓ & ✗ & 5G Wireless Networks \\ [PERSON] et al. [39] & 2020 & ✓ & ✗ & Wireless Networks \\ [PERSON] et al. [40] & 2020 & ✓ & ✗ & Software-Defined Networking \\ [PERSON] et al. [41] & 2020 & ✓ & ✗ & Intent-Based Networking \\ [PERSON] et al. [42] & 2021 & ✓ & ✗ & Internet of Things \\ [PERSON] et al. [43] & 2021 & ✓ & ✗ & Edge Computing \\ [PERSON] et al. [44] & 2021 & ✓ & ✗ & 6G Networks \\ [PERSON] et al. [45] & 2021 & ✓ & ✗ & Edge Computing \\ [PERSON] et al. [46] & 2022 & ✓ & ✗ & Networking Systems \\ [PERSON] et al. [47] & 2022 & ✓ & ✗ & Mobile Networks \\ [PERSON] et al. [48] & 2022 & ✓ & ✗ & Cybersecurity \\ [PERSON] et al. [49] & 2022 & ✓ & ✗ & Wireless Networks \\ [PERSON] et al. [50] & 2023 & ✓ & ✗ & Edge Computing \\ [PERSON] et al. [51] & 2023 & ✓ & ✗ & 6G Networks \\ [PERSON] et al. [52] & 2023 & ✓ & ✗ & Edge Computing \\ [PERSON] [53] & 2023 & ✓ & ✓ & Cybersecurity \\ [PERSON] et al. [54] & 2023 & ✓ & ✓ & 6G Systems \\ [PERSON] et al. [55] & 2024 & ✓ & ✗ & Edge Computing \\ [PERSON] et al. [56] & 2024 & ✓ & ✗ & 6G Wireless Networks \\ [PERSON] et al. [57] & 2024 & ✓ & ✗ & Software-Defined Networking \\ [PERSON] et al. [58] & 2024 & ✓ & ✗ & 6G Wireless Networks \\ [PERSON] et al. [59] & 2024 & ✓ & ✓ & Cybersecurity \\ [PERSON] et al. [60] & 2024 & ✓ & ✓ & 6G Networks \\ [PERSON] et al. [61] & 2024 & ✓ & ✓ & Next-Generation Wireless Networks \\ [PERSON] et al. [62] & 2024 & ✓ & ✓ & Edge Computing \\ [PERSON] et al. [63] & 2024 & ✓ & ✓ & Mobile Networks \\ [PERSON] et al. [64] & 2024 & ✓ & ✓ & Telecommunications \\ This Study & 2024 & ✓ & ✓ & Next-Generation Network \\ \hline \hline \end{tabular}
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Table 1: A comparison of related reviews on AI in networking. To summarize, the contributions of this paper are as follows:  
* We provide a comprehensive review of the potential applications of LLMs across the four key stages of network engineering: Network Design and Planning, Network Implementation, Network Analytics, and Network Management. * We present strategies for enhancing network performance and management using network intelligence, highlighting the limitations of current AI-driven methods. Additionally, we demonstrate how LLMs can address these limitations and improve existing network intelligence. * We identify key challenges in integrating LLMs into network engineering and, based on recent developments, provide future research directions to further optimize network functionality and efficiency through the use of LLMs and AI. This paper is organized as follows. In Section 2, we provide a background on network intelligence and LLMs, detailing how LLMs can be applied to networking. In Section 3, we explore application domains where LLMs can be beneficial for networks. Section 4 discusses the current open research challenges and suggests future research opportunities related to LLMs in networking. We conclude the paper in Section 5.