# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 4 Open Challenges and Opportunities
### Congestion Control with Large Language Models
For example, AutoML models could analyze historical Starlink network performance data and predict the optimal AIMD settings for various traffic scenarios, ensuring better network performance across diverse geographic regions and environmental conditions. Additionally, the models could continuously learn from live network data to optimize AIMD parameters on the fly, significantly improving throughput and reducing latency even as Starlink satellites move in and out of range. Meanwhile, LLMs can complement this approach by providing intelligent recommendations and insights to network operators. By analyzing large datasets of Starlink network traffic, LLMs can help identify patterns in packet loss, latency, and bandwidth fluctuations that might not be immediately apparent. This can inform adjustments to AIMD settings or even suggest new variations of congestion control algorithms tailored to satellite networks. LLMs can also facilitate better human-AI collaboration by translating network insights into natural language, allowing operators to make informed decisions more easily. As such, while traditional AIMD congestion control mechanisms face limitations in satellite environments like Starlink, integrating AutoML and LLMs can significantly improve their overall performance and reliability. In the past, around the 1980s, end users had no visibility into the underlying network conditions, making it difficult to optimize the AIMD algorithm effectively. Back then, network environments were simpler, and the performance of the AIMD algorithm depended largely on static assumptions. However, with the advent of more complex systems such as 5G, 6G, and satellite-based internet of SpaceX, optimizing network resource allocation even with AIMD has become increasingly challenging due to the dynamic and intricate nature of these networks. Emerging technologies like satellite-based non-terrestrial networks and Open Radio Access Network architecture in future wireless networks [195], further complicate the landscape, requiring more adaptive congestion control solutions. TCP Machina represents a significant advancement in the field of congestion control, utilizing supervised learning to automate the design of congestion control algorithms. By training on network simulations, TCP Machina is able to optimize congestion control strategies for various network scenarios, learning the most effective methods for resource allocation. However, while this approach offers improvements, it is limited by its reliance on predefined conditions and lacks the ability to dynamically adapt in real time to the constantly changing environments found in modern networks. The integration of LLMs and network log analysis introduces a new dimension of adaptability to this challenge. By analyzing network logs, LLMs can infer network conditions that are typically opaque to end users. Based on these insights, LLMs can dynamically adjust the parameters of the AIMD algorithm, specifically fine-tuning \(\alpha\) (Additive Increase) to optimize resource allocation in response to evolving network conditions, and \(\beta\) (Multiplicative Decrease) to efficiently manage congestion. This approach allows for continuous, data-driven refinement of AIMD parameters, ensuring that network resource allocation is precisely tailored to the unique and fluctuating conditions of complex networks. In doing so, LLMs provide a level of responsiveness and efficiency in congestion control that surpasses static, scenario-based methods, making them particularly well-suited for the demands of modern networks. This approach utilizes unsupervised learning, offering greater flexibility and adaptability to unanticipated network conditions as compared to supervised learning methods.