# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 4 Open Challenges and Opportunities
### Congestion Control with Large Language Models
Utilizing computational power, Remy automatically generates algorithms based on predefined network assumptions and objectives. Unlike traditional TCP protocols that manually define endpoint reactions to congestion signals, Remy synthesizes control algorithms by specifying network conditions and performance goals. This method allows for creating distributed algorithms that can adapt to various network environments, optimizing throughput and minimizing delay without requiring intrusive changes to network infrastructure. Remy-generated algorithms offer a significant improvement over manually designed protocols, providing a more adaptable and efficient approach to congestion control in diverse network conditions. The success of the automation approach proposed in TCP ex Machina represents a groundbreaking step towards automating the process of optimal congestion control, highlighting the potential for integrating advanced intelligence with TCP congestion control mechanisms. Central to TCP ex Machina is the use of network objectives or performance goals set by network operators to optimize network performance alongside network conditions. These crucial elements can be effectively managed by leveraging the capabilities of LLMs. For instance, network operators may find it challenging to articulate clear and precise network objectives or performance goals. LLMs can analyze detailed descriptions provided by network operators to accurately identify and distill the main objectives and goals that can be realistically achieved. This automated identification process ensures that network performance targets are well-defined and aligned with the intentions of the operators. Additionally, network operators may lack comprehensive visibility into current network conditions. LLMs can analyze incident reports and operator logs to identify patterns and infer network conditions. By synthesizing this information, LLMs can suggest adjustments to network objectives or performance goals, providing a more dynamic and responsive approach to congestion control. This approach can potentially facilitate informed decision-making and optimized network performance before actual implementation, demonstrating a significant advancement in network optimization. In satellite networks like Starlink [193], the behavior of AIMD must be adapted to account for the higher latencies inherent in satellite communication. In terrestrial networks, AIMD increases the transmission rate incrementally until packet loss occurs, then reduces the rate significantly. However, in satellite environments, packet loss is often caused by long propagation delays or changes in satellite positioning, not necessarily congestion. This misinterpretation by AIMD could lead to unnecessary reductions in transmission rates, resulting in suboptimal throughput for users. To address this, modifications to AIMD are necessary for satellite networks. One such approach, inspired by the work of [PERSON] on HighSpeed TCP [194], could involve adjusting the AIMD parameters (the additive increase rate and multiplicative decrease factor) to be more tolerant of delay and packet loss caused by satellite dynamics rather than congestion. In this way, the algorithm would better suit the fluctuating bandwidth and latency conditions found in satellite-to-ground communications of Starlink. Automated machine learning (AutoML) and LLMs can further enhance the adaptability of AIMD in these challenging environments. AutoML can automatically fine-tune the parameters of AIMD by analyzing real-time network conditions, such as latency, throughput, and packet loss rates, and dynamically adjusting the behavior of the algorithm. This enables more efficient and resilient congestion control mechanisms that are responsive to the unique demands of satellite-based internet services.