# Adapting Large Language Models for Improving TCP Fairness over WiFi

arXiv: https://arxiv.org/abs/2412.18200 · HTML: https://mlsanity.com/paper/2412.18200/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Utility Function is All You Need: LLM-based Congestion Control](https://mlsanity.com/paper/2603.10357/) (2026)

## Abstract (arXiv)

The new transmission control protocol (TCP) relies on Deep Learning (DL) for prediction and optimization, but requires significant manual effort to design deep neural networks (DNNs) and struggles with generalization in dynamic environments. Inspired by the success of large language models (LLMs), this study proposes TCP-LLM, a novel framework leveraging LLMs for TCP applications. TCP-LLM utilizes pre-trained knowledge to reduce engineering effort, enhance generalization, and deliver superior performance across diverse TCP tasks. Applied to reducing flow unfairness, adapting congestion control, and preventing starvation, TCP-LLM demonstrates significant improvements over TCP with minimal fine-tuning.