Large Language Model-Based Semantic Communication System for Image Transmission

TL;DR

This paper introduces a novel semantic communication system for image transmission using LLMs, significantly reducing data size and increasing data rate, suitable for future 6G networks.

Contribution

It presents an OFDM-based semantic communication framework leveraging LLMs for encoding and decoding images, a novel approach for efficient semantic data transmission.

Findings

Reduces data size by 4250 times

Achieves higher data rate than traditional methods

Demonstrates robustness in urban macro-cell scenarios

Abstract

The remarkable success of Large Language Models (LLMs) in understanding and generating various data types, such as images and text, has demonstrated their ability to process and extract semantic information across diverse domains. This transformative capability lays the foundation for semantic communications, enabling highly efficient and intelligent communication systems. In this work, we present a novel OFDM-based semantic communication framework for image transmission. We propose an innovative semantic encoder design that leverages the ability of LLMs to extract the meaning of transmitted data rather than focusing on its raw representation. On the receiver side, we design an LLM-based semantic decoder capable of comprehending context and generating the most appropriate representation to fit the given context. We evaluate our proposed system under different scenarios, including Urban Macro-cell environments with varying speed ranges. The evaluation metrics demonstrate that our proposed system reduces the data size 4250 times, while achieving a higher data rate compared to conventional communication methods. This approach offers a robust and scalable solution to unlock the full potential of 6G connectivity.