ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence

TL;DR

This paper introduces ChannelGPT, a large model-based digital twin for 6G wireless channels that utilizes environment intelligence to generate accurate, multi-scenario channel data and support adaptive network decision-making.

Contribution

The paper presents a novel large model driven digital twin, ChannelGPT, capable of multi-scenario channel generation using multimodal data and environment intelligence for 6G networks.

Findings

ChannelGPT accurately generates diverse channel data.

It demonstrates strong generalization across scenarios.

Supports real-time adaptive network decisions.

Abstract

6G is envisaged to provide multimodal sensing, pervasive intelligence, global coverage, global coverage, etc., which poses extreme intricacy and new challenges to the network design and optimization. As the core part of 6G, wireless channel is the carrier and enabler for the flourishing technologies and novel services, which intrinsically determines the ultimate system performance. However, how to describe and utilize the complicated and high-dynamic characteristics of wireless channel accurately and effectively still remains great hallenges. To tackle this, digital twin is envisioned as a powerful technology to migrate the physical entities to virtual and computational world. In this article, we propose a large model driven digital twin channel generator (ChannelGPT) embedded with environment intelligence (EI) to enable pervasive intelligence paradigm for 6G network. EI is an iterative and interactive procedure to boost the system performance with online environment adaptivity. Firstly, ChannelGPT is capable of utilization the multimodal data from wireless channel and corresponding physical environment with the equipped sensing ability. Then, based on the fine-tuned large model, ChannelGPT can generate multi-scenario channel parameters, associated map information and wireless knowledge simultaneously, in terms of each task requirement. Furthermore, with the support of online multidimensional channel and environment information, the network entity will make accurate and immediate decisions for each 6G system layer. In practice, we also establish a ChannelGPT prototype to generate high-fidelity channel data for varied scenarios to validate the accuracy and generalization ability based on environment intelligence.