LLM4WM: Adapting LLM for Wireless Multi-Tasking

TL;DR

LLM4WM introduces a multi-task fine-tuning framework for large language models to improve wireless channel-related tasks by leveraging joint learning and transfer capabilities, outperforming existing methods.

Contribution

It presents a novel multi-task fine-tuning approach using MoE-LoRA tailored for wireless channel tasks, enhancing transfer learning and joint modeling in this domain.

Findings

Outperforms existing methods in full-sample evaluations.

Effective in few-shot learning scenarios.

Demonstrates robust multi-task joint modeling.

Abstract

The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. To capitalize on this advantage, LLM4WM is proposed--a large language model (LLM) multi-task fine-tuning framework specifically tailored for channel-associated tasks. This framework utilizes a Mixture of Experts with Low-Rank Adaptation (MoE-LoRA) approach for multi-task fine-tuning, enabling the transfer of the pre-trained LLM's general knowledge to these tasks. Given the unique characteristics of wireless channel data, preprocessing modules, adapter modules, and multi-task output layers are designed to align the channel data with the LLM's semantic feature space. Experiments on a channel-associated multi-task dataset demonstrate that LLM4WM outperforms existing methodologies in both full-sample and few-shot evaluations, owing to its robust multi-task joint modeling and transfer learning capabilities.