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Large Language Model Enabled Multi-Task Physical Layer Network
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AI
DOI：10.1109/TCOMM.2025.3626010.png)
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Abstract
En 中文
The advance of Artificial Intelligence (AI) is continuously reshaping the future 6G wireless communications. Particularly, the development of Large Language Models (LLMs) offers a promising approach to effectively improve the performance and generalization of AI in different physical-layer (PHY) tasks. However, most existing works finetune dedicated LLM networks for a single wireless communication task separately. Thus, performing diverse PHY tasks requires extremely high training resources, memory usage, and deployment costs. To solve the problem, we propose a LLM-enabled multi-task PHY network to unify multiple tasks with a single LLM, by exploiting the excellent semantic understanding and generation capabilities of LLMs. Specifically, we first propose a multi-task LLM framework, which finetunes LLM to perform multiple tasks including multi-user precoding, signal detection, and channel prediction. Besides, the multi-task instruction module, input encoders, as well as output decoders, are elaborately designed to distinguish different tasks and adapt LLM for different tasks in the wireless domain. Moreover, low-rank adaptation (LoRA) is utilized for LLM fine-tuning. To reduce the memory requirement during LLM fine-tuning, a LoRA fine-tuning-aware quantization method is introduced. Extensive numerical simulations are also displayed to verify the effectiveness of the proposed method.
Keywords:
Large language models (LLMs)
multi-task LLM
physical layer communications
Journal
IF:
8.3
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1.2W
Citations:
3.6W
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