# Large Language Model Enabled Multi-Task Physical Layer Network

arXiv: https://arxiv.org/abs/2412.20772 · HTML: https://mlsanity.com/paper/2412.20772/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2024.

## Built on by (most cited first, 13 of 13)

- [A Wireless Foundation Model for Multi-Task Prediction](https://mlsanity.com/paper/2507.05938/) (2025)
- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [Wireless Power Control Based on Large Language Models](https://mlsanity.com/paper/2603.00474/) (2026)
- [BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization](https://mlsanity.com/paper/2509.11056/) (2025)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning](https://mlsanity.com/paper/2509.12089/) (2025)
- [When marine radar target detection meets pretrained large language models](https://mlsanity.com/paper/2509.12110/) (2025)
- [WALoMA: A Multitask Wireless Foundation Model via Adaptive Low-Rank Masked Autoencoders](https://mlsanity.com/paper/2607.25763/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

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 multi-user precoding, signal detection and channel prediction simultaneously. Besides, multi-task instruction module, input encoders, as well as output decoders, are elaborately designed to distinguish different tasks. The proposed design allows different wireless data types to be well aligned with the LLM input format. 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.