# RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

arXiv: https://arxiv.org/abs/2501.17888 · HTML: https://mlsanity.com/paper/2501.17888/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://mlsanity.com/paper/1905.13545/) (2019)
- [TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting](https://mlsanity.com/paper/2310.04948/) (2023)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Inception Transformer](https://mlsanity.com/paper/2205.12956/) (2022)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder](https://mlsanity.com/paper/2011.08295/) (2020)
- [S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting](https://mlsanity.com/paper/2403.05798/) (2024)
- [An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation](https://mlsanity.com/paper/2110.04980/) (2021)
- [Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification](https://mlsanity.com/paper/2203.15932/) (2022)
- [LLM4WM: Adapting LLM for Wireless Multi-Tasking](https://mlsanity.com/paper/2501.12983/) (2025)
- [Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution](https://mlsanity.com/paper/2308.05022/) (2023)
- [Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications](https://mlsanity.com/paper/2308.04033/) (2023)

## Built on by (most cited first, 6 of 6)

- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [PReD: An LLM-based Foundation Multimodal Model for Electromagnetic Perception, Recognition, and Decision](https://mlsanity.com/paper/2603.28183/) (2026)
- [RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation](https://mlsanity.com/paper/2606.01862/) (2026)
- [The Hitchhiker's Guide to Agentic AI: From Foundations to Systems](https://mlsanity.com/paper/2606.24937/) (2026)
- [EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals](https://mlsanity.com/paper/2608.24086/) (2026)
- [Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization](https://mlsanity.com/paper/2610.12142/) (2026)

## Abstract (arXiv)

The growing scarcity of spectrum resources and rapid proliferation of wireless devices make efficient radio network management critical. While deep learning-enhanced Cognitive Radio Technology (CRT) provides promising solutions for tasks such as radio signal classification (RSC), denoising, and spectrum allocation, existing DL-based CRT frameworks are typically task-specific and lack scalability in diverse real-world applications. This limitation naturally leads to the exploration of Large Language Models (LLMs), whose exceptional cross-domain generalization capabilities offer new potential for advancing CRT. To bridge this gap, we propose RadioLLM, a novel framework that integrates Hybrid Prompt and Token Reprogramming (HPTR) for combining radio signal features with expert knowledge, and a Frequency-Attuned Fusion (FAF) module for enhanced high-frequency feature modeling. Extensive evaluations on multiple benchmark datasets demonstrate that RadioLLM achieves superior performance compared to existing baselines in the majority of testing scenarios.