# WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

arXiv: https://arxiv.org/abs/2502.06877 · HTML: https://mlsanity.com/paper/2502.06877/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Large Wireless Model (LWM): A Foundation Model for Wireless Channels](https://mlsanity.com/paper/2411.08872/) (2024)
- [Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges](https://mlsanity.com/paper/2104.11457/) (2021)
- [Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization](https://mlsanity.com/paper/2410.00617/) (2024)
- [Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction](https://mlsanity.com/paper/2309.11811/) (2023)
- [ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence](https://mlsanity.com/paper/2410.13379/) (2024)
- [Integrating Pre-Trained Language Model with Physical Layer Communications](https://mlsanity.com/paper/2402.11656/) (2024)
- [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024)
- [Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities](https://mlsanity.com/paper/2401.06274/) (2024)
- [Sensiverse: A dataset for ISAC study](https://mlsanity.com/paper/2308.13789/) (2023)

## Built on by (most cited first, 30 of 40)

- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [6G WavesFM: A Foundation Model for Sensing, Communication, and Localization](https://mlsanity.com/paper/2504.14100/) (2025)
- [Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks](https://mlsanity.com/paper/2505.10134/) (2025)
- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [RF-GPT: Teaching AI to See the Wireless World](https://mlsanity.com/paper/2602.14833/) (2026)
- [IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G](https://mlsanity.com/paper/2506.06718/) (2025)
- [Multimodal Wireless Foundation Models](https://mlsanity.com/paper/2511.15162/) (2025)
- [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [Towards SISO Bistatic Sensing for ISAC](https://mlsanity.com/paper/2508.12614/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction](https://mlsanity.com/paper/2603.18865/) (2026)
- [A Graph Foundation Model for Wireless Resource Allocation](https://mlsanity.com/paper/2604.07390/) (2026)
- [Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models](https://mlsanity.com/paper/2605.00968/) (2026)
- [LatentWave: JEPA Pretraining for Wireless Foundation Models](https://mlsanity.com/paper/2606.06373/) (2026)
- [Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels](https://mlsanity.com/paper/2505.13055/) (2025)
- [Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence](https://mlsanity.com/paper/2602.17096/) (2026)
- [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026)
- [Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models](https://mlsanity.com/paper/2606.10277/) (2026)
- [Fast Wireless Foundation Models with Early-Exits](https://mlsanity.com/paper/2606.29640/) (2026)
- [Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization](https://mlsanity.com/paper/2607.15713/) (2026)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [A Short Overview of Multi-Modal Wi-Fi Sensing](https://mlsanity.com/paper/2505.06682/) (2025)
- [Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks: Concepts, Perspectives and Case Study](https://mlsanity.com/paper/2508.00256/) (2025)
- [SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling](https://mlsanity.com/paper/2508.02742/) (2025)
- [SCA-LLM: Spectral-Attentive LLM-Based Wireless World Modeling for Agentic Communications](https://mlsanity.com/paper/2509.08139/) (2025)
- [Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing](https://mlsanity.com/paper/2510.17816/) (2025)
- [Agentic Link Construction for Environment and Intent Aware 6G Communication](https://mlsanity.com/paper/2511.05094/) (2025)

## Abstract (arXiv)

This paper introduces WirelessGPT, a pioneering foundation model specifically designed for multi-task learning in wireless communication and sensing. Specifically, WirelessGPT leverages large-scale wireless channel datasets for unsupervised pretraining and extracting universal channel representations, which captures complex spatiotemporal dependencies. In fact,this task-agnostic design adapts WirelessGPT seamlessly to a wide range of downstream tasks, using a unified representation with minimal fine-tuning. By unifying communication and sensing functionalities, WirelessGPT addresses the limitations of task-specific models, offering a scalable and efficient solution for integrated sensing and communication (ISAC). With an initial parameter size of around 80 million, WirelessGPT demonstrates significant improvements over conventional methods and smaller AI models, reducing reliance on large-scale labeled data. As the first foundation model capable of supporting diverse tasks across different domains, WirelessGPT establishes a new benchmark, paving the way for future advancements in multi-task wireless systems.