# A Wireless Foundation Model for Multi-Task Prediction

arXiv: https://arxiv.org/abs/2507.05938 · HTML: https://mlsanity.com/paper/2507.05938/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Beam Prediction based on Large Language Models](https://mlsanity.com/paper/2408.08707/) (2024)
- [Cram'er-Rao Bound Optimization for Joint Radar-Communication Design](https://mlsanity.com/paper/2101.12530/) (2021)
- [Large Language Model Enabled Multi-Task Physical Layer Network](https://mlsanity.com/paper/2412.20772/) (2024)
- [Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing](https://mlsanity.com/paper/2001.09306/) (2020)
- [Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach](https://mlsanity.com/paper/2005.07698/) (2020)
- [Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictions](https://mlsanity.com/paper/1912.11330/) (2019)
- [Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks](https://mlsanity.com/paper/2209.12368/) (2022)
- [Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems](https://mlsanity.com/paper/1904.00798/) (2019)

## Built on by (most cited first, 12 of 12)

- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G](https://mlsanity.com/paper/2605.00020/) (2026)
- [Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models](https://mlsanity.com/paper/2606.10277/) (2026)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels](https://mlsanity.com/paper/2605.22856/) (2026)
- [CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models](https://mlsanity.com/paper/2607.14975/) (2026)
- [MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation](https://mlsanity.com/paper/2608.05076/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)
- [Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization](https://mlsanity.com/paper/2610.07550/) (2026)

## Abstract (arXiv)

With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location, and network traffic, has become essential for a wide range of physical (PHY)-layer and medium access control (MAC)-layer tasks. Although traditional deep learning (DL)-based methods have been widely applied to such prediction tasks, they often struggle to generalize across different scenarios and tasks. In response, we propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals. The proposed model enforces univariate decomposition to unify heterogeneous tasks, encodes granularity for interval awareness, and uses a causal Transformer backbone for accurate predictions. Additionally, we introduce a patch masking strategy during training to support arbitrary input lengths. After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and achieves zero-shot performance on new tasks that surpass traditional full-shot baselines.