--- tags: - wireless - foundation-model - competition - data-limited - transformer - channel-estimation - localization - beam-prediction - channel-interpolation - los-nlos-classification - pytorch - self-supervised-learning datasets: - DeepMIMO metrics: - f1 - mse - normalized-mse - localization-error model-index: - name: lwm-v1.1 results: - task: type: classification name: LoS/NLoS Classification dataset: name: Wireless Channel Dataset type: custom metrics: - type: f1 value: baseline - task: type: regression name: Channel Estimation dataset: name: Wireless Channel Dataset type: custom metrics: - type: normalized-mse value: baseline - task: type: regression name: Channel Interpolation dataset: name: Wireless Channel Dataset type: custom metrics: - type: normalized-mse value: baseline - task: type: classification name: Beam Prediction dataset: name: Wireless Channel Dataset type: custom metrics: - type: f1 value: baseline - task: type: regression name: Localization dataset: name: Wireless Channel Dataset type: custom metrics: - type: normalized-localization-error value: baseline pipeline_tag: feature-extraction library_name: pytorch ---

# ⚡ LARGE WIRELESS MODELS (LWMs) 2025 CHALLENGE **The goal is to improve performance across five wireless downstream tasks by optimizing a baseline LWM and/or designing new downstream models** [![Model Hub](https://img.shields.io/badge/🤗%20HuggingFace-Model%20Hub-orange?style=flat-square)](https://huggingface.co/wi-lab/lwm-v1.1) [![Tutorials](https://img.shields.io/badge/🎓%20Tutorials-Available-brightgreen?style=flat-square)](https://lwm-wireless.net/tutorials) [![Website](https://img.shields.io/badge/🌐%20Website-lwm--wireless.net-blue?style=flat-square)](https://lwm-wireless.net/) [![Contact](https://img.shields.io/badge/📬%20Contact-lwmwireless@gmail.com-red?style=flat-square)](mailto:lwmwireless@gmail.com)

Challenge Overview • Provided Materials • Getting Started • Submission Process • Tutorials • Citation • Community & Support • Team

# 📡 Large Wireless Model (LWM) Challenge Welcome to the official repository of the **LWM 2025 Challenge**, a competition designed to advance the state of foundation models in wireless communications and sensing. Participants are invited to optimize a provided baseline Large Wireless Model (LWM) and design downstream models to tackle five core wireless tasks with limited labeled data. --- ## 🧠 About LWM **Large Wireless Model (LWM) 1.1** is a Transformer-based foundation model pre-trained using self-supervised learning on over 1 million unlabeled wireless channel samples. It generates rich, task-agnostic embeddings that significantly outperform raw channel representations on downstream tasks—especially when data is scarce or noisy or downstream models need to be simple. --- ## 🏁 Challenge Overview Participants are given: - A pre-trained LWM 1.1 checkpoint - Baseline downstream task models - Training, validation, and public test sets for each task - Helper functions and templates Your goal is to improve the **Composite Generalization Score (CG-Score)** across these five tasks: 1. **LoS/NLoS Classification** – F1-score 2. **Sub-6 GHz Channel to mmWave Beam Prediction** – Top-1 Beam F1-score 3. **Channel Interpolation** – Normalized MSE 4. **Channel Estimation** – Normalized MSE 5. **Localization** – Normalized Localization Error Final rankings are based on hidden test sets evaluated by the organizers. --- ## 📦 Provided Materials This repository contains: - `pretrained_model.py` — Loads the baseline or your refined LWM model - `train_heads.py` — The main script for training and evaluating all task-specific models. **This file must not be modified.** It is provided as a standardized template to ensure fairness and consistency across all teams. Participants must design their submissions to align with this script. The organizers will use an equivalent version of `train_heads.py` for final evaluation, and any deviation from the expected structure will result in automatic disqualification. - `train_heads_config.py` — Contains training configs and model head definitions - `train_lwm.py` — Contains LWM 1.1 pre-training and dataset reproducibility script - `utils.py` — Helper functions (training, scoring, data handling) - `task_{t}/` — Contains the training, validation, and public test sets for each downstream task. These datasets are used for jointly fine-tuning your refined LWM and training the corresponding task-specific models. While downstream training is restricted to the provided datasets, you are free to use any dataset for LWM pre-training. Participants are granted early access to the **DeepMIMO v4** dataset, which offers new, large-scale scenarios suitable for extended LWM refinement. - `requirements.yml` — Conda environment file for dependency setup --- ## 🚀 Getting Started ### 📥 Clone the repo ```bash git clone https://github.com/wireless-intelligence-lab/lwm-competition-2025.git cd lwm-competition-2025 ``` ### 🛠️ Set up the environment ```bash conda env create -f requirements.yml conda activate lwm_env ``` ### 🧪 Run baseline pipeline ```bash python train_heads.py ``` This jointly finetunes LWM and trains downstream heads, evaluates on public test sets, and creates a submission ZIP file. ### 🧩 Submission Process 1. Refine your LWM or downstream heads 2. Update `pretrained_model.py`, `train_heads_config.py`, and `utils.py`. 3. Run: ```bash python train_heads.py ``` 4. Submit the generated ZIP file to the competition portal 🛑 **Do not modify `train_heads.py`.** While you may adapt it for local development or experimentation, your final submission **must be fully compatible with the original, unmodified version** provided. The evaluation script used by the organizers assumes this exact structure—any deviation may result in disqualification. --- ## 📚 Tutorials Visit the official tutorials page: 👉 [https://lwm-wireless.net/tutorials](https://lwm-wireless.net/tutorials) --- ## 🧪 Citation If you use the LWM model or its components, please cite: ```bibtex @misc{alikhani2025largewirelessmodellwm, title={Large Wireless Model (LWM): A Foundation Model for Wireless Channels}, author={Sadjad Alikhani and Gouranga Charan and Ahmed Alkhateeb}, year={2025}, eprint={2411.08872}, archivePrefix={arXiv}, primaryClass={cs.IT}, url={https://arxiv.org/abs/2411.08872}, } ``` --- ## 👥 Community & Support * 💬 [Discussion Forum](https://huggingface.co/wi-lab/lwm-v1.1/discussions) * 📨 Contact: [lwmwireless@gmail.com](mailto:lwmwireless@gmail.com) --- ## 👨‍🔬 Team Developed by the [Wireless Intelligence Lab](https://wi-lab.net) at Arizona State University.