📡 LWM: Large Wireless Model

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LWM is a powerful pre-trained model developed as a universal feature extractor for wireless channels. As the world's first foundation model crafted for this domain, LWM leverages transformer architectures to extract refined representations from simulated datasets, such as DeepMIMO and Sionna, and real-world wireless data.

How is LWM built?

The LWM model’s structure is based on transformers, allowing it to capture both fine-grained and global dependencies within channel data. Unlike traditional models that are limited to specific tasks, LWM employs a self-supervised approach through our proposed technique, Masked Channel Modeling (MCM). This method trains the model on unlabeled data by predicting masked channel segments, enabling it to learn intricate relationships between antennas and subcarriers. Utilizing bidirectional attention, LWM interprets the full context by attending to both preceding and succeeding channel segments, resulting in embeddings that encode comprehensive spatial information, making them applicable to a variety of scenarios.

What does LWM offer?

LWM provides a universal feature extraction framework that can be applied across diverse wireless communication and sensing tasks. It is built to handle complex wireless environments, capturing channel characteristics in a way that facilitates robust performance across different scenarios and conditions.

Trained on hundreds of thousands of wireless channel samples, LWM has been designed to generalize across varied environments—from dense urban areas to synthetic setups, ensuring its adaptability and consistency across a broad spectrum of wireless tasks.

How is LWM used?

LWM is designed to be easily integrated into downstream applications as a source of high-quality embeddings that encapsulate complex channel features. By feeding raw wireless channel data into the pre-trained model, users obtain embeddings that capture essential spatial relationships and interactions within the channel environment.

These embeddings provide a versatile and contextualized representation of wireless data, which can be leveraged across different applications. By utilizing the pre-trained model in this way, users can reduce the need for extensive labeled data while benefiting from embeddings that retain the critical properties of the original channel.

Advantages of Using LWM

• Various Tasks: Self-supervised and pre-trained without labels, LWM excels in a wide range of wireless tasks, offering flexibility and performance
• Limited Data: With LWM embeddings, downstream tasks achieve high accuracy with less data, cutting reliance on large datasets
• Various Environments: Pre-trained on diverse data, LWM excels in various environments from urban to rural areas, ensuring reliable performance

Join the growing community of researchers using LWM for their wireless communication and sensing research, and unlock a new level of performance and insight in your models!

Please cite the following paper if you use the LWM model or any modifiled parts:

@misc{alikhani2024largewirelessmodellwm,
      title={Large Wireless Model (LWM): A Foundation Model for Wireless Channels}, 
      author={Sadjad Alikhani and Gouranga Charan and Ahmed Alkhateeb},
      year={2024},
      eprint={2411.08872},
      archivePrefix={arXiv},
      primaryClass={cs.IT},
      url={https://arxiv.org/abs/2411.08872}, 
}

🛠 How to Use

1. Install Conda

First, ensure that you have a package manager like Conda installed to manage your Python environments and packages. You can install Conda via Anaconda or Miniconda.

• Anaconda includes a comprehensive scientific package suite. Download it here.
• Miniconda is a lightweight version that includes only Conda and Python. Download it here.

Once installed, you can use Conda to manage environments.

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2. Create a New Environment

After installing Conda, follow these steps to create a new environment and install the required packages.

Step 1: Create a new environment

To begin, open the Anaconda PowerShell Prompt and create a new Conda environment named lwm_env:

conda create -n lwm_env

Step 2: Activate the environment

Activate the environment:

conda activate lwm_env

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3. Install Required Packages

Once the environment is activated, install the necessary packages.

Install CUDA-enabled PyTorch

Although inference can run efficiently on a CPU, you may need a GPU for training more resource-intensive downstream tasks. Visit this page and select the appropriate options based on your system's specifications. The website will generate a tailored installation command.

For instance, on an NVIDIA system, you can use a command like the following with the appropriate CUDA version for your system:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

This command installs PyTorch with CUDA support for GPU-accelerated training. Ensure that the specified CUDA version is compatible with your system, adjusting it if necessary.

Note: If you encounter issues installing CUDA-enabled PyTorch, verify your CUDA version compatibility. It might also be due to conflicting installation attempts—try a fresh environment.

Install Other Required Packages via Conda Forge

conda install python numpy pandas matplotlib tqdm -c conda-forge

Install DeepMIMOv3 with pip

pip install DeepMIMOv3

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4. Clone the Dataset Scenarios

The following functions will help you clone specific dataset scenarios from a repository:

import subprocess
import os

# Function to clone a specific dataset scenario folder
def clone_dataset_scenario(scenario_name, repo_url, model_repo_dir="./LWM", scenarios_dir="scenarios"):
    current_dir = os.path.basename(os.getcwd())
    if current_dir == "LWM":
        model_repo_dir = "."

    # Create the scenarios directory if it doesn't exist
    scenarios_path = os.path.join(model_repo_dir, scenarios_dir)
    if not os.path.exists(scenarios_path):
        os.makedirs(scenarios_path)

    scenario_path = os.path.join(scenarios_path, scenario_name)

    # Initialize sparse checkout for the dataset repository
    if not os.path.exists(os.path.join(scenarios_path, ".git")):
        print(f"Initializing sparse checkout in {scenarios_path}...")
        subprocess.run(["git", "clone", "--sparse", repo_url, "."], cwd=scenarios_path, check=True)
        subprocess.run(["git", "sparse-checkout", "init", "--cone"], cwd=scenarios_path, check=True)
        subprocess.run(["git", "lfs", "install"], cwd=scenarios_path, check=True)  # Install Git LFS if needed

    # Add the requested scenario folder to sparse checkout
    print(f"Adding {scenario_name} to sparse checkout...")
    subprocess.run(["git", "sparse-checkout", "add", scenario_name], cwd=scenarios_path, check=True)
    
    # Pull large files if needed (using Git LFS)
    subprocess.run(["git", "lfs", "pull"], cwd=scenarios_path, check=True)

    print(f"Successfully cloned {scenario_name} into {scenarios_path}.")

def clone_dataset_scenarios(selected_scenario_names, dataset_repo_url, model_repo_dir):
    for scenario_name in selected_scenario_names:
        clone_dataset_scenario(scenario_name, dataset_repo_url, model_repo_dir)

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5. Clone the Model Repository

Now, clone the LWM model repository to your local system.

# Step 1: Clone the model repository (if not already cloned)
model_repo_url = "https://huggingface.co/wi-lab/lwm"
model_repo_dir = "./LWM"

if not os.path.exists(model_repo_dir):
    print(f"Cloning model repository from {model_repo_url}...")
    subprocess.run(["git", "clone", model_repo_url, model_repo_dir], check=True)

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6. Clone the Desired Dataset Scenarios

You can now clone specific scenarios from the DeepMIMO dataset, as detailed in the table below:

📊 Dataset Overview

📊 Dataset	🏙️ City	👥 Number of Users	🔗 DeepMIMO Page
Dataset 0	🌆 Denver	1354	DeepMIMO City Scenario 18
Dataset 1	🏙️ Indianapolis	3248	DeepMIMO City Scenario 15
Dataset 2	🌇 Oklahoma	3455	DeepMIMO City Scenario 19
Dataset 3	🌆 Fort Worth	1902	DeepMIMO City Scenario 12
Dataset 4	🌉 Santa Clara	2689	DeepMIMO City Scenario 11
Dataset 5	🌅 San Diego	2192	DeepMIMO City Scenario 7

It is important to note that these six datasets were not used during the pre-training of the LWM model, and the high-quality embeddings produced are a testament to LWM’s robust generalization capabilities rather than overfitting.

The operational settings below were used in generating the datasets for both the pre-training of LWM and the downstream tasks. If you intend to use custom datasets, please ensure they adhere to these configurations:

Operational Settings:

• Antennas at BS: 32
• Antennas at UEs: 1
• Subcarriers: 32
• Paths: 20
• Frequency: 3.5GHz (By the way, our results are consistent across different frequency ranges.)

Clone the Scenarios:

import numpy as np
dataset_repo_url = "https://huggingface.co/datasets/wi-lab/lwm"  # Base URL for dataset repo
scenario_names = np.array([
    "city_18_denver", "city_15_indianapolis", "city_19_oklahoma", 
    "city_12_fortworth", "city_11_santaclara", "city_7_sandiego"
])

scenario_idxs = np.array([0, 1, 2, 3, 4, 5])  # Select the scenario indexes
selected_scenario_names = scenario_names[scenario_idxs]

# Clone the requested scenarios
clone_dataset_scenarios(selected_scenario_names, dataset_repo_url, model_repo_dir)

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7. Change the Working Directory to LWM

if os.path.exists(model_repo_dir):
    os.chdir(model_repo_dir)
    print(f"Changed working directory to {os.getcwd()}")
else:
    print(f"Directory {model_repo_dir} does not exist. Please check if the repository is cloned properly.")

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8. Tokenize and Load the Model

Before we dive into tokenizing the dataset and loading the model, let's understand how the tokenization process is adapted to the wireless communication context. In this case, tokenization refers to segmenting each wireless channel into patches, similar to how Vision Transformers (ViTs) work with images. Each wireless channel is structured as a 32x32 matrix, where rows represent antennas and columns represent subcarriers.

The tokenization process involves dividing the channel matrix into patches, with each patch containing information from 16 consecutive subcarriers. These patches are then embedded into a 64-dimensional space, providing the Transformer with a richer context for each patch. In this process, positional encodings are added to preserve the structural relationships within the channel, ensuring the Transformer captures both spatial and frequency dependencies.

If you choose to apply Masked Channel Modeling (MCM) during inference (by setting gen_raw=False), LWM will mask certain patches, as it did during pre-training. However, for standard inference, masking isn't necessary unless you want to test LWM's robustness to noisy inputs! The printed LWM loss after inference could show you how well it has predicted the masked patches.

Now, let's move on to tokenize the dataset and load the pre-trained LWM model.

from input_preprocess import tokenizer
from lwm_model import lwm
import torch

preprocessed_chs = tokenizer(
    selected_scenario_names=selected_scenario_names,  # Selects predefined DeepMIMOv3 scenarios. Set to None to load your own dataset.
    manual_data=None,  # If using a custom dataset, ensure it is a wireless channel dataset of size (N,32,32) based on the settings provided above.
    gen_raw=True  # Set gen_raw=False to apply masked channel modeling (MCM), as used in LWM pre-training. For inference, masking is unnecessary unless you want to evaluate LWM's ability to handle noisy inputs.
)

device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Loading the LWM model on {device}...")
model = lwm.from_pretrained(device=device)

With this setup, you're ready to pass your tokenized wireless channels through the pre-trained model, extracting rich, context-aware embeddings that are ready for use in downstream tasks.

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9. Perform Inference

Before running the inference, it's important to understand the benefits of the different embedding types. The CLS embeddings (cls_emb) provide a highly compressed, holistic view of the entire wireless channel, making them ideal for tasks requiring a general understanding, such as classification or high-level decision-making. On the other hand, channel embeddings (channel_emb) capture detailed spatial and frequency information from the wireless channel, making them more suitable for complex tasks like beamforming or channel prediction.

You can now perform inference on the preprocessed data using the LWM model.

from inference import lwm_inference, create_raw_dataset
input_types = ['cls_emb', 'channel_emb', 'raw']
selected_input_type = input_types[1]  # Change the index to select LWM CLS embeddings, LWM channel embeddings, or the original input channels.

if selected_input_type in ['cls_emb', 'channel_emb']:
    dataset = lwm_inference(preprocessed_chs, selected_input_type, model, device)
else:
    dataset = create_raw_dataset(preprocessed_chs, device)

By selecting either cls_emb or channel_emb, you leverage the pre-trained model's rich feature extraction capabilities to transform raw channels into highly informative embeddings. If you prefer to work with the original raw data, you can choose the raw input type.

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10. Generate Labels if Necessary

If your dataset requires labels, you can easily generate them using DeepMIMO data. Here's an example to create labels for either LoS/NLoS classification or beam prediction, depending on the scenario selected:

from input_preprocess import create_labels
tasks = ['LoS/NLoS Classification', 'Beam Prediction']
task = tasks[1] # Choose 0 for LoS/NLoS labels or 1 for beam prediction labels.
labels = create_labels(task, selected_scenario_names, n_beams=64) # For beam prediction, n_beams specifies the number of beams in the codebook. If you're generating labels for LoS/NLoS classification, you can leave this value unchanged as it doesn't impact the label generation.

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11. Leverage the Dataset for Downstream Tasks

LWM, pre-trained on a vast and diverse dataset using self-supervised learning, does not rely on labeled data. During inference, it transforms raw channels into rich embeddings in real time, capturing both general and intricate patterns within the wireless channels. These embeddings can be directly applied to various downstream tasks, offering a more powerful alternative to using the original channel data.

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12. Explore the Interactive Demo

To experience LWM interactively, visit our demo hosted on Hugging Face Spaces:

Try the Interactive Demo!

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You're now ready to explore the power of LWM in wireless communications! Start processing datasets and generate high-quality embeddings to advance your research or applications.