# 📡 **LWM: Large Wireless Model**

**[🚀 Click here to try the Interactive Demo!](https://huggingface.co/spaces/sadjadalikhani/LWM-Interactive-Demo)**

Welcome to the **LWM** (Large Wireless Model) repository! This project hosts a pre-trained model designed to process and extract features from wireless communication datasets, specifically the **DeepMIMO** dataset. Follow the instructions below to set up your environment, install the required packages, clone the repository, load the data, and perform inference with LWM.

---

## 🛠 **How to Use for Beginners**

### 1. **Install Conda or Mamba (via Miniforge)**

First, you need to have a package manager like **Conda** or **Mamba** (a faster alternative) installed to manage your Python environments and packages.

#### **Option A: Install Conda**

If you prefer to use **Conda**, you can download and install **Anaconda** or **Miniconda**.

- **Anaconda** includes a full scientific package suite, but it is larger in size. Download it [here](https://www.anaconda.com/products/distribution).
- **Miniconda** is a lightweight version that only includes Conda and Python. Download it [here](https://docs.conda.io/en/latest/miniconda.html).

#### **Option B: Install Mamba (via Miniforge)**

**Mamba** is a much faster alternative to Conda. You can install **Mamba** by installing **Miniforge**.

- **Miniforge** is a smaller, community-based installer for Conda that includes **Mamba**. Download it [here](https://github.com/conda-forge/miniforge/releases/latest).

After installation, you can use `conda` or `mamba` for environment management. The commands will be the same except for replacing `conda` with `mamba`.

---

### 2. **Create a New Environment**

Once you have Conda or Mamba installed, follow these steps to create a new environment and install the necessary packages.

#### **Step 1: Create a new environment**

You can create a new environment called `lwm_env` (or any other name) with Python 3.9 or any required version:

```bash
# If you're using Conda:
conda create -n lwm_env python=3.9

# If you're using Mamba:
mamba create -n lwm_env python=3.9
```

#### **Step 2: Activate the environment**

Activate the environment you just created:

```bash
# For both Conda and Mamba:
conda activate lwm_env
```

---

### 3. **Clone the Repository**

After setting up the environment, clone the Hugging Face repository to your local machine using the following Python code:

```python
import subprocess
import os
import sys
import importlib.util
import torch

# Hugging Face public repository URL
repo_url = "https://huggingface.co/sadjadalikhani/LWM"

# Directory where the repo will be cloned
clone_dir = "./LWM"

# Step 1: Clone the repository if it hasn't been cloned already
if not os.path.exists(clone_dir):
    print(f"Cloning repository from {repo_url} into {clone_dir}...")
    result = subprocess.run(["git", "clone", repo_url, clone_dir], capture_output=True, text=True)

    if result.returncode != 0:
        print(f"Error cloning repository: {result.stderr}")
        sys.exit(1)
    print(f"Repository cloned successfully into {clone_dir}")
else:
    print(f"Repository already cloned into {clone_dir}")

# Step 2: Add the cloned directory to Python path
sys.path.append(clone_dir)

# Step 3: Import necessary functions
def import_functions_from_file(module_name, file_path):
    try:
        spec = importlib.util.spec_from_file_location(module_name, file_path)
        module = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(module)

        for function_name in dir(module):
            if callable(getattr(module, function_name)) and not function_name.startswith("__"):
                globals()[function_name] = getattr(module, function_name)
        return module
    except FileNotFoundError:
        print(f"Error: {file_path} not found!")
        sys.exit(1)

# Step 4: Import functions from the repository
import_functions_from_file("lwm_model", os.path.join(clone_dir, "lwm_model.py"))
import_functions_from_file("inference", os.path.join(clone_dir, "inference.py"))
import_functions_from_file("load_data", os.path.join(clone_dir, "load_data.py"))
import_functions_from_file("input_preprocess", os.path.join(clone_dir, "input_preprocess.py"))
print("All required functions imported successfully.")
```

---

### 4. **Install Required Packages**

Install the necessary packages inside your new environment.

```bash
# If you're using Conda:
conda install pytorch torchvision torchaudio -c pytorch
pip install -r requirements.txt

# If you're using Mamba:
mamba install pytorch torchvision torchaudio -c pytorch
pip install -r requirements.txt
```

This will install **PyTorch**, **Torchvision**, and other required dependencies from the `requirements.txt` file in the cloned repository.

---

### 5. **Load the DeepMIMO Dataset**

Before proceeding with tokenization and data processing, the **DeepMIMO** dataset—or any dataset generated using the operational settings outlined below—must first be loaded. The table below provides a list of available datasets and their respective links for further details:

📊 **Dataset Overview**

| 📊 **Dataset** | 🏙️ **City**         | 👥 **Number of Users** | 🔗 **DeepMIMO Page**                                                                                       |
|----------------|----------------------|------------------------|------------------------------------------------------------------------------------------------------------|
| Dataset 0      | 🌆 Denver             | 1354                   | [DeepMIMO City Scenario 18](https://www.deepmimo.net/scenarios/deepmimo-city-scenario18/)                   |
| Dataset 1      | 🏙️ Indianapolis       | 3248                   | [DeepMIMO City Scenario 15](https://www.deepmimo.net/scenarios/deepmimo-city-scenario15/)                   |
| Dataset 2      | 🌇 Oklahoma           | 3455                   | [DeepMIMO City Scenario 19](https://www.deepmimo.net/scenarios/deepmimo-city-scenario19/)                   |
| Dataset 3      | 🌆 Fort Worth         | 1902                   | [DeepMIMO City Scenario 12](https://www.deepmimo.net/scenarios/deepmimo-city-scenario12/)                   |
| Dataset 4      | 🌉 Santa Clara        | 2689                   | [DeepMIMO City Scenario 11](https://www.deepmimo.net/scenarios/deepmimo-city-scenario11/)                   |
| Dataset 5      | 🌅 San Diego          | 2192                   | [DeepMIMO City Scenario 7](https://www.deepmimo.net/scenarios/deepmimo-city-scenario7/)                     |

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

#### **Load Data Code**:
Select and load specific datasets by adjusting the `dataset_idxs`. In the example below, we select the first two datasets.

```python
# Step 5: Load the DeepMIMO dataset
print("Loading the DeepMIMO dataset...")

# Load the DeepMIMO dataset
deepmimo_data = load_DeepMIMO_data()

# Select datasets to load
dataset_idxs = torch.arange(2)  # Adjust the number of datasets as needed
print("DeepMIMO dataset loaded successfully.")
```

---

### 6. **Tokenize the DeepMIMO Dataset**

After loading the data, tokenize the selected **DeepMIMO** datasets. This step prepares the data for the model to process.

#### **Tokenization Code**:

```python
# Step 6: Tokenize the dataset
print("Tokenizing the DeepMIMO dataset...")

# Tokenize the loaded datasets
preprocessed_chs = tokenizer(deepmimo_data, dataset_idxs, gen_raw=True)
print("Dataset tokenized successfully.")
```

---

### 7. **Load the LWM Model**

Once the dataset is tokenized, load the pre-trained **LWM** model using the following code:

```python
# Step 7: Load the LWM model (with flexibility for the device)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Loading the LWM model on {device}...")
model = LWM.from_pretrained(device=device)
```

---

### 8. **LWM Inference**

Once the dataset is tokenized and the model is loaded, generate either **raw channels** or the **inferred LWM embeddings** by choosing the input type.

```python
# Step 8: Generate the dataset for inference
input_type = ['cls_emb', 'channel_emb', 'raw'][1]  # Modify input type as needed
dataset = dataset_gen(preprocessed_chs, input_type, model)
```

You can choose between:
- `cls_emb`: LWM CLS token embeddings
- `channel_emb`: LWM channel embeddings
- `raw`: Raw wireless channel data

---

###

 9. **Post-processing for Downstream Task**

#### **Use the Dataset in Downstream Tasks**

Finally, use the generated dataset for your downstream tasks, such as classification, prediction, or analysis.

```python
# Step 9: Print results
print(f"Dataset generated with shape: {dataset.shape}")
print("Inference completed successfully.")
```

---

## 📋 **Requirements**

- **Python 3.x**
- **PyTorch**
- **Git**

---

### Summary of Steps:

1. **Install Conda/Mamba**: Install a package manager for environment management.
2. **Create Environment**: Use Conda or Mamba to create a new environment.
3. **Clone the Repository**: Download the project files from Hugging Face.
4. **Install Packages**: Install PyTorch and other dependencies.
5. **Load and Tokenize Data**: Load the DeepMIMO dataset and prepare it for the model.
6. **Load Model and Perform Inference**: Use the LWM model for generating embeddings or raw channels.