Update README.md

README.md

@@ -86,14 +86,39 @@ This repository contains the implementation of **LWM** (Large Wireless Model), a
 
 4. **Tokenize the DeepMIMO Dataset**
 
-   Tokenize the loaded dataset. You can choose the scenario indices to select specific scenarios from DeepMIMO:
+After loading the dataset, you can tokenize the DeepMIMO dataset based on specific scenarios. The table below lists the available scenarios, their corresponding DeepMIMO pages, and relevant details:
+
+| **Scenario**  | **City**      | **Link to DeepMIMO Page**                                                                                     |
+|---------------|---------------|----------------------------------------------------------------------------------------------------------------|
+| Scenario 0    | Denver        | [DeepMIMO City Scenario 18](https://www.deepmimo.net/scenarios/deepmimo-city-scenario18/)                      |
+| Scenario 1    | Indianapolis  | [DeepMIMO City Scenario 15](https://www.deepmimo.net/scenarios/deepmimo-city-scenario15/)                      |
+| Scenario 2    | Oklahoma      | [DeepMIMO City Scenario 19](https://www.deepmimo.net/scenarios/deepmimo-city-scenario19/)                      |
+| Scenario 3    | Fort Worth    | [DeepMIMO City Scenario 12](https://www.deepmimo.net/scenarios/deepmimo-city-scenario12/)                      |
+| Scenario 4    | Santa Clara   | [DeepMIMO City Scenario 11](https://www.deepmimo.net/scenarios/deepmimo-city-scenario11/)                      |
+| Scenario 5    | San Diego     | [DeepMIMO City Scenario 7](https://www.deepmimo.net/scenarios/deepmimo-city-scenario7/)                        |
+
+#### **Operational Settings**:
+- **Antennas at BS**: 32
+- **Antennas at UEs**: 1
+- **Subcarriers**: 32
+- **Paths**: 20
+
+#### **Tokenization Code**:
+You can adjust the number of scenarios by changing the `scenario_idxs`. In the example below, scenario 0 and 1 are selected.
+
+```python
+# Step 7: Tokenize the dataset
+scenario_idxs = torch.arange(2)  # Adjust the number of scenarios you want
+print("Tokenizing the dataset...")
+preprocessed_chs = tokenizer(deepmimo_data, scenario_idxs, gen_raw=True)
+```
+
+- Use the `scenario_idxs` variable to select specific scenarios from the DeepMIMO dataset.
+- The dataset will be tokenized according to the chosen scenarios and preprocessing configurations.
 
-    ```python
-    # Step 7: Tokenize the dataset (direct call, no module prefix)
-    scenario_idxs = torch.arange(1)  # Adjust the number of scenarios you want
-    print("Tokenizing the dataset...")
-    preprocessed_chs = tokenizer(deepmimo_data, scenario_idxs, gen_raw=True)
-    ```
+---
+
+This format separates the **scenarios**, **operational settings**, and the **code** clearly, making it more readable. The table provides a structured overview of the available scenarios with direct links to their respective pages on DeepMIMO.
 
 5. **LWM Inference**