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Update README.md

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@@ -25,7 +25,7 @@ Once installed, you can use Conda to manage environments.
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  ### 2. **Create a New Environment**
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- After installing Conda (https://conda.io/projects/conda/en/latest/user-guide/install/index.html), follow these steps to create a new environment and install the required packages.
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  #### **Step 1: Create a new environment**
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@@ -179,9 +179,9 @@ from lwm_model import lwm
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  import torch
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  preprocessed_chs = tokenizer(
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- selected_scenario_names=selected_scenario_names,
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- manual_data=None,
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- gen_raw=True
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  )
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  device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
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  ### 2. **Create a New Environment**
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+ After installing Conda, follow these steps to create a new environment and install the required packages.
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  #### **Step 1: Create a new environment**
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  import torch
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  preprocessed_chs = tokenizer(
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+ selected_scenario_names=selected_scenario_names, # Selects predefined DeepMIMOv3 scenarios. Set to None to load your own dataset.
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+ 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.
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+ 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.
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  )
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  device = 'cuda' if torch.cuda.is_available() else 'cpu'