diff --git a/src/core/csi_processor.py b/src/core/csi_processor.py
index b610481..6c21b65 100644
--- a/src/core/csi_processor.py
+++ b/src/core/csi_processor.py
@@ -44,4 +44,36 @@ class CSIProcessor:
         if processed.std() > 0:
             processed = (processed - processed.mean()) / processed.std()
         
-        return processed
\ No newline at end of file
+        return processed
+    
+    def process_csi_batch(self, csi_data: np.ndarray) -> torch.Tensor:
+        """Process a batch of CSI data for neural network input.
+        
+        Args:
+            csi_data: Complex CSI data array of shape (batch, antennas, subcarriers, time)
+            
+        Returns:
+            Processed CSI tensor ready for neural network input
+        """
+        if csi_data.ndim != 4:
+            raise ValueError(f"Expected 4D input (batch, antennas, subcarriers, time), got {csi_data.ndim}D")
+        
+        batch_size, num_antennas, num_subcarriers, time_samples = csi_data.shape
+        
+        # Extract amplitude and phase
+        amplitude = np.abs(csi_data)
+        phase = np.angle(csi_data)
+        
+        # Process each component
+        processed_amplitude = self.process_raw_csi(amplitude)
+        processed_phase = self.process_raw_csi(phase)
+        
+        # Stack amplitude and phase as separate channels
+        processed_data = np.stack([processed_amplitude, processed_phase], axis=1)
+        
+        # Reshape to (batch, channels, antennas, subcarriers, time)
+        # Then flatten spatial dimensions for CNN input
+        processed_data = processed_data.reshape(batch_size, 2 * num_antennas, num_subcarriers, time_samples)
+        
+        # Convert to tensor
+        return torch.from_numpy(processed_data).float()
\ No newline at end of file
diff --git a/src/core/phase_sanitizer.py b/src/core/phase_sanitizer.py
index cfdbc28..fd5371f 100644
--- a/src/core/phase_sanitizer.py
+++ b/src/core/phase_sanitizer.py
@@ -1,6 +1,7 @@
 """Phase sanitizer for WiFi-DensePose CSI phase data processing."""
 
 import numpy as np
+import torch
 from typing import Optional
 from scipy import signal
 
@@ -60,6 +61,35 @@ class PhaseSanitizer:
         
         return result
     
+    def sanitize_phase_batch(self, processed_csi: torch.Tensor) -> torch.Tensor:
+        """Sanitize phase information in a batch of processed CSI data.
+        
+        Args:
+            processed_csi: Processed CSI tensor from CSI processor
+            
+        Returns:
+            CSI tensor with sanitized phase information
+        """
+        if not isinstance(processed_csi, torch.Tensor):
+            raise ValueError("Input must be a torch.Tensor")
+        
+        # Convert to numpy for processing
+        csi_numpy = processed_csi.detach().cpu().numpy()
+        
+        # The processed CSI has shape (batch, channels, subcarriers, time)
+        # where channels = 2 * antennas (amplitude and phase interleaved)
+        batch_size, channels, subcarriers, time_samples = csi_numpy.shape
+        
+        # Process phase channels (odd indices contain phase information)
+        for batch_idx in range(batch_size):
+            for ch_idx in range(1, channels, 2):  # Phase channels are at odd indices
+                phase_data = csi_numpy[batch_idx, ch_idx, :, :]
+                sanitized_phase = self.sanitize(phase_data)
+                csi_numpy[batch_idx, ch_idx, :, :] = sanitized_phase
+        
+        # Convert back to tensor
+        return torch.from_numpy(csi_numpy).float()
+    
     def smooth_phase(self, phase_data: np.ndarray) -> np.ndarray:
         """Apply smoothing filter to reduce noise in phase data.