diff --git a/monai/networks/blocks/patchembedding.py b/monai/networks/blocks/patchembedding.py
index 7d56045814..44774ce5da 100644
--- a/monai/networks/blocks/patchembedding.py
+++ b/monai/networks/blocks/patchembedding.py
@@ -123,6 +123,7 @@ def __init__(
             with torch.no_grad():
                 pos_embeddings = build_sincos_position_embedding(grid_size, hidden_size, spatial_dims)
                 self.position_embeddings.data.copy_(pos_embeddings.float())
+                self.position_embeddings.requires_grad = False
         else:
             raise ValueError(f"pos_embed_type {self.pos_embed_type} not supported.")
 
diff --git a/monai/networks/blocks/pos_embed_utils.py b/monai/networks/blocks/pos_embed_utils.py
index e03553307e..21586e56da 100644
--- a/monai/networks/blocks/pos_embed_utils.py
+++ b/monai/networks/blocks/pos_embed_utils.py
@@ -46,7 +46,7 @@ def build_sincos_position_embedding(
         temperature (float): The temperature for the sin-cos position embedding.
 
     Returns:
-        pos_embed (nn.Parameter): The sin-cos position embedding as a learnable parameter.
+        pos_embed (nn.Parameter): The sin-cos position embedding as a fixed parameter.
     """
 
     if spatial_dims == 2:
diff --git a/tests/test_patchembedding.py b/tests/test_patchembedding.py
index f8610d9214..d059145033 100644
--- a/tests/test_patchembedding.py
+++ b/tests/test_patchembedding.py
@@ -93,6 +93,32 @@ def test_shape(self, input_param, input_shape, expected_shape):
             result = net(torch.randn(input_shape))
             self.assertEqual(result.shape, expected_shape)
 
+    def test_sincos_pos_embed(self):
+        net = PatchEmbeddingBlock(
+            in_channels=1,
+            img_size=(32, 32, 32),
+            patch_size=(8, 8, 8),
+            hidden_size=96,
+            num_heads=8,
+            pos_embed_type="sincos",
+            dropout_rate=0.5,
+        )
+
+        self.assertEqual(net.position_embeddings.requires_grad, False)
+
+    def test_learnable_pos_embed(self):
+        net = PatchEmbeddingBlock(
+            in_channels=1,
+            img_size=(32, 32, 32),
+            patch_size=(8, 8, 8),
+            hidden_size=96,
+            num_heads=8,
+            pos_embed_type="learnable",
+            dropout_rate=0.5,
+        )
+
+        self.assertEqual(net.position_embeddings.requires_grad, True)
+
     def test_ill_arg(self):
         with self.assertRaises(ValueError):
             PatchEmbeddingBlock(