diff --git a/channel.py b/channel.py
new file mode 100644
index 0000000..32ef5c4
--- /dev/null
+++ b/channel.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+channel_response = np.array([1, 0, 1 - 1j])
+
+def sim(in_data):
+    out_data = np.ndarray((len(in_data), len(in_data[0])), dtype=np.csingle)
+
+    for i in range(len(in_data)):
+        convolved = np.convolve(channel_response, in_data[i])
+
+
+
diff --git a/data.txt b/data.txt
new file mode 100644
index 0000000..c953ffa
--- /dev/null
+++ b/data.txt
@@ -0,0 +1 @@
+The quick brown fox jumped over the lazy dog.
diff --git a/main.py b/main.py
index d307470..5b43723 100755
--- a/main.py
+++ b/main.py
@@ -8,8 +8,23 @@ import qam
 
 from serpar import parallelise, serialise
 
-def cyclic_prefix(n, in_data, prefix_len):
-    out_data = np.ndarray((len(in_data), n + prefix_len), dtype=np.csingle)
+def cp_add(in_data, prefix_len):
+    out_data = np.ndarray((len(in_data), len(in_data[0]) + prefix_len), dtype=np.csingle)
+
+    for i in range(len(in_data)):
+        cp = in_data[i][-prefix_len:]
+        out_data[i] = np.hstack([cp, in_data[i]])
+
+    return out_data
+
+def cp_remove(in_data, prefix_len):
+    out_data = np.ndarray((len(in_data), len(in_data[0]) - prefix_len), dtype=np.csingle)
+
+    for i in range(len(in_data)):
+        out_data[i] = in_data[i][prefix_len:]
+
+    return out_data
+
 
 
 
@@ -23,10 +38,18 @@ if __name__ == '__main__':
 
     modulated = qam.modulate(parallel)
 
-    tx = np.fft.ifft(modulated)
+    ofdm_time = np.fft.ifft(modulated)
 
-    rx = channel.sim(tx)
-    
+    ofdm_prefixed = cp_add(ofdm_time, 4)
 
+    # Put the channel simulator stuff here
 
+    ofdm_cp_removed = cp_remove(ofdm_prefixed, 4)
 
+    to_decode = np.fft.fft(ofdm_cp_removed)
+
+    to_serialise = qam.demodulate(to_decode)
+
+    data = serialise(to_serialise)
+
+    print(data)
diff --git a/qam.py b/qam.py
index 47a51dd..a052442 100644
--- a/qam.py
+++ b/qam.py
@@ -30,7 +30,7 @@ def modulate(in_data):
 
     return out_data
 
-def demodulate(n, in_data):
+def demodulate(in_data):
     out_data = np.ndarray((len(in_data), len(in_data[0])), dtype=np.uint8)
 
     # Just pull the constellation array data out
@@ -43,7 +43,7 @@ def demodulate(n, in_data):
             # Here we have to map to the closest constellation point,
             # because floating point error
             for k in range(len(constellation)):
-                distances[k] = euclidean(in_data[i][j], constellation[i][j])
+                distances[k] = euclidean(in_data[i][j], constellation[k])
 
             # output is the index of the constellation, essentially
             # this may have to change if I want to generalise