diff --git a/torchsig/datasets/synthetic.py b/torchsig/datasets/synthetic.py
index ca15842..a98b8bc 100644
--- a/torchsig/datasets/synthetic.py
+++ b/torchsig/datasets/synthetic.py
@@ -155,7 +155,7 @@ def __init__(
             modulations=gfsks,
             num_iq_samples=num_iq_samples,
             num_samples_per_class=num_samples_per_class,
-            iq_samples_per_symbol=8 if iq_samples_per_symbol is None else iq_samples_per_symbol,
+            iq_samples_per_symbol=8,
             random_data=random_data,
             random_pulse_shaping=random_pulse_shaping,
             **kwargs
@@ -692,13 +692,15 @@ def __init__(
 
         for freq_idx, freq_name in enumerate(map(str.lower, self.modulations)):
             for idx in range(self.num_samples_per_class):
-                if "g" in freq_name:
-                    bandwidth = np.random.uniform(0.1, 0.5) if self.random_pulse_shaping else 0.35
-                else:
-                    bandwidth = np.random.uniform(
-                        (1 / self.iq_samples_per_symbol) * 1.25,
-                        (1 / self.iq_samples_per_symbol) * 3.75,
-                    ) if self.random_pulse_shaping else 0.0
+                # modulation index scales the bandwidth of the signal, and
+                # iq_samples_per_symbol is used as an oversampling rate in 
+                # FSKDataset class, therefore the signal bandwidth can be 
+                # approximated by mod_idx/iq_samples_per_symbol
+                mod_idx = self._mod_index(freq_name)
+                bandwidth = np.random.uniform(
+                    (mod_idx / self.iq_samples_per_symbol) * 1.25,
+                    (mod_idx / self.iq_samples_per_symbol) * 3.75,
+                ) if self.random_pulse_shaping else 0.0
                 signal_description = SignalDescription(
                     sample_rate=0,
                     bits_per_symbol=np.log2(len(freq_map[freq_name])),
@@ -739,28 +741,24 @@ def _generate_samples(self, item: Tuple) -> np.ndarray:
 
         symbols = const_oversampled[symbol_nums]
         symbols_repeat = xp.repeat(symbols, samples_per_symbol_recalculated)
-        symbols_repeat = np.insert(symbols_repeat,0,0) # start at zero phase
 
-        filtered = symbols_repeat
         if "g" in const_name:
+            # GMSK, GFSK
             taps = self._gaussian_taps(samples_per_symbol_recalculated,bandwidth)
             signal_description.excess_bandwidth = bandwidth
             filtered = xp.convolve(xp.array(symbols_repeat), xp.array(taps), "same")
-
-        if ("gfsk" in const_name):
-            # bluetooth
-            mod_idx = 0.32
-        elif ("msk" in const_name):
-            # MSK, GMSK
-            mod_idx = 0.5
         else:
-            # FSK
-            mod_idx = 1.0
+            # FSK, MSK
+            filtered = symbols_repeat
+
+        # insert a zero at first sample to start at zero phase
+        filtered = np.insert(filtered,0,0)
 
+        mod_idx = self._mod_index(const_name)
         phase = xp.cumsum(xp.array(filtered) * 1j * mod_idx * np.pi)
         modulated = xp.exp(phase)
 
-        if "g" not in const_name and self.random_pulse_shaping:
+        if self.random_pulse_shaping:
             # Apply a randomized LPF simulating a noisy detector/burst extractor, then downsample to ~fs/2 bw
             lpf_bandwidth = bandwidth
             num_taps = int(np.ceil(50 * 2 * np.pi / lpf_bandwidth / .125 / 22))
@@ -814,6 +812,20 @@ def _gaussian_taps(self, samples_per_symbol, BT: float = 0.35) -> np.ndarray:
         return p
 
 
+    def _mod_index(self, const_name):
+        # returns the modulation index based on the modulation
+        if ("gfsk" in const_name):
+            # bluetooth
+            mod_idx = 0.32
+        elif ("msk" in const_name):
+            # MSK, GMSK
+            mod_idx = 0.5
+        else:
+            # FSK
+            mod_idx = 1.0
+        return mod_idx
+
+
 class AMDataset(SyntheticDataset):
     """AM Dataset