CC1200 TX energy: Use energy model, not inaccurate power * duration

1 files changed, 81 insertions, 4 deletions

diff --git a/lib/size_to_radio_energy.py b/lib/size_to_radio_energy.py
index 1cc9fd0..808ff9b 100644
--- a/lib/size_to_radio_energy.py
+++ b/lib/size_to_radio_energy.py
@@ -11,6 +11,8 @@ def get_class(radio_name: str):
     """Return model class for radio_name."""
     if radio_name == 'CC1200tx':
         return CC1200tx
+    if radio_name == 'NRF24L01tx':
+        return NRF24L01tx
     if radio_name == 'NRF24L01dtx':
         return NRF24L01dtx
 
@@ -37,6 +39,7 @@ class CC1200tx:
         if type(params) != dict:
             return CC1200tx.get_energy(_param_list_to_dict(CC1200tx, params))
 
+        # Mittlere TX-Leistung, gefitted von AEMR
         power  = 8.18053941e+04
         power -= 1.24208376e+03 * np.sqrt(params['symbolrate'])
         power -= 5.73742779e+02 * np.log(params['txbytes'])
@@ -46,24 +49,98 @@ class CC1200tx:
         power -= 3.31365158e-01 * np.log(params['txbytes']) * (params['txpower'])**2
         power += 1.32784945e-01 * np.sqrt(params['symbolrate']) * np.log(params['txbytes']) * (params['txpower'])**2
 
+        # txDone-Timeout, gefitted von AEMR
         duration  = 3.65513500e+02
         duration += 8.01016526e+04 * 1/(params['symbolrate'])
         duration -= 7.06364515e-03 * params['txbytes']
         duration += 8.00029860e+03 * 1/(params['symbolrate']) * params['txbytes']
 
-        return power * 1e-6 * duration * 1e-6
+        # TX-Energie, gefitted von AEMR
+        # Achtung: Energy ist in µJ, nicht (wie in AEMR-Transitionsmodellen üblich) in pJ
+
+        energy  = 1.74383259e+01
+        energy += 6.29922138e+03 * 1/(params['symbolrate'])
+        energy += 1.13307135e-02 * params['txbytes']
+        energy -= 1.28121377e-04 * (params['txpower'])**2
+        energy += 6.29080184e+02 * 1/(params['symbolrate']) * params['txbytes']
+        energy += 1.25647926e+00 * 1/(params['symbolrate']) * (params['txpower'])**2
+        energy += 1.31996202e-05 * params['txbytes'] * (params['txpower'])**2
+        energy += 1.25676966e-01 * 1/(params['symbolrate']) * params['txbytes'] * (params['txpower'])**2
+
+        return energy * 1e-6
+
+    def get_energy_per_byte(params):
+        A  = 8.18053941e+04
+        A -= 1.24208376e+03 * np.sqrt(params['symbolrate'])
+        A += 1.76945886e+01 * (params['txpower'])**2
+        A -= 6.99137635e-01 * np.sqrt(params['symbolrate']) * (params['txpower'])**2
+        B  = -5.73742779e+02
+        B += 2.33469617e+02 * np.sqrt(params['symbolrate'])
+        B -= 3.31365158e-01 * (params['txpower'])**2
+        B += 1.32784945e-01 * np.sqrt(params['symbolrate']) * (params['txpower'])**2
+        C  = 3.65513500e+02
+        C += 8.01016526e+04 * 1/(params['symbolrate'])
+        D  = -7.06364515e-03
+        D += 8.00029860e+03 * 1/(params['symbolrate'])
+
+        x = params['txbytes']
+
+        # in pJ
+        de_dx = A * D + B * C * 1/x + B * D * (np.log(x) + 1)
+
+        # in µJ
+        de_dx  = 1.13307135e-02
+        de_dx += 6.29080184e+02 * 1/(params['symbolrate'])
+        de_dx += 1.31996202e-05 * (params['txpower'])**2
+        de_dx += 1.25676966e-01 * 1/(params['symbolrate']) * (params['txpower'])**2
+
+        #de_dx = (B * 1/x) * (C + D * x) + (A + B * np.log(x)) * D
+
+        return de_dx * 1e-6
+
+class NRF24L01tx:
+    """NRF24L01+ TX energy based on aemr measurements (32B fixed packet size, ack-await, no retries)."""
+    name = 'NRF24L01'
+    parameters = {
+        'datarate' : [250, 1000, 2000], # kbps
+        'txbytes' : [],
+        'txpower' : [-18, -12, -6, 0], # dBm
+        'voltage' : [1.9, 3.6],
+    }
+    default_params = {
+        'datarate' : 1000,
+        'txpower' : -6,
+        'voltage' : 3,
+    }
+
+    def get_energy(params):
+        if type(params) != dict:
+            return NRF24L01tx.get_energy(_param_list_to_dict(NRF24L01tx, params))
+
+        power = 6.30323056e+03
+        power += 2.59889924e+06 * 1/params['datarate']
+        power += 7.82186268e+00 * (19.47+params['txpower'])**2
+        power += 8.69746093e+03 * 1/params['datarate'] *  (19.47+params['txpower'])**2
+
+        duration = 1624.06589147
+        duration += 332251.93798766 * 1/params['datarate']
+
+        energy = power * 1e-6 * duration * 1e-6 * np.ceil(params['txbytes'] / 32)
+
+        return energy
+
 
 class NRF24L01dtx:
     """nRF24L01+ TX energy based on datasheet values (probably unerestimated)"""
     name = 'NRF24L01'
     parameters = {
-        'symbolrate' : [250, 1000, 2000], # kbps
+        'datarate' : [250, 1000, 2000], # kbps
         'txbytes' : [],
         'txpower' : [-18, -12, -6, 0], # dBm
         'voltage' : [1.9, 3.6],
     }
     default_params = {
-        'symbolrate' : 1000,
+        'datarate' : 1000,
         'txpower' : -6,
         'voltage' : 3,
     }
@@ -89,6 +166,6 @@ class NRF24L01dtx:
         elif params['txpower'] == 0:
             current = 11.3e-3
 
-        energy += current * params['voltage'] * ((header_bytes + params['txbytes']) * 8 / (params['symbolrate'] * 1e3))
+        energy += current * params['voltage'] * ((header_bytes + params['txbytes']) * 8 / (params['datarate'] * 1e3))
 
         return energy