def logNormalShadowing(self, intf):
"""Log-Normal Shadowing Propagation Loss Model
distance is the range of the transmitter (m)"""
from mn_wifi.wmediumdConnector import WmediumdGRandom, w_server, \
wmediumd_mode
mean = 0
ref_d = 1
dist = intf.range
gain = intf.antennaGain
variance = ppm.variance
gRandom = round(gauss(mean, variance), 2)
ppm.gRandom = gRandom
if wmediumd_mode == 3:
sleep(0.001) # notice problem when there are many threads
w_server.update_gaussian_random(WmediumdGRandom(
intf.wmIface, gRandom))
pl = self.path_loss(intf, ref_d) - gRandom
self.txpower = 10 * ppm.exp * math.log10(dist / ref_d) + \
ppm.noise_th + pl - (gain * 2)
if self.txpower < 0: self.txpower = 1
return self.txpower
def logNormalShadowing(self, **kwargs):
"""Log-Normal Shadowing Propagation Loss Model
distance is the range of the transmitter (m)"""
from mn_wifi.wmediumdConnector import WmediumdGRandom, w_server
mean = 0
d = kwargs['dist']
ref_d = 1
gain = kwargs['node'].params['antennaGain'][kwargs['wlan']]
variance = propagationModel.variance
gRandom = float('%.2f' % gauss(mean, variance))
propagationModel.gRandom = gRandom
if kwargs['interference']:
sleep(0.001) # notice problem when there are many threads
w_server.update_gaussian_random(
WmediumdGRandom(kwargs['node'].wmIface[kwargs['wlan']],
gRandom))
pl = self.pathLoss(kwargs['node'], ref_d, kwargs['wlan']) - gRandom
self.txpower = 10 * ppm.exp * math.log10(d / ref_d) + \
ppm.noise_threshold + pl - (gain * 2)
if self.txpower < 0:
self.txpower = 1
return self.txpower
def logNormalShadowing(self, intf):
"""Log-Normal Shadowing Propagation Loss Model"""
from mn_wifi.wmediumdConnector import WmediumdGRandom, w_server, \
wmediumd_mode
ref_d = 1
txpower = int(intf.txpower)
gain = int(intf.antennaGain)
gains = txpower + (gain * 2)
mean = 0
variance = ppm.variance
gRandom = round(gauss(mean, variance), 2)
ppm.gRandom = gRandom
if wmediumd_mode == 3:
sleep(0.002) # noticed problem when there are multiple threads
w_server.update_gaussian_random(
WmediumdGRandom(intf.wmIface, gRandom))
pl = self.path_loss(intf, ref_d) - gRandom
numerator = -ppm.noise_th - pl + gains
denominator = 10 * ppm.exp
self.range = math.pow(10, (numerator / denominator)) * ref_d
return self.range
def logNormalShadowing(self, **kwargs):
"""Log-Normal Shadowing Propagation Loss Model"""
from mn_wifi.wmediumdConnector import WmediumdGRandom, w_server
ref_d = 1
txpower = kwargs['node'].params['txpower'][kwargs['wlan']]
gain = kwargs['node'].params['antennaGain'][kwargs['wlan']]
gains = txpower + (gain * 2)
mean = 0
variance = propagationModel.variance
gRandom = float('%.2f' % gauss(mean, variance))
propagationModel.gRandom = gRandom
if kwargs['interference']:
sleep(0.002) #notice problem when there are multiple threads
w_server.update_gaussian_random(
WmediumdGRandom(kwargs['node'].wmIface[kwargs['wlan']],
gRandom))
pl = self.pathLoss(kwargs['node'], ref_d, kwargs['wlan']) - gRandom
numerator = -ppm.noise_threshold - pl + gains
denominator = 10 * ppm.exp
self.dist = math.pow(10, (numerator / denominator)) * ref_d
return self.dist