def logNormalShadowing(self, **kwargs):
"""Log-Normal Shadowing Propagation Loss Model
distance is the range of the transmitter (m)"""
from mininet.wmediumdConnector import WmediumdGaussianRandom, \
WmediumdServerConn
ref_dist = 1
antGain = kwargs['node'].params['antennaGain'][kwargs['wlan']]
mean = 0
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
WmediumdServerConn.update_gaussian_random(
WmediumdGaussianRandom(kwargs['node'].wmIface[kwargs['wlan']],
gRandom))
pathLoss = self.pathLoss(kwargs['node'], ref_dist,
kwargs['wlan']) - gRandom
self.txpower = 10 * ppm.exp * math.log10(
kwargs['dist'] / ref_dist) - 92 + pathLoss - (antGain * 2)
if self.txpower < 0:
error('*** Error: tx power is negative! (%s)\n' % self.txpower)
exit(1)
return self.txpower
def logNormalShadowing(self, **kwargs):
"""Log-Normal Shadowing Propagation Loss Model"""
from mininet.wmediumdConnector import WmediumdGaussianRandom, \
WmediumdServerConn
ref_dist = 1
txpower = kwargs['node'].params['txpower'][kwargs['wlan']]
antGain = kwargs['node'].params['antennaGain'][kwargs['wlan']]
gains = txpower + (antGain * 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 many threads
WmediumdServerConn.update_gaussian_random(
WmediumdGaussianRandom(kwargs['node'].wmIface[kwargs['wlan']],
gRandom))
pathLoss = self.pathLoss(kwargs['node'], ref_dist,
kwargs['wlan']) - gRandom
self.dist = math.pow(10, ((-ppm.noise_threshold - pathLoss + gains) /
(10 * ppm.exp))) * ref_dist
return self.dist
