def testSpecificPermutation(self, nrPlayers, nodesIdList):
"""
Check power allocation game convergence rule for specific setup.
Keyword arguments:
nrPlayers -- number of players
nodesIdList -- list describing player setup, e.g. [1,2,3,4] 2 player game, player 1 tx node is 1, rx node is 2,
player 2 tx node is 3, rx node is 4.
"""
nodeObjects = [GameNode(self.coordinatorId, ids) for ids in nodesIdList]
directGains = getDirectGainsNPl(self.coordinatorId, nodeObjects)
# check if bad measurement, if so then stop - no reason to continue
badMeasurements = False
if directGains is None:
badMeasurements = True
else:
for x in directGains:
if x is None: badMeasurements = True
if not badMeasurements:
# wait for things to cool down
time.sleep(10)
if nrPlayers == 2:
# reuse some code
crossGains = getCrossGains2Pl(self.coordinatorId, nodeObjects)
else:
crossGains = getCrossGainsNPl(self.coordinatorId, nodeObjects)
if crossGains is None:
badMeasurements = True
else:
for x in crossGains:
if x is None: badMeasurements = True
if badMeasurements:
print "BAD MEASUREMENT"
else:
print "\n"
print "h_ii: %s" % (', '.join([str(x) for x in directGains]))
print "h_ii: %s" % (', '.join([str(x) for x in crossGains]))
if not checkConvergenceRuleForSearch(nrPlayers, directGains, crossGains):
print "nodes:", nodesIdList, "DO NOT MEET PAPU convergence rule"
else:
print "nodes SATISFY convergence condition"
stringToAppend = '\t'.join([str(x) for x in nodesIdList]) + '\t' + '\t'.join([str(x) for x in directGains])
stringToAppend += '\t' + '\t'.join([str(x) for x in crossGains])
print stringToAppend
def findNodes(self, nrPlayers, nrCases, startWithRowFromFile=2):
""" Find candidate nodes, exhaustive search. Do real world measurements and check convergence rule.
First create a list of node objects and pass elements of this list, identified by their id read
from file, to methods that compute instant and cross gain.
nrPlayers -- number of players to check for
nrCases -- stop the search when the number of cases sought is found, for
a 5 player game there a re 3.6e6 possible combinations!!
startWithRowFromFile -- if something goes wrong experiments can resume from
a specific combination. Read line numbering starts from 1 not from 0!!
"""
# check if we have enough nodes
if self.nrNodes < 4:
raise Exception("The node ID list must contain at least 4 nodes!! "
"A player is formed by a transmitter node and a receiver node.")
# check if the number of players can be realized by the available nodes
if self.nrPayrs < nrPlayers:
raise Exception("The number of available nodes cannot cover the number "
"searched players. (nrAvailableNodes/2) should be > nrPlayers")
totalExperiments = scipy.math.factorial(self.nrNodes) / scipy.math.factorial(self.nrNodes - nrPlayers * 2) - startWithRowFromFile
question = "total number of possible experiments %d proceed? - yes/no:" % (totalExperiments)
choice = raw_input(question)
if choice.lower() == "no":
print "finishing..."
return
else:
print "ok let's proceed"
# create a list of vesna nodes and pass these nodes to methods
vesnaNodes = [GameNode(self.coordinatorId, ids) for ids in self.nodeIDsList]
path = getNodeCombinations(self.coordinatorId, self.nodeIDsList, nrPlayers)
print "read permutations from", path
casesFound = 0
while casesFound < nrCases:
"""Repeat experiments with different node permutations until I find nrCases that satisfy
PAPU algorithm convergence condition.
"""
for i in range(startWithRowFromFile, totalExperiments):
# get line that contains node ids, first line is a header
line = linecache.getline(path, i)
# use ids like this: tx_1, rx_1, tx_2, rx_2...
nodesUsed = map(int, line.split())
print "nodes used in this experiment ", nodesUsed
nodeObjects = list()
# get object list of nodes used in this experiment
for j in nodesUsed:
nodeObjects.append(vesnaNodes[self.nodeIDsList.index(j)])
# compute direct gains for nodes used in experiment
directGains = getDirectGainsNPl(self.coordinatorId, nodeObjects)
if nrPlayers == 2:
# reuse some code
crossGains = getCrossGains2Pl(self.coordinatorId, nodeObjects)
else:
crossGains = getCrossGainsNPl(self.coordinatorId, nodeObjects)
# if lists contain None then a bad measurement has occur, i.e. noise > gain
badMeasurement = False
for x in directGains:
if x is None:
badMeasurement = True
for x in crossGains:
if x is None:
badMeasurement = True
if badMeasurement:
printBadResultToFile(self.coordinatorId, nrPlayers, self.nodeIDsList, nodesUsed, directGains, crossGains, badMeasurement)
else:
# check convergence condition
if not checkConvergenceRuleForSearch(nrPlayers, directGains, crossGains):
print "nodes:", nodesUsed, "do not satisfy PAPU convergence rule"
printBadResultToFile(self.coordinatorId, nrPlayers, self.nodeIDsList, nodesUsed, directGains, crossGains, badMeasurement)
else:
# save results to file
casesFound += 1
printPAPUtoFile(self.coordinatorId, nrPlayers, self.nodeIDsList, nodesUsed, directGains, crossGains)
