def optimizePC(channel, noiseIfPower, rate, linkBandwidth, pMax, p0, m, verbosity=0):
''' Uses channel values, PHY parameters and power consumption characteristics to find minimal resource allocation. Returns resource allocation, objective value and IPOPT status.
Input:
channel - 3d array. 0d users, 1d n_tx, 2d n_rx
noiseIfPower - total noise power over the linkbandwidth
rate - target rate in bps
pMax - maximum allowed transmission power
p0 - power consumption at zero transmission (not sleep)
m - power consumption load factor
verbosity - IPOPT verbosity level
Output:
obj - solution objective value
solution - resource share per user
status - IPOPT status '''
# the channel dimensions tell some more parameters
users = channel.shape[0]
n_tx = channel.shape[1]
n_rx = channel.shape[2]
# preparing IPOPT parameters
nvar = users # for readability
x_L = zeros((nvar), dtype=float_) * 0.0
x_U = ones((nvar), dtype=float_) * 1.0
ncon = nvar + 1 # transmit power constraints and the unit sum
g_L = zeros(1+nvar) # unit sum and all power constraints
g_L[0] = 1.
g_U = pMax * ones(1+nvar) # unit sum and all power constraints
g_U[0] = 1.
nnzj = nvar * (1+nvar)
nnzh = 0 #used?
x0 = repeat([1./(nvar+1)],nvar) # Starting point
# IPOPT requires single parameter functions
if n_tx is 2 and n_rx is 2:
eval_f = lambda mus: optimMinPow2x2.eval_f(mus, noiseIfPower, channel, rate, linkBandwidth, p0, m)
eval_grad_f = lambda mus: optimMinPow2x2.eval_grad_f(mus, noiseIfPower, channel, rate, linkBandwidth, p0, m)
eval_g = lambda mus: optimMinPow2x2.eval_g(mus, noiseIfPower, channel, rate, linkBandwidth)
eval_jac_g = lambda mus, flag: optimMinPow2x2.eval_jac_g(mus, noiseIfPower, channel, rate, linkBandwidth, flag)
else:
raise NotImplementedError # other combinations may be needed later
# Call solve()
pyipopt.set_loglevel(min([2,verbosity])) # verbose
nlp = pyipopt.create(nvar, x_L, x_U, ncon, g_L, g_U, nnzj, nnzh, eval_f, eval_grad_f, eval_g, eval_jac_g)
#nlp.int_option("max_iter", 3000)
#nlp.num_option("tol", 1e-8)
#nlp.num_option("acceptable_tol", 1e-2)
#nlp.int_option("acceptable_iter", 0)
nlp.str_option("derivative_test", "first-order")
nlp.str_option("derivative_test_print_all", "no")
#nlp.str_option("print_options_documentation", "yes")
nlp.int_option("print_level", min([verbosity,12])) # maximum is 12
nlp.str_option("print_user_options", "yes")
solution, zl, zu, obj, status = nlp.solve(x0)
nlp.close()
return obj, solution, status
def test_eval_grad_f(self):
ans = optimMinPow2x2.eval_grad_f(self.x0, self.noisepower, self.H, self.rate, self.linkBandwidth, self.p0, self.m)
answer = np.array([-162.07682481, -71.55787938, -106.63302593])
np.testing.assert_array_almost_equal(ans, answer)
def optimizePC(channel,
noiseIfPower,
rate,
linkBandwidth,
pMax,
p0,
m,
verbosity=0):
''' Uses channel values, PHY parameters and power consumption characteristics to find minimal resource allocation. Returns resource allocation, objective value and IPOPT status.
Input:
channel - 3d array. 0d users, 1d n_tx, 2d n_rx
noiseIfPower - total noise power over the linkbandwidth
rate - target rate in bps
pMax - maximum allowed transmission power
p0 - power consumption at zero transmission (not sleep)
m - power consumption load factor
verbosity - IPOPT verbosity level
Output:
obj - solution objective value
solution - resource share per user
status - IPOPT status '''
# the channel dimensions tell some more parameters
users = channel.shape[0]
n_tx = channel.shape[1]
n_rx = channel.shape[2]
# preparing IPOPT parameters
nvar = users # for readability
x_L = zeros((nvar), dtype=float_) * 0.0
x_U = ones((nvar), dtype=float_) * 1.0
ncon = nvar + 1 # transmit power constraints and the unit sum
g_L = zeros(1 + nvar) # unit sum and all power constraints
g_L[0] = 1.
g_U = pMax * ones(1 + nvar) # unit sum and all power constraints
g_U[0] = 1.
nnzj = nvar * (1 + nvar)
nnzh = 0 #used?
x0 = repeat([1. / (nvar + 1)], nvar) # Starting point
# IPOPT requires single parameter functions
if n_tx is 2 and n_rx is 2:
eval_f = lambda mus: optimMinPow2x2.eval_f(mus, noiseIfPower, channel,
rate, linkBandwidth, p0, m)
eval_grad_f = lambda mus: optimMinPow2x2.eval_grad_f(
mus, noiseIfPower, channel, rate, linkBandwidth, p0, m)
eval_g = lambda mus: optimMinPow2x2.eval_g(mus, noiseIfPower, channel,
rate, linkBandwidth)
eval_jac_g = lambda mus, flag: optimMinPow2x2.eval_jac_g(
mus, noiseIfPower, channel, rate, linkBandwidth, flag)
else:
raise NotImplementedError # other combinations may be needed later
# Call solve()
pyipopt.set_loglevel(min([2, verbosity])) # verbose
nlp = pyipopt.create(nvar, x_L, x_U, ncon, g_L, g_U, nnzj, nnzh, eval_f,
eval_grad_f, eval_g, eval_jac_g)
#nlp.int_option("max_iter", 3000)
#nlp.num_option("tol", 1e-8)
#nlp.num_option("acceptable_tol", 1e-2)
#nlp.int_option("acceptable_iter", 0)
nlp.str_option("derivative_test", "first-order")
nlp.str_option("derivative_test_print_all", "no")
#nlp.str_option("print_options_documentation", "yes")
nlp.int_option("print_level", min([verbosity, 12])) # maximum is 12
nlp.str_option("print_user_options", "yes")
solution, zl, zu, obj, status = nlp.solve(x0)
nlp.close()
return obj, solution, status
def test_eval_grad_f(self):
ans = optimMinPow2x2.eval_grad_f(self.x0, self.noisepower, self.H,
self.rate, self.linkBandwidth,
self.p0, self.m)
answer = np.array([-162.07682481, -71.55787938, -106.63302593])
np.testing.assert_array_almost_equal(ans, answer)