def test_ptxOfMu(self):
"""docstring for test_ptxOfMu"""
ans = optimMinPow2x2DTX.ptxOfMu(0.1, self.rate, self.linkBandwidth, self.noisepower[0], self.H[0,:,:]) # 59.16
answer = 59.16385275
np.testing.assert_almost_equal(ans, answer)
ans = optimMinPow2x2DTX.ptxOfMu(0.1, self.rate, self.linkBandwidth, self.noisepower[0], self.Htrivial[0,:,:]) # 59.16
answer = 511.5
np.testing.assert_almost_equal(ans, answer)
ans = optimMinPow2x2DTX.ptxOfMu(0.1, self.rate, self.linkBandwidth, self.noisepower[0], self.H1[0,:,:]) # 59.16
answer = 0.15357658182477138
np.testing.assert_almost_equal(ans, answer)
def test_optimPCDTX(self):
# test for simple problem with known outcome
H = np.array([[[1. - 1j, -1.], [-1., 1.]], [[1. - 1j, 1.], [-1., 1.]],
[[0.5, 1.j], [1., -1.j]]])
for k in np.arange(3):
H[k, :, :] = scipy.dot(H[k, :, :], H[k, :, :].conj().T)
noisepower = np.ones(3)
rate = 1
linkBandwidth = 1
p0 = 10
m = 2
pS = 5
pMax = 10
obj, solution, status = optimMinPow.optimizePCDTX(
H, noisepower, rate, linkBandwidth, pMax, p0, m, pS, 0)
answerObj = 13.9204261
answerSol = np.array([0.32342002, 0.24371824, 0.27855287, 0.15430887])
np.testing.assert_almost_equal(obj, answerObj)
np.testing.assert_almost_equal(solution, answerSol)
for k in np.arange(H.shape[0]):
ptx = optimMinPow2x2DTX.ptxOfMu(solution[k], rate, linkBandwidth,
noisepower[k], H[k, :, :])
rate_test = solution[k] * np.real(
utils.ergMIMOCapacityCDITCSIR(H[k, :, :], ptx))
np.testing.assert_almost_equal(rate_test, rate)
def test_optimPCDTX_trivial(self):
# test for simple problem with known outcome
H = np.ones([3, 2, 2])
for k in np.arange(3):
H[k, :, :] = scipy.dot(H[k, :, :], H[k, :, :].conj().T)
noisepower = np.ones(3)
rate = 1
linkBandwidth = 1
p0 = 10
m = 2
pS = 5
pMax = 10
obj, solution, status = optimMinPow.optimizePCDTX(
H, noisepower, rate, linkBandwidth, pMax, p0, m, pS, 0)
answerObj = 17.000000115485285
answerSol = np.array([0.33333334, 0.33333334, 0.33333334, 0.])
np.testing.assert_almost_equal(obj, answerObj)
np.testing.assert_almost_equal(solution, answerSol)
for k in np.arange(H.shape[0]):
ptx = optimMinPow2x2DTX.ptxOfMu(solution[k], rate, linkBandwidth,
noisepower[k], H[k, :, :])
rate_test = solution[k] * np.real(
utils.ergMIMOCapacityCDITCSIR(H[k, :, :], ptx))
np.testing.assert_almost_equal(rate_test, rate)
def test_optimPCDTXrandomChannel(self):
# test for simple problem with known outcome
users = 22
n_tx = 2
n_rx = 2
H = np.empty([users, n_tx, n_rx],
dtype=complex) # very important to make it complex!
for k in np.arange(users):
H[k, :, :] = 10e-7 * utils.rayleighChannel(n_tx, n_rx)
H[k, :, :] = scipy.dot(H[k, :, :], H[k, :, :].conj().T)
noisepower = np.ones(users) * 4e-14
rate = 1.2e7 / users # bps
linkBandwidth = 1e7
p0 = 100
m = 2.4
pS = 50
pMax = 40
obj, solution, status = optimMinPow.optimizePCDTX(
H, noisepower, rate, linkBandwidth, pMax, p0, m, pS, 0)
# Test that all calls were correct and their order. What goes in must come out.
for k in np.arange(users):
ptx = optimMinPow2x2DTX.ptxOfMu(
solution[k], rate, linkBandwidth, noisepower[k],
H[k, :, :]) # power as a function of the MIMO link
rate_test = solution[k] * np.real(
utils.ergMIMOCapacityCDITCSIR(
H[k, :, :], ptx / noisepower[k])) * linkBandwidth # bps
np.testing.assert_almost_equal(rate_test, rate)
def test_ptxOfMu(self):
"""docstring for test_ptxOfMu"""
ans = optimMinPow2x2DTX.ptxOfMu(0.1, self.rate, self.linkBandwidth,
self.noisepower[0],
self.H[0, :, :]) # 59.16
answer = 59.16385275
np.testing.assert_almost_equal(ans, answer)
ans = optimMinPow2x2DTX.ptxOfMu(0.1, self.rate, self.linkBandwidth,
self.noisepower[0],
self.Htrivial[0, :, :]) # 59.16
answer = 511.5
np.testing.assert_almost_equal(ans, answer)
ans = optimMinPow2x2DTX.ptxOfMu(0.1, self.rate, self.linkBandwidth,
self.noisepower[0],
self.H1[0, :, :]) # 59.16
answer = 0.15357658182477138
np.testing.assert_almost_equal(ans, answer)
def test_optimPCDTX(self):
# test for simple problem with known outcome
H = np.array([[[1.-1j,-1.],[-1.,1.]],[[1.-1j,1.],[-1.,1.]],[[0.5,1.j],[1.,-1.j]]])
for k in np.arange(3):
H[k,:,:] = scipy.dot(H[k,:,:], H[k,:,:].conj().T)
noisepower = np.ones(3)
rate = 1
linkBandwidth = 1
p0 = 10
m = 2
pS = 5
pMax = 10
obj, solution, status = optimMinPow.optimizePCDTX(H, noisepower, rate, linkBandwidth, pMax, p0, m, pS, 0)
answerObj = 13.9204261
answerSol = np.array([ 0.32342002, 0.24371824, 0.27855287, 0.15430887])
np.testing.assert_almost_equal(obj, answerObj)
np.testing.assert_almost_equal(solution, answerSol)
for k in np.arange(H.shape[0]):
ptx = optimMinPow2x2DTX.ptxOfMu(solution[k], rate, linkBandwidth, noisepower[k], H[k,:,:])
rate_test = solution[k]*np.real(utils.ergMIMOCapacityCDITCSIR(H[k,:,:], ptx))
np.testing.assert_almost_equal(rate_test, rate)
def test_optimPCDTX_trivial(self):
# test for simple problem with known outcome
H = np.ones([3,2,2])
for k in np.arange(3):
H[k,:,:] = scipy.dot(H[k,:,:], H[k,:,:].conj().T)
noisepower = np.ones(3)
rate = 1
linkBandwidth = 1
p0 = 10
m = 2
pS = 5
pMax = 10
obj, solution, status = optimMinPow.optimizePCDTX(H, noisepower, rate, linkBandwidth, pMax, p0, m, pS, 0)
answerObj = 17.000000115485285
answerSol = np.array([ 0.33333334, 0.33333334, 0.33333334, 0. ])
np.testing.assert_almost_equal(obj, answerObj)
np.testing.assert_almost_equal(solution, answerSol)
for k in np.arange(H.shape[0]):
ptx = optimMinPow2x2DTX.ptxOfMu(solution[k], rate, linkBandwidth, noisepower[k], H[k,:,:])
rate_test = solution[k]*np.real(utils.ergMIMOCapacityCDITCSIR(H[k,:,:], ptx))
np.testing.assert_almost_equal(rate_test, rate)
def test_optimPCDTXrandomChannel(self):
# test for simple problem with known outcome
users = 22
n_tx = 2
n_rx = 2
H = np.empty([users, n_tx, n_rx],dtype=complex) # very important to make it complex!
for k in np.arange(users):
H[k,:,:] = 10e-7*utils.rayleighChannel(n_tx,n_rx)
H[k,:,:] = scipy.dot(H[k,:,:], H[k,:,:].conj().T)
noisepower = np.ones(users) * 4e-14
rate = 1.2e7/users # bps
linkBandwidth = 1e7
p0 = 100
m = 2.4
pS = 50
pMax = 40
obj, solution, status = optimMinPow.optimizePCDTX(H, noisepower, rate, linkBandwidth, pMax, p0, m, pS, 0)
# Test that all calls were correct and their order. What goes in must come out.
for k in np.arange(users):
ptx = optimMinPow2x2DTX.ptxOfMu(solution[k], rate, linkBandwidth, noisepower[k], H[k,:,:]) # power as a function of the MIMO link
rate_test = solution[k]*np.real(utils.ergMIMOCapacityCDITCSIR(H[k,:,:], ptx/noisepower[k]))*linkBandwidth # bps
np.testing.assert_almost_equal(rate_test, rate)
