接下来我们将注意力放在这样的信道上:在离散时间连续无记忆信道 (定义 9.4) 的基础上,我们进一步要求信道的噪声是可加 (additive) 的 Gaussian 白噪声 (white noise),且采用平均功率约束。具体而言:对于时刻 i=1,2,\ldots, ,信道输入为 X_i ,那么信道输出就是 ...
wgngenerates normal random noise samples usingrandn. Therandnfunction uses one or more uniform values from theRandStreamobject to generate each normal value. For information about producing repeatable noise samples, seeTips. seed—Random number generator seed ...
x1 =gnoise(sqrt(sig1sq))// Gaussian random generatorx2 =gnoise(sqrt(sig2sq))Y1 =(x1 + x2)/sqrt(2)// joint (CORRELATED) dataY2 =(x1 - x2)/sqrt(2)// obeying given pdfBiHistSetBins(Y1, Y2,201, -5,0.05,201, -5,0.05,"BivariateHistWave")BivariateHistWave =log(BivariateHist...
We study the long time behaviour of a nonlinear oscillator subject to a random multiplicative noise with a spectral density (or power-spectrum) that decays as a power law at high frequencies. When the dissipation is negligible, physical observables, such as the amplitude, the velocity and the ...
Set the random number generator seed. Get seed = 12345; Generate repeatable random noise using the rng function before calling the awgn function. Get rng(seed); y = awgn(x,snr); Compute the bit errors. Get dataHat = pskdemod(y,M); numErr1 = biterr(data,dataHat,k) numErr1 =...
Detection in Complex-Valued White Gaussian Noise (Coherent Samples) The NP detector for complex-valued signals is similar to that discussed in Source Localization Using Generalized Cross Correlation. In addition, the function makes these assumptions: The variance of the complex-valued Gaussian random va...
random.normal(0, noise_sigma, size=x.shape) return y.tolist() train_X = np.array([3, 1, 4, 5, 9]).reshape(-1, 1) train_y = y(train_X, noise_sigma=1e-4) test_X = np.arange(0, 10, 0.1).reshape(-1, 1) gpr = GPR() gpr.fit(train_X, train_y) mu, cov = gpr...
If the input signal is a multichannel signal, the function calculates thesignalpowervalue across all channels as a single value. It then uses the value to calculate the noise level for all the channels. Data Types:double randobject—Random number stream object ...
defy_2d(x, noise_sigma=0.0): x = np.asarray(x) y = np.sin(0.5* np.linalg.norm(x, axis=1)) y += np.random.normal(0, noise_sigma, size=y.shape) returny train_X = np.random.uniform(-4,4, (100,2)).tolist() t...
从应用的角度,很多问题都可以归结为寻找关键要素(变量/random variable)之间的关系(函数/概率):已知一些观测点(数据,可以看成function graph上的有误差的点,或samples from a joint distribution),如何推测underlying unknown function/distribution。从这句话可以看出,对同一个问题可以有deterministic和stochastic两种建模(...