Generators

class SymbolGenerator(M: int, seed: int = None, name: str = 'generator')

A generator for creating independent and identically distributed (IID) symbols.

This class generates a sequence of integer randomly chosen uniformly from the set \(\{0, 1, ..., M-1\}\).

Signal Model

Each symbol \(y[n]\) is drawn independently from the same distribution :

\[\begin{split}p(y[n]=m) = \begin{cases} \frac{1}{M} & \text{if } m \in \{0, 1, \ldots, M-1 \}, \\ 0 & \text{otherwise}. \end{cases}\end{split}\]

Attributes

Mint

The size of the alphabet.

is_mimobool, optional

Indicates whether the generator is used in a MIMO context (default: True).

seedint, optional

Seed for the random number generator, for reproducibility. Default is None, which initializes the generator without a fixed seed.

namestr, optional

Name of the generator instance (default: “generator”).

Examples

>>> generator = SymbolGenerator(M=4, seed=42)
>>> symbols_1D = generator(5)  # Generates a 1D array of size 5
>>> print(symbols_1D)
[0 3 2 1 1]
>>> symbols_2D = generator((2, 3))  # Generates a 2D array of shape (2, 3)
>>> print(symbols_2D)
[[3 0 2]
 [0 0 2]]

class GaussianGenerator(sigma2: float = 1, seed: int = None, name: str = 'gaussian_generator')

A generator for creating Gaussian-distributed symbols.

This class generates a sequence of symbols drawn from a Gaussian distribution with mean 0 and standard deviation sigma.

Signal Model

Each symbol \(\mathbf{y}[n]\) is drawn independently from a Gaussian distribution:

\[\mathbf{y}[n] \sim \mathcal{N}_c(0, \sigma^2)\]

Attributes

sigma2float

The variance of the Gaussian distribution.

seedint, optional

Seed for the random number generator, for reproducibility. Default is None, which initializes the generator without a fixed seed.

namestr, optional

Name of the generator instance (default: “gaussian_generator”).

Examples

>>> generator = GaussianGenerator(sigma2=1.0, seed=42)
>>> symbols_1D = generator(5)  # Generates a 1D array of size 5
>>> print(symbols_1D)
[ 0.49671415 -0.1382643   0.64768854  1.52302986 -0.23415337]
>>> symbols_2D = generator((3, 3))  # Generates a 2D array of shape (3, 3)
>>> print(symbols_2D)
[[ 0.49671415 -0.1382643   0.64768854]
 [ 1.52302986 -0.23415337  1.57921282]
 [ 0.76743473 -0.46947439  0.54256004]]