Random number generation

In order to make your simulations reproducible, it is important to configure a random seed which makes your code deterministic. When Sionna is loaded, it instantiates random number generators (RNGs) for Python, NumPy, and TensorFlow. You can then set a single seed which will make all of your results deterministic, as long as only these RNGs are used. In the cell below, you can see how this seed is set and how the different RNGs can be used in your code. All of Sionna PHY’s built-in functions realy on these RNGs.

from sionna.phy import config
config.seed = 40

# Python RNG - use instead of
# import random
# random.randint(0, 10)
print(config.py_rng.randint(0,10))

# NumPy RNG - use instead of
# import numpy as np
# np.random.randint(0, 10)
print(config.np_rng.integers(0,10))

# TensorFlow RNG - use instead of
# import tensorflow as tf
# tf.random.uniform(shape=[1], minval=0, maxval=10, dtype=tf.int32)
print(config.tf_rng.uniform(shape=[1], minval=0, maxval=10, dtype=tf.int32))

7
5
tf.Tensor([2], shape=(1,), dtype=int32)