plot_ber#

sionna.phy.utils.plot_ber(snr_db: numpy.ndarray | List[numpy.ndarray], ber: numpy.ndarray | List[numpy.ndarray], legend: str | List[str] = '', ylabel: str = 'BER', title: str = 'Bit Error Rate', ebno: bool = True, is_bler: bool | List[bool] | None = None, xlim: Tuple[float, float] | None = None, ylim: Tuple[float, float] | None = None, save_fig: bool = False, path: str = '') → Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes][source]#

Plot error-rates.

Parameters:

  • snr_db (numpy.ndarray | List[numpy.ndarray]) – Array defining the simulated SNR points.

  • ber (numpy.ndarray | List[numpy.ndarray]) – Array defining the BER/BLER per SNR point.

  • legend (str | List[str]) – Legend entries.

  • ylabel (str) – Label for the y-axis.

  • title (str) – Figure title.

  • ebno (bool) – If True, the x-label is set to “EbNo [dB]” instead of “EsNo [dB]”.

  • is_bler (bool | List[bool] | None) – If True, the corresponding curve is dashed.

  • xlim (Tuple[float, float] | None) – x-axis limits.

  • ylim (Tuple[float, float] | None) – y-axis limits.

  • save_fig (bool) – If True, the figure is saved as .png.

  • path (str) – Path to save the figure if save_fig is True.

Outputs:

  • fig – matplotlib.figure.Figure. Figure handle.

  • ax – matplotlib.axes.Axes. Axes object.

Examples

import numpy as np
from sionna.phy.utils import plot_ber

snr = np.array([0, 2, 4, 6, 8, 10])
ber = np.array([0.2, 0.1, 0.05, 0.01, 0.001, 0.0001])
fig, ax = plot_ber(snr, ber, legend="AWGN", title="BER vs SNR")
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