LinkPredict

LinkPredict alows you to predict the performance of radio links. The flow of constructing such a link is from transmitter towards receiver, taking into account all elements that contribute with gains and losses.

LinkPredict is written in Python and is:

• Generic: construct radio links for terrestrial applications as well as space-to-ground, ground-to-space, orbiter-to-rover, and others.

• Dynamic: every element of the radio link can be time-depended and/or depend on other parameters. This way, time-dependent effects (such as change of distance) can be analyzed and visualized.

• Extensible: subclasses can be easily created and used for the radio link computations.

Installation

Install LinkPredict by running:

$ pip install linkpredict

Example

Let’s create the link budget for the example defined in Table 1 from this reference: http://www.waves.utoronto.ca/prof/svhum/ece422/notes/22-linkbudget.pdf

import linkpredict as lp

# Transmitter
circuit_loss = lp.Device(gain=-2.0)
transmitter = lp.Transmitter(amplifier_power=20, devices=[circuit_loss])
transmit_antenna = lp.MainLobeAntenna(peak_gain=51.6, beam_3db_width=6)

# Path
geometry = lp.SimpleGeometry(slant_range=40.721e6, rx_antenna_angle=2.5)
fade = lp.SimpleMediumLoss(4.0)
other = lp.SimpleMediumLoss(6.0)
medium_losses= [fade, other]

# Channel
modulation = lp.AnalogModulation(bandwidth=2e6)
channel = lp.Channel(frequency=8e9, modulation=modulation)

# Receiver
receive_antenna = lp.MainLobeAntenna(peak_gain=35.1, beam_3db_width=6)
receive_antenna_noise = lp.SimpleAntennaNoise(300)
receiver = lp.Receiver(noise_temperature=3806)

# Link Budget
link = lp.Link(
    channel=channel,
    geometry=geometry,
    medium_losses=medium_losses,
    transmitter=transmitter,
    transmit_antenna=transmit_antenna,
    receive_antenna=receive_antenna,
    receive_antenna_noise=receive_antenna_noise,
    receiver=receiver)
result = link.calculate_link_budget()

k = lp.LinkBudgetKeys
fields = (
    k.tx_amplifier_power,
    k.tx_circuit_loss,
    k.tx_antenna_gain,
    k.eirp,
    k.free_space_path_loss,
    k.medium_loss,
    k.total_path_loss,
    k.received_isotropic_signal_level,
    k.rx_antenna_gain,
    k.rx_antenna_pointing_loss,
    k.received_power,
    k.received_noise_power_density,
    k.cno_ratio,
    k.bandwidth,
    k.snr,
)
for field in fields:
    print("{}: {:0.1f} {}".format(field.name, result[field], field.unit))

Output:

tx_amplifier_power: 20.0 dBW
tx_circuit_loss: 2.0 dB
tx_antenna_gain: 51.6 dBi
eirp: 69.6 dBW
free_space_path_loss: 202.7 dB
medium_loss: 10.0 dB
total_path_loss: 212.7 dB
received_isotropic_signal_level: -143.1 dB
rx_antenna_gain: 35.1 dBi
rx_antenna_pointing_loss: 2.0 dB
received_power: -110.0 dBW
received_noise_power_density: -192.5 dBW/Hz
cno_ratio: 82.4 dB-Hz
bandwidth: 2000000.0 Hz
snr: 19.4 dB

You can find more examples as Jupyter notebooks in the docs folder. Try them out right now using Binder.

Documentation

The API documentation is in docs/api.md.

Contribute

• Issue Tracker: https://gitlab.com/librecube/lib/python-linkpredict/-/issues
• Source Code: https://gitlab.com/librecube/lib/python-linkpredict

To learn more on how to successfully contribute please read the contributing information in the LibreCube guidelines.

Support

If you are having issues, please let us know. Reach us at Matrix or via Email.

License

The project is licensed under the MIT license. See the LICENSE file for details.