Package: linkpredict
LinkPredictLinkPredict 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.InstallationInstall LinkPredict by running:$pipinstalllinkpredictExampleLet'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.pdfimportlinkpredictaslp# Transmittercircuit_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)# Pathgeometry=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]# Channelmodulation=lp.AnalogModulation(bandwidth=2e6)channel=lp.Channel(frequency=8e9,modulation=modulation)# Receiverreceive_antenna=lp.MainLobeAntenna(peak_gain=35.1,beam_3db_width=6)receive_antenna_noise=lp.SimpleAntennaNoise(300)receiver=lp.Receiver(noise_temperature=3806)# Link Budgetlink=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.LinkBudgetKeysfields=(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,)forfieldinfields: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 dBYou can find more examples as Jupyter notebooks in the docs folder.Try them out right now using Binder.DocumentationThe API documentation is indocs/api.md.ContributeIssue Tracker:https://gitlab.com/librecube/lib/python-linkpredict/-/issuesSource Code:https://gitlab.com/librecube/lib/python-linkpredictTo learn more on how to successfully contribute please read the contributing
information in theLibreCube guidelines.SupportIf you are having issues, please let us know. Reach us atMatrixor viaEmail.LicenseThe project is licensed under the MIT license. See theLICENSEfile for details.