TutorialΒΆ
This webpage introduces a detailed tutorial for the imtoolkit
command and APIs.
The imtoolkit
command is a stand-alone toolkit for the spatial modulation and subcarrier-index modulation schemes, which supports Windows, Mac, and Linux.
By contrast, the imtoolkit
APIs provide various classes and functions for the general MIMO/OFDM scenarios.
Before using imtoolkit
, one can activate the CuPy-aided GPGPU acceleration by setting an environment variable USECUPY=1
as follows.
> export USECUPY=1
In case you would like to use the CPU backend, NumPy, please unset the environment variable.
> unset USECUPY
How to use the imtoolkit command
API examples for the imtoolkit package
CoherentMIMO-IdealRayleigh-BER.py
This example compares the BER performance of the coherent Bell laboratories layered space-time (BLAST) and spatial modulation (SM) schemes.
CoherentMIMO-IdealRayleigh-AMI.py
This example compares the AMI performance of the coherent BLAST and SM schemes.
CoherentOFDM-IdealRayleigh-BER.py
This example compares the BER performance of the coherent OFDM and subcarrier-index modulation (SIM) schemes.
CoherentOFDM-IdealRayleigh-BER-diversity.py
This example evaluates the BER performance of the coherent SIM scheme, where three subcarrier activation patterns are considered.
CoherentOFDM-IdealRayleigh-AMI.py
This example compares the AMI performance of the coherent OFDM and SIM schemes.
DifferentialMIMO-IdealRayleigh-BER.py
This example compares the BER performance of the differential BPSK, differential orthogonal space-time block code (DOSTBC), diagonal unitary code (DUC), algebraic differential spatial modulation (ADSM), and differential threaded algebraic space-time (DTAST) schemes.
SemiUnitaryDifferentialMIMO-IdealRayleigh-BER.py
This example compares the BER performance of the differential star-QAM (SQAM), DOSTBC with SQAM, and DUC schemes.
NonSquareDifferentialMIMO-IdealRayleigh-BER.py
This example compares the BER performance of the differential SQAM, square DUC, and nonsquare DUC schemes.