Skip to main content

IMToolkit: An open-source index modulation toolkit for reproducible research based on massively parallel algorithms

Project description

IMToolkit

IMToolkit, an open-source index modulation (IM) toolkit, attempts to facilitate reproducible research in wireless communications. The major advantages of this toolkit are highlighted as follows:

  • It accelerates bit error ratio and average mutual information simulations by relying on a state-of-the-art Nvidia GPU and the massively parallel algorithms proposed in [1].
  • It also supports the representative multiplexing schemes for ideal MIMO and OFDM scenarios, in addition to the IM family.
  • It contains a comprehensive database of designed active indices, that determine the achievable performance of the generalized spatial modulation or the subcarrier-index modulation.

For more information, please refer to the following webpages.

Installation Guide

IMToolkit is available from the Python official package repository PyPi.

> pip install imtoolkit

This installation requires NumPy, Pandas, SciPy, SymPy, Numba, and tqdm, all of which are popular Python packages. Additionally, it is strongly recommended to install CuPy 5.40+. IMToolkit is heavily dependent on CuPy to achieve significantly fast Monte Carlo simulations. The key components required by CuPy are listed here. In case CuPy is not installed in your environment, IMToolkit uses NumPy only. Note that the CuPy-based simulation is 145 times faster than the NumPy-based calculation, as reported in [1].

The above PyPi package excluded the designed active indices due to their large file size, which exceeds 500MB. Hence, this reduced-size PyPi package will automatically download a required file from the GitHub repository or a mirror website. If you need all the project files, to use imtoolkit offline, it is recommended to obtain the whole package from GitHub as follows:

> pip install git+https://github.com/imtoolkit/imtoolkit

The IMToolkit development team welcomes other researchers' contributions and pull requests. In that case, it would be better to install the latest package and activate the editable mode as follows:

> git clone https://github.com/imtoolkit/imtoolkit
> pip install -e ./imtoolkit # this activates the editable mode

A detailed tutorial for the installed imtoolkit command is available here.

Citations

It would be highly appreciated if you cite the following reference when using IMToolkit.

Of course, if your project relies on CuPy, the following reference is strongly recommended.

Contributor(s)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

imtoolkit-0.8-py3-none-any.whl (77.9 kB view details)

Uploaded Python 3

File details

Details for the file imtoolkit-0.8-py3-none-any.whl.

File metadata

  • Download URL: imtoolkit-0.8-py3-none-any.whl
  • Upload date:
  • Size: 77.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.2.0.post20200210 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.7.6

File hashes

Hashes for imtoolkit-0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 228073986b6e5a57132145023e207313641e5b725d2c65e1db3bb4468e21e5c9
MD5 e8f1cdaa3c803bed1b9a7fdb33e9783e
BLAKE2b-256 999282203c0d53e43ba90cbe310e96b9eb878f67fb6d510414d841c4c03f4888

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page