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        "author": "Naoki Ishikawa",
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        "description": "# IMToolkit\n\n<a href=\"https://github.com/imtoolkit/imtoolkit\"><img align=\"right\" width=\"150px\" height=\"150px\" src=\"https://github.com/imtoolkit/imtoolkit/blob/master/docs/source/_static/imtoolkit-logo.png?raw=true\"></a>\nIMToolkit, an open-source index modulation (IM) toolkit, attempts to facilitate reproducible research in wireless communications.\nThe major advantages of this toolkit are highlighted as follows:\n\n- 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].\n- It also supports the representative multiplexing schemes for ideal MIMO and OFDM scenarios, in addition to the IM family.\n- It contains [a comprehensive database of designed active indices](https://ishikawa.cc/imtoolkit/db/index.html), that determine the achievable performance of the generalized spatial modulation or the subcarrier-index modulation.\n\nFor more information, please refer to the following webpages.\n- [IMToolkit official website](https://ishikawa.cc/imtoolkit/).\n- [A detailed tutorial for the imtoolkit command](https://ishikawa.cc/imtoolkit/tutorial.html).\n- [A comprehensive database of the designed active indices](https://ishikawa.cc/imtoolkit/db/index.html).\n- [A compute capsule on Code Ocean](https://codeocean.com/capsule/4685246/tree), that reproduces the same results as those reported in [1].\n\n## Installation Guide\n\nIMToolkit is available from the Python official package repository [PyPi](https://pypi.org/project/imtoolkit/).\n\n    > pip install imtoolkit\n\nThis installation requires NumPy, Pandas, SciPy, SymPy, Numba, and tqdm, all of which are popular Python packages.\nAdditionally, it is strongly recommended to install [CuPy](https://cupy.chainer.org/) 5.40+. \nIMToolkit is heavily dependent on CuPy to achieve significantly fast Monte Carlo simulations.\n[The key components required by CuPy are listed here.](https://docs-cupy.chainer.org/en/stable/install.html)\nIn case CuPy is not installed in your environment, IMToolkit uses NumPy only.\nNote that the CuPy-based simulation is 145 times faster than the NumPy-based calculation, as reported in [1].\n\n[The above PyPi package](https://pypi.org/project/imtoolkit/) excluded the designed active indices due to their large file size, which exceeds 500MB.\nHence, this reduced-size PyPi package will automatically download a required file from the GitHub repository or a mirror website.\nIf you need all the project files, to use `imtoolkit` offline, it is recommended to obtain the whole package from GitHub as follows:\n\n    > pip install git+https://github.com/imtoolkit/imtoolkit\n\nThe IMToolkit development team welcomes other researchers' contributions and pull requests.\nIn that case, it would be better to install the latest package and activate the editable mode as follows:\n\n    > git clone https://github.com/imtoolkit/imtoolkit\n    > pip install -e ./imtoolkit # this activates the editable mode\n\n[A detailed tutorial for the installed imtoolkit command is available here.](https://ishikawa.cc/imtoolkit/tutorial.html)\n\n## Citations\n\nIt would be highly appreciated if you cite the following reference when using IMToolkit.\n\n- [1] N. Ishikawa, ``[IMToolkit: An open-source index modulation toolkit for reproducible research based on massively parallel algorithms](https://doi.org/10.1109%2Faccess.2019.2928033),'' IEEE Access, vol. 7, pp. 93830--93846, July 2019.\n\nOf course, if your project relies on CuPy, the following reference is strongly recommended.\n\n- [2] R. Okuta, Y. Unno, D. Nishino, S. Hido, and C. Loomis, ``[CuPy: A NumPy-compatible library for NVIDIA GPU calculations](http://learningsys.org/nips17/assets/papers/paper_16.pdf),'' in Conference on Neural Information Processing Systems Workshop, Long Beach, CA, USA, December 4--9, 2017.\n\n## Contributor(s)\n\n- Naoki Ishikawa ([Web](https://ishikawa.cc) / [Google Scholar](https://scholar.google.co.jp/citations?user=JHnisGYAAAAJ) / [ResearchGate](https://www.researchgate.net/profile/Naoki_Ishikawa) / [Publons](https://publons.com/researcher/3012020/naoki-ishikawa/))\n- You might become a valuable contributor of this project. Any contributions and issues are appreciated.\n\n\n\n",
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