Ahan Kak on Muck Rack

Ahan Kak

As seen in: ScienceDirect, IEEE
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Articles

inRAN: Interpretable Online Bayesian Learning for Network Automation in Open Radio Access Networks

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By Yuru Zhang, Qiang Liu, Ahan Kak, Nakjung Choi
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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The High Ground: The Case for U.S. Space Dominance

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By Ian F. Akyildiz, Ahan Kak, Chan-Hee Kim, Baek-Il Kim
| ScienceDirect Verified
Abstract Modern society depends on satellites in space, and the United States relies on satellites more than any other country. It is from space that much of America's military advantage is assured. Without the satellites that link our forces together, America's modern military would cease to function and would risk being overwhelmed. However, U.S. space architecture is more vulnerable to attack than ever.
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InSlicing: Interpretable Learning-Assisted Network Slice Configuration in Open Radio Access Networks

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By Yuru Zhang, Qiang Liu, Ahan Kak, Nakjung Choi
| arxiv.org
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