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The umkc/networkslicing5g dataset (2022-03-22)
University of Missouri Kansas City 5G Dataset, 6G Dataset, Network Slicing, Wireless Dataset, eMBB, URLLC, mMTC. DOI: https://doi.org/10.15783/k0w0-js18
Contributed by Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj.
We have created a Deep Learning model for 5G and Network Slicing. (eMBB, URLLC, IoT). I encourage developers and researchers working on the 4G/LTE, 5G, 6G and similar interest to use and provide feedback: Our research can be found at 1. IEEE paper "DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks" (https://ieeexplore.ieee.org/document/8993066) 2. IEEE paper "Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond" (https://ieeexplore.ieee.org/document/9031158)
details of the umkc/networkslicing5g dataset (2022-03-22)
This dataset contains the following traceset:
how to cite this dataset
When writing a paper that uses CRAWDAD datasets, we would appreciate it if you could cite both the authors of the dataset and CRAWDAD itself, and identify the exact dataset using the appropriate version number. For this dataset, this citation would look like:
We also provide bibliographic information in common citation formats below:
Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj, CRAWDAD dataset umkc/networkslicing5g (v. 2022‑03‑22), downloaded from https://crawdad.org/umkc/networkslicing5g/2022‑03‑22, https://doi.org/10.15783/k0w0‑js18, Mar 2022.
If you do not use the provided citation formats, please include a reference with the same information, as described in the CRAWDAD FAQ.