CRAWDAD dataset umkc/networkslicing5g (v.2022-03-22)

Anurag Thantharate; Cory Beard; Rahul Paropkari; Vijay Walunj

doi:10.15783/k0w0-js18

University of Missouri Kansas City 5G Dataset, 6G Dataset, Network Slicing, Wireless Dataset, eMBB, URLLC, mMTC. DOI: https://doi.org/10.15783/k0w0-js18

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)

This dataset contains the following traceset:

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:

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.

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The umkc/networkslicing5g dataset (2022-03-22)

This dataset contains the following traceset: