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CRAWDAD umkc/networkslicing5g
- Citation Author(s):
- Submitted by:
- CRAWDAD Team
- Last updated:
- Mon, 03/21/2022 - 08:00
- DOI:
- 10.15783/k0w0-js18
- License:
1886 Views- Collection:
- CRAWDAD
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Abstract
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)
date/time of measurement start: 2019-05-01
date/time of measurement end: 2019-10-30
measurement purpose: Computer Malware (Worms) Investigation, Energy-Efficient Wireless Network, Network Diagnosis, Network Performance Analysis, Network Security, Routing Protocol
file: 5G_Dataset_Network_Slicing_CRAWDAD_Shared.zip
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.
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These datasets are part of Community Resource for Archiving Wireless Data (CRAWDAD). CRAWDAD began in 2004 at Dartmouth College as a place to share wireless network data with the research community. Its purpose was to enable access to data from real networks and real mobile users at a time when collecting such data was challenging and expensive. The archive has continued to grow since its inception, and starting in summer 2022 is being housed on IEEE DataPort.
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