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)

DOI Link: 
10.15783/k0w0-js18
Read more

Dataset Files

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[1] Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj, "CRAWDAD umkc/networkslicing5g", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.15783/k0w0-js18. Accessed: Jun. 06, 2023.
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author = {Anurag Thantharate; Cory Beard; Rahul Paropkari; Vijay Walunj },
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Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj. (2022). CRAWDAD umkc/networkslicing5g. IEEE Dataport. http://dx.doi.org/10.15783/k0w0-js18
Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj, 2022. CRAWDAD umkc/networkslicing5g. Available at: http://dx.doi.org/10.15783/k0w0-js18.
Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj. (2022). "CRAWDAD umkc/networkslicing5g." Web.
1. Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj. CRAWDAD umkc/networkslicing5g [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.15783/k0w0-js18
Anurag Thantharate, Cory Beard, Rahul Paropkari, Vijay Walunj. "CRAWDAD umkc/networkslicing5g." doi: 10.15783/k0w0-js18