MACHINE LEARNING BASED TECHNIQUE TO DYNAMICALLY ALLOCATE A NETWORK SLICE BASED ON USER EQUIPMENT TYPE AND NETWORK SLICE SELECTION FUNCTION FACTOR FOR 5G WIRELESS INTERNET OF THINGS NETWORKS

Inventor(s)

Akram Sheriff
Santosh Patil
M. David Hanes
Gonzalo Salgueiro

Abstract

Techniques are described for dynamically allocating different Long Term Evolution (LTE) User Equipment (UE) variants (e.g., Cat-M1 and Narrowband Internet of Things (NB-IOT) UEs) to the correct Network Slice. This may be accomplished by using a Machine Learning (ML) based software component running on 4G LTE evolved Node B (eNB) or in 5G next generation NodeB (gNB).

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Sheriff, Akram; Patil, Santosh; Hanes, M. David; and Salgueiro, Gonzalo, "MACHINE LEARNING BASED TECHNIQUE TO DYNAMICALLY ALLOCATE A NETWORK SLICE BASED ON USER EQUIPMENT TYPE AND NETWORK SLICE SELECTION FUNCTION FACTOR FOR 5G WIRELESS INTERNET OF THINGS NETWORKS", Technical Disclosure Commons, (December 12, 2018)
https://www.tdcommons.org/dpubs_series/1774