A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

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Abstract for Research Paper Telecommunications Engineering


The expected massive connectivity in 5G wireless
the network is bound to have become a challenge to service providers.
Many services over the 5G network will be aligned to a particular
radio access network (RAN). As a result, admitting a service-based user to a particular RAN will depend on the most efficient
radio access technology selection(RAT). This is because 5G
the network will adopt multi-tier radio access networks ranging
from high power macro base stations to extremely low power
Bluetooth connectivity. The selection of a service-oriented RAT is
critical because some wireless services have a superior quality of
service under certain RATs. Maintaining efficient RAT selection
by network operators will improve power allocation efficiency,
bandwidth allocation efficiency, and operational expenditure. The
complexity of associating a RAT to a service-based user while
considering network states such as service packet size, the turn
around time, the power allocation has not to been fully explored.
In this paper, we propose a reinforcement learning approach
to user admission based on efficient RAT selection considering
wireless services in a cross-tier wireless radio access network
domain. The proposed algorithm is expected to improve RAT
selection efficiency while minimizing the computation complexity.
We perform extensive simulation using Python dynamic libraries
and present our results alongside existing approaches.

Primary author

Mr Mourice Ojijo (Kabarak University)


Mr Dismas Ombuya (Kabarak University) Mr Wyclife Ayako (Kabarak University) Mr Andrew Kipkebut (Kabarak University) Mr Cleophas Mochoge (Kabarak University)

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