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Dynamic Resource Management in Next Generation Networks based on Deep Q Learning

Aslan, Aysun
Bal Bozkurt, Gülce
Toker, Cenk
In next generation networks, with the increasing number of diverse mobile network service types, a major challenge lies in how to manage and support all mobile service users who have different Quality of Service (QoS) requirements. Network slicing term can be a solution to satisfy the heterogeneous network requests over a common physical infrastructure. Splitting the network into slices which have different properties (e.g., bandwidth requirements, delay tolerance, user density, etc.) allows to schedule and optimize the requests in constraint of limited resources. The network has to decide to accept or reject the requests, and scale up/down the slices by considering the user density in accepted requests, and then, schedule the accepted requests to serve them in an order. In this paper, effect of scaling up/down the slices by using deep reinforcement learning (DRL) algorithm to the speed of satisfaction of user requests is examined.