Improving 5G Radio Resource Management with Simulation and Reinforcement Learning

WN4SS lab logo.

Use deep reinforcement learning to improve radio resource scheduling in advanced cellular networks (LTE/5G).

Project description

With the rising number of mobile users and the large set of critical applications, such as Internet of Things, Smart Grid, and Smart Cities, it is fundamental to wisely and efficiently allocate the available radio resources in 5G networks.

In this project, we aim at using deep reinforcement learning techniques to improve the scheduling of radio resources in advanced cellular networks (LTE/5G). OpenAI Gym, NS-3 and NS-3 gym will be used:

  • OpenAI Gym is a software toolkit for reinforcement learning (RL), widely employed in the research community.
  • The network simulator ns–3 is the de-facto standard for academic and industry studies in the areas of networking protocols and communication technologies.
  • ns3-gym is a framework that integrates both OpenAI Gym and ns-3 in order to encourage usage of RL in networking research.

The objective is to use existing LTE/5G simulation libraries in NS-3 and integrate them with the NS3-gym framework

Project outcome

The objective is to use existing LTE/5G simulation libraries in NS-3 and integrate them with the NS3-gym framework. Possibility to present the outcome of this project at international conferences and to publish in peer-reviewed journals.

Project details

Timing, eligibility and other details
Length of commitment At least a semester.
Start time Spring, Summer, Anytime 
In-person, remote, or hybrid?
Level of collaboration Large group collaboration (4+ students)
Benefits Academic Credit
Who is eligible Sophomores, Juniors and Seniors who have programming experience with Python and C++. Working knowledge of Machine learning concepts would be preferred, but it is not mandatory.

Core partners

  • Dr. Mastronarde

Project mentor

Filippo Malandra

Assistant Professor of Research

Electrical Engineering

Phone: (716) 645-1151


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Electrical Engineering