AI-enabled Performance Enhancement for the Reconfigurable Multi-Player RAN

In 5G and beyond networks, softwarization of network functions, as well as disaggregation of software and hardware, are the recent moves pushing Radio Access Networks (RAN) to be ultra-agile, reconfigurable and flexible. This flexibility comes along with complexity that goes beyond traditional algorithms’ capabilities to optimize the RAN. In addition, in future RANs, multiple-players interacting within the same RAN environment will increase the burden on proper decision making. Many researchers in academia and the telecom industry have turned to AI/ML to handle the rising complexity of wireless networks. Dr. Erol-Kantarci, one of the leading researchers in the area, will join forces with Ericsson to address this bleeding-edge challenge and develop advanced machine learning tools for future RANs. With this project, her team will develop hierarchical and planned distributed learning techniques under partial observability to optimize the multi-player RAN based on policies. These techniques will provide Ericsson an edge over the rapidly changing technology scene.

Faculty Supervisor:

Melike Erol-Kantarci

Student:

Partner:

Ericsson Canada Inc (Quebec);Ericsson Canada Inc (Montreal, QC)

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Ottawa

Program: