Thesis defence in the University of Oulu

Doctoral Candidate

M.Sc. Samad Ali

Faculty and research unit

University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, CWC-NS

Field of study

Wireless Communications

Date and time of the thesis defence

7.2.2020 12:15

Place of the thesis defence


Topic of the dissertation

Learning-based predictive resource allocation for machine type communications


D.Sc. Jyri Hämäläinen, Aalto University


Ph.D. Nandana Rajatheva, University of Oulu

Learning-based predictive resource allocation for machine type communications

The goal of this thesis is to design novel frameworks that will facilitate the development of predictive resource allocation mechanisms for machine type communications (MTC). This is done by exploiting a set of machine learning tools such as causal inference, multi-armed bandits (MABs) and deep learning. MTC networks often encompass a very large number of machine type devices (MTDs) that require wireless radio resources to send small data packets in the uplink of cellular networks. A predictive resource allocation framework based on the concept of the so-called fast uplink grant is proposed for MTC. In this scheme, the base station (BS) predicts the set of active MTDs at each time step and proactively allocates resources to them. Challenges such as source traffic prediction and optimal resource allocation for predictive resource allocation are addressed in this thesis.


Last updated: 3.2.2020