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Machine Learning for Wireless Communications

Hybrid Data-Driven and Model-Based Approaches

Time: Thu 2022-12-15 13.00

Location: F3, Lindstedtsvägen 26 & 28, Stockholm

Video link:

Language: English

Subject area: Electrical Engineering

Doctoral student: Lissy Pellaco , Teknisk informationsvetenskap

Opponent: Assistant Professor Santiago Segarra, Rice University, Department of Electrical and Computer Engineering

Supervisor: Joakim Jaldén, Teknisk informationsvetenskap

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QC 20221115


Machine learning has enabled extraordinary advancements in many fields and penetrates every aspect of our lives. Autonomous driving cars and automatic speech translators are just two examples of the numerous applications that have become a reality yet seemed so distant a few years ago. Motivated by this unprecedented success of machine learning, researchers have started investigating its potential within the field of wireless communications, and a plethora of outstanding data-driven solutions have appeared. 

In this thesis, we acknowledge the success of machine learning, and we corroborate its role in shaping the future generation of cellular systems. However, we argue that machine learning should be combined with solid theoretical foundations and expert knowledge as the basis of wireless systems. Machine learning allows a substantial performance gain when traditional approaches fall short, e.g., when modeling assumptions fail to capture reality accurately or when conventional algorithms are computationally costly. Likewise, the injection of domain knowledge into data-driven solutions can compensate for typical machine learning shortcomings, such as a lack of interpretability and performance guarantees, poor scalability, and questionable robustness. 

In this thesis, composed of five technical papers, we present novel hybrid model-based and data-driven approaches in three application areas: interference detection for satellite signals, channel prediction for link adaptation, and downlink beamforming in MU-MISO and MU-MIMO settings. We go beyond a mere application of machine learning and adopt a reasoned approach to integrate domain knowledge synergistically. As a result, the proposed approaches, on the one hand, achieve remarkable empirical performance and, on the other hand, are supported by theoretical analysis. Furthermore, we pay particular attention to the explainability of all our proposed approaches since the typical black-box nature of data-driven solutions constitutes one of the major obstacles to their actual deployment, especially in the wireless communications field.