Machine Learning for Wireless Communications D
Aalto University
<p>This course consists of the following modules:</p><p>Module I: Channel Models:</p><p>Machine learning methods are data hungry and to obtain reliable results huge amount of real world measurements are needed. This problem can be circumvented by synthetic data which mimics the behavior of realistic channels. Channel models appropriate for Fifth Generation (5G) and Beyond Fifth Generation (B5G) systems will be studied. Channel model characteristics including path loss, large-scale fading, small-scale fading models and spatial consistency will be discussed. The differences of Milimeter-Wave and microwave models will be explained. Several radio channel simulators will be introduced.</p><p>Module II: Massive MIMO and Beam Management:</p><p>Massive multiple-input multiple-output (MIMO) is an important technology in 5G and B5G systems. Beam management procedures are used to acquire and maintain a set of beam pair links (a beam used at gNB paired with a beam used at user equipment). These procedures aim to maintain high-quality communication links despite challenges like path loss, blockages, and rapid changes in user equipment position and orientation. We will discuss some use cases of applying machine learning for beam management in future communication systems.</p><p> </p><p>Module III: Radio-frequency Positioning:</p><p>Location awareness is essential for enabling location based services and for improving network management in future communication systems. We will discuss the use of radio frequency based approaches to localization. We review the radio frequency features and techniques that can be utilized for positioning. We will discuss the challenges for indoor positioning and utilize machine learning techniques to address these challenges.</p><p>Module IV: Channel Charting and Applications:</p><p>Channel charting is a self-supervised machine learning framework, that is applied to the channel state information of the users in wireless systems to create a logical radio map of radio environment. The channel chart can be then used for several radio resource management applications. We will study dimensionality reduction techniques that can be used for channel charting and discuss several radio resource management applications based on channel charting.</p><p> </p><p>Module V: Neural Network Structures and Applications</p><p>We will study neural network structures and concepts to jointly optimize the transmitter and receiver in communication system by a single process. We will utilize the autoencoder structure to represent the end-to-end communication system.</p><p> </p>
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Course dates
09 January 2026 - 15 April 2026
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Course organizer
Hanan Al-Tous
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Place/Venue
School of Electrical Engineering / Department of lnformation and Communications Engineering
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City
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Country
Finland
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Workload
5
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Link
https://mycourses.aalto.fi/course/search.php?...
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