Mobile Radio Channels

Course description: A precise knowledge of mobile radio channels is indispensable for the development, evaluation, and test of present and future mobile communication systems. After all, from digital modulation techniques over channel coding to network aspects, nearly all relevant components of mobile radio systems are determined by the propagation characteristics of the channel. This course deals with the modelling, analysis, and simulation of mobile radio channels. It provides a fundamental understanding of many issues that are currently being investigated in the area of mobile radio channel modelling. Several classes of singleinput single-output (SISO), and multiple-input multiple-output (MIMO) fading channels are treated in detail. Furthermore, the description of efficient methods for the simulation of mobile radio channels is in the centre of attention.

Topics: The course strives for providing a fundamental and comprehensive understanding of many topics in the area of mobile radio channel modelling. Among them are the following:

  • Introduction to mobile radio channels
  • Methodologies for the modelling of mobile radio channels
  • Relationship between geometrical models, reference models, simulation models, and measured channels
  • Principles of sum-of-sinusoids and sum-of-cisoids for channel modelling
  • Fundamentals of stochastic and deterministic channel models
  • Parametrization methods for the design of channel simulators
  • Methods for the modelling of Doppler, delay, and angular profiles
  • Methods for the design of measurement-based channel models
  • Classes of frequency-nonselective channels (Rayleigh channel, Rice channel, generalised Rice channel, Nakagami-m channel, Suzuki channel, Loo model)
  • Classes of frequency-selective channels
  • Geometry- and measurement-based MIMO channel models
  • Mobile-to-mobile and vehicle-to-vehicle MIMO channel models
  • Mobile radio channels for cooperative networks (double Rayleigh and double Rice channels)
  • Modelling, analysis, and simulation of non-stationary mobile radio channels
  • Design of high-speed channel simulators
  • Test and performance analysis of channel simulators
  • Design of multiple uncorrelated Rayleigh fading waveforms
  • Modelling and simulation of shadowing effects.

This course is offered as part of the PhD Programme in “Information and Communication Technology.”

Mobile Radio Communications

Course description: To always communicate everywhere and at all times, very flexible and cost-effective communication solutions are needed, which can only be offered by mobile radio systems. In this course, students are introduced to the principles of modern mobile communications and are given an overview of physical layer aspects of wireless systems and standards. In addition, the course provides an overview of radio communication system design with detailed knowledge of important system trade-offs and a deep understanding of radio propagation mechanisms, as well as a description of important fading channel characteristics for different types of radio links.


  • Introduction to wireless systems and standards
  • Overview of the evolution from 1G to 6G
  • Propagation and modelling of multipath fading channels
  • Co-channel interference
  • Modulated signals and their power spectra
  • Digital signalling on flat fading channels
  • Antenna diversity schemes
  • Equalization and interference cancellation
  • Block codes and convolutional coding
  • Spread spectrum techniques.

This lecture will be offered as part of the new Master’s Programme “Intelligent Data Sensing and Wireless Communications” in 2023.

Machine Learning and Deep Learning in Data Sensing

Course description: Machine learning and deep learning (ML/DL) is the science of teaching computers to learn from experience without explicitly programming them. ML/DL has been the forefront of a majority technical innovations of recent decades that are revolutionizing many industries, such as E-commerce, banking and financial services, education, healthcare, food, travel, manufacturing, entertainment, transportation, robotics, natural language processing and translation, and gaming. Nowadays, ML/DL has become so pervasive that we even barely notice it. For example, every time we type in a query, the Google search engine uses ML/DL to not only prompts with what it thinks we are looking for, but also renders appropriate results for us. This course provides an introduction to the fundamentals of modern machine learning and deep learning. In this course, we will cover both the theory and the main algorithms of machine learning and deep learning. In addition, students will learn how to analyse and process data and then apply machine learning and deep learning algorithms by solving concrete examples. The course will be delivered through a series of formal lectures and tutorials. The workload for the average student is approximately 200 hours.


  • Introduction to artificial intelligence, machine learning, and deep learning
  • Fundamentals of machine learning
  • Data processing, feature extraction, and curse of dimensionality
  • Generalization, model evaluation, bias variance trade-off, and model selection
  • Nearest neighbour classifiers, decision trees, regression, and logistic regression
  • Ensemble techniques (bagging and boosting)
  • Perceptron and large margin classifiers
  • Principal component analysis and k-means clustering
  • Fundamentals of artificial neural networks (architecture, forward propagation, activation functions, and back propagation)
  • Deep learning intuition, optimization algorithms, and hyper parameter tuning
  • Deep convolutional neural networks
  • Recurrent neural networks
  • Generative adversarial networks.

This lecture will be offered as part of the new Master’s Programme “Intelligent Data Sensing and Wireless Communications” in 2023.

Probability Theory and Stochastic Processes

Course description: Probability and statistics help to bring logic to a world, which is replete with randomness and uncertainty. This course will give students the tools they need to understand data, science, philosophy, engineering, economics, and finance. It will allow them to solve challenging technical problems, but also how those solutions can be applied in everyday life. With examples ranging from communication systems performance to optimization and estimation, you will gain a strong foundation for the study of statistical inference, stochastic processes, and other subjects where probability is needed.


  • Definitions and axioms of probability
  • Repeated trials
  • Continuous and discrete probability density functions
  • Functions of random variables
  • Joint distributions and joint densities
  • Sums of random variables and limit theorems
  • Stochastic processes
  • Applications of stochastic processes.

This lecture will be offered as part of the new Master’s Programme “Intelligent Data Sensing and Wireless Communications” in 2023.

Radar Sensing

Course description: The word radar was initially coined as an acronym for “radio detection and ranging”, which later became a noun in many languages. Radar utilizes electromagnetic waves to find out relative range, radial velocity, and angular information of objects. This course is designed to provide a theoretical and practical understanding of radar systems. The course provides an overview of the radar system design and radar signal processing techniques, thus enabling the students to comprehend the basic principles, signal analysis, and design of the building blocks of the radar systems. By the end of the course, students should be able to generate and analyse different radar waveforms, understand radar detection of objects in noise, and use radar systems for data sensing.

Topics: The course starts with the theoretical basics of radar systems. Subsequently, the more complex concepts of radar systems are explained step by step in an understandable way. In addition to building up the theoretical knowledge of radar techniques, laboratory exercises on state-of-the-art millimetre-wave radar sensors will help students to grasp the practical aspects of RF sensor technology. The course covers the following topics:

  • Introduction and overview of radar techniques
  • Radar range equation
  • Radar search and detection in interference
  • Fundamentals of radio wave propagation
  • Characteristics of Clutter
  • Target reflectivity and target fluctuation models
  • Doppler phenomenology and range-Doppler spectrum
  • Acquisition of radar sensing data
  • Radar antennas
  • Digital signal processing for radar sensing
  • Moving target indication (MTI) and moving target detection (MTD)
  • Radar pulse compression and pulse integration techniques
  • Analysis of delay, range, and Doppler characteristics of radar sensing data.

This lecture will be offered as part of the new Master’s Programme “Intelligent Data Sensing and Wireless Communications” in 2023.

Advanced Time-Frequency Signal Processing

Course description: This course deals with the theory and algorithms used for time-frequency signal analysis and processing, which is a set methods based on two types of variables: time and frequency. The methods provide an alternative to traditional approaches in which either time or frequency is used independently for signal analysis. This is in contrast to time-frequency signal analysis and processing, where both the time and frequency variables are used jointly for signal analysis. This is an important feature that helps to analyse nonstationary signals whose spectral properties vary over time. The topics of time-frequency signal analysis and processing find a wide range of applications in human activity recognition systems, mobilecommunications, radar, and biomedical engineering. For example, time-frequency analysis methods can be used to solve classical signal processing problems such as noise reduction or detrending. They can also be a part of recognition systems to extract features in the time-frequency domain. Moreover, time-frequency analysis methods can be used for enhancing images from radars and sonars as well as for signal detection and image segmentation.

Topics: The course starts with an introduction to time-frequency signal analysis and processing. After showing the advantages of these techniques, the concept of the short-time Fourier transform is introduced, which is one of the most commonly used methods of time-frequency signal analysis. The idea behind this method is to apply the Fourier transform to a portion of the signal. Subsequently, more advanced time-frequency analysis methods, such as the wavelet transform, Wigner-Ville distribution, Rihaczek, Levin, and Page distributions, will be introduced. More specific, the course covers the following topics:

  • Introduction and overview of time-frequency signal processing
  • Limitations of traditional signal representations
  • Benefits of joint time-frequency representations
  • Analytic signals and Hilbert transform
  • Instantaneous frequency and spectral delay
  • Wigner distribution and Wigner-Ville distribution
  • Spectra of nonstationary random processes
  • Short-time Fourier transform and spectrogram
  • Time-varying power spectral densities
  • Rihaczek, Levin, and Page distributions
  • Formulations of quadratic time-frequency distributions (TFDs)
  • Properties of quadratic TFDs
  • Relationship between TFDs using time-lag kernels
  • Cohen’s class of TFDs
  • Ambiguity function of radar systems

This lecture will be offered as part of the new Master’s Programme “Intelligent Data Sensing and Wireless Communications” in 2023.

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