5G Phased Arrays

International Summer School on 5G Phased Arrays

Understanding of phased array operation requires multi- disciplinary approach, which is based on the antenna array, microwave circuit and signal processing theories. By bringing these three areas together, the school provides integral approach to phased array front-ends for 5G communication systems.

At the school the phased array foundations will be considered from antenna, RF technology and signal processing points of view. Realization of 5G capabilities such as high data-rate communication link to moving objects will be discussed. The education will be concluded by a design project.

The summer school is open for all young specialists and researchers from both industry and academia. The attendees should have basic knowledge about EM, electrical circuits and signal processing (graduate courses on electromagnetic waves, electrical circuits including microwave (RF) circuits, and signal processing).


  • Foundations of antenna arrays
  • Antenna array topologies for 5G applications
  • Analog and digital beamforming in antenna arrays
  • Front-end architecture and performance
  • 5G applications and system requirements

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Active Implantable Biomedical Microsystems Course

Active Implantable Biomedical Microsystems Course

Vasiliki Giagka, Virgilio Valente, Christos Strydis, Wouter Serdijn
Delft University of Technology and Erasmus Medical Center

Course on the understanding, design and future developments of active implantable biomedical microsystems, such as cochlear implants, cardiac pacemakers, spinal cord implants, neurostimulators and bioelectronic medicine.

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Signal Processing Seminar

Time-Varying System Theory - Can it be connected to Graph Signal Processing?

Alle-Jan van der Veen

Long time ago, I was developing time-varying system theory. The rows of an "arbitrary" matrix can be viewed as impulse responses of a time-varying system. Next, there is a notion of causality, which relates to upper triangular matrices. And there is a "shift operator" which provides connections between the rows. From these ingredients, it turned out that we can develop a state-space theory where the matrix is implemented by a series of "nodes" that communicate to each other via "states". The ARMA graph filtering work of Elvin e.a. results in similar expressions, where the shift operator is the Laplacian. An open question is if this can be connected to the TV system theory? If so, we know how to do realization theory (given the responses, find a minimal realization, i.e. minimize the number of communication links) and approximation theory (given a realization, find one of lower complexity that has approximately the same response). The talk won't give the answers, but I hope it can start a discussion.

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MSc CE Thesis Presentation

Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks

Eralp Kolagasioglu

Cardiovascular diseases are the leading cause of death in the developed world. Preventing these deaths, require long term monitoring and manual inspection of ECG signals, which is a very time consuming process. Consequently, a wearable system that can automatically categorize beats is essential.

Neuromorphic machines have been introduced relatively recently in the science community. The aim of these machines is to emulate the brain. Their low power design makes them an optimal choice for a low power wearable ECG classifier.

As features are crucial in any machine learning system, this thesis aims at proposing an energy efficient feature extraction algorithm for ECG arrhythmia classification using neuromorphic machines. The feature extraction algorithm proposed in this thesis consists of the merger of a low power feature detection and a feature selection algorithm. Also, different network configurations have been investigated to achieve classification using an LSM architecture. The resulting system can accurately cluster seven beat types, has an overall classification rate of 95.5%, and consumes an estimate of 803.62 nW.

MSc SS Thesis Presentation

The cocktail party problem: GSVD-beamformers in reverberant environments

Derk-Jan Hulsinga

Hearing aids as a form of audio preprocessing is increasingly common in everyday life. The goal of this thesis is to implement a blind approach to the cocktail party problem and challenge some of the regular assumptions made in literature. We approach the problem as wideband FD-BSS. From this field of research, the common assumption of continuous activity is dropped. Instead a number of users detection is implemented as a preprocessing step and ensure the appropriate number of demixing vectors for each time frequency bin. The validity of the standard mixing model used for STFT’s is challenged by looking at the response of a linear array.

Source separation is achieved by demixing vectors based on the GSVD, derived in a model-based approach. While most permutation solvers offer an a posteriori solution for all users, we looked at finding local solutions for a single user. Combining this with the user identification called the alignment step, we conclude that the permutation problem can be reduced to selecting a demixing vector for each discrete time-frequency instance. The correlation coefficient proves to be a sufficient metric to couple reconstructions to the original data as it selects most of the active time-frequency bins.

In simulations, our demixing vectors achieve comparable inteligibility, measured by STOI, as the compared techniques and it is more robust against smaller sample sizes than the theoretically SINR optimal MVDR.

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Signal Processing Seminar

An introduction to distributed signal processing

Richard Heusdens

Due to the explosion in size and complexity of modern data sets, it is increasingly important to be able to solve problems with a very large number of features or training examples. In industry, this trend has been referred to as ‘Big Data’, and it has had a significant impact in areas as varied as artificial intelligence, internet applications, computational biology, medicine, finance, marketing, journalism, network analysis, weather forecast, telecommunication, and logistics. As a result, both the decentralized collection or storage of these data sets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this talk, we will give an introduction to the design of distributed algorithms. We will discuss the basic requirements of these algorithms, like being simple, resource efficient, scalable, robust against changes in network topology, asynchronous, etc. We will demonstrate the design of such algorithm by considering the example of distributed averaging in a sensor network.

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Signal Processing Seminar

Synchronization for underwater communications based on dual Zadoff-Chu sequences

Yiyin Wang

Abstract: Underwater acoustic channels are not only characterized by multipath propagation but also by Doppler scaling effects. These characteristics challenge the preliminary tasks of an acoustic receiver, such as timing and frequency synchronization, and Doppler scale and channel estimation. In this talk, we propose a novel preamble design based on a dual Zadoff-Chu (ZC) sequence. With the help of the well design preamble, a cyclic feature based detector is developed to bypass the requirement of channel statistic information. The Doppler scale estimation is simplified as the frequency estimation adopting the ESPRIT type algorithm. Furthermore, the special structure of the preamble facilitates the estimation of the residual carrier frequency offset (CFO), and the good correlation properties of the preamble enable a low-cost channel estimation. Therefore, with a single preamble, multiple preliminary tasks of the receiver are accomplished. Simulation results indicate the superior performance of the proposed methods.

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