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

Compressive Covariance Sensing

Geert Leus

There are many engineering applications that rely on frequency or angular spectrum sensing, such as cognitive radio, radio astronomy, radar, seismic acquisition, and so on. Many of these applications do not require the reconstruction of the full signal, and can perfectly rely on an estimate of the power spectral density (PSD), or in other words, the second-order statistics of the signal. However, the large bandwidths of the involved signals lead to high sampling rates and thus high sampling costs, which can be prevented by a direct compression step carried out in the analog domain (e.g., by means of an analog-to-information converter, multi-coset sampling, analog beamforming, antenna selection, etc.). This leads to the problem of sensing the PSD or covariance using compressive observations, labeled as compressive covariance sensing (CCS). In this tutorial we will give an overview of the state-of-the-art in CCS and present its connections to compressive sensing (CS). We focus on the design constraints of the compression matrices, which are completely different as in classical CS, and elaborate on the estimation/detection techniques to sense the covariance using compressive measurements. In this context, both non-uniform and random sampling are discussed. We further elaborate on distributed CCS, where compressive measurements in one domain are fused in the dual domain, i.e., temporal compressive measurements are gathered at different spatial sensors or spatial compressive measurements from different time slots are combined. Finally, connections to super resolution techniques such as atomic norm minimization are discussed. We end this tutorial by sketching some open issues and presenting the concluding remarks.

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PhD Thesis Defence

Front-End ASICs for 3-D Ultrasound: From Beamforming to Digitization

Chao Chen

12:00 - 12:15 Introductory presentation
12:30 - 13:30 Public defense
13:45 - 14:00 Diploma ceremony
Address: Senaatszaal of the Aula Congress Center

This thesis describes the analysis, design and evaluation of front-end application-specific integrated circuits (ASICs) for 3-D medical ultrasound imaging, with the focus on the receive electronics. They are specifically designed for next-generation miniature 3-D ultrasound devices, such as transesophageal echocardiography (TEE), intracardiac echocardiography (ICE) and intravascular ultrasound (IVUS) probes. These probes, equipped with 2-D array transducers and thus the capability of volumetric visualization, are crucial for both accurate diagnosis and therapy guidance of cardiovascular diseases. However, their stringent size constraints, as well as the limited power budget, increase the difficulty in integrating in-probe electronics. The mismatch between the increasing number of transducer elements and the limited cable count that can be accommodated, also makes it challenging to acquire data from these probes. Front-end ASICs that are optimized in both system architecture and circuit-level implementation are proposed in this thesis to tackle these problems.
The techniques described in this thesis have been applied in several prototype realizations, including one LNA test chip, one PVDF readout IC, two analog beamforming ASICs and one ASIC with on-chip digitization and datalinks. All prototypes have been evaluated both electrically and acoustically. The LNA test chip achieved a noise-efficiency factor (NEF) that is 2.5 × better than the state-of-the-art. One of the analog beamforming ASIC achieved a 0.27 mW/element power efficiency with a compact layout matched to a 150 µm element pitch. This is the highest power-efficiency and smallest pitch to date, in comparison with state-of-the-art ultrasound front-end ASICs. The ASIC with integrated beamforming ADC consumed only 0.91 mW/element within the same element area. A comparison with previous digitization solutions for 3-D ultrasound shows that this work achieved a 10 × improvement in power-efficiency, as well as a 3.3 × improvement in integration density.

The dissertation can be found in the TU Delft repository:

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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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