Signal processing for communication
Contact: Geert Leus
Array signal processing refers both to parallel signal processing of an array of signals as it occurs in a multiple antennas/receivers situation, and to the mapping of such algorithms onto parallel hardware platforms. This topic has great relevance in a number of contexts: the mobile communication context, separation of airplane transponder signals using phased antenna arrays, and spatial filtering for radio telescope arrays.
Topics studied can be classified under "techniques" and "applications".
- Estimation and detection theory; statistical signal processing
- Array calibration and beamforming algorithms; source localization
- Performance analysis and bounds
- Distributed processing; optimization techniques
- Sampling and reconstruction theory
- Adaptive signal processing
- Space-time coding; modulation; acquisition, synchronization, tracking
- CDMA and spread spectrum; multicarrier and OFDM; UWB
- Sensor networks
- MIMO communication; wireless networks
- Radar and sonar signal processing; radio astronomy; geophysics; remote sensing
ResearchIn our work, we combine "techniques" with "applications". We work on a variety of applications, such as sensor networks for the process industry, a distributed radio telescope in space, an underwater communication system. Typically, there is a receiver (antenna array) which receives a collection of transmitted signals, perturbed by a multipath channel that may even be highly time-varying. The challenge then is to derive algorithms that estimate the channel and detect the transmitted data. Our work is theoretical: the development of new algorithms and the derivation of their performance, as well as practical: the development of experimental phased array measurement systems and the verification of the algorithms on the obtained data.
The focus of our work is as follows:
- Smart antenna technology for wireless communications: This includes research on algorithms for source separation, equalization and parameter estimation of communication signals, and application of blind source separation/equalization techniques to W-CDMA and OFDM.
- Signal processing over time-varying channels: If the transmitter and/or receiver is moving fast, a large Doppler spread makes the communication channel time-varying. This occurs in DVB-T systems (e.g., digital television received in high-speed trains), but is even more pronounced in acoustic underwater communication channels. The challenge is to estimate and compensate for these effects, especially for wideband (OFDM) signals.
- Signal processing for sensor networks: Distributed sensor systems consist of a large number of nodes with only local communication capabilities. Challenges include localization of the nodes, low-power communication protocols, and distributed estimation algorithms, where local estimates are combined to form global parameters estimates.
- Signal processing for radio astronomy: The trend in radio astronomy is to construct large arrays of small antennas. An example is the LOFAR system, where 13,000 antennas are distributed over 100 stations in The Netherlands and Germany. In the future, we will have SKA (square kilometer array, consisting of 1 million antennas) and OLFAR, a distributed radio telescope in space. Central issues for us are array calibration, interference cancellation, and image formation using array processing techniques.
Projects under this theme
Task-cognizant sparse sensing for inference
Low-cost sparse sensing designed for specific tasks
SuperGPS: Accurate timing and positioning
Accurate timing and positioning through an optical-wireless distributed time and frequency reference
Data reduction and image formation for future radio telescopes
The future SKA telescope will produce large amounts of correlation data that cannot be stored and needs to be processed quasi real-time. Image formation is the main bottleneck--can compressive sampling and advanced algebraic techniques help?
Quality of Service-driven Channel Selection for Cognitive Radio Networks
Improving the reliability of disaster relief networks using cognitive radio with strict QoS requirements.
Ultra WideBand (UWB) Radio Indoor Positioning System
How can we accomplish effective, scalable and low-cost indoor positioning systems for practical applications using UWB radio signals?
Extreme Wireless Distributed Systems
EWiDS is one of the projects of the COMMIT program, concentrating on extreme wireless distributed systems. In EWiDS, we aim at a better understanding of using wireless, user-centric sensor technology to monitor and manage the behavior of people.
Radio astronomical calibration and imaging techniques
New, larger and more complex radio telescopes bring new challenges. Foremost among these is the calibration of the data in order to remove atmospheric and instrumental effects which corrupt the exceedingly faint signals from cosmic sources.
PulsarPlane: Worldwide Air Transport Operations
We investigate if pulsar navigation for aviation is positive, and analyse the impact on aviation.
Sensing Heterogeneous Information Network Environment
How can heterogeneous resources (people, mobile sensors, fixed sensors, social media, information systems, etc.) self-organize for answering information needs?
Autonomous, self-learning, optimal and complete underwater systems
Can we develop robust, cooperative and cognitive communication for Autonomous Underwater Vehicles?
Reliable and fast wireless communication for lithography machines
Connecting a sensor network on a moving platform to a control unit; this requires high-speed links with low latency, and accurate wireless clock synchronization.
Separation of AIS Transponder Signals
AIS is a VHF communication system for ship transponders. Seen from a satellite, transponder messages overlap. The aim is to separate these using an antenna array.
Dependable Distributed Sensing Systems
The D2S2 project aims at developing an algorithmic framework for operating large-scale distributed sensor systems.
Low-frequency distributed radio telescope in space
Below 15 MHz, the ionosphere blocks EM signals from the sky. Therefore, can we design a radio telescope in space, using a swarm of inexpensive nano-satellites? Accurate localization and clock recovery is important.
Signal Processing for Self-Organizing Wireless Networks
Mathematical foundations to develop large self-organizing networks based on cognitive radio devices that are capable of sensing the radio spectrum and adapt accordingly.
Smart moving Process Environment Actuators and Sensors
Can an RF sensor network be developed for an underwater environment (chemical reaction tank)? Main issues are localization and UWB communication. This is a difficult environment for RF.
- Tue, 26 Sep 2017
- EWI HB 17.150
MSc SS Thesis Presentation
Multiway Component Analysis for the Removal of Far Ventricular Signal in Unipolar Epicardial Electrograms of Patients with Atrial Fibrillation
- Vincent van der Knaap
- Bart Coonen
- Feng Ma
- Christos Tzotzadinis
- Franklin van Putten
- Aulia Recky Soepeno
- Ashvant Mahabir
- Vinay Pathi Balaji
- Yiling Zhang (2016)
- Nambur Ramamohan Krishnaprasad (2016)
- Hongrun Zhang (2015)
- Shailja Shukla (2015)
- Francesco Giorgi (2015)
- Ruijie Zhang (2015)
- Shilpa Rao (2015)
- Yun Wang (2015)
- Samira Bahrami (2014)
- Elvin Isufi (2014)
- Shahrzad Naghibzadeh (2014)
- Joost Geelhoed (2014)
- Yongwei Wang (2014)
- Yi Lu (2014)
- Cong Nie (2014)
- Keke Hu (2014)
- Ning Pan (2014)
- Viktor Stoev (2013)
- Apostolos Kontakis (2013)
- Siavash Shakeri (2013)
- Lyubomir Zegov (2013)
- A. Ramkumar (2012)
- D. Shastry Ravishankar (2012)
- Rakshith Jagannath (2012)
- Z. Huang (2011)
- Sundeep Chepuri (2011)
- Millad Mouri Sardarabadi (2011)
- Stefan Kok (2011)
- Maxim Volkov (2010)
- Andre Raposo dos Santos Silva (2010)
- A.D. Visser (2010)
- David Caicedo (2010)
- Deheng Liu (2010)
- Shahzad Gishkori (2009)
- M.N. Khan (2009)
- Sajit Aqeel (2009)
- A.O. Adejuwon (2009)
- Bruhtesfa Godana (2009)
- Yukang Tu (2009)
- Zixia Hu (2009)