Signal processing for communication

Contact: Geert Leus

Signal processing for communications is an important area within signal prodcessing, as it covers around 20% of research. Among many questions, an important one is, how can multiple overlapping signals be separated? This problem is very relevant in wireless communications, where signals are overlapping in time, space and frequency (the 'cocktail party problem'). With multiple antennas and advanced separation techniques, the capacity and robustness of wireless links can greatly be improved. Similar problems are relevant in radio astronomy, where the sky signals of interest are contaminated by man-made interference (e.g., communication signals), which need to be estimated and suppressed. The resulting algorithms are formulated in terms of linear algebra operations and provide interesting test cases for embedded system design.

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


In 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.