Communication systems fundamentals

 

Research area leader: Professor Giuseppe Durisi
Our researchers in Communication systems fundamentals are listed below.

About the research area Communication systems fundamentals

Within the research area of Communication Systems Fundamentals, we aim to find mathematically precise answers to practically relevant problems in the areas of wireless and wired communications, and signal processing. This involves the study of mathematical models for communication and signals acquisition.
 
Currently, we are targeting the theories behind next generation communication networks. Our research includes the characterization of the ultimate limit on the rate of reliable communications over wireless and wired channels, and the study of the minimum number of measurements necessary to acquire and reconstruct a given signal under a fidelity criterion.

​Information and Communication Theory       

Wireless and wired communication systems have experienced a formidable growth in the past years. The dramatic increase in the number of mobile subscribers and, in particular, of mobile broadband subscribers resulted in an enormous growth of mobile data traffic. The surge in the use of broadband applications such as video streaming and social media poses a significant challenge on all parts of current communication networks, from the core (backhaul) to the periphery (cellular base stations).
 
As already happened in the past, information theory is expected to play a significant role in tackling the challenges ahead. Information theory studies the ultimate limit on the rate of reliable communications, which is usually referred to as channel capacity.
 
Our research activity aims at developing an information theory that accounts for the impairments typically encountered in the development of wireless and wired network. Typical research questions include the characterization of the cost of acquiring and distributing channel state information and the impact on capacity of nonlinear phenomena such as phase noise. Our vision is that this theoretical effort will be instrumental for the design of next generation wireless and wired communication networks.

Compressive sensing

Compressive sensing is a recently developed theory, which asserts that signals and images that are sparse in an appropriate domain (e.g., frequency, wavelet) can be recovered from far less measurements (samples) than traditionally thought.
 
A classic problem in compressed sensing is to seek - by solving an appropriate convex-optimization problem - the sparsest representation of a given vector, as a linear combination of vectors from a certain set, usually referred to as dictionary. It turns out that this convex-optimization problem yields the unique sparsest representation of a given vector whenever the sparsity level (i.e., the number of nonzero coefficients) of the solution lies below a threshold, which depends on the properties of the dictionary.

Research projects

 
Our research activity aims at developing an information theory that accounts for the impairments typically encountered in the development of wireless and wired network. Typical research questions include the characterization of the cost of acquiring and distributing channel state information and the impact on capacity of nonlinear phenomena such as phase noise...
 

Published: Wed 05 Sep 2012. Modified: Mon 01 May 2017