“Lattice Detection of MIMO Systems”
Frem 205, Byblos campus.
The Department of Electrical and Computer Engineering is organized a seminar entitled “Lattice Detection of MIMO Systems”, by Dr. Oussama Damen, associate professor at the University of Waterloo.
Biography: Oussama Damen received his Ph.D. (in Electronics and Communications) from Telecom ParisTech, France, in October 1999. He has done post-doctoral research at the Telecom ParisTech from November 1999 to August 2000, and at the Electrical and Computer Engineering department of the University of Minnesota from September 2000 to March 2001. From March 2001 to June 2004, he was with the Electrical and Computer Engineering department of the University of Alberta, working as a Senior Research Associate of Alberta Informatics Circle of Research Excellence (ICORE). In June 2004, he joined the Electrical and Computer Engineering department of the University of Waterloo, Ontario, where he is now working as an Associate Professor and held the NSERC/Nortel Networks Associate Chair in Advanced Telecommunications from April 2005 until April 2010. He also held a visiting position at Ohio-State University in the summer of 2002 and summer 2010, EPFL, September 2010, UNIK, Norway from October 2010 to December 2010, and UAB, Barcelona in March 2011. He is a senior member of IEEE. His current research interests are in the general areas of wireless communications and coding theory with a special emphasis on lattices, coding and decoding algorithms, Network MIMO, asynchronous communications, cooperative diversity and storage systems.
Abstract: In multi-input multi-output (MIMO) systems, the outputs (i.e. received signals) can be represented as a linear combination of the inputs (transmitted signals) affected by independent coefficients (channel gains) and corrupted by additive noise (thermal noise at each receiver). When using integer constellations such as QAM or PAM, the optimal detection problem in the presence of Gaussian noise is equivalent to the closest lattice point search (CLPS) problem. Solving the CLPS as well as the maximum likelihood (ML) optimal detection is a very difficult problem with an exponential complexity in the number of inputs.
Lattice reduction methods that find a “better conditioned” lattice basis, have proved themselves to be powerful tools in approximating the CLPS problem while keeping a polynomial complexity. It is shown that the use of lattice reduction methods significantly improves the performance of suboptimal detection algorithms such as zero-forcing and minimum mean square error (MMSE) filters.
The LLL algorithm introduced by Lenstra, Lenstra, and Lovasz is the most widely used lattice reduction method due to its efficiency in finding near orthogonal vectors with short norms. In this talk, we overview the problem of applying lattice reduction methods in order to approximate the optimal solution of detecting useful signals in the presence of additive Gaussian noise. We also discuss the extension of these methods over correlated channels and some recent advances on improving their performance using conditional optimization.