There has been increased interest in sparse signal reconstruction algorithms (commonly known as compressed sensing) due to their wide applicability in various fields. Recently, focus has shifted to Bayesian based approaches that are able to perform sparse recovery at much lower complexity while invoking constraint and/or a priori information about the data. In this talk we introduce a Bayesian algorithm which is able to deal with sparse signals with non?Gaussian or unknown distributions. The talk will also introduce variants of this algorithm that deal with various sparsity structures. The discussion will be illuminated with various applications including impulse noise cancellation, PAPR reduction in OFDM, feedback reduction in Broadcast channels, and massive MIMO.
Tareq Y. Al-Naffouri received the B.S. degrees in mathematics and electrical engineering (with first honors) from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, the M.S. degree in electrical engineering from Georgia Institute of Technology, Atlanta, and the Ph.D. degree in electrical engineering from Stanford University, CA, in 2004. He was a visiting scholar at the California Institute of Technology, Pasadena, from January to August 2005 and during summer 2006. He was a Fulbright Scholar at the University of Southern California from February to September 2008. He is currently an Associate Professor at the Electrical Engineering Department at King Abdullah University of Science & Technology (KAUST) and jointly at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. His research interests lie in the areas of adaptive and statistical signal processing and in compressed sensing and their applications to wireless communications, and in multiuser information theory.