Keynote Speakers

SSPC-K1: Georgios B. Giannakis, University of Minnesota

Sparsity and Low Rank for Inference of Cognitive Network States

Viewed through a statistical inference lens, many network analytics challenges boil down to (non-) parametric regression and classification, dimensionality reduction, or clustering. Adopting such a vantage point, this keynote presentation will put forth novel learning approaches for comprehensive situation awareness of cognitive radio (CR) networks that includes spatio-temporal sensing via RF spectrum and channel gain cartography, flagging of network anomalies, prediction of network processes, and dynamic topology inference. Key emphasis will be placed on parsimonious models leveraging sparsity, low-rank or low-dimensional manifolds, attributes that are instrumental for complexity reduction.

Sparse signal processing has extensively been used in various fields of communications. Most of the communicational signals possess the property of being sparse in some domain which can be leveraged to process them more efficiently and accurately. Using the modern techniques of sparse signal processing, we can make a great progress in the communication areas such as: sparse channel estimation, compressive spectrum sensing and wireless parameter estimation, distributed networks, smart antennas and MIMO systems, wireless sensor networks, radar systems, cognitive radio, smart green. This symposium on “ sparse signal processing for communications” aims to discuss some of the recent advances in this area.

Submissions are welcome on topics including:

• Sparsity for Smart antennas, MIMO systems, large scale MIMO, channel estimation, power allocation and beam forming
• Sparse signal processing for Big data applications and distributed networks
• Sparsity and compressive sensing in co-located/distributed radars
• Applications of Statistical sparsity models and algorithms (such as Bayesian, likelihood-based, entropy and variational Bayes) in communications
• Compressive sensing and learning in communications and wireless networks
• Sparse network theory and analysis, including dynamic (time-varying) networks and large networks
• Compressed sensing in cognitive radio, spectrum estimation, Ultra-wideband radio
• Compressive Sensing in Wireless Sensor Networks, energy harvesting, and green communications
• Sparsity-based techniques for inverse problems in different fields such as Microwave Imaging and Magnetic Resonance Imaging systems
• Sparsity for signal sampling, data compression and Analog to Digital converters

[Download the PDF Call for Papers]

Paper Submission

Prospective authors are invited to submit full-length papers, with 3 to 5 pages for technical content including figures and possible references. Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Submission is through the GlobalSIP website at http://2016.ieeeglobalsip.org/Papers.asp.

Organizing Committee

• Masoumeh Azghani, Sahand University of Technology, Tabriz, Iran
• Farokh Marvasti, Sharif University of Technology, Tehran, Iran

Technical Program Chair

• Masoumeh Azghani, Sahand University of Technology, Tabriz, Iran

Technical Program Committee

• Arash Amini, Sharif University of Technology, Tehran, Iran.
• Justin Romberg, Georgia Institute of Technology, Atlanta, United States.
• Saeed Sanei, University of Surrey, Guildford, England.
• Holger Rahut, RWTH Aachen University, Aachen, Germany.
• Holger Boche, Technische Universität München, Munich, Germany
• Mahdi Soltankotabi, University of Southern California, California, United States.
• Babak hassibi, California Institute of Technology, California, United States.
• Mohammad Ghavami, London South Bank University, London, England.