The theoretical and computational results in the field of signal design have been of interest to both engineers and mathematicians in the last decades. Signal optimization for active sensing and communications usually deals with various measures of quality (including estimation/detection and information-theoretic criteria), and moreover, the practical condition that the employed signals must belong to a limited signal set. Such diversity of design metrics and signal constraints paves the way for many interesting research works in signal optimization. We study the latest techniques facilitating signal design for optimized actuation, sensing, and communication over constrained sets. In particular, we focus on three different methodologies:
- Alternating Projections on Converging Sets (ALPS-CS) -- an alternating projections-based approach specialized for constrained alphabets;
- Power Method-Like Iterations -- a fast approach for alphabet-constrained signal design that resembles power method; and a
- Monotonically Error-Bound Improving Technique for Optimization (MERIT)-- a novel optimization framework that lays the ground for obtaining computational data-dependent sub-optimality guarantees for the obtained solutions. The new guarantees typically outperform the a priori known guarantees of semidefinite relaxation (SDR) -- a widely used approach for constrained signal design
Mojtaba Soltanalian received the Ph.D. degree in electrical engineering (with applications in signal processing) under the supervision of Prof. Peter Stoica at the Department of Information Technology, Uppsala University, Sweden, in 2014. He is currently with California Institute of Technology. His research interests include different aspects of signal design and optimization for active sensing, communications and biology.
He has been a recipient of -or supported in part by- different research grants from the European Research Council (ERC), the Swedish Research Council (VR), and Ericsson.