Abstract

Multiple measurements are involved in many applications including radar/sonar processing, hyper-spectral imaging, sensor networks, wireless communications, etc. In the simplest scenario where the underlying signals remain the same for all the measurement instances, multiple measurements help in averaging out the noise and hence improving the signal-to-noise performance. In the more general setting, the signals in different measurement instances can be different but somewhat dependent; the measurement processes can be designed so that the measurement operators can be distinct (Measurement Diversity); the sampling rates and consumed energy can be optimized across measurement instances (Resource Allocation). Our contribution is to exactly quantify the performance gains from measurement diversity and resource allocation in an asymptotic region. The asymptotic results provide important insights on the system design.

BIO: Dr. Wei Dai is currently a Lecturer in the Electrical and Electronic Engineering Department at Imperial College London. He received his Ph.D. degree in Electrical and Computer Engineering from the University of Colorado at Boulder in 2007. From 2007 to 2010, he was a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests include sparse signal processing, wireless communications, and more recently topics in big data processing. In theoretical study, one of his works on compressive sensing reconstruction has been cited around 1000 times. On the more practical side, he was involved in the development of the first compressive sensing DNA microarray prototype in the world, and was the PI of the first hardware implementation of compressed sampling in the UK.