Optimized compressed sensing matrix design for noisy communication channels
2015 (English)Conference paper (Refereed)
We investigate a power-constrained sensing matrix design problem for a compressed sensing framework. We adopt a mean square error (MSE) performance criterion for sparse source reconstruction in a system where the source-to-sensor channel and the sensor-to-decoder communication channel are noisy. Our proposed sensing matrix design procedure relies upon minimizing a lower-bound on the MSE. Under certain conditions, we derive closed-form solutions to the optimization problem. Through numerical experiments, by applying practical sparse reconstruction algorithms, we show the strength of the proposed scheme by comparing it with other relevant methods. We discuss the computational complexity of our design method, and develop an equivalent stochastic optimization method to the problem of interest that can be solved approximately with a significantly less computational burden. We illustrate that the low-complexity method still outperforms the popular competing methods.
Place, publisher, year, edition, pages
, IEEE International Conference on Communications, ISSN 1550-3607
Research subject Electrical Engineering with specialization in Signal Processing
IdentifiersURN: urn:nbn:se:uu:diva-253314ISI: 000371708104126ISBN: 9781467364324OAI: oai:DiVA.org:uu-253314DiVA: diva2:814128
IEEE International Conference on Communications (ICC), London, UK