2010/01/31 by Romain Couillet, Jack W. Silverstein, Jack W Silverstein +3 · 40 citations
Computer Science · Engineering · Mathematics · #Covariance matrix #Distributed Sensor Networks and Detection Algorithms #Energy (signal processing) #Estimation theory #Estimator #Fading #Field (mathematics) #Matrix (chemical analysis) #Power (physics) #Random Matrices and Applications #Simple (philosophy) #Sparse and Compressive Sensing Techniques #cs.IT #math.IT
paper · pdf · doi:10.1109/tit.2011.2109990
published in IEEE Transactions on Information Theory 57(4), 2420-2439 (Institute of Electrical and Electronics Engineers) · to appear in IEEE Trans. on Information Theory, 17 pages, 13 figures
arxiv created 2010/10/24 · openalex publication_date 2011/03/15 · openalex created_date 2016/06/24 · arxiv updated 2016/11/15 · openalex updated_date 2026/08/05
In this paper, a new method is introduced to blindly estimate the transmit power of multiple signal sources in multiantenna fading channels, when the number of sensing devices and the number of available samples are sufficiently large compared to the number of sources. Recent advances in the field of large dimensional random matrix theory are used that result in a simple and computationally efficient consistent estimator of the power of each source. A criterion to determine the minimum number of sensors and the minimum number of samples required to achieve source separation is then introduced. Simulations are performed that corroborate the theoretical claims and show that the proposed power estimator largely outperforms alternative power inference techniques.