Eigen-Inference for Energy Estimation of Multiple Sources

Romain Couillet (1) , Jack W. Silverstein (2) , Zhidong Bai (3, 4) , Merouane Debbah (5)

1 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
2 NC State - North Carolina State University [Raleigh]
3 KLASMOE - Key Laboratory of Applied Statistics under Ministry of Education
4 DSAP - Department of Statistics and Applied Probability
5 Chaire Radio Flexible - Chaire Radio Flexible Alcatel-Lucent/Supélec

In this paper, a new method is introduced to blindly estimate the transmit power of multiple signal sources in multi-antenna 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.

Domaines: Théorie de l'information [cs.IT]; Théorie de l'information et codage [math.IT]

Romain Couillet, Jack W. Silverstein, Zhidong Bai, Merouane Debbah. Eigen-Inference for Energy Estimation of Multiple Sources. IEEE Transactions on Information Theory, 2011, 57 (4), pp.2420-2439. ⟨10.1109/TIT.2011.2109990⟩. ⟨hal-00553497⟩