Eigen-Inference for Energy Estimation of Multiple Sources

Abstract

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.

to appear in IEEE Trans. on Information Theory, 17 pages, 13 figures

References in corpus (1)

• Statistical eigen-inference from large Wishart matrices

Cited by in corpus (7)

• Robust Estimates of Covariance Matrices in the Large Dimensional Regime
• Statistical Inference in Large Antenna Arrays under Unknown Noise Pattern
• Performance Analysis of Tyler's Covariance Estimator
• Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations
• Estimation of the Covariance Matrix of Large Dimensional Data
• Convolution operations arising from Vandermonde matrices
• Large system analysis of a GLRT for detection with large sensor arrays in temporally white noise