Eigen-Inference for Energy Estimation of Multiple Sources

TL;DR

This paper presents a novel, computationally efficient method for blindly estimating the transmit power of multiple sources in multi-antenna fading channels using large-dimensional random matrix theory, with proven accuracy and source separation criteria.

Contribution

It introduces a new eigen-inference technique for energy estimation that outperforms existing methods and provides criteria for sensor and sample requirements.

Findings

The proposed estimator is consistent and accurate.

It outperforms alternative power inference techniques.

The method enables source separation with minimal sensors and samples.

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.