Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

TL;DR

This paper introduces a novel 2-D sensing scheme combining spectrum and harvested power information in energy harvesting cognitive radio networks, enhancing primary user detection and power estimation accuracy.

Contribution

It is the first to jointly consider spectrum and power dimensions for primary user detection in energy harvesting cognitive radio networks, using a hidden input Markov model.

Findings

Significant performance improvement in primary user detection.

Effective primary transmit power level estimation.

Validation through theoretical analysis and simulations.

Abstract

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem.