Energy-Efficient Power Loading for OFDM-based Cognitive Radio Systems with Channel Uncertainties

TL;DR

This paper introduces a new algorithm to optimize energy efficiency in OFDM-based cognitive radio systems considering channel uncertainties and limited sensing, ensuring rate and interference constraints are met.

Contribution

It presents a novel energy-efficient power loading algorithm that accounts for imperfect CSI and sensing limitations, unlike previous works.

Findings

Energy efficiency decreases with higher channel estimation errors.

The proposed method prevents excessive interference to primary users.

Accounting for sensing errors slightly reduces energy efficiency.

Abstract

In this paper, we propose a novel algorithm to optimize the energy-efficiency (EE) of orthogonal frequency division multiplexing-based cognitive radio systems under channel uncertainties. We formulate an optimization problem that guarantees a minimum required rate and a specified power budget for the secondary user (SU), while restricting the interference to primary users (PUs) in a statistical manner. The optimization problem is non-convex and it is transformed to an equivalent problem using the concept of fractional programming. Unlike all related works in the literature, we consider the effect of imperfect channel-stateinformation (CSI) on the links between the SU transmitter and receiver pairs and we additionally consider the effect of limited sensing capabilities of the SU. Since the interference constraints are met statistically, the SU transmitter does not require perfect CSI feedback from the PUs receivers. Simulation results sho w that the EE deteriorates as the channel estimation error increases. Comparisons with relevant works from the literature show that the interference thresholds at the PUs receivers can be severely exceeded and the EE is slightly deteriorated if the SU does no t account for spectrum sensing errors.