Analysis of the Packet Loss Probability in Energy Harvesting Cognitive Radio Networks

TL;DR

This paper models energy state variations in energy harvesting cognitive radio networks using a Markovian battery model, deriving packet loss probabilities to optimize transmission policies and improve throughput.

Contribution

It introduces a novel Markovian battery model for EH secondary users and derives packet loss probabilities considering sensing inaccuracy and energy outage.

Findings

The analysis accurately predicts packet loss probabilities.

Simulation results validate the analytical model.

The method can optimize upper layer transmission policies.

Abstract

A Markovian battery model is proposed to provide the variation of energy states for energy harvesting (EH) secondary users (SUs) in the EH cognitive radio networks (CRN). Based on the proposed battery model, we derive the packet loss probability in the EH SUs due to sensing inaccuracy and energy outage. With the proposed analysis, the packet loss probability can easily be predicted and utilized to optimize the transmission policy (i.e., opportunities for successful transmission and EH) of EH SUs to improve their throughput. Especially, the proposed method can be applied to upper layer (scheduling and routing) optimization. To this end, we validate the proposed analysis through Monte-Carlo simulation and show an agreement between the analysis and simulations results.