The Dynamic Spectrum Aggregation Strategy for Cognitive Networks Based on Markov Model

TL;DR

This paper proposes a dynamic spectrum aggregation strategy using Markov prediction to enhance spectrum efficiency and network throughput in cognitive relay networks with multiple users and relays.

Contribution

It introduces a novel spectrum aggregation strategy based on Markov prediction for cooperative relay networks, improving throughput and reducing outage rates.

Findings

Spectrum prediction lowers outage rate

Strategy significantly improves network throughput

Effective in multi-user, multi-relay scenarios

Abstract

In order to meet the constantly increasing demand by mobile terminals for higher data rates with limited wireless spectrum resource, cognitive radio and spectrum aggregation technologies have attracted much attention due to its capacity in improving spectrum efficiency. Combing cognitive relay and spectrum aggregation technologies, in this paper, we propose a dynamic spectrum aggregation strategy based on the Markov Prediction of the state of spectrum for the cooperatively relay networks on a multi-user and multi-relay scenario aiming at ensuring the user channel capacity and maximizing the network throughput. The spectrum aggregation strategy is executed through two steps. First, predict the state of spectrum through Markov prediction. Based on the prediction results of state of spectrum, a spectrum aggregation strategy is proposed. Simulation results show that the spectrum prediction process can observably lower the outage rate, and the spectrum aggregation strategy can greatly improve the network throughput.