Greedy versus dynamic channel aggregation strategy in CRNs: Markov models and performance evaluation

Authors:
Lei Jiao;Frank Y. Li;Vicent Pla
Affiliations:
Dept. of Information and Communication Technology, University of Agder, Norway;Dept. of Information and Communication Technology, University of Agder, Norway;Dept. of Communications, Universitat Politècnica de València, València, Spain
Venue:
NETWORKING'11 Proceedings of the IFIP TC 6th international conference on Networking
Year:
2011

Citing 7
Cited 0

Generating representative Web workloads for network and server performance evaluation

SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
CRAHNs: Cognitive radio ad hoc networks

Ad Hoc Networks
MAC Protocol for Opportunistic Cognitive Radio Networks with Soft Guarantees

IEEE Transactions on Mobile Computing
Analysis of cognitive radio spectrum access with finite user population

IEEE Communications Letters
Primary user behavior in cellular networks and implications for dynamic spectrum access

IEEE Communications Magazine
Comments on "Analysis of cognitive radio spectrum access with optimal channel reservation"

IEEE Communications Letters
HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In cognitive radio networks, channel aggregation techniques which aggregate several channels together as one channel have been proposed in many MAC protocols. In this paper, we consider elastic data traffic and spectrum adaptation for channel aggregation, and propose two new strategies named as Greedy and Dynamic respectively. The performance of channel aggregation represented by these strategies is evaluated using continuous time Markov chain models. Moreover, simulation results based on various traffic distributions are utilized in order to evaluate the validity and preciseness of the mathematical models.