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

7Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

This article is free to access.

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. © 2011 IFIP International Federation for Information Processing.

References Powered by Scopus

CRAHNs: Cognitive radio ad hoc networks

1136Citations
N/AReaders
Get full text

HC-MAC: A hardware-constrained cognitive MAC for efficient spectrum management

572Citations
N/AReaders
Get full text

Analysis of cognitive radio spectrum access with optimal channel reservation

299Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Queuing method in combined channel aggregation and fragmentation strategy for dynamic spectrum access

8Citations
N/AReaders
Get full text

Analysis and performance evaluation of resource management mechanisms in heterogeneous traffic cognitive radio networks

2Citations
N/AReaders
Get full text

Erlang capacity performance evaluation of spectrum adaptation strategies in cognitive radio networks

2Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Jiao, L., Li, F. Y., & Pla, V. (2011). Greedy versus dynamic channel aggregation strategy in CRNs: Markov models and performance evaluation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6827 LNCS, pp. 22–31). https://doi.org/10.1007/978-3-642-23041-7_3

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 3

60%

Professor / Associate Prof. 2

40%

Readers' Discipline

Tooltip

Computer Science 3

50%

Business, Management and Accounting 1

17%

Nursing and Health Professions 1

17%

Engineering 1

17%

Save time finding and organizing research with Mendeley

Sign up for free