Learning-Based Routing in Cognitive Networks

Authors

  • Tahir Alyas NCBA&E, Lahore, Pakistan
  • Nadia Tabassum Virtual University, Pakistan
  • Shahid Naseem Lahore Leeds University, Lahore, Pakistan
  • Fahad Ahmed NCBA&E, Lahore, Pakistan
  • Qura Tul Ein Lahore Garrison University, Lahore, Pakistan

DOI:

https://doi.org/10.51611/iars.irj.v4i2.2014.40

Keywords:

ROUTING STRATEGIES, COGNITIVE NETWORK, PACKET ROUTING, DIRECTED ACYCLIC GRAPHS

Abstract

Intelligent Routing can influence the overall performance of a communication network’s throughput and efficiency. Routing strategies is required to adapt to changing network loads and different topologies. Learning from the network environment, in order to optimally adapt the network settings, is an essential requirement for providing efficient communication services in such environments. Cognitive networks are capable of learning and reasoning. They can energetically adapt to varying network conditions in order to optimize end-to-end performance and utilize network resources. In this paper we will focus machine learning in routing scheme that includes routing awareness, a routing reconfiguration.

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References

Friend, Daniel H. "COGNITIVE NETWORKS: FOUNDATIONS TO APPLICATIONS." Dissertation, Blacksburg, Virginia, 2009.
K. –C. Chen, Y. –J. Peng, N. Prasad. "COGNITIVE RADIO NETWORK ARCHITECTURE". National Science Council, Taiwan, 2008.
Edwill Nel, C.W. Omlin. "MACHINE LEARNING ALGORITHMS FOR PACKET ROUTING IN TELECOMMUNICATION NETWORKS." Bellville, South Africa.
Giorgio Quer, Hemanth Meenakshisundaram, Bheemarjuna. "USING BAYESIAN NETWORKS FOR COGNITIVE CONTROL OF MULTI-HOP WIRELESS NETWORKS." The 2010 Military Communications Conference, 2010: 6.

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Published

2014-08-29

Issue

Section

Peer Reviewed Research Manuscript

How to Cite

Alyas, T. (2014) “Learning-Based Routing in Cognitive Networks”, IARS’ International Research Journal, 4(2). doi:10.51611/iars.irj.v4i2.2014.40.

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