Clustering and Reinforcement-Learning-Based Routing for Cognitive Radio Networks

Yasir Saleem, Kok-Lim Alvin Yau, Hafizal Mohamad, Nordin Ramli, Mubashir Husain Rehmani, Qiang Ni

Research output: Contribution to journalArticlepeer-review

53 Citations (SciVal)


The CRN is a future generation wireless communication system that allows SUs to use the underutilized or unused spectrum, known as white spaces, in licensed spectrum with minimum interference to PUs. However, the dynamic conditions of CRNs (e.g., PUs' activities and channel availability) make routing more challenging compared to traditional wireless networks. In this tutorial, we focus on solving the routing problem in CRNs with the help of a clustering mechanism. Cluster-based routing in CRNs enhances network scalability by reducing the flooding of routing overheads, as well as network stability by reducing the effects of dynamicity of channel availability. Additionally, RL, an artificial intelligence approach, is applied as a tool to further enhance network performance. We present SMART, which is a cluster-based routing scheme designed for the CRN, and evaluate its performance via simulations in order to show the effectiveness of cluster-based routing in CRNs using RL.

Original languageEnglish
Article number8014298
Pages (from-to)146-151
Number of pages6
JournalIEEE Wireless Communications
Issue number4
Publication statusPublished - 22 Aug 2017
Externally publishedYes


  • Cognitive Radio Networks
  • Wireless Networks
  • Wireless communication
  • Routing
  • Reinforcement learning
  • Cluster-based routing


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