CogMesh (Cognitive Mesh): A Bridge from Cognitive Radio towards Cognitive Networking & Cognitive Cloud


Figure: Cognitive wireless mesh networking (CogMesh) scenarios.    

Why does Cognitive Wireless Mesh Networking (CogMesh) Make Sense?

  • Cognition, Cooperation (altruistic or non-altruistic) and Competition are the basis to break up the traditional cellular architecture and enrich it by multi-hop, peer-to-peer (end-to-end), Grid, Cluster and Cloud Networking functionalities;
  • A tendency to evolve the Cognitive Radio concept towards Cognitive Cloud (CogCloud) and Cognitive Networks;
  • Cognitive Mesh (CogMesh) may be a necessary step/bridge towards the ubiquitous Cognitive Networking paradigm;
  • Based on intelligence enabled cognition and cooperation, Cognitive Mesh (CogMesh) will be able to achieve energy-efficiency (Green Communications & Networks), spectrum-efficiency (dynamic spectrum access) and IAC (interference alignment & cancellation) for the future converged heterogeneous networks.
As radio spectrum usage paradigm moving from the traditional command-and-control allocation solution to the open spectrum allocation & access scheme in heterogeneous and converged networking environment, wireless networks meet new opportunities and challenges. Accordingly, we introduce the concept of cognitive wireless mesh networking (CogMesh) and address the unique problems in such a dynamically networking environment. Basically, CogMesh is a self-organized distributed network architecture combining cognitive wireless access technologies (e.g. Cognitive Radio) with the flexible mesh (ad-hoc) structure in order to provide an integrated & converged service platform over a wide range of heterogeneous networks. CogMesh is based on opportunistic spectrum access (OSA) and featured by self-organization, self-configuration, self-protecting and self-healing. Within the CogMesh framework, in order to achieve the OSA goal, we develop the "Cloud" based master common control channel (CCC) selection scheme and the "Cluster" based networking formation techniques, which can be further extended to consensus based multiple rendezvous approach for combined "Cloud" and "Cluster" evolution in a dynamic manner. Moreover, we also show that advanced learning algorithms (e.g., reinforcement learning & Swarm Intelligence) is a good candidate for dealing with the complicated control channel problem in CogMesh.



  • Tao Chen, Honggang Zhang, G. M. Maggio, and Imrich Chlamtac, "CogMesh: A Cluster-based Cognitive Radio Network," Proc. the 2007 IEEE Symp. on New Frontiers in Dynamic Spectrum Access Networks (IEEE DySPAN 2007), Dublin, Ireland, April 17-20, 2007 (, download in PDF).
  • Tao Chen, Honggang Zhang, and Marcos Kartz, "Cloud Networking Formation in CogMesh Environment,">cs> arXiv:0904.2028 (, April 2009.
  • Xianfu Chen, Zhifeng Zhao, Honggang Zhang, and Tao Chen,“Reinforcement Learning Enhanced Iterative Power Allocation in Stochastic Cognitive Wireless Mesh Networks, ” Springer Wireless Personal Communications Journal, Special Issue on "Cognitive Radio Networks and Communications", April 2010. (
  • Tao Chen, Honggang Zhang, Xiaofei Zhou,G. M. Maggio, and Imrich Chlamtac, "CogMesh: A Cluster Based Cognitive Radio Mesh Network," Cognitive Wireless Networks: Concepts, Methodologies and Visions, Springer, August 2007 (
  • Xianfu Chen, Zhifeng Zhao, and Honggang Zhang, “Power Entangling and Matching in Cognitive Wireless Mesh Networks by Applying Conjecture Based Multi-agent QQ-learning Approach,” IEEE Globecom 2010 (MCECN Workshop), Miami, USA, December 2010. (Download in PDF)
  • Loukas Lazos, S. Liu, and Marwan Krunz, "Spectrum Opportunity-based Control Channel Assignment in Cognitive Radio Networks," Proc. the 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2009), Rome, Italy, June 2009, (
  • Alfred Asterjadhi, Nicola Baldo, and Michele Zorzi, "A Distributed Network Coded Control Channel for Multihop Cognitive Radio Networks," IEEE Network,Volume 23, Issue 4, pp. 26-32, July/August 2009.
  • Tao Chen, Honggang Zhang, Marko Hoyhty, and Marcos Kartz, "Spectrum Coexistence in Cognitive Wireless Access Networks," Proc. IEEE GLOBECOM 2009, Hawaii, USA, December 2009.
  • Xianfu Chen, Zhifeng Zhao, Tao Jiang, David Grace, and Honggang Zhang, "Inter-Cluster Connection in Cognitive Wireless Mesh Networks Based on Intelligent Network Coding," Proc. IEEE PIMRC 2009, Tokyo, Japan, September 2009.
  • K. R. Chowdhury and Ian F. Akyildiz, "Cognitive Wireless Mesh Networks with Dynamic Spectrum Access," IEEE Journal on Selected Areas in Communications (JSAC), vol.26, no.1, pp.168-181, Jan.2008.
  • Qin Xin, Yan Zhang, and Jie Xiang, “Optimal Spectrum Scheduling in Cognitive Wireless Mesh Networks,” ISWCS 2008, Iceland, October 2008.
  • Y. T. Hou, Y. Shi, and H. D. Sherali, "Spectrum Sharing for Multi-hop Networking with Cognitive Radios", IEEE Journal on Selected Areas in Communications (JSAC), , vol.26, no.1, pp.146-155, Jan. 2008.
  • G. Cheng, W. Liu, Y. Li, and W. Cheng, "Spectrum Aware on-demand Routing in Cognitive Radio Networks," IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2007), pp.571-574, April 2007.
  • Q. Wang and Heather Zheng, "Route and Spectrum Selection in Dynamic Spectrum Networks,” IEEE Consumer Communications and Networking Conference (CCNC 2006), pp.625-629, 2006.
  • C. Xin, B. Xie, and C. Shen, "A Novel Layered Graph Model for Topology Formation and Routing in Dynamic Spectrum Access Networks,” IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), pp.308- 317, 2005.
  • Lance Hester and Ahmad D. Ridley, "Cognitive Radio Networks: Not Your Father Wireless Network," The Telecommunications Review, 2008.
  • A. Attar, S. A. Ghorashi, M. Sooriyabandara, and A. H. Aghvami, "Challenges of Real-time Secondary Usage of Spectrum," Computer Networks: The International Journal of Computer and Telecommunications Networking, Volume 52, Issue 4, March 2008.
  • P. D. Sutton, K. E. Nolan, and Linda E. Doyle, "Cyclostationary Signatures in Practical Cognitive Radio Applications," IEEE Journal on Selected Areas in Communications, Vol: 26, Issue: 1, pp.13-24, January 2008.
  • Manuj Sharma, Anirudha Sahoo, and K. D. Nayak, "Channel Selection under Interference Temperature Model in Multi-hop Cognitive Mesh Networks," IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2007), April 2007 (
  • Hiroyuki Yomo , H. Yomo, P. Popovski, K. Nishimori, and Ramjee Prasad, "Cognitive Mesh Network Under Interference from Primary User Wireless," International Journal of Personal Communications, Volume 45 , Issue 3, Pages: 385-401, May 2008.



Figure: Network architecture of Cognitive Mesh (CogMesh).

Figure: A typical transceiver of Cognitive Mesh node.

Figure: Cognitive Cloud and the master Common Control Channel (CCC).