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.

 

[Reference]

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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).