Time-efficient distributed layer-2 auto-configuration for cognitive radio networks

  • Authors:

  • Srinivasan Krishnamurthy;Mansi Thoppian;Srikant Kuppa;R. Chandrasekaran;Neeraj Mittal;S. Venkatesan;Ravi Prakash

  • Affiliations:

  • Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA;Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA;Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA;Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA;Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA;Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA;Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75083, USA

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2008

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Abstract

Cognitive radios (CR) have the ability to dynamically adapt to local spectrum availability. In a network comprised of CR-enabled devices, layer-2 auto-configuration involves determining a common set of channels to facilitate communication among participating nodes. This is a unique challenge as nodes in the CR network may be unaware of (a) their neighbors and (b) the channels on which they can communicate with a neighbor. In this paper, we propose a time-efficient distributed algorithm for layer-2 auto-configuration for a CR network. Our algorithm finds the globally common channel set in 2MN+O(DN) timeslots, where each node is assigned a unique identifier from the range [1,...,N], M is the maximum number of channels available for communication, and D is the diameter of the network. All nodes know M and N. We present both diameter-aware and diameter-unaware versions of the algorithm. We then show that the proposed algorithms are efficient by proving a matching lower bound. Finally, we investigate a special case when nodes have more knowledge available at their disposal and discuss how the time-complexity of our algorithm can be improved under this case.