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Optimal Algorithm for Cognitive Spectrum Decision Making

Authors:
Ming-Xue Liao
Xiao-Xin He
Xiao-Hong Jiang

Keywords: cognitive radio network; spectrum decision making; maximal biclique; dynamic threshold

Abstract:
In this paper, an optimal algorithm of spectrum decision making is presented for a real cognitive radio network with tree-based topology. All nodes of a subnet in such network have the capability in being aware of spectrum information by both energy detector based sensing and centralized cooperative sensing. After gathering sensed information, the master node will decide which frequency can be used by the subnet and which slave nodes should leave the subnet if there is no common frequency among all nodes. The problem is how to keep nodes staying in the subnet as many as possible. Traditionally, this is a combination-optimization problem. By mapping the node set and frequency set to be both parts of a bipartite graph respectively, the problem can be turned into a special case of searching for maximal bicliques. Based on a well-known LCM (Linear time Closed itemset Miner) algorithm, and using some new techniques in terms of dynamic thresholds and efficient management of closeness states, we have solved this problem for our application requiring real-time performance. For some special cases where nodes in a subnet may have different weights, our algorithm can also find an optimal solution with maximal weights in real time.

Pages: 50 to 56

Copyright: Copyright (c) IARIA, 2012

Publication date: April 29, 2012

Published in: conference

ISSN: 2308-4251

ISBN: 978-1-61208-197-7

Location: Chamonix, France

Dates: from April 29, 2012 to May 4, 2012