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Effective Channel Assignment Based on Dynamic Source Routing in Cognitive Radio Networks
Ying Dai, Jie Wu and Andrew Daniels

The channel assignment problem is one of the most important issues in cognitive radio networks (CRNs). Under a SINR-driven model, we consider channel assignments in a network using dynamic source routing (DSR). In this unicasting model, channel assignments are conducted in a relatively small scale of nodes, which are on the chosen route. In addition, we can make use of the route reply (RREP) message in DSR to estimate the SINR and the maximum data transmission rate of nodes on the chosen route. In this way, the source node can conduct the channel assignment in a more efficient way. We propose two algorithms for the single route and multi-route channel assignments, where the multi-route scheme uses alternative nodes to help transmitting. We give a complexity analysis of two algorithms and an extension of reducing complexity for the multi-route channel assignment algorithm. In addition, we conduct simulations of our two algorithms under networks with different densities and study the performance of our algorithms. Finally, we testify our channel assignment model of a single route in the USRP/Gnuradio testbed, and show the efficiency of our scheme using experimental results.

Keywords: Channel assignment, cognitive radio networks, dynamic source routing, piggyback, SINR estimation.

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