Resource discovery algorithms for channel aggregation in Cognitive Radio Networks

In Cognitive Radio Networks (CRNs) secondary users (SUs) are allowed to transmit data exploiting the wireless resources (i.e., channels) not utilized by licensed primary users (PUs). Efficient resource discovery for finding available channels is a key aspect for successful SUs' operation. In this paper we investigate and propose different algorithms to discover the best CRN channels that, exploited for transmission jointly as aggregate OFDM carriers, will maximize SUs' throughput. By using CRN channels statistics such as channel availability and link quality, together with current channel discovery results, SUs attempt to make the best choice at each decision point. This is choosing to continue the discovery process or stop and transmit using the aggregated resources found available so far. All the proposed algorithms run in polynomial time, which is a significant complexity reduction from the typical exponential order of optimal stopping rule algorithms. The simulation study confirms the validity of our algorithms in different CRN scenarios, comparing their throughput performance with the theoretically best achievable results.