Improving Routing Performance Using Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

The traditional fixed spectrum assignment policy, in wireless networks, has led to significant underutilization (both spatially and temporally) of some licensed spectrum bands and crowdedness of unlicensed spectrum bands. These challenges gave birth to the new spectrum utilization paradigm called “Opportunistic Spectrum Access (OSA)”. Networks that operate under this new paradigm are called Cognitive Radio Networks, named after the enabling technology of OSA. The new paradigm allows unlicensed wireless users, also called Secondary Users (SUs), to use licensed spectrum bands as long as they are not in use by their licensed users, also called Primary Users (PUs). One of the most important properties of CRNs is that channel availability changes over time depending on the activity of PUs. Therefore, SUs must be able to detect PU activity on licensed spectrum bands, in order to use those bands for data communication when they are not in use by their PUs. This nature affects many functions in the network including routing. Routing in CRNs is different from traditional network routing, since it requires spectrum availability awareness. Therefore in CRNs, all intermediate SUs must sense channels availability periodically. However, the overall sensing time over a selected route cannot be neglected. In fact, the overall transmission time for SUs along a route is reduced, due to the time spent on the required periodic sensing for these SUs. In this paper, we introduce a novel Cooperative Spectrum Sensing (CSS) strategy, in which SUs along a selected route cooperate with their neighboring SUs to monitor PUs’ activities. In our proposed strategy, a SU along the route selects a neighboring SU, if exists, to conduct spectrum sensing on its behalf for a particular channel. This selection is based on the required channel sensing time, and the remaining available time of the candidate SU. Simulation results show that the proposed model improves routing performance such that it reduces the overall required sensing time along selected routes, and therefore, the available time that SUs can offer for data transmission is increased. Also, the end-to-end delay and the achieved bottleneck link rate are enhanced.
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