Compressive Spectrum Sensing in Centralized Vehicular Cognitive Radio Networks

Cognitive radio enabled vehicular networks (CR-VNETs) is a new communication paradigm that enables moving vehicles to identify spectrum opportunities along busy streets and freeways. This detected spectrum may possibly lie in licensed frequency bands, and can be used for emergency communications, such as by primary responders during crises events. Spectrum sensing ensures that this spectrum is not currently occupied by licensed users, who have priority access rights. However, as the vehicles are in motion, the spectrum sensing at a given location must be completed with minimum delay, a challenge for classical energy and feature based detection schemes. This paper presents a new distributed compressive sampling technique that allows individual vehicles to report partial information to a centralized base station (BS), with an overhead of only few bytes. Thus, we tradeoff reporting time with processing complexity at the BS, which is tasked with re-constructing the overall spectrum utilization from these portions. Simulation results reveal significant improvements in detection time and accuracy, making our approach suitable for CR-VNETs.