Adaptive spectrum sensing for cognitive radios: An experimental approach

In order to avoid packet collisions in wireless communications, cognitive radio users require spectrum sensing to increase their awareness. Spectrum sensing methods must show good performance with low complexity, particularly in low-power wireless sensor networks. Motivated by the non-continuous signal patterns observed from realistic signals, an adaptive spectrum sensing algorithm that exploits information on the primary user traffic is proposed. The proposed method is based on a modified energy detector, which incorporates an estimate on the primary user occupancy. The performance of the proposed adaptive method is shown based on real ZigBee signal data obtained from a commercial transmitter, and a Universal Software Radio Peripheral (USRP) device where the spectrum sensing algorithms are executed. Our results show that this method is mostly useful in high occupancy environments with limited available free spaces. Knowledge on the primary user occupancy helps us to reduce the chance of having packet collisions and retransmissions based on a single spectrum occupation parameter, without additional statistical information. Furthermore, it allows us to decrease and adapt the spectrum sensing observation time to maintain a target performance, in terms of probability of detection and false alarm.