Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the 'single packet loss' and 'imperfect spectrum monitoring' issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.