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In this paper, we have presented different threshold computation schemes for spectrum sensing using Fixed threshold (FT), Dynamic threshold (DT), Neyman-Pearson (NP) and Bayesian detection (BD) approach for energy detection technique. Further, we have analyzed the effect of different threshold selection approaches on the spectrum sensing performance parameters. We have also analyzed the effect of prior probability of occupancy of channel over different sensing performance parameters in BD approach.

This paper introduces the concept of imperfect spectrum monitoring for realistic high-traffic cognitive radio networks (HTCRN). The effect of imperfect spectrum monitoring in the HTCRN is analyzed in term of probability of the data loss (in the form of packets) and interference at primary user (PU). In addition to this, two algorithms to compute the data-loss and interference at PU, considering various scenarios of the traffic intensity and spectrum monitoring error, are proposed. Moreover, the closed-form expression for the ratio of the achieved-throughput to the data-loss is derived. Furthermore, the simulation results are presented in order to validate the proposed work.