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The spectrum sensing is a key process of the cognitive radio technology in which the cognitive users identify the unutilized/underutilized primary users (PUs)/licensed users spectrum for its efficient utilization. The sensing performance of cognitive radio (CR) is generally measured in terms of false-alarm probability ( $$ P_{f} $$ ) and detection probability ( $$ P_{d} $$ ). IEEE 802.22 wireless regional area network is one of the typical cognitive radio standards to access unused licensed frequencies of TV band and according to this standard, the false-alarm probability of CR should be ≤ 0.1 and the detection probability must be ≥ 0.9. Further, the detection and false-alarm probabilities are greatly affected by the selected threshold value in the spectrum sensing approach and selection of threshold is a crucial step to yield the status (presence/absence) of PU. In most of the available literatures, the threshold is decided by fixing one parameter ( $$ P_{f} $$ or $$ P_{d} $$ ) and optimizing the other parameter ( $$ P_{d} $$ or $$ P_{f} $$ ). Moreover, at low SNR, while achieving one of the targeted sensing parameter, there is significant degradation in the other sensing parameter. Therefore, in this paper, we are motivated to decide the optimal threshold at low SNR (signal-to-noise ratio) in such a way where we can jointly achieve both sensing matrices ( $$ P_{f} $$ ≤ 0.1 and $$ P_{d} \ge 0.9 $$ ) and provided better sensing performance in comparison to that of the traditional constant false-alarm rate and constant detection rate (CDR) threshold selection approaches. Further, we have illustrated that at low SNR, the proposed optimal threshold selection approach has provided better throughput as compare to that of the threshold selected by traditional CDR approach. The proposed approach has improved throughput approximately 24.63% when compared with CDR at chosen SNR.

The imperfect spectrum monitoring (SM) is a major obstacle to detect the emergence of primary user (PU) quickly during the cognitive users’ (CUs’) data transmission which results data-loss and introduces the interference at PU. The cooperation in CUs for SM is an effective solution to improve its performance. Therefore, in this paper, a scenario, where CUs can cooperate with each other for SM is presented and have analyzed the effect of cooperation on various performance metrics namely, the data-loss, interference efficiency, and energy efficiency. An algorithm is illustrated for the computation of data-loss under various conditions of the traffic intensity of PU and probability of SM error. Moreover, the closed-form expressions of these metrics are derived for the cooperative and non-cooperative SM. Further, the simulation results are presented for various scenarios of traffic intensity, probability of SM error and channel gain between the CUs’ transmitter to PU receiver. Furthermore, the Monte-Carlo simulation results are exploited to consider the random nature of the PUs’ traffic intensity as well as to support the numerically simulated results.

In this paper, the hybrid spectrum access and prediction techniques are exploited simultaneously in the high-traffic cognitive radio communication system, in order to enhance the throughput and overcome the problem of waiting states. The hybrid spectrum access is responsible for throughput enhancement by escaping the waiting states whereas the spectrum prediction alleviates the sensing errors in the high-traffic communication environment. The closed-form expression for the throughput of cognitive user (CU) communication is derived and validated the proposed approach with the reported literature. Moreover, a new framework is proposed to conquer the sharing issues of conventional and proposed approaches. In addition to this, the performance metrics of proposed framework such as the data-loss, energy-loss of the CU and interference at the PU have been analyzed.