Secure Cooperative Spectrum Sensing via a Novel User-Classification Scheme in Cognitive Radios for Future Communication Technologies

Abstract

Future communication networks would be required to deliver data on a far greater scale than is known to us today, thus mandating the maximal utilization of the available radio spectrum using cognitive radios. In this paper, we have proposed a novel cooperative spectrum sensing approach for cognitive radios. In cooperative spectrum sensing, the fusion center relies on reports of the cognitive users to make a global decision. The global decision is obtained by assigning weights to the reports received from cognitive users. Computation of such weights requires prior information of the probability of detection and the probability of false alarms, which are not readily available in real scenarios. Further, the cognitive users are divided into reliable and unreliable categories based on their weighted energy by using some empirical threshold. In this paper, we propose a method to classify the cognitive users into reliable, neutral and unreliable categories without using any pre-defined or empirically-obtained threshold. Moreover, the computation of weights does not require the detection, or false alarm probabilities, or an estimate of these probabilities. Reliable cognitive users are assigned the highest weights; neutral cognitive users are assigned medium weights (less than the reliable and higher than the unreliable cognitive users' weights); and unreliable users are assigned the least weights. We show the performance improvement of our proposed method through simulations by comparing it with the conventional cooperative spectrum sensing scheme through different metrics, like receiver operating characteristic (ROC) curve and OPEN ACCESSSymmetry 2015, 7 676 mean square error. For clarity, we also show the effect of malicious users on detection probability and false alarm probability individually through simulations.

A Novel Grasshopper Optimization-based Particle Swarm Algorithm for Effective Spectrum Sensing in Cognitive Radio Networks

“…The local choices are sent to The FC as 𝐸𝐸 𝑗𝑗 in which = ,3, ⋯ , 𝑂𝑂. Due to the fact that different CR users in CSS have different sensing capacities, different local sensing outcomes are produced [44]. We employ a weight-oriented decision combination at the FC in the suggested approach.…”

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

“…Sharifi et al presented a weighted likelihood ratio rest (WLRT) in [15] to mitigate the effect of Byzantine attack, in which the collaborative weight is calculated by comparing the sensing history of each user with the reliable anchor nodes' global decision. In another work, Usman and Insoo proposed a simple, but the efficient classification method in [16] that classifies the SUs into reliable, neutral and unreliable categories. Byzantines are restricted by assigning the lowest weight, and the trustworthy SUs are encouraged by assigning higher weights accordingly.…”

Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

“…, . In CSS, the sensing capabilities of CR users are different from each other which results in different local sensing results [40]. In the proposed scheme, we use a weightbased decision combination at the FC.…”