Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

Cognitive Radio Networks(CRN)are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems.CRN is a promising alternative approach that allows spectrum sharing in many applications.The licensed users considered Primary Users(PU)and unlicensed users as Secondary Users(SU).Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service(QoS).Irrespective of using different optimization techniques,the same methodology is to be updated for the task.So that,learning and optimization go hand in hand.It ensures the security in CRN,risk factors in spectrum sharing to SU for secure communication.The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource.Ant Colony Optimization(ACO)is implemented to increase the overall efficiency and utilization of the CRN.ACO is used to form clusters of SUs in the co-operative spectrum sensing technique.This paper deals with threat detection and classifying threats using parameters such as unlikability,context privacy,anonymity,conditional traceability,and trade-off.In this privacy-preserving model,overall accuracy is 97.4%,and it is 9%higher than the conventional models without Privacy-Preserving Architecture(PPA).

Analysis of secondary throughput and optimization in cooperative spectrum sensing

In cooperative spectrum sensing, more secondary user makes more opportunity for detecting the vacant spectrums, which resulting the spectrum utilization improved, however much bandwidth would be occupied for sending the local observation results, inducing the reduced secondary throughput. In this paper, an adaptive algorithm for selecting detection threshold was proposed, where the maximized secondary throughput can always be achieved while assuring sufficient protection to primary user, regardless of the number of sensing users in cognitive radio networks. Theoretical analysis and simulation results validate the proposed scheme.

CR–RSS location algorithm for primary user in cognitive radio

This article introduces the classic locating method based on the receiving signal strength in the cognitive radio and puts forward a cognitive radio-receiving signal strength（CR-RSS） localization algorithm which solves the problem of secondary users locating the primary user and succeeds in estimating the primary user＇s location and transmission power. Through the establishment of cognitive radio network, evaluating the number of secondary users,sampling and the environmental factors to the results in CR-RSS approach. The consequence shows that this approach can effectively locate the primary user and the technology of localization in cognitive radio can assist network optimization.

SNR-based weighted cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing （CSS） is an approach to confront fading environment. However, in conventional cooperative spectrum sensing （CCSS）, the difference among the secondary users （SUs） is ignored when SUs suffer from different fading. In this article, a signal-to-noise ratio （SNR）-based weighted spectrum sensing scheme is proposed to improve the sensing performance. The sensing performance of the weighted spectrum sensing scheme is then derived. Considering the minor contribution of the SUs with small weighted factor, a selective CSS scheme is proposed, where SUs with low SNR are not selected into cooperative spectrum sensing. The simulation results conftrm the analytical results. And the performance of weighted scheme is better than that of conventional schemes. In the case where the SNR of SUs are randomly distributed, the performance of selective scheme is almost the same as the weighted scheme while the number of cooperative SUs is reduced to save the consumption of system resource in cooperation with little additional complexity.

ROPAS:Cross-Layer Cognitive Architecture for Mobile UWB Networks

The allocation of bandwidth to unlicensed users, without significantly increasing the interference on the existing licensed users, is a challenge for Ultra Wideband （UWB） networks. Our research work presents a novel Rake Optimization and Power Aware Scheduling （ROPAS） architecture for UWB networks. Since UWB communication is rich in multipath effects, a Rake receiver is used for path diversity. Our idea of developing an optimized Rake receiver in our ROPAS architecture stems from the intention of reducing the computation complexity in terms of the number of multiplications and additions needed for the weight derivation attached to each finger of the Rake receiver. Our proposed work uses the Cognitive Radio （CR） for dynamic channel allocation among the requesting users while limiting the average power transmitted in each sub-band. In our proposed novel ROPAS architecture, dynamic channel allocation is achieved by a CR-based cross-layer design between the PHY and Medium Access Control （MAC） layers. Additionally, the maximum number of parallel transmissions within a frame interval is formulated as an optimization problem. This optimal decision is based on the distance parameter between a transmitter-receiver pair, bit error rate and frequency of request by a particular application. Moreover, the optimization problem improvises a differentiation technique among the requesting applications by incorporating priority levels among user applications. This provides fairness and higher throughput among services with varying power constraint and data rates required for a UWB network.