The different realistic propagation channels are faced frequently the multipath fading environments. The main goal of this system design (cognitive radio network) is to improve the efficiency of spectrum access on a n...The different realistic propagation channels are faced frequently the multipath fading environments. The main goal of this system design (cognitive radio network) is to improve the efficiency of spectrum access on a non-interfering basis. This system achieves high utilization for the limited spectrum in order to fulfill needs for all users’ demands which are considered as a problem in wireless communications due to rapidly increasing in wireless applications and service. This system is exposed to attack due to the vulnerabilities existence in this system. So, the main outcome of this paper is to investigate the performance of the cooperative sensing in cognitive radio networks under malicious attacks over different channel impairments, and to illustrate the most suitable individual probability of detection in real faded channel by using Nakagami model. This paper illustrates the effectiveness of the attacks and fading on the performance of spectrum sensing process.展开更多
Data selection-based summation fusion （DSSF） was developed to overcome the shortcomings ot previously developed likelihood ratio tests based on channel statistics （LRT-CS） for the problem of fusing censored binary...Data selection-based summation fusion （DSSF） was developed to overcome the shortcomings ot previously developed likelihood ratio tests based on channel statistics （LRT-CS） for the problem of fusing censored binary decisions transmitted over Nakagami fading channels in a wireless sensor network （WSN）. The LRT-CS relies on detection probabilities of the local sensors, while the detection probabilities are a priori unknown for uncooperative targets. Also, for Nakagami fading channels, the LRT-CS involves an infinite series, which is cumbersome for real-time application. In contrast, the DSSF only involves data comparisons and additions and does not require the detection probabilities of local sensors. Furthermore, the performance of DSSF is only slightly degraded in comparison with the LRT-CS when the detection probabilities of local sensors are a priori unknown. Therefore, the DSSF should be used in a WSN with limited resources.展开更多
文摘The different realistic propagation channels are faced frequently the multipath fading environments. The main goal of this system design (cognitive radio network) is to improve the efficiency of spectrum access on a non-interfering basis. This system achieves high utilization for the limited spectrum in order to fulfill needs for all users’ demands which are considered as a problem in wireless communications due to rapidly increasing in wireless applications and service. This system is exposed to attack due to the vulnerabilities existence in this system. So, the main outcome of this paper is to investigate the performance of the cooperative sensing in cognitive radio networks under malicious attacks over different channel impairments, and to illustrate the most suitable individual probability of detection in real faded channel by using Nakagami model. This paper illustrates the effectiveness of the attacks and fading on the performance of spectrum sensing process.
文摘Data selection-based summation fusion （DSSF） was developed to overcome the shortcomings ot previously developed likelihood ratio tests based on channel statistics （LRT-CS） for the problem of fusing censored binary decisions transmitted over Nakagami fading channels in a wireless sensor network （WSN）. The LRT-CS relies on detection probabilities of the local sensors, while the detection probabilities are a priori unknown for uncooperative targets. Also, for Nakagami fading channels, the LRT-CS involves an infinite series, which is cumbersome for real-time application. In contrast, the DSSF only involves data comparisons and additions and does not require the detection probabilities of local sensors. Furthermore, the performance of DSSF is only slightly degraded in comparison with the LRT-CS when the detection probabilities of local sensors are a priori unknown. Therefore, the DSSF should be used in a WSN with limited resources.
