4,519 research outputs found
Energy Detection of Unknown Signals over Cascaded Fading Channels
Energy detection is a favorable mechanism in several applications relating to
the identification of deterministic unknown signals such as in radar systems
and cognitive radio communications. The present work quantifies the detrimental
effects of cascaded multipath fading on energy detection and investigates the
corresponding performance capability. A novel analytic solution is firstly
derived for a generic integral that involves a product of the Meijer
function, the Marcum function and arbitrary power terms. This solution
is subsequently employed in the derivation of an exact closed-form expression
for the average probability of detection of unknown signals over *Rayleigh
channels. The offered results are also extended to the case of square-law
selection, which is a relatively simple and effective diversity method. It is
shown that the detection performance is considerably degraded by the number of
cascaded channels and that these effects can be effectively mitigated by a
non-substantial increase of diversity branches.Comment: 12 page
Generalized detector as a spectrum sensor in cognitive radio networks
The implementation of the generalized detector (GD) in cognitive radio (CR) systems allows us to improve the spectrum sensing performance in comparison with employment of the conventional detectors. We analyze the spectrum sensing performance for the uncorrelated and spatially correlated receive antenna array elements. AddiÂŹtionally, we consider a practical case when the noise power at the output of GD linear systems (the preliminary and additional filters) is differed by value. The choice of the optimal GD threshold based on the minimum total error rate criterion is also discussed. Simulation results demonstrate superiority of GD implementation in CR sysÂŹtem as spectrum sensor in comparison with the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, and generalized likelihood ratio test (GLRT) detecto
Cognitive node selection and assignment algorithms for weighted cooperative sensing in radar systems
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Low Complexity Energy-Efficient Collaborative Spectrum Sensing for Cognitive Radio Networks
Clustering approach is considered a management technology that arranged the distributed cognitive radio users into logical groups to improve the sensing performance of the network. A lot of works in this area showed that cluster-based spectrum sensing (CBSS) technique efficiently tackled the trade-off between performance and overhead issue. By employing the tree structure of the cluster, a multilevel hierarchical cluster-based spectrum sensing (MH-CBSS) algorithm was proposed to compromise between the gained performance and incurred overhead. However, the MH-CBSS iterative algorithm incurs high computational requirements. In this thesis, an energy-efficient low computational hierarchical cluster-based algorithm is proposed which reduces the incurred computational burden. This is achieved by predetermining the number of cognitive radios (CRs) in the cluster, which provides an advantage of reducing the number of iterations of the MH-CBSS algorithm. Furthermore, for a comprehensive study, the modified algorithm is investigated over both Rayleigh and Nakagami fading channels. Simulation results show that the detection performance of the modified algorithm outperforms the MH-CBSS algorithm over Rayleigh and Nakagami fading channels. In addition, a conventional energy detection algorithm is a fixed threshold based algorithm. Therefore, the threshold should be selected properly since it significantly affects the sensing performance of energy detector. For this reason, an energy-efficient hierarchical cluster-based cooperative spectrum sensing algorithm with an adaptive threshold is proposed which enables the CR dynamically adapts its threshold to achieve the minimum total cluster error. Besides, the optimal threshold level for minimizing the overall cluster detection error rate is numerically determined. The detection performance of the proposed algorithm is presented and evaluated through simulation results
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