Cooperative Spectrum Sensing under Imperfect Channel Estimation

2010 ◽  
Author(s):  
Qing-hua Shen ◽  
Xuan-li Wu ◽  
Xue-jun Sha ◽  
Nai-tong Zhang
Keyword(s):  
Channel Estimation ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Imperfect Channel Estimation

Cooperative Spectrum Sensing with Censoring of Cognitive Radios and MRC-Based Fusion in Fading and Shadowing Channels

2015 ◽  
pp. 38-67
Author(s):  
Srinivas Nallagonda ◽  
Sanjay Dhar Roy ◽  
Sumit Kundu ◽  
Gianluigi Ferrari ◽  
Riccardo Raheli
Keyword(s):  
Channel Estimation ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Minimum Mean Square Error ◽  
Cognitive Radios ◽  
Decision Fusion ◽  
Soft Data ◽  
Fading And Shadowing ◽  
Log Normal ◽  
Reporting Channels

In this chapter, the authors study the performance of Cooperative Spectrum Sensing (CSS) with soft data fusion, given by Maximal Ratio Combining (MRC)-based fusion with Weibull faded channels, and Log-normal shadowed channels. More precisely, they evaluate the performance of a MRC-based CSS with Cognitive Radios (CRs) censored on the basis of the quality of the reporting channels. The performance of CSS with two censoring schemes, namely rank-based and threshold-based, is studied in the presence of Weibull fading, Rayleigh fading, and Log-normal shadowing in the reporting channels, considering MRC fusion. The performance is compared with those of schemes based on hard decision fusion rules. Furthermore, depending on perfect or imperfect Minimum Mean Square Error (MMSE) channel estimation, the authors analyze the impact of channel estimation strategy on the censoring schemes. The performance is studied in terms of missed detection probability as a function of several network, fading, and shadowing parameters.

scholarly journals An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
S. Tephillah ◽  
J. Martin Leo Manickam
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Trust Management ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Second Phase ◽  
Common Channel ◽  
Secondary Users ◽  
Management Algorithm ◽  
Different Types

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

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