By Mohamed Ibnkahla

ISBN-10: 1466570792

ISBN-13: 9781466570795

Cooperative Cognitive Radio Networks: the full Spectrum Cycle presents a superb figuring out of the principles of cognitive radio expertise, from spectrum sensing, entry, and handoff to routing, buying and selling, and safeguard. Written in an educational type with a number of illustrative examples, this finished book:

  • Gives an summary of cognitive radio structures and explains different elements of the spectrum cycle
  • Features step by step analyses of the various algorithms and structures, supported via broad computing device simulations, figures, tables, and references
  • Fulfills the necessity for a unmarried resource of data on all elements of the spectrum cycle, together with the actual, hyperlink, medium entry, community, and alertness layers

Offering a unifying view of a number of the techniques and methodologies, Cooperative Cognitive Radio Networks: the entire Spectrum Cycle presents the cutting-edge of cognitive radio know-how, addressing all levels of the spectrum entry cycle.

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Additional resources for Cooperative cognitive radio networks : the complete spectrum cycle

Example text

In addition, the proposed cyclic-CS (C-CS) can be used to estimate the PSD at lower complexity if the PU signals are assumed to be stationary. , it does not appear when the cyclic frequency α ≠ 0). 13 Compressed sensing: The AIC (t denotes continues time domain). The previous techniques assume that the signals are discrete. For analog signals, an analog-to-information converter (AIC) is used (also known as the random demodulator) [64,65]. The AIC basically extracts the information of the signal, and because the information rate of a sparse signal is less than its Nyquist rate, AIC promises to reduce the sampling burden.

2 SB detector !  λM Detectors Thresholds (a) Y1 ( f ) y S/P FFT Y2 ( f ) YM ( f ) SB detector !  λ1 SB detector !  λ2 SB detector ! 9 (a) Filter bank structure; (b) frequency-based parallel SB detectors. banks demand many RF components, which increase the cost as well as the size of the receiver. The complexity will also be further increased if the detectors are not of the same type. 9b. This is done using a serial-to-parallel (S/P) converter and fast Fourier transform (FFT) before feeding the signal to SB detectors.

7b) In practice, few cyclic frequencies are sufficient for a good performance, and the more cyclic frequencies are used, the further the improvements [18]. The cyclostationarity detector outperforms the energy detector in several aspects. , Sv( f,α) = 0 for α ≠ 0) [19]. This helps discriminate noise from the PU signals with periodic statistical characteristics. In fact, this may help differentiate between different PU signals since different signal structures have different cyclic frequencies. 6 illustrates the performance of the first-order cyclostationarity detector, where the periodicity in the mean of the received signal is utilized.

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Cooperative cognitive radio networks : the complete spectrum cycle by Mohamed Ibnkahla


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