Computationally efficient algorithms for cyclic spectral analysis

Roberts, Royston M.; Brown, W. A.; Loomis, H.H.

doi:10.1109/79.81008

Cited by 245 publications

(149 citation statements)

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“…Although we would consider comparing our approach with the techniques described in [23], there is a large difference between the employed architectures, which would need porting to the multi-core architecture used here. On the other hand, the work described in [28] strongly relies on a priori knowledge of the signal characteristics such as modulation, roll-off factor and symbol rate, while our approach does not rely on such information.…”

Section: The Resulting Tmentioning

confidence: 99%

“…In general, the feasible cyclostationarity strategies for microprocessed and battery operated devices that have been proposed so far reduce the energy consumption by reducing the area in the plane ( f, α) where the functions for extraction of cyclostationary features are calculated [23], [28].…”

Section: The Resulting Tmentioning

confidence: 99%

“…The CPD algorithm directly implements the expressions described in the theory of cyclostationarity, what is helpful to its parallelization. Furthermore, in [26], a comparative analysis among the algorithms FFT Accumulation Method (FAM) [23], Strip Spectral Correlation Analyzer (SSCA) [23] and CPD shows the equivalence of those techniques.…”

Section: Sensing By Cyclostationaritymentioning

confidence: 99%

“…Many algorithms for cyclic spectral analysis proposed in the literature (i.e. FAM, SSCA and CPD algorithms) were derived from the methods originally introduced by Gardner [23]- [25].…”

Section: Sensing By Cyclostationaritymentioning

confidence: 99%

“…This allows the detection of a primary user but also enables the identification of signal characteristics [20], [21], such as the type of modulation used. Detecting cyclostationary characteristics proves to be quite robust even in the detection of signals with low SNR, but it bares a high computational cost [22], [23]. Many algorithms for cyclic spectral analysis proposed in the literature (i.e.…”

Section: Sensing By Cyclostationaritymentioning

confidence: 99%

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Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

Lima

Barros

Silveira

et al. 2014

IEICE Trans. Commun.

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SUMMARYThe evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

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Section: The Resulting Tmentioning

confidence: 99%

Section: The Resulting Tmentioning

confidence: 99%

Section: Sensing By Cyclostationaritymentioning

confidence: 99%

“…Many algorithms for cyclic spectral analysis proposed in the literature (i.e. FAM, SSCA and CPD algorithms) were derived from the methods originally introduced by Gardner [23]- [25].…”

Section: Sensing By Cyclostationaritymentioning

confidence: 99%