A Novel Spectrum-Sensing Method Based on Maximum Cyclic Autocorrelation Selection for Cognitive Radio System

Muraoka, Kazuyuki; Ariyoshi, Masayuki; Fujii, Takeo

doi:10.1109/dyspan.2008.11

Cited by 24 publications

(13 citation statements)

References 12 publications

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“…A spectrum sensing method based on maximum cyclic autocorrelation selection has been proposed in [37], where the peak and non-peak values of the cyclic autocorrelation function are compared to determine whether the primary signal is present or not. This method does not require noise variance estimation, and is robust against noise uncertainty and interference signals.…”

Section: ) Energy Detectormentioning

confidence: 99%

Advances in cognitive radio networks: A survey

Wang

Liu

2011

IEEE J. Sel. Top. Signal Process.

1,149

107

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Abstract-With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.Index Terms-Cognitive radio (CR), platforms and standards, radio spectrum management, software radio, spectrum sensing, wireless communication.

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Section: ) Energy Detectormentioning

confidence: 99%

Advances in cognitive radio networks: A survey

Wang

Liu

2011

IEEE J. Sel. Top. Signal Process.

1,149

107

View full text Add to dashboard Cite

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“…In addition, some of the coherent sensing methods (e.g. cyclostationary detectors) require significant computing resources [9].…”

Section: Introductionmentioning

confidence: 99%

Robust Sensing of DVB-T Signals

Gaddam

Ghosh

2010

2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN)

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Following the recent Federal Communications Commission's (FCC) decision to allow unlicensed use of the television white spaces (TVWS) in the US on a secondary basis, other regulatory agencies such as Office of communications (Ofcom) will follow suit as well. Since the incumbent television signals in UK, Europe and many other countries follow the DVB-T standard, there is a need for sensing algorithms that are capable of detecting these signals at very low levels (-114 dBm). In this paper we describe sensing algorithms for extremely robust sensing of DVB-T signals. We present simulation results as well as results from the lab and field tests using real over-the-air signals to verify the algorithms.

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“…Among all sensing techniques, cyclostationarity-based feature [7] sensing has recently gained attention due to better immunity to noise uncertainty and the ability to distinguish signal types. For example, OFDM signals exhibit periodicities embedded in equally spaced sinusoidal carriers, cyclic prefix and pilot patterns [2].…”

Section: Introductionmentioning

confidence: 99%

“…For example, OFDM signals exhibit periodicities embedded in equally spaced sinusoidal carriers, cyclic prefix and pilot patterns [2]. These periodicities are exploited to detect PU through the Cyclostationary Autocorrelation Function (CAF) [7] or the Spectral Correlation Function (SCF) [2], the Fourier transform of CAF.…”

Section: Introductionmentioning

confidence: 99%

OFDM Signal Type Recognition and Adaptability Effects in Cognitive Radio Networks

Vizziello

Akyildiz

Augusti

et al. 2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-The ability of adapting to the environment in the most efficient way is a crucial issue in Cognitive Radio (CR) networks. For this purpose, an accurate estimation of the characteristics and activity of the Primary Users (PUs) is required. A system that takes into account heterogeneous PUs with several features is developed. A new scheme is integrated in the system to exploit these motleys and to improve the adaptability in CR networks. Through the proposed PU signal type recognition, the PU signal is detected and classified. The features of each PU type: the allowed interference levels, the bandwidth and the idle time, are extracted and exploited for CR adaptability effects. For this, a new CR throughput/interference adapter is proposed. The CR throughput is efficiently increased depending on the specific characteristics of PU types. Simulation results show that the proposed PU type recognition detects, distinguishes and classifies PU signals in Additive White Gaussian Noise (AWGN). It is shown that CR throughput varies with PU features for the improvement of CR adaptability.

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A Novel Spectrum-Sensing Method Based on Maximum Cyclic Autocorrelation Selection for Cognitive Radio System

Cited by 24 publications

References 12 publications

Advances in cognitive radio networks: A survey

Advances in cognitive radio networks: A survey

Robust Sensing of DVB-T Signals

OFDM Signal Type Recognition and Adaptability Effects in Cognitive Radio Networks

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