Frequency-Domain Differential Energy Detection Based on Extreme Statistics for OFDM Source Sensing

Cheraghi, Parisa; Ma, Yi; Tafazolli, Rahim

doi:10.1109/vetecs.2011.5956357

Cited by 9 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was expected since noise power fluctuation increases such that it makes the edge detection used in this approach reluctant. The performance comparison is also carried out for pilot based detection [50] and differential based energy detection [51]. In order to carry out simulations for the pilot based detection, it is assumed that the pilot symbols are embedded in each OFDM block, which are equally spaced for every 16 or 32 sub-carriers.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Cheraghi

Tafazolli

et al. 2012

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-This paper presents a novel differential energy detection scheme for multi-carrier systems, which can form fast and reliable decision of spectrum availability even in very low signalto-noise ratio (SNR) environment. For example, the proposed scheme can reach 90% in probability of detection (PD) and 10% in probability of false alarm (PFA) for the SNRs as low as −21 dB, while the observation length is equivalent to 2 multi-carrier symbol duration. The underlying initiative of the proposed scheme is applying order statistics on the clustered differential energy-spectral-density (ESD) in order to exploit the channel frequency diversity inherent in high data-rate communications. Specifically, to enjoy a good frequency diversity, the clustering operation is utilized to group uncorrelated subcarriers, while, the differential operation applied onto each cluster can effectively remove the noise floor and consequently overcome the impact of noise uncertainty while exploiting the frequency diversity. More importantly, the proposed scheme is designed to allow robustness in terms of both, time and frequency offsets. In order to analytically evaluate the proposed scheme, PFA and PD for Rayleigh fading channel are derived. The closed-form expressions show a clear relationship between the sensing performance and the cluster size, which is an indicator of the diversity gain. Moreover, we are able to observe up to 10 dB gain in the performance compared to the state-of-the-art spectrum sensing schemes.Index Terms-Differential, energy detection, low signal-tonoise ratio (SNR), multi-carrier, spectrum sensing.

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Since the detection technique introduced in [51], also exploits the frequency diversity of the channel our main concern is to observe how much improvement can be delivered by the clustered-based energy detection. Fig.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Cheraghi

Tafazolli

et al. 2012

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Among these signal sensing techniques cyclostationary detection is recognized as one of the most robust techniques which can provide an excellent sensing performance [5,26,27], however, with the disadvantage of introducing a long latency into the system [28].…”

Section: Related Studymentioning

confidence: 99%

“…In case of blind spectrum sensing and stationary noise the optimal detection technique is energy detection which provides low computational complexity and is recognized for its simple architecture [28,33,34]. However, energy detection has the shortcoming that it requires perfect information of the noise variance to perform satisfac- torily and thus is susceptible to noise uncertainty at the receiver.…”

Section: Related Studymentioning

confidence: 99%

Removing non-stationary noise in spectrum sensing using matrix factorization

Bloem

Schiphorst

Slump

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Spectrum sensing is key to many applications like dynamic spectrum access (DSA) systems or telecom regulators who need to measure utilization of frequency bands. The International Telecommunication Union (ITU) recommends a 10 dB threshold above the noise to decide whether a channel is occupied or not. However, radio frequency (RF) receiver front-ends are non-ideal. This means that the obtained data is distorted with noise and imperfections from the analog front-end. As part of the front-end the automatic gain control (AGC) circuitry mainly affects the sensing performance as strong adjacent signals lift the noise level. To enhance the performance of spectrum sensing significantly we focus in this article on techniques to remove the noise caused by the AGC from the sensing data. In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis. In addition, we use live measurement results to verify the performance and to remove the effects of the AGC from the sensing data using above mentioned techniques, i.e., applied on block-wise available spectrum data. In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum usage by 60%.

show abstract

“…In this case, the cognitive radio (CR) has to estimate robustly the primary users locations in the network in order to adjust its transmission power function of the estimated location in the network. The knowledge of position information in CR system (CRS) is also an enabler of location-based beamforming as shown in [14] and also as shown in the ICT-WHERE2 project, a whole framework of location-aided 2 Journal of Sensors PHY/MAC layer design for advanced cognitive radios [15] with novel concepts of spectrum sensing techniques based on location information [16], to multicell multiuser MIMO systems with location-based CSIT [17].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

Guibène

Slock

2013

Journal of Sensors

View full text Add to dashboard Cite

We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism. In this way, we exploit sparse characteristics of primary units to be detected, both in terms of spectrum used and location occupied. The compressed sensing approach also allows to overcome hardware limitations, in terms of the incapacity to acquire measurements and signals at the Nyquist rate when the spectrum to be scanned is large. Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single formalism the two main features of cognitive sensor networks.

show abstract

Frequency-Domain Differential Energy Detection Based on Extreme Statistics for OFDM Source Sensing

Cited by 9 publications

References 11 publications

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems

Removing non-stationary noise in spectrum sensing using matrix factorization

Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

Contact Info

Product

Resources

About