The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Chintha Tellambura - One of the best experts on this subject based on the ideXlab platform.
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Energy Detection for spectrum sensing in cognitive radio
2014Co-Authors: Saman Atapattu, Chintha Tellambura, Hai JiangAbstract:This Springer Brief focuses on the current state-of-the-art research on spectrum sensing by using Energy Detection, a low-complexity and low-cost technique. It includes a comprehensive summary of recent research, fundamental theories, possible architectures, useful performance measurements of Energy Detection and applications of Energy Detection. Concise, practical chapters explore conventional Energy detectors, alternative forms of Energy detectors, performance measurements, diversity techniques and cooperative networks. The careful analysis enables reader to identify the most efficient techniques for improving Energy Detection performance. Energy Detection for Spectrum Sensing in Cognitive Radio is a valuable tool for researchers and practitioners interested in spectrum sensing and cognitive radio networks. Advanced-level students studying wireless communication will also benefit from this brief.
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Energy Detection of Unknown Signals in Fading and Diversity Reception
IEEE Transactions on Communications, 2011Co-Authors: Sanjeewa P Herath, Nandana Rajatheva, Chintha TellamburaAbstract:A comprehensive performance analysis of the Energy detector over fading channels with single antenna reception or with antenna diversity reception is developed. For the no-diversity case and for the maximal ratio combining (MRC) diversity case, with either Nakagami-m or Rician fading, expressions for the probability of Detection are derived by using the moment generating function (MGF) method and probability density function (PDF) method. The former, which avoids some difficulties of the latter, uses a contour integral representation of the Marcum-Q function. For the equal gain combining (EGC) diversity case, with Nakagami-m fading, expressions for the probability of Detection are derived for the cases L =2,3,4 and L >; 4, where L is the number of diversity branches. For the selection combining (SC) diversity, with Nakagami-m fading, expressions for the probability of Detection are derived for the cases L =2 and L >; 2. A discussion on the comparison between MGF and PDF methods is presented. We also derive several series truncation error bounds that allow series termination with a finite number of terms for a given figure of accuracy. These results help quantify and understand the achievable improvement in the Energy detector's performance with diversity reception. Numerical and simulation results are also provided.
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Energy Detection based cooperative spectrum sensing in cognitive radio networks
IEEE Transactions on Wireless Communications, 2011Co-Authors: Saman Atapattu, Chintha Tellambura, Hai JiangAbstract:Detection performance of an Energy detector used for cooperative spectrum sensing in a cognitive radio network is investigated over channels with both multipath fading and shadowing. The analysis focuses on two fusion strategies: data fusion and decision fusion. Under data fusion, upper bounds for average Detection probabilities are derived for four scenarios: 1) single cognitive relay; 2) multiple cognitive relays; 3) multiple cognitive relays with direct link; and 4) multi-hop cognitive relays. Under decision fusion, the exact Detection and false alarm probabilities are derived under the generalized "k-out-of-n" fusion rule at the fusion center with consideration of errors in the reporting channel due to fading. The results are extended to a multi-hop network as well. Our analysis is validated by numerical and simulation results. Although this research focuses on Rayleigh multipath fading and lognormal shadowing, the analytical framework can be extended to channels with Nakagami-m multipath fading and lognormal shadowing as well.
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performance of Energy Detection a complementary auc approach
Global Communications Conference, 2010Co-Authors: Saman Atapattu, Chintha Tellambura, Hai JiangAbstract:This paper investigates Detection capability of Energy detectors. With the help of receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), a new measure, Complementary AUC (CAUC), is introduced as a proxy for the overall Detection capability. When relays are available to help forward the target signal, the upper bound of the CAUC under Rayleigh fading channels is derived without and with a direct path. In addition, the average CAUC is discussed for Nakagami-m fading channels without and with diversity combining. The analytical results are validated by numerical examples.
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analysis of area under the roc curve of Energy Detection
IEEE Transactions on Wireless Communications, 2010Co-Authors: Saman Atapattu, Chintha Tellambura, Hai JiangAbstract:A simple figure of merit to describe the performance of an Energy detector is desirable. The area under the receiver operating characteristic (ROC) curve, denoted (AUC), is such a measure, which varies between 1/2 and 1. If the detector's performance is no better than flipping a coin, then the AUC is 1/2 , and it increases to one as the detector performance improves. However, in the wireless literature, the AUC measure has gone unnoticed. In this paper, to address this gap, we comprehensively analyze the AUC of an Energy detector with no-diversity reception and with several popular diversity schemes. The channel model is assumed to be Nakagami-m fading. First, the average AUC is derived for the case of no-diversity reception. Second, the average AUC is derived for diversity reception cases including maximal ratio combining (MRC), square-law combining (SLC) and selection combining (SC). Further, for Rayleigh fading channels, the impacts of channel estimation errors and fading correlations are analyzed. High SNR (signal-to-noise ratio) approximations and the Detection diversity gain are also derived. The analytical results are verified by numerical computations and by Monte-Carlo simulations.
Marco Chiani - One of the best experts on this subject based on the ideXlab platform.
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effects of noise power estimation on Energy Detection for cognitive radio applications
IEEE Transactions on Communications, 2011Co-Authors: Andrea Mariani, Andrea Giorgetti, Marco ChianiAbstract:An uncertain knowledge of the noise power level can severely limit the Energy detector (ED) spectrum sensing capability. In some situations this uncertainty can cause signal-to-noise ratio (SNR) penalties or even the rise of the SNR wall phenomenon. In this paper we analyze the performance of the ED with estimated noise power (ENP), addressing the threshold design and giving the conditions for the existence of the SNR wall. We derive analytical expressions for the design curves (SNR vs. observation time for a target performance) for the ENP-ED. Then we apply our analysis to cognitive radio (CR) systems where Energy Detection is used for fast sensing. For example it is shown that the SNR penalty with respect to ideal ED is of 5 log 10 (1+λ/λ) dB, when the time dedicated to noise power estimation is a multiple λ of the ED observation interval.
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snr wall for Energy Detection with noise power estimation
International Conference on Communications, 2011Co-Authors: Andrea Mariani, Andrea Giorgetti, Marco ChianiAbstract:In this work we perform an asymptotic analysis of estimated noise power (ENP) Energy detector (ED) to derive the condition for the existence of the SNR wall phenomenon. We prove that an ED with noise estimation does not exhibit the SNR wall if the variance of the estimate reduces when the observation time increases. In the absence of SNR wall, we show that the maximum slope of the design curves (SNR vs. observation time for an arbitrary target probability of false alarm (Pfa) and probability of Detection (Pd)), equal to -5 dB/decade for the ideal ED, can be reached also by an ENP-ED. Finally, we derive analytical expressions for the design curves when maximum likelihood (ML) noise power estimation is adopted, and we prove that, asymptotically, the signal-to-noise ratio (SNR) penalty with respect to ideal ED is of 1.5 dB, when the number of noise-only samples is equal to the number of observed samples.
Sanjeewa P Herath - One of the best experts on this subject based on the ideXlab platform.
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Energy Detection of Unknown Signals in Fading and Diversity Reception
IEEE Transactions on Communications, 2011Co-Authors: Sanjeewa P Herath, Nandana Rajatheva, Chintha TellamburaAbstract:A comprehensive performance analysis of the Energy detector over fading channels with single antenna reception or with antenna diversity reception is developed. For the no-diversity case and for the maximal ratio combining (MRC) diversity case, with either Nakagami-m or Rician fading, expressions for the probability of Detection are derived by using the moment generating function (MGF) method and probability density function (PDF) method. The former, which avoids some difficulties of the latter, uses a contour integral representation of the Marcum-Q function. For the equal gain combining (EGC) diversity case, with Nakagami-m fading, expressions for the probability of Detection are derived for the cases L =2,3,4 and L >; 4, where L is the number of diversity branches. For the selection combining (SC) diversity, with Nakagami-m fading, expressions for the probability of Detection are derived for the cases L =2 and L >; 2. A discussion on the comparison between MGF and PDF methods is presented. We also derive several series truncation error bounds that allow series termination with a finite number of terms for a given figure of accuracy. These results help quantify and understand the achievable improvement in the Energy detector's performance with diversity reception. Numerical and simulation results are also provided.
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unified approach for Energy Detection of unknown deterministic signal in cognitive radio over fading channels
International Conference on Communications, 2009Co-Authors: Sanjeewa P Herath, Nandana Rajatheva, Chintha TellamburaAbstract:Detection of an unknown deterministic signal by using an Energy detector is of promising for cognitive radio networks. In this paper, a new approach is proposed to analyze the performance of the Energy detector. It is based on the contour integral representation of the Marcum-Q function and the use of the moment generating function (MGF) of the signal-to-noise ratio (SNR). A new decision variable is constructed for the case of maximal ratio combining (MRC) reception. With its help and the MGF based approach, the performance of the MRC Energy detector over i.i.d. Rician fading channels is analyzed. This case is intractable with the conventional probability density function (PDF) based approach. Further the Detection probability of MRC combined Energy detector over Nakagami-m fading branches is derived. The simulation results are presented to support the developed MGF based method, decision variable formulation and derivations. The detector performance is evaluated over different fading and diversity parameters with the help of numerical and simulation examples. I. INTRODUCTION Cognitive radio technology allows unlicensed users (sec- ondary users) to dynamically use unoccupied free spectrum bands of primary users (licensed users), while avoiding in- terference to primary users. Energy Detection can be used to explore the presence of primary user transmissions and hence to identify the available spectrum holes. When using this technique, the Energy detector of the secondary user treats the received primary user transmission as an unknown deterministic signal. Hence, secondary users do not requires unauthorized, irrelevant details of the primary transmissions. Due to this application, the performance of the Energy detector using diversity reception techniques and over various wireless fading environments is of interest. The technique of detecting an unknown deterministic signal by using an Energy detector is introduced in (1). It is shown that the Detection problem is a test of binary hypotheses and statistics of the decision variable is chi-square distributed, irre- spective of whether the process model is lowpass or bandpass. Kostylev (2) extends the formulation to fading channels. In his work, average Detection (Pd) and false alarm (Pf ) probabilities over Rayleigh, Rician and Nakagami-m fading channels are presented. But the Nakagami-m channel result is limited to an integral form expression. In (3) Nakagami-m and Rician fading channels are considered. But the derivation of Nakagami-m is restricted to integer values of the shape parameter (m) while the result of Rician fading channel is limited to unity time bandwidth product (u) in the decision variable. Maximal ratio, selection and switch and stay diversity detectors are analyzed over i.i.d. Rayleigh fading channels in (3). Expressions of Pd over i.i.d. and correlated Rayleigh fading channels with square-law combining is derived in
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on the Energy Detection of unknown deterministic signal over nakagami channelswith selection combining
Canadian Conference on Electrical and Computer Engineering, 2009Co-Authors: Sanjeewa P Herath, Nandana Rajatheva, Chintha TellamburaAbstract:Blind sensing for identifying unused frequency bands is of particular interest in cognitive radio and ultra wide-band applications. Energy Detection is one such method proposed to identify the presence of an unknown band-limited deterministic signal. In this paper, by using an alternative series representation of the Marcum-Q function, the exact average Detection probability over the Nakagami-m fading channel is derived. Moreover, we formulate the decision variable of a selection diversity combined Energy detector and derive the exact average Detection and false alarm probabilities.
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analysis of equal gain combining in Energy Detection for cognitive radio over nakagami channels
Global Communications Conference, 2008Co-Authors: Sanjeewa P Herath, Nandana RajathevaAbstract:This paper addresses the problem of Energy Detection of unknown deterministic signal of a primary user in a cognitive radio environment. As an extension to the previous works, we focus on equal gain combining technique when the wireless channel is modeled as Nakagami-m. We derive series form exact expressions for probability of Detection and false alarm when the number of diversity branches are 1, 2, 3 and L ges 4. Finally, performance variation is shown against the number of diversity branches and the time bandwidth product in decision statistic with the aid of numerical results.
Przemyslaw Pawelczak - One of the best experts on this subject based on the ideXlab platform.
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multinode spectrum sensing based on Energy Detection for dynamic spectrum access
Vehicular Technology Conference, 2008Co-Authors: F E Visser, G J M Janssen, Przemyslaw PawelczakAbstract:Sharing of the frequency spectrum between licensed primary users and unlicensed secondary users (SUs) requires reliable Detection of spectrum occupancy by the SUs. Due to fading, single terminal Detection is unreliable and results in a high probability of missed Detection. This problem is solved by applying cooperative Detection. In this paper two novel Energy-based cooperative Detection methods using weighted combining for Dynamic Spectrum Access are presented and analyzed. Weighting is based on the local mean SNR and the optimum log-likelihood ratio. Simulation results show a substantial improvement for the proposed weighting methods compared to equal gain combining and hard decision combining.
Andrea Mariani - One of the best experts on this subject based on the ideXlab platform.
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effects of noise power estimation on Energy Detection for cognitive radio applications
IEEE Transactions on Communications, 2011Co-Authors: Andrea Mariani, Andrea Giorgetti, Marco ChianiAbstract:An uncertain knowledge of the noise power level can severely limit the Energy detector (ED) spectrum sensing capability. In some situations this uncertainty can cause signal-to-noise ratio (SNR) penalties or even the rise of the SNR wall phenomenon. In this paper we analyze the performance of the ED with estimated noise power (ENP), addressing the threshold design and giving the conditions for the existence of the SNR wall. We derive analytical expressions for the design curves (SNR vs. observation time for a target performance) for the ENP-ED. Then we apply our analysis to cognitive radio (CR) systems where Energy Detection is used for fast sensing. For example it is shown that the SNR penalty with respect to ideal ED is of 5 log 10 (1+λ/λ) dB, when the time dedicated to noise power estimation is a multiple λ of the ED observation interval.
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snr wall for Energy Detection with noise power estimation
International Conference on Communications, 2011Co-Authors: Andrea Mariani, Andrea Giorgetti, Marco ChianiAbstract:In this work we perform an asymptotic analysis of estimated noise power (ENP) Energy detector (ED) to derive the condition for the existence of the SNR wall phenomenon. We prove that an ED with noise estimation does not exhibit the SNR wall if the variance of the estimate reduces when the observation time increases. In the absence of SNR wall, we show that the maximum slope of the design curves (SNR vs. observation time for an arbitrary target probability of false alarm (Pfa) and probability of Detection (Pd)), equal to -5 dB/decade for the ideal ED, can be reached also by an ENP-ED. Finally, we derive analytical expressions for the design curves when maximum likelihood (ML) noise power estimation is adopted, and we prove that, asymptotically, the signal-to-noise ratio (SNR) penalty with respect to ideal ED is of 1.5 dB, when the number of noise-only samples is equal to the number of observed samples.