Unified Approach for Energy Detection of Unknown Deterministic Signal in Cognitive Radio Over Fading Channels

Herath, Sanjeewa P.; Rajatheva, Nandana; Tellambura, Chintha

doi:10.1109/iccw.2009.5208031

Cited by 58 publications

(35 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…35.2. For selecting cluster heads [7,8], the distances is calculated from all the cognitive users in each cluster to the fusion center, the user with the shortest distance to fusion center is cluster head (CH), then the CH sends the cooperative sensing result of its cluster to the fusion center. In this way, not only ensure the accuracy of the information transmission, but also be able to save transmission channel bandwidth.…”

Section: Cluster Algorithmmentioning

confidence: 99%

An Improved Clustering Cooperative Spectrum Sensing Algorithm

Liu

Wang

et al. 2015

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

For the fading channel effect on the performance of cooperative spectrum sensing in cognitive wireless network, an improved clustering cooperative spectrum sensing algorithm based on double-threshold energy detection is presented in this paper. Within each cluster, the cognitive user that the channel quality is best to the data fusion center (DFC) is chosen as the cluster head (CH), other cognitive users in the cluster use the double-threshold energy detection on the local position. The detective information is sent to the CH, the CH makes the decision of the cluster, and then the decision information of each cluster is sent to the DFC by each CH. The DFC use the "or" rule to fuse each clusters' results, and make a final decision whether the authorized user (AU) exists. Simulation results show that the proposed algorithm can further improve the detection probability of cognitive wireless network than traditional cooperative spectrum sensing based on double threshold detection.

show abstract

Section: Cluster Algorithmmentioning

confidence: 99%

An Improved Clustering Cooperative Spectrum Sensing Algorithm

Liu

Wang

et al. 2015

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

show abstract

“…With every iteration, the detector updates each belief value using the observation from its corresponding observed channels via (4). Based on the updated belief vector, the detector decides on the action at time t based on the following policy:…”

Section: Reduced Complexity Multiband Multi-sensor Spectrum Sensinmentioning

confidence: 99%

“…Methods based on feature detection to detect different classes of signals are studied for various applications, such as for wireless microphones and analog TV signals [1], [2], or OFDM pilot signals [3]. There is also work on exploring alternative sensing methods such as energy detection [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reduced complexity multiband multi-sensor spectrum sensing

Blostein

2011

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

View full text Add to dashboard Cite

A spectrum sensing problem in which multiple sensors are used to detect an idle period in multiple channels is considered in this paper. By casting the problem using a partially observable Markov decision process (POMDP), a sequential detection scheme that minimizes the expected detection time and false alarm is described. Recent research shows that the POMDP formulation can be applied to spectrum sensing problems that sense multiple channels using only one sensor. This paper shows that this approach can be generalized to systems that incorporate an arbitrary number of sensors. Based on this more general procedure, this paper proposes two sequential detection schemes that exploit the additional sensors to reduce the detection time in the spectrum sensing system while maintaining its false alarm rate under a desired design constraint. The performances of the two detectors are investigated through Monte Carlo simulation.

show abstract

“…More recently, Kostylev [2], Digham [3], [4], Herath [5], [6] and Atapattu [7] concentrate on the flat fading channel.…”

Section: Introductionmentioning

confidence: 99%

Performance of energy detection in NLOS frequency-selective fading channels

Bober¹,

Moorfeld²,

Jorswieck³

2011

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

View full text Add to dashboard Cite

A closed-form solution for the bit error probability for an energy detection receiver using OnOff-Keying in a frequency selective multipath fading channel with zero-mean complex-normal distributed fading amplitudes is introduced. Independent and correlated fading are considered. The presented results are valid for for all kinds of frequency selective fading channels, especially ultra wideband channels, where the Gaussian assumption for the decision statistic and the flat fading assumption do not hold. I. IntroductionAnalysis of the detection performance of energy detectors has been started by Urkowitz in 1967 [1] considering a simple AWGN channel model. More recently, Kostylev [2], Digham [3], [4], Herath [5], [6] and Atapattu [7] concentrate on the flat fading channel.These channel models only fit for narrowband systems, where the transmitted signal bandwidth is much smaller than the channel's coherence bandwidth. A wideband system, for example an ultra-wideband (UWB) receiver, uses an extreme high bandwidth, collects many different reflections and requires a more general model for channel fading: the frequency-selective fading (FSF). The product of integration time and channel bandwidth equals equals the number of signal paths distinguishable by the receiver.An independent decay coefficient is assigned to each signal path. The vector of those decay coefficients, sorted by path arrival time, represents the samples of the channel impulse response.Given free choice of the distribution of each decay coefficient, one fit this channel model to observed systems with arbitrary power spectrum. Thus this fading model is called frequency-selective.Because of the simple design of energy detection receivers, this method has been used in cognitive radio systems to detect spectrum holes [8], [9] and to transmit data for different low and high data rate UWB receivers [10], [11] including location and tracking [12].In this paper we focus on UWB energy detection receivers in FSF channels. We do not use the Gaussian assumption to simplify the decision statistics [13] of the energy detection receiver, because we do not want to limit system parameters in order to fulfil the central limit theorem and we do not limit our result to flat fading channels.

show abstract

Unified Approach for Energy Detection of Unknown Deterministic Signal in Cognitive Radio Over Fading Channels

Cited by 58 publications

References 8 publications

An Improved Clustering Cooperative Spectrum Sensing Algorithm

An Improved Clustering Cooperative Spectrum Sensing Algorithm

Reduced complexity multiband multi-sensor spectrum sensing

Performance of energy detection in NLOS frequency-selective fading channels

Contact Info

Product

Resources

About