The automatic modulation classification (AMC) is linked to the accurate identification of a received signal modulation. The AMC represents an important part of cognitive radio (CR) systems recently envisioned to be an...
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ISBN:
(纸本)9781509001545
The automatic modulation classification (AMC) is linked to the accurate identification of a received signal modulation. The AMC represents an important part of cognitive radio (CR) systems recently envisioned to be an appropriate platform to adjust against changing work conditions. The two main distinguished streams of AMC are either by using the likelihood based (LB) statistical tests or featured based (FB) recognitions. The LB is viewed to be optimum providing that all statistical signal descriptions are available to a receiver, while the FB usually viewed as suboptimal. In some practical situations, especially when the AMC process is carried out blindly, a signal enhancement is viewed necessary to boost the detection accuracy. The multiple-input multiple-output (MIMO) antennas configuration is widely accepted as key enhancer to signal and system performance. This paper is intended to explore opportunities of the AMC detection accuracy improvements using MIMO and diversity combining settings. The probability of error detection is reformulated using diagonalized MIMO channel through singular value decomposition (SVD) realization. Simulation results show that classification performance is improved by adopting multiple antennas with appropriate signal combining configuration.
The automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful ...
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ISBN:
(纸本)9781479958290
The automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful guidance and discussion on the most practical approaches of automatic modulation classification (AMC) in cognitive radio (CR) using likelihood based (LB) statistical tests. It also suggests a novel idea of storing the known constellation sets on the receiver side using a look-up table (LUT) to detect the transmitted replica. Relevant performance measures with simulated comparisons in flat fading additive white Gaussian noise (AWGN) channels are examined. Namely, the average likelihood ratio test (ALRT), generalized LRT (GLRT) and hybrid LRT (HLRT) are particularly illustrated using linearly phase-modulated signals such as M-ary phase shift keying (MPSK) and quadrature amplitude modulation (MQAM). When the unknown signal constellation is estimated using the maximum likelihood (ML) method, results indicate that the HLRT performs well and near optimal in most situations without extra computational burden.
The automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful ...
详细信息
ISBN:
(纸本)9781479958306
The automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful guidance and discussion on the most practical approaches of automatic modulation classification (AMC) in cognitive radio (CR) using likelihood based (LB) statistical tests. It also suggests a novel idea of storing the known constellation sets on the receiver side using a look-up table (LUT) to detect the transmitted replica. Relevant performance measures with simulated comparisons in flat fading additive white Gaussian noise (AWGN) channels are examined. Namely, the average likelihood ratio test (ALRT), generalized LRT (GLRT) and hybrid LRT (HLRT) are particularly illustrated using linearly phase-modulated signals such as M-ary phase shift keying (MPSK) and quadrature amplitude modulation (MQAM). When the unknown signal constellation is estimated using the maximum likelihood (ML) method, results indicate that the HLRT performs well and near optimal in most situations without extra computational burden.
RICH-1 is a large size RICH detector in operation at the COMPASS experiment since 2001 and recently upgraded implementing a new photon detection system with increased performance. A dedicated software package has been...
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RICH-1 is a large size RICH detector in operation at the COMPASS experiment since 2001 and recently upgraded implementing a new photon detection system with increased performance. A dedicated software package has been developed to perform RICH-1 data reduction, pattern recognition and particle identification as well as a number of accessory tasks for detector studies. The software package, the algorithms implemented and the detector characterisation and performance are reported in detail. (C) 2010 Elsevier B.V. All rights reserved.
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