Modulation classification of QAM and PSK from their constellation using Genetic Algorithm and hierarchical clustering

Ahmadi, Negar; Berangi, Reza

doi:10.1109/ictta.2008.4530242

Cited by 28 publications

(12 citation statements)

References 2 publications

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“…However, as Fig. 6 shown, only when the SNR must be higher than 30dB, the recognition ratio can reaches 100% which uses Genetic Algorithm and Hierarchical Clustering identification method [6], and which also need 20-25dB to reaches 100% [7][8]. …”

Section: B Recognition Rate Under the Multipath Channel Environmentmentioning

confidence: 99%

Modulation recognition based on constellation diagram for M-QAM signals

Zhendong

Jiang

Cheng

et al. 2013

2013 IEEE 11th International Conference on Electronic Measurement &Amp; Instruments

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This paper proposed a modulation recognition algorithm for M-QAM signals by the constellation diagram which does not require the prior information. Firstly, this scheme estimates the modulation parameters. Secondly, it reconstructs the received signals' constellation and uses kmeans cluster algorithm to compute the number of the signal constellation points which are as a recognition feature used for classification. The experimental simulation results proves that this method is effective for M-QAM signals, and the correct recognition ratio reaches 100% under the condition that the SNR is higher than 15dB. So it has relatively preferable practical value.

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Section: B Recognition Rate Under the Multipath Channel Environmentmentioning

confidence: 99%

Modulation recognition based on constellation diagram for M-QAM signals

Zhendong

Jiang

Cheng

et al. 2013

2013 IEEE 11th International Conference on Electronic Measurement &Amp; Instruments

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“…Assume DA is the energy consumption for data fusion per bit in the cluster by the cluster head in each round [25]. Then the energy consumption of data fusion is given by:…”

Section: Energy Consumption For Intra Cluster Communicationmentioning

confidence: 99%

A Novel Energy-efficient Cooperative Transmission Scheme Based on V-BLAST Processing and ICA clustering for WSNs Lifetime Maximization

Kerdabadi

Ghazizadeh²

2015

AJST

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In wireless sensor network, one of the main objectives is how to improve its lifetime because the sensor nodes are generally energy-limited. Thus, providing ways to optimize energy Consumption, which ultimately increases the network lifetime, is of great importance. In this paper, we propose and evaluate an energy-efficient cooperative MIMO transmission scheme based on V-BLAST technique. In proposed scheme the clustering is done based on Imperialist Competitive Algorithm (ICA) then V-BLAST technique based virtual MIMO transmission are used. This proposed protocol performance is analytically and experimentally is compared with previous presented works. From the analysis and the simulation results, it is seen that the proposed method can reduce the energy consumption of the network and prolong the sensor networks lifetime compared with existing techniques.

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“…This diagram is extracted from the analytic form of the IF signal after down conversion to the baseband. Many features exist in literature that exploit different aspects of the constellation diagram for classification of 16, 32, and 64QAM modulations [38][39][40][41][42]. In this article, a new feature based on partitioning magnitude of constellation diagram is proposed.…”

Section: Oqpskmentioning

confidence: 99%

Automatic modulation classification of digital modulations in presence of HF noise

Hazza

Shoaib

Alshebeili

et al. 2012

EURASIP J. Adv. Signal Process.

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Designing an automatic modulation classifier (AMC) for high frequency (HF) band is a research challenge. This is due to the recent observation that noise distribution in HF band is changing over time. Existing AMCs are often designed for one type of noise distribution, e.g., additive white Gaussian noise. This means their performance is severely compromised in the presence of HF noise. Therefore, an AMC capable of mitigating the time-varying nature of HF noise is required. This article presents a robust AMC method for the classification of FSK, PSK, OQPSK, QAM, and amplitude-phase shift keying modulations in presence of HF noise using feature-based methods. Here, extracted features are insensitive to symbol synchronization and carrier frequency and phase offsets. The proposed AMC method is simple to implement as it uses decision-tree approach with pre-computed thresholds for signal classification. In addition, it is capable to classify type and order of modulation in both Gaussian and non-Gaussian environments.

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Modulation classification of QAM and PSK from their constellation using Genetic Algorithm and hierarchical clustering

Cited by 28 publications

References 2 publications

Modulation recognition based on constellation diagram for M-QAM signals

Modulation recognition based on constellation diagram for M-QAM signals

A Novel Energy-efficient Cooperative Transmission Scheme Based on V-BLAST Processing and ICA clustering for WSNs Lifetime Maximization

Automatic modulation classification of digital modulations in presence of HF noise

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