Digital Modulation Classification using Fuzzy Neural Networks

Zong, Wenbo; Lai, Edmund Ming-Kit; Quek, Chai

doi:10.1007/978-3-540-34353-0_7

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…In [8], the wavelet transform for feature extraction on QAM, PSK and FSK signal samples is used, while authors in [5] use features obtained from wavelet domain to perform AMC with the use of an ANN. There are also works proposing different AMC techniques based on higher order statistics and cyclostationary features from the modulated signals [4], Principal Component Analysis (PCA) [7], and ANN with Fuzzy Logic [17]. In fact, some of those pre-processing tasks demand a high computational cost, which limits its application, or even make it not practical for real-time systems with current off-the-shelf technology.…”

Section: Automatic Modulation Classification (Amc)mentioning

confidence: 99%

“…AMC techniques currently reported on literature [2]- [5], [7], [8], [10], [16], [17] employ a pre-processing module in order to extract signal features usable for classification, which may, depending on the applied mechanism, make assumptions about the received signal which may not hold (e.g. AWGN being the unique source of noise), or even can demand a high computational cost to be implemented.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Automatic Modulation Classification using Correntropy Coefficient

Fontes¹,

Cavalcante²,

Silveira³

2012

Anais De XXX Simpósio Brasileiro De Telecomunicações

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This paper deals with automatic modulation classification (AMC) of communication signals. A new method for the automatic classification using a similarity measure derived from Information Theoretic Learning (ITL), called correntropy coefficient, is proposed. Unlike many of the conventional methods, the proposed method does not require any signal pre-processing. Further, the proposed AMC technique uses a simple scheme of evaluating the correntropy coefficients, calculated over templates containing the common features of digitally modulated signals, in the classification task. The performance of the classifier is presented in the form of classification hit-rates under AWGN noisy conditions, with Signal-to-Noise Ratios (SNRs) at the range of −5 dB to 15 dB. Simulation results with binary modulations show classification hit-rates of 83% for −5 dB and 99% for 0 dB.

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