Blind Digital Modulation Identification for Spatially-Correlated MIMO Systems

Hassan, Kaïs; Dayoub, Iyad; Hamouda, Walaa; Nzeza, C. Nsiala; Berbineau, Marion

doi:10.1109/twc.2011.122211.110236

Cited by 112 publications

(97 citation statements)

References 16 publications

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“…Theoretical values of κ (4,0) and κ (4,2) for different unit variance constellations are provided in Table I. For the theoretical values of the corresponding moments, the reader is referred to [1].…”

Section: System Modelmentioning

confidence: 99%

“…For the theoretical values of the corresponding moments, the reader is referred to [1]. Note that the estimates of κ (4,0) and κ (4,2) are obtained from (2) and (3) with the moments replaced by their corresponding finite sample estimates [6]. Clearly, the cumulant features cannot be directly estimated from r[k] in (1) due to the channel effects, which are required to be compensated for prior to feature estimation.…”

Section: System Modelmentioning

confidence: 99%

“…is the cross-covariance matrix ofκ (4,0) and κ (4,2) . The mean of the feature vector depends on the modulation type M q , while its covariance matrix additionally depends on the channel matrix H and the noise variance σ 2 w .…”

Section: A Classification With Perfect Channel Knowledgementioning

confidence: 99%

“…However, both algorithms exhibit a high computational complexity, which severely limits their applicability. The feature-based approach is followed in [4] and [5]. Diverse higher-order signal statistics are used as features in [4] with a neural network-based algorithm that requires prior training and is sensitive to channel estimation errors.…”

mentioning

confidence: 99%

“…The feature-based approach is followed in [4] and [5]. Diverse higher-order signal statistics are used as features in [4] with a neural network-based algorithm that requires prior training and is sensitive to channel estimation errors. The algorithm in [5] employs higher-order signal statistics with a sub-optimal criterion of decision.…”

mentioning

confidence: 99%

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A Low Complexity Modulation Classification Algorithm for MIMO Systems

et al. 2013

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Abstract-A novel algorithm is proposed for automatic modulation classification in multiple-input multiple-output spatial multiplexing systems, which employs fourth-order cumulants of the estimated transmit signal streams as discriminating features and a likelihood ratio test (LRT) for decision making. The asymptotic likelihood function of the estimated feature vector is analytically derived and used with the LRT. Hence, the algorithm can be considered as asymptotically optimal for the employed feature vector when the channel matrix and noise variance are known. Both the case with perfect channel knowledge and the practically more relevant case with blind channel estimation are considered. The results show that the proposed algorithm provides a good classification performance while exhibiting a significantly lower computational complexity when compared with conventional algorithms.

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