Semi-blind algorithms for automatic classification of digital modulation schemes

Wong, M. L. Dennis; Nandi, Asoke K.

doi:10.1016/j.dsp.2007.02.007

Cited by 37 publications

(15 citation statements)

References 25 publications

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“…Remark: For the time being, we assume that A, σ 2 , and the modulation sets are known at the receiver, as did in many modulation classification works [10], [11].…”

Section: A Signal Modelmentioning

confidence: 99%

Sequential Detection Aided Modulation Classification in Cognitive Radio Networks

Han¹,

Gao²,

Zhang³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we target at cognitively detecting the presence of the primary user (PU) as well as recognizing PU's signal modulation. Since the existing modulation classification methods rely on fixed sensing period which may waste time when the modulations are easier to distinguish, we propose an automatic modulation classification (AMC) approach using likelihood-based (LB) and feature-based (FB) sequential detection methods, where SU calculates the likelihood ratio (LLR) sequentially to determine whether or not to stop listening. Referring to asymptotic analysis of the sequential methods, we formulate an optimization problem and derive the minimum sensing time under a constrained misclassification rate. Simulation results demonstrate that both LB and FB methods could significantly reduce the sensing time compared to fixed sensing period method.

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“…Remark: For the time being, we assume that A, σ 2 , and the modulation sets are known at the receiver, as did in many modulation classification works [10], [11].…”

Section: A Signal Modelmentioning

confidence: 99%

Sequential Detection Aided Modulation Classification in Cognitive Radio Networks

Han¹,

Gao²,

Zhang³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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“…A similar purely distance-based classifier is proposed in [18]. Since the average distance between constellation points reduces with increasing bandwidth efficiency, this algorithm suffers from an implicit tendency to prefer higher-order modulation schemes.…”

Section: ) Likelihood Functionmentioning

confidence: 99%

Joint optimization of bit loading and modulation classification in wireless OFDM systems

Haring

Kisters

2013

2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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This paper presents a novel adaptive transmission technique for time-division duplex (TDD)-based wireless orthogonal frequency division multiplexing (OFDM) systems which jointly optimizes the bit loading algorithm at the transmitter side and the modulation classification method at the receiver side. Instead of signaling the complete bit allocation table (BAT) to the receiver, a novel maximum-a-posteriori (MAP) modulation classification algorithm is applied to increase the effective data rate. Constraints on the bit loading algorithm aid to significantly increase the classification reliability while maintaining a high link quality and a low signaling overhead. Simulation results in terms of the classification reliability and the effective bandwidth efficiency reveal the superior performance of the proposed adaptive scheme for the transmission of short bursts in a typical indoor propagation scenario.Index Terms-bit loading, modulation classification, adaptive orthogonal frequency division multiplexing, quadrature amplitude modulation

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“…The first approach usually depends on a priori knowledge such as the accurate estimation of the carrier or symbol rate [1,2] . Its result may be invalid once the error of estimation rises.…”

Section: Introductionmentioning

confidence: 99%

“…Currently there are several approaches to this problem. They are the classical decision algorithm based on the likelihood function [1,2] , pattern classification algorithm [3][4][5][6][7][8][9][10] based on the constellation recovery [4,5] and the character extraction algorithm [6,10] .…”

Section: Introductionmentioning

confidence: 99%

QAM signals recognition based on Amplitude distribution

Ren

Wei³

2011

J. Electron.(China)

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In this paper, a Quadrature Amplitude Modulation (QAM) signal recognition algorithm is proposed based on amplitude distribution of the signal. The algorithm uses envelop amplitude distribution information extracted by wavelet analysis to do modulation classification. It provides robustness for symbol rate determination. Simulation shows that it is more effective and convenient than the recognition algorithm of likelihood function at moderate Signal-to-Noise Ratio (SNR).

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Semi-blind algorithms for automatic classification of digital modulation schemes

Cited by 37 publications

References 25 publications

Sequential Detection Aided Modulation Classification in Cognitive Radio Networks

Sequential Detection Aided Modulation Classification in Cognitive Radio Networks

Joint optimization of bit loading and modulation classification in wireless OFDM systems

QAM signals recognition based on Amplitude distribution

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