A log-likelihood function-based algorithm for QAM signal classification

In order to rapidly and automatically identify the modulation level of digital amplitude modulated signals at low signal-to-noise ratio (SNR), a method of identifying the modulation levels of M-ary quadrature amplitude modulation (M-QAM) and M-ary amplitude shift keying (M-ASK) is proposed. In this method, wavelet transform with the optimal scale is used to identify the modulation levels of M-QAM and M-ASK signals. The performance of this method was investigated through simulations. Simulation results show that when the SNR is not lower than -4 dB, the percentage of correct identification of M-QAM is higher than 93 %, and when the SNR is not lower than -10 dB, the percentage of correct identification of M-ASK is higher than 90 %, using only 100 observed symbols. It shows that this method can rapidly acquire good performance at a low SNR.

show abstract

“…Label 1 is the average identification for 16-QAM and 32-QAM in Ref. [4] using 1 024 symbols. Label 2 is the identification for 16-QAM in Ref.…”

Section: Fig 2 Relationship Between Snr Gain and Wt Scalementioning

confidence: 99%

Fast identification of digital amplitude modulation level at low signal-to-noise ratio

Cao

Wei

2006

Front. Electr. Electron. Eng. China

View full text Add to dashboard Cite

show abstract

“…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%

“…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%

QAM signals recognition based on Amplitude distribution

Ren

Wei³

2011

J. Electron.(China)

View full text Add to dashboard Cite

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).

show abstract

“…EASUREMENT of the probability density function (PDF) plays a very important role in digital communication applications, such as signal detection and modulation classification [1], [2]. The amplitude probability distribution function has been found to be useful in characterizing signals and evaluating effects of interference on victim receivers [3].…”

mentioning

confidence: 99%

“…The amplitude probability distribution function has been found to be useful in characterizing signals and evaluating effects of interference on victim receivers [3]. The theoretical derivation of the PDF of the envelope of baseband digital signals in narrowband (color) Gaussian noise has been presented in the literature [2]- [6]. In this paper, we consider additive white noise channel and drive the PDF of the envelopes of the linearly modulated signals (BPSK, M-PSK, and M-QAM) as well as the PDF of the phases of M-PSK signals.…”

mentioning

confidence: 99%