Recognition of QAM Signals with Low SNR Using a Combined Threshold Algorithm

Hashim, Ivan A.; Sadah, Jafar W. Abdul; Saeed, Thamir R.; Ali, Jawad K.

doi:10.1080/03772063.2014.985263

Cited by 12 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the complexity of the classification algorithm, this research suggests a different method of classifications to conventional modulation [10,11], which requires a different logarithmic operations. Yet, still it is crucial in terms of high probabilities to correct the recognition rate and less process is required to calculate the cumulant orders; at the same time, the other studies do not highlight these promising lines [21,23,[33][34][35].…”

Section: Evaluation Accuracy and Complexity With Other Methodsmentioning

confidence: 99%

“…e most commonly used features are high-order cumulants (HOC) and high-order moment (HOM) [10]. e HOC-based techniques show superior performance to classify the digital modulation signals at a low signal-to-noise ratio (SNR); these algorithms were documented in literature [11][12][13]. Nevertheless, they were not appropriate to classify higher-order M-QAM modulation formats.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Ali

Erçelebi

2019

Scientific Programming

View full text Add to dashboard Cite

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of automatic modulation classification (AMC) which deals with modulation classification and is a pattern recognition problem. However, the algorithms that focus on multilevel quadrature amplitude modulation (M-QAM) which underneath different channel scenarios is well detailed. A search of the literature revealed that few studies were performed on the classification of high-order M-QAM modulation schemes such as 128-QAM, 256-QAM, 512-QAM, and 1024-QAM. is work focuses on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting higher order cumulant's (HOC) features from input data received raw. e HOC signals were extracted under the additive white Gaussian noise (AWGN) channel with four effective parameters which were defined to distinguish the types of modulation from the set: 4-QAM∼1024-QAM. is approach makes the classifier more intelligent and improves the success rate of classification. e simulation results manifest that a very good classification rate is achieved at a low SNR of 5 dB, which was performed under conditions of statistical noisy channel models. is shows the potential of the logarithmic classifier model for the application of M-QAM signal classification. furthermore, most results were promising and showed that the logarithmic classifier works well under both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero). It can be considered as an integrated automatic modulation classification (AMC) system in order to identify the higher order of M-QAM signals that has a unique logarithmic classifier to represent higher versatility. Hence, it has a superior performance in all previous works in automatic modulation identification systems.

show abstract

Section: Evaluation Accuracy and Complexity With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Ali

Erçelebi

2019

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Higher-order constellation (HOC)-based techniques are well-known pattern recognition methods that perform well at classifying digital modulation signals when the signal-to-noise ratio (SNR) is low. However, they might not work well for classifying higher-order MQAM modulation formats [5]. The constellation diagram varies between different modulation signal types and serves as a robust signature.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Enhanced MQAM Modulation Identification in Communication Systems: A Subtractive Clustering-Based PSO-FCM Algorithm Study

Quan,

Zhang,

Luo

et al. 2024

Information

View full text Add to dashboard Cite

Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FCM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FCM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the SC-based PSO-FCM algorithm outperforms the conventional FCM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.

show abstract

“…For the AMC step, in the literature, various al-gorithms for modulation classification are considered such us Maximum-likelihood [8] [9] and order cyclic cumulants [10] [11]. In this paper, we use the KNN algorithm with the fourth-order cumulants [12] [13]. Consequently, the main contributions of our work are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Multi-user generic channel equalization and sources separation for MIMO communication systems

Farhati,

Fki,

Aïssa-El-Bey

et al. 2023

2023 IEEE Tenth International Conference on Communications and Networking (ComNet)

View full text Add to dashboard Cite

In this work, we study blind equalization techniques to mitigate inter-symbol interference (ISI), and blind sources separation (BSS) in multiple inputs multiple outputs (MIMO) communication systems. Mainly, we are interested in the multi-user generic blind equalizer (MU-GBE). A MU-GBE has no prior information about the transmission channels and the used constellations. To solve this challenge, a joint MU-GBE, based on a new multi-criteria cost function and automatic modulation classification (AMC) is proposed. The new multi-users multi-criteria cost function is based on the probability density fitting (PDF) and the Knearest neighbors (KNN) algorithm is considered for the AMC stage. This approach is implemented in its linear and nonlinear versions. For the nonlinear case, we use a neural network. Numerical results, in terms of mean square error (MSE) and symbol error rate (SER) with quadrature amplitude modulation (QAM) signals show that our multi users multi-criteria GBE is effective in alleviating the ISI.

show abstract

Recognition of QAM Signals with Low SNR Using a Combined Threshold Algorithm

Cited by 12 publications

References 4 publications

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Simulation-Enhanced MQAM Modulation Identification in Communication Systems: A Subtractive Clustering-Based PSO-FCM Algorithm Study

Multi-user generic channel equalization and sources separation for MIMO communication systems

Contact Info

Product

Resources

About