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Open AccessJournal ArticleDOI

Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems

TLDR
In this paper, the authors proposed a scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth generation (5G) and beyond.
Abstract
In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes’ histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0–35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0–35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

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Citations
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Journal ArticleDOI

Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models

TL;DR: Simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs.
Journal ArticleDOI

Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models

- 01 Jan 2022 - 
TL;DR: In this paper , the authors implemented and compared digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naïve Bayes (NB) algorithms.
Journal ArticleDOI

A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

TL;DR: In this article, a new modulation recognition method based on flying fish swarm algorithm is proposed, which combines short-time Fourier transform, Choi-Williams distribution, and cyclic spectrum to complete multi-channel signal processing.
Journal ArticleDOI

Novel Successive Interference Cancellation (SIC) With Low-Complexity for GFDM Systems

TL;DR: A new model is proposed that classifies the GFDM self-interferences into two independent categories as inband and adjacent sub-carriers interferences and show their corresponding mathematical formulations and indicates that the proposed model and SIC technique considerably improve the system performance in terms of bit error rate and signal to interference ratio.
Journal ArticleDOI

Novel Successive Interference Cancellation (SIC) With Low-Complexity for GFDM Systems

- 01 Jan 2022 - 
TL;DR: In this article , the authors proposed a new model that classifies the GFDM self-interference into two independent categories as inband and adjacent sub-carriers interferences and show their corresponding mathematical formulations.
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