USRP RIO-Based Testbed for Real-Time Blind Digital Modulation Recognition in MIMO Systems

Thameur, Hayfa Ben; Dayoub, Iyad; Hamouda, Walaa

doi:10.1109/lcomm.2022.3191787

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…According to the observations of the received signal, AMC determines what modulation is being used at the transmitter. Over the past few decades, it has had various applications in both military and civilian scenarios, such as cognitive radio, signals control, spectrum management, surveillance analysis, and intelligent software-defined radios [2], [3], [4]. It is true that significant progress has been achieved using The associate editor coordinating the review of this manuscript and approving it for publication was Eyuphan Bulut .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some recent works have tackled these issues. For example, the authors in [3], [6] successfully developed a testbed based on USRP-RIO and simulated a similar scenario to real-world MIMO communication. Currently, the number of communication devices has dramatically increased due to breakthrough innovations in technology and services such as 6G and massive Multiple-Input Multiple-Output (MIMO) [7].…”

Section: Introductionmentioning

confidence: 99%

“…The FB-AMC makes feature extraction and classification. In order to extract features, expert systems carefully extract various manual features, such as wavelet-based features [9], instantaneous features [10], and statistical features [3], [11].…”

Section: Introductionmentioning

confidence: 99%

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Robust Automatic Modulation Classification in Low Signal to Noise Ratio

Lee

2023

IEEE Access

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In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

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Section: Introductionmentioning

confidence: 99%