Cooperative Modulation Classification of multiple signals in Cognitive Radio Networks

Abdelbar, Mahi; Tranter, Bill; Bose, T.

doi:10.1109/icc.2014.6883531

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…Conventional MC requires signal feature extraction either at waveform level or symbol level; and the signal features can be categorized into temporal, spectral and vector-space [8], [9]. The drawbacks of feature-based MC techniques include high computational cost or high sensitivity to noise and distortion [3], [8], [10].…”

Section: Introductionmentioning

confidence: 99%

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Oyedeji¹,

Ajayi

Aladesae

2022

EJENG

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Cognitive Radio (CR) system has been adopted for efficient utilization of radio frequency spectrum. The classification of signal modulation schemes is one of the main characteristics of the CR for appropriate demodulation of sensed signals. However, conventional Modulation Classification (MC) techniques require extensive extraction of signal features, which is not often guaranteed. Thus, Deep Learning (DL) has been seen as a promising solution to this drawback in MC. This paper proposes a DL-based MC technique using the Long Short-Term Memory (LSTM) network architecture. The proposed LSTM model was trained on M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude Modulation (MQAM) signal types. The LSTM directly learns the features of a given modulation scheme of a signal sample during training. The signal samples were generated via computer simulation for Signal-to-Noise Ratios (SNRs) from -10 dB to 20 dB with an interval of 5 dB over flat fading channel and Additive white Gaussian noise (AWGN). Simulation results show that the proposed LSTM model achieves an average classification accuracy of 95% at SNRs above 0 dB.

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Section: Introductionmentioning

confidence: 99%

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Oyedeji¹,

Ajayi

Aladesae

2022

EJENG

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“…There are few researches introducing deep neuron network, such as CNN or LSTM, for the AMC in MIMO systems. In this paper, the CNN and cooperative decision rules are proposed for the cooperative AMC (Co-AMC) method in the MIMO systems, different from [11], [12], where AMC methods are based on HOC and ANN. In detail, the CNN is trained on the dataset from all of the received antennas, and then the CNN gives the recognition sub-results, based on each received antennas.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Cooperative Automatic Modulation Classification Method for MIMO Systems

Wang

Zhang

et al. 2020

IEEE Trans. Veh. Technol.

103

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Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input singleoutput (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition subresults via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

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“…This requirement calls for MC as an intermediate step before the demodulation in CR networks . Therefore, MC has been a fruitful area of study for over three decades because of its applicability to several practical problems.…”

Section: Introductionmentioning

confidence: 99%

Modulation classification for cognitive radios using stacked denoising autoencoders

Zhu

Fujii

2016

Satell Commun Network

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Summary This paper proposes a modulation classification method based on stacked denoising autoencoders (SDAE). This method can extract the modulation features automatically and classify the input signals based on the extracted features. The scenarios of rapid classification and high‐accuracy classification are considered. In a rapid classification scenario, the classification speed has priority over the classification accuracy. Therefore, a long‐symbol sequence is not attainable for this scenario. Moreover, expert features are not necessary for this scenario, simplifying the modulation classification procedure and rendering rapid classification more achievable. In addition, in a high‐accuracy classification scenario, higher cumulants are used as the expert features owing to their advantage over the other features at noise resistance. We use complex symbols rather than pulse shaped complex signals as the network inputs, simplifying the network topology and reducing the calculation overhead. The results of the average classification accuracy, the individual classification accuracy, the execution time and the influence of the signal sampling synchronization are presented, demonstrating significant performance advantages over the other methods. Copyright © 2016 John Wiley & Sons, Ltd.

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Cooperative Modulation Classification of multiple signals in Cognitive Radio Networks

Cited by 16 publications

References 23 publications

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Deep Learning-Based Cooperative Automatic Modulation Classification Method for MIMO Systems

Modulation classification for cognitive radios using stacked denoising autoencoders

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