Optimized Distributed Automatic Modulation Classification in Wireless Sensor Networks Using Information Theoretic Measures

Hakimi, Saeed; Hodtani, Ghosheh Abed

doi:10.1109/jsen.2017.2688240

Cited by 22 publications

(7 citation statements)

References 37 publications

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“…In a feature-level fusion mechanism, each sensing node independently extracts features from the received signal data and then transmits the extracted features to the FC, which requires all sensing nodes to be highly synchronized with each other for making the global AMC decision. The decision-level fusion mechanisms can be further split into two categories, namely, (i) optimal hard decision fusion (OHDF) mechanisms [16][17][18][19] and (ii) soft decision fusion (SDF) mechanisms [20]. In an OHDF mechanism, each sensing node makes a local decision based on its extracted features and then such a local decision is transmitted to the FC for making the global decision.…”

Section: Introductionmentioning

confidence: 99%

Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks

Yan

Zhang

Rao

et al. 2022

Sensors

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Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node’s Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by η≥0dB, our proposed new CAMC scheme’s correct classification probability Pcc could reach up close to 100%. On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime.

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Section: Introductionmentioning

confidence: 99%

Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks

Yan

Zhang

Rao

et al. 2022

Sensors

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“…Chetty and Yamin proposed a novel intelligent processing method based on soft and hard sensor fusion for obtaining better actionable intelligence from automated computer-based decision support systems [9]. Hakimi and Abed Hodtani solved the problem of digital amplitude phase modulated signals in multi-sensor systems for distributed classification problem for these sensors observing unknown signals corrupted by additive Gaussian white noise [10]. e proposed scheme improved the classification accuracy but did little to improve performance.…”

Section: Introductionmentioning

confidence: 99%

Construction of Teaching and Education Quality Improvement Model Based on Distributed Information Fusion

Zhang

2022

Mobile Information Systems

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Following the growth of educational informatization, the integrated adoption of the Internet in classroom teaching has facilitated the change of instructional pattern from the conventional instructional pattern to a novel information-based instructional pattern. Based on the background of “Internet+,” a variety of new information-based classroom teaching models have emerged. However, in practice, there are still many deficiencies due to the immature application of the model. This paper proposes to use the design-based research paradigm to construct and optimize the teaching mode, and to clarify the design principles of the teaching mode. This paper analyses and designs the English lesson instruction pattern in the distributed information convergence condition from five dimensions: theoretical foundation, online instructional setting, instructional targets, instructional events, and instructional assessment, which construct an instructional pattern. It adopts the method of combining quantitative research with qualitative research and combines the experimental method, questionnaire survey method, classroom observation method, and interview method to assist the research. Through the investigation of 122 valid data, it is shown that the overall amount of classroom codes before the experiment is 57, and the overall amount of classroom codes after the experiment is 150. There is a big difference between the two. It validates the effectiveness of the English lesson instruction pattern in distributed information integration terms in improving students’ English achievement, cooperative learning ability, information literacy, and problem-solving ability.

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“…Automatic modulation classification (AMC) is an essential technology in non-cooperative communication systems for demodulation tasks of unknown signals [1]- [4]. It has various applications, such as intercepted enemy signal recovery, adaptive modulator [5], and spectrum sensing [6], in both military and civilian strategies.…”

Section: Introductionmentioning

confidence: 99%

Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

Wang¹,

Gui²,

Ohtsuki³

et al. 2020

Preprint

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Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

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Optimized Distributed Automatic Modulation Classification in Wireless Sensor Networks Using Information Theoretic Measures

Cited by 22 publications

References 37 publications

Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks

Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks

Construction of Teaching and Education Quality Improvement Model Based on Distributed Information Fusion

Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

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