Non‐cooperative burst detection and synchronisation in downlink TDMA‐based wireless communication networks

Zebarjadi, M.; Teimouri, Mehdi

doi:10.1049/iet-com.2018.5536

Cited by 12 publications

(2 citation statements)

References 35 publications

(40 reference statements)

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“…Modulation recognition technology allows the collection and extraction of signal features like the signal spectrum, instantaneous amplitude, and instantaneous phase, thus facilitating efficient signal processing. In non-cooperative communication, modulation recognition significantly alleviates the scarcity of spectrum resources, serving as an important technique in both civil and military applications [1,2]. Deep learning (DL), in its ascendancy, has fostered considerable interest in automatic modulation recognition (AMR) technology based on DL in recent years [3,4].…”

Section: Introductionmentioning

confidence: 99%

Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

Jiang,

Zeng,

Zhou

et al. 2023

Electronics

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Automatic modulation recognition (AMR) serves as a crucial component in domains such as cognitive radio and electromagnetic countermeasures, acting as a significant prerequisite for the efficient signal processing of receivers. Deep neural networks (DNNs), despite their effectiveness, are known to be vulnerable to adversarial attacks. This vulnerability has inspired the introduction of subtle interference to wireless communication signals—interference so minuscule that it is difficult for the human eye to discern. Such interference can mislead eavesdroppers into erroneous modulation pattern recognition when using DNNs, thereby camouflaging communication signal modulation patterns. Nonetheless, the majority of current camouflage methods used for electromagnetic signal modulation recognition rely on a global perturbation of the signal. They fail to consider the local agility of signal disturbance and the concealment requirements for bait signals that are intercepted by the interceptor. This paper presents a generator framework designed to produce perturbations with sparse properties. Furthermore, we introduce a method to reduce spectral loss, which minimizes the spectral difference between adversarial perturbation and the original signal. This method makes perturbation more challenging to monitor, thereby deceiving enemy electromagnetic signal modulation recognition systems. The experimental results validated that the proposed method significantly outperformed existing methods in terms of generation time. Moreover, it can generate adversarial signals characterized by high deceivability and transferability even under extremely sparse conditions.

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Section: Introductionmentioning

confidence: 99%

Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

Jiang,

Zeng,

Zhou

et al. 2023

Electronics

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show abstract

“…Consequently, it is important to investigate modulation recognition techniques for communication signals in depth. In non-cooperative communication systems [2,3], modulation recognition primarily serves to process the received signals; analyze the modulation type; and subsequently perform signal demodulation, decoding and other operations to obtain valuable information. In cooperative communication systems, modulation recognition techniques are also applied in numerous fields, including spectrum sensing [4,5], spectrum resource management [6], cognitive radio [7] and others.…”

Section: Introductionmentioning

confidence: 99%

An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction

et al. 2023

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Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of −8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes.

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The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

Zhang

et al. 2021

IEEE Access

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Non‐cooperative burst detection and synchronisation in downlink TDMA‐based wireless communication networks

Cited by 12 publications

References 35 publications

Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction

The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

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