Poster: Anti-jamming automatic gain control and packet detection for vehicular receiver

Yeh, Shao-Ying; Chu, Jian-Ya; Hsu, Terng-Yin

doi:10.1109/vnc.2015.7385564

Cited by 3 publications

(3 citation statements)

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“…Sangho et al utilized the spectrum sensing method for interference signals to opportunistically avoid the jamming spectrum; specifically, they designed a tactical data link with cognitive anti-jamming capability for improved bit-error-rate performance [35]. Yeh et al verified the system performance of resisting impulse interference and continuous wave interference under the effect of multipath fading and shadow [36]. However, most of these conventional methods can only suppress a specific interference, and reconstruction of the interference cannot be achieved by varying the parameters; consequently, these methods fail to achieve effective and robust performance in communication systems.…”

Section: Sparse Interference Eliminationmentioning

confidence: 99%

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

Wang¹

2021

Preprint

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<p>Broadband reliable communication is a competitive 5G technology for cognitive communication scenarios, but meanwhile introduces multiform interference to existing broadband transform domain communication system (TDCS) transmission. In order to facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and TDCS signals in wireless heterogeneous networks, it is important to separated and eliminate various interference to TDCS systems. In this paper, a novel sparse learning method-based cognitive transformation framework of interference separation is formulated for accurate interference recovery, which can be efficiently solved by iteratively learning the prior sparse probability distribution of the interference support. To further improve the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning are exploited to singly recover the multiple interference and TDCS signals. Moreover, utilizing different sparsity states of spectrum analysis, the proposed novel interference separation algorithm is extended to simultaneous separation based on state evolving of approximation message passing, which iteratively learns the belief propagation posteriors and keeps shrunk by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods are effective in separating and recovering the sparse diversities of interference to TDCS systems, and significantly outperform the state-of-the-art methods.</p>

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Section: Sparse Interference Eliminationmentioning

confidence: 99%

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

Wang¹

2021

Preprint

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“…The authors of [24] realized the recovery of sparse signals by using the method of sparse dictionary, which can effectively improve the ability to deal with impulse interference and narrow band interference in OFDM system. S. Yeh et al verified the system performance of resisting impulse interference and continuous wave interference under the effect of multipath fading and shadow [25].…”

Section: B Interference Processingmentioning

confidence: 99%

“…where α > 0 and β > 0 are respective weights for row-sparse partition and element-sparse partition. In [29], the convex l 1 norm and the convex l 1,2 norm are used instead for the problem (25), which results in solving the followings…”

Section: Sparse Approximation (Sa) For Unknown Interferencementioning

confidence: 99%

Classification Methods With Signal Approximation for Unknown Interference

Wang

Huang

et al. 2020

IEEE Access

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There has been growing interest in the classification of interference types in communication systems, especially under large samples and unknown interference, which severely restrict anti-jamming performance of the system. In this paper, we present two signal approximation algorithms for classification restricted different conditions, and the transform learning label consistency (TLLC) is embedded into the evaluation owing to the imperfect performance for classification and feature library. First, the interference signals are converted into the signal feature space, and then the interference processing and feature extraction are conducted based on the Hilbert signal space theory. Second, the projection approximation (PA) for signal approximation is used to approximate the unknown interference, and the restricted projection property is demonstrated as well. Furthermore, in order to ease the restrictions, the sparse approximation (SA) for interference signals is demonstrated. Moreover, an unsupervised learning method and the unknown interference classifier are proposed based on the self-organizing map (SOM) neural network. Based on l1 minimization functions, we improve the accuracy of TLLC with sparse approximation, which is more suitable for general interference signals. Finally, the simulation results demonstrate that, compared with the traditional classification method, the proposed method improves the classification accuracy of known interference by 3.44%. In this case, the overall accuracy is close to that of the supervised learning method, and the speed of processing interference is increased by more than 10 times. When the SNR reaches 5 dB, the accuracy of unknown interference classification exceeds 94%. Finally, yet importantly, owing to the imperfect performance for classification and feature library at present, we acquire the final accuracy for them at 92.23% by intervening measures, and the time availability also has been obtained advantages on signal processing.

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An Efficient EVM Based Jamming Detection in 5G Networks

Örnek

Kartal²

2022

2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)

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Poster: Anti-jamming automatic gain control and packet detection for vehicular receiver

Cited by 3 publications

References 0 publications

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

Classification Methods With Signal Approximation for Unknown Interference

An Efficient EVM Based Jamming Detection in 5G Networks

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