Artificial Intelligence for Automatic Classification of Unintentional Electromagnetic Interference in Air Traffic Control Communications

Eliardsson, Patrik; Stenumgaard, Peter

doi:10.1109/emceurope.2019.8872082

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…1 depicts the communication between a sender and a receiver, simulated using a single PCB trace with matched source and load impedances. The sender encodes the data (4 bits) into codewords of 7 bits using a Hamming code (7,4). Those codewords are sent to the receiver using a particular voltage encoding.…”

Section: Fig 1: Simulation Framework Outlinementioning

confidence: 99%

“…The Hamming code is a linear, single-error correction code capable of detecting up to two-bit errors and correcting single-bit errors [27]. This section considers the encoding and decoding scheme Hamming (7,4).…”

Section: Hamming Codementioning

confidence: 99%

“…Hamming (7,4) encodes four bits into seven bits by adding three parity bits, ensuring that the minimum Hamming distance between any two correct codewords is three. If the distance between the received word and the transmitted codeword is at least one, the Hamming code decodes it to the most likely data word.…”

Section: A Hamming Encoding and Decodingmentioning

confidence: 99%

“…One of the areas that is gaining attention is the use of Artificial intelligence (AI) techniques to predict/classify EMI [3], [4], or to address problems in electromagnetic compatibility (EMC) [5]. These techniques include using neural networks to model EMI and predict its impact on electronic systems [6], or using genetic algorithms to optimize electronic device design to improve EMC performance [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Decision Algorithm Based on the Modified Bellman Equation to Deal With EMI-Induced Errors in Hamming-Based Communications

Gonzalez-Atienza

Vanoost

Verbeke

et al. 2023

IEEE Trans. Electromagn. Compat.

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This paper presents a decision-making algorithm based on a modified version of the Bellman Equation to deal with Electromagnetic Interference errors in a communication channel inside a harsh electromagnetic reverberant environment.The Bellman equation is a fundamental concept in decisionmaking problems such as Markov Decision Processes. Such processes model decision-making in situations where the outcomes are partly random and partly controlled by an agent (decisionmaker).Recent studies have implemented Markov Decision Processes as a tool for risk assessment in areas such as robotics or aviation. However, so far, no research has been reported that uses the Bellman Equation or Markov Decision Processes to deal with risks related to electromagnetic disturbances.In our study, a wired communication channel that uses Non-Return-to-Zero-Level data encoding and Hamming code for error detection and correction is disturbed. First, the packet error rate is calculated and compared with and without the proposed algorithm for different electromagnetic disturbance frequencies and bit rates. The gain is compared at different packet error rates when the algorithm's parameters (called rewards) are optimized and when these are set as random. Last, the influence of the rewards and the maximum number of resends on the algorithm's performance is also studied.

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Section: Fig 1: Simulation Framework Outlinementioning

confidence: 99%

Section: Hamming Codementioning

confidence: 99%

Section: A Hamming Encoding and Decodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decision Algorithm Based on the Modified Bellman Equation to Deal With EMI-Induced Errors in Hamming-Based Communications

Gonzalez-Atienza

Vanoost

Verbeke

et al. 2023

IEEE Trans. Electromagn. Compat.

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show abstract

“…In order to automatically monitor different types of communication jamming signals in the air to facilitate anti-jamming processing, researchers have used machine learning (ML) to automatically identify and classify jamming signals in the whole electromagnetic environment for speed and accuracy of classification [9]. Interestingly, researchers have also studied the vulnerability of automatic recognition model, to make the model vulnerable to attacks.…”

mentioning

confidence: 99%

Adversarial Attack and Defense on Deep Learning for Air Transportation Communication Jamming

Liu

Zhang

Chen

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker's carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

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Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites

Dananjaya,

Marimuthu,

Yang

et al. 2024

Progress in Materials Science

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Artificial Intelligence for Automatic Classification of Unintentional Electromagnetic Interference in Air Traffic Control Communications

Cited by 6 publications

References 7 publications

Decision Algorithm Based on the Modified Bellman Equation to Deal With EMI-Induced Errors in Hamming-Based Communications

Decision Algorithm Based on the Modified Bellman Equation to Deal With EMI-Induced Errors in Hamming-Based Communications

Adversarial Attack and Defense on Deep Learning for Air Transportation Communication Jamming

Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites

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