Framework for Developing Neural Network Regression Models Predicting the Influence of EMI on Integrated Circuits

Zupan, Dominik; Czepl, Nikolaus; Kircher, Daniel

doi:10.1109/smacd55068.2022.9816240

Cited by 1 publication

(1 citation statement)

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“…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%

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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