6G secure quantum communication: a success probability prediction model

Muhammad Azeem Akbar, Arif Ali Khan, Sami Hyrynsalmi, Javed Ali Khan

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Abstract

The emergence of 6G networks initiates significant transformations in the communication technology landscape. Yet, the melding of quantum computing (QC) with 6G networks although promising an array of benefits, particularly in secure communication. Adapting QC into 6G requires a rigorous focus on numerous critical variables. This study aims to identify key variables in secure quantum communication (SQC) in 6G and develop a model for predicting the success probability of 6G-SQC projects. We identified key 6G-SQC variables from existing literature to achieve these objectives and collected training data by conducting a questionnaire survey. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the 6G-SQC matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each 6G-SQC project variable determined using NBC and LR indicated a strong positive correlation (rs = 0.895). The t-test results (t = 0.752, p = 0.502 > 0.05) show no significant differences between the rankings calculated using both prediction models (NBC and LR). The results reveal that the developed success probability prediction model, based on 15 identified 6G-SQC project variables, highlights the areas where practitioners need to focus more to facilitate the cost-effective and successful implementation of 6G-SQC projects.
Original languageEnglish
Article number31
Pages (from-to)1-40
Number of pages40
JournalAutomated Software Engineering
Volume31
Issue number1
Early online date29 Mar 2024
DOIs
Publication statusE-pub ahead of print - 29 Mar 2024

Keywords

  • Secure communication
  • 6G Technology
  • Quantum computing
  • Prediction model

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