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<p>The increasing demand for secure communications in mobile and critical infrastructure networks has led to the emergence of Quantum Key Distribution (QKD) as a promising technique for enhancing cryptographic security in next-generation systems. This paper reviews recent progress in integrating QKD within mobile and wireless networks. It focuses on key enabling technologies, such as Software-Defined Networking (SDN), satellite-based, and aerial networks. The paper critically analyzes how these frameworks can address evolving security requirements in sixth-generation (6G) networks and smart city infrastructures. Additionally, it examines the role of Machine Learning (ML) in improving QKD performance and adaptability. Challenges such as key management, mobility support, scalability, and interoperability are explored. These challenges help identify current barriers and research directions. By consolidating developments and highlighting unresolved issues, this study offers researchers valuable insights. It discusses the convergence of quantum communication and advanced mobile architectures, facilitating the design of secure and resilient future communication infrastructures.</p>

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Quantum key distribution in next-generation networks: a survey of space-based, aerial, and SDN-enabled frameworks for secure communication

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Dalal Aljebry,
  • Ahmed Barnawi

摘要

The increasing demand for secure communications in mobile and critical infrastructure networks has led to the emergence of Quantum Key Distribution (QKD) as a promising technique for enhancing cryptographic security in next-generation systems. This paper reviews recent progress in integrating QKD within mobile and wireless networks. It focuses on key enabling technologies, such as Software-Defined Networking (SDN), satellite-based, and aerial networks. The paper critically analyzes how these frameworks can address evolving security requirements in sixth-generation (6G) networks and smart city infrastructures. Additionally, it examines the role of Machine Learning (ML) in improving QKD performance and adaptability. Challenges such as key management, mobility support, scalability, and interoperability are explored. These challenges help identify current barriers and research directions. By consolidating developments and highlighting unresolved issues, this study offers researchers valuable insights. It discusses the convergence of quantum communication and advanced mobile architectures, facilitating the design of secure and resilient future communication infrastructures.