Autonomous 6G Physical Layer Architectures for Space-Air-Ground Integrated Networks

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 02 | Page :
    By

    Patel Karm,

  • Rizwan Alad,

  • Ashish Pandya,

  • Nirav Desai,

  • Purvang Dalal,

  1. Student, Department of Electronics & Communication, Faculty of Technology, Dharmsinh Desai University, Nadiad, Gujarat, India
  2. Associate Professor, Department of Electronics & Communication, Faculty of Technology, Dharmsinh Desai University, Nadiad, Gujarat, India
  3. Assistant Professor, Department of Electronics & Communication, Faculty of Technology, Dharmsinh Desai University, Nadiad, Gujarat, India
  4. Research Assistant, Department of Electronics & Communication, Faculty of Technology, Dharmsinh Desai University, Nadiad, Gujarat, India
  5. Professor & Head, Department of Electronics & Communication, Faculty of Technology, Dharmsinh Desai University, Nadiad, Gujarat, India

Abstract

The emergence of sixth generation (6G) wireless systems calls for a significant shift away from conventional deterministic communication models. As communication infrastructures evolve into Space- Air-Ground Integrated Networks (SAGIN), traditional physical layer (PHY) techniques struggle to operate effectively under the severe Doppler effects and long propagation delays associated with space environments. This paper examines the role of artificial intelligence embedded directly within the 6G transceiver architecture to enable ultra-reliable and low-latency communication (URLLC). By replacing conventional signal estimation approaches with AI-driven PHY mechanisms, communication systems can conventional signal estimation approaches with AI-driven PHY mechanisms, communication systems can avoid the computational burden of matrix inversion operations that often limit resource-constrained spaceborne platforms. A mathematical framework is developed to illustrate how intelligent edge nodes can autonomously deliver critical Smart Grid telemetry through Low Earth Orbit (LEO) satellite constellations. In addition, complexity analysis shows that deep learning-based modulation techniques exhibit near linear scaling behavior, offering a more efficient and resilient communication foundation for both decentralized energy networks and Spacecraft Internet of Things (IoT) applications. The dynamic channel variability, spectrum utilization issues, and real-time decision-making demands present in varied SAGIN contexts are all addressed by the suggested architecture. Incorporating AI-enhanced transceiver intelligence lowers computing overhead while increasing communication flexibility, dependability, and spectrum efficiency. Performance analysis shows how intelligent communication systems may facilitate large-scale Internet of Things connectivity, autonomous satellite operations, and mission-critical data sharing.

Keywords: 6G, Space-Air-Ground Integrated Networks (SAGIN), URLLC, Deep Reinforcement Learning, Channel Estimation, Internet of Things.

How to cite this article:
Patel Karm, Rizwan Alad, Ashish Pandya, Nirav Desai, Purvang Dalal. Autonomous 6G Physical Layer Architectures for Space-Air-Ground Integrated Networks. Journal of Telecommunication, Switching Systems and Networks. 2026; 13(02):-.
How to cite this URL:
Patel Karm, Rizwan Alad, Ashish Pandya, Nirav Desai, Purvang Dalal. Autonomous 6G Physical Layer Architectures for Space-Air-Ground Integrated Networks. Journal of Telecommunication, Switching Systems and Networks. 2026; 13(02):-. Available from: https://journals.stmjournals.com/jotssn/article=2026/view=247054


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Ahead of Print Subscription Review Article
Volume 13
02
Received 15/06/2026
Accepted 17/06/2026
Published 19/06/2026
Publication Time 4 Days
38

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