Blockchain-assisted intelligent symbiotic radio in space-air-ground integrated networks

Cheng, R. , Sun, Y. , Mohjazi, L. , Liang, Y.-C. and Imran, M. (2023) Blockchain-assisted intelligent symbiotic radio in space-air-ground integrated networks. IEEE Network, 37(2), 94–101-94–101. (doi: 10.1109/MNET.004.2200277)

Abstract

In a space-air-ground integrated network (SAGIN), managing resources for the growing number of highly-dynamic and heterogeneous radios is a challenging task. Symbiotic communication (SC) is a novel paradigm, which leverages the analogy of the natural ecosystem in biology to create a radio ecosystem in wireless networks that achieves cooperative service exchange and resource sharing, that is, service/resource trading, among numerous radios. As a result, the potential of symbiotic communication can be exploited to enhance resource management in SAGIN. Despite the fact that different radio resource bottlenecks can complement each other via symbiotic relationships, unreliable information sharing among heterogeneous radios and multi-dimensional resources managing under diverse service requests impose critical challenges on trusted trading and intelligent decision-making. In this article, we propose a secure and smart symbiotic SAGIN (S⁴) framework by using blockchain for ensuring trusted trading among heterogeneous radios and machine learning (ML) for guiding complex service/resource trading. A case study demonstrates that our proposed S⁴ framework provides better service with rational resource management when compared with existing schemes. Finally, we discuss several potential research directions for future symbiotic SAGIN.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liang, Professor Ying-Chang and Imran, Professor Muhammad and Sun, Dr Yao and CHENG, RUNZE and Mohjazi, Dr Lina
• Authors: Cheng, R., Sun, Y., Mohjazi, L., Liang, Y.-C., and Imran, M.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Network
• Publisher: IEEE
• ISSN: 0890-8044
• ISSN (Online): 1558-156X
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Network 37(2):94–101
• Publisher Policy: Reproduced in accordance with the publisher copyright policy