Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy

Towards sixth-generation networks (6G), satellite communication systems, especially based on Low Earth Orbit (LEO) networks, become promising due to their unique and comprehensive capabilities. These advantages are accompanied by a variety of challenges such as security vulnerabilities, management of hybrid systems, and high mobility. In this paper, firstly, a security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems, highlighting the potential attacks. Secondly, a learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed. The performance of the designed CNN architecture is compared with a prevalent machine learning algorithm, support vector machine (SVM). The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.

Learning-Driven Decision Mechanisms in Physical Layer: Facts, Challenges, and Remedies

Future communication systems must include extensive capabilities as they will embrace a vast diversity of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements due to the frequent use of impracticable and oversimplifying assumptions that lead to a trade-off between complexity and efficiency. By utilizing past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and provide a quick response even under exceptional circumstances. The corresponding design solutions should evolve following the learning-driven paradigms that offer increased autonomy and robustness. This evolution must take place by considering the facts of real-world systems without restraining assumptions. This paper introduces the common assumptions in the physical layer to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering implementation steps and challenges. Additionally, these issues are discussed through a real-time case study that uses software-defined radio nodes, demonstrating the potential performance improvement. A remedial perspective is presented to guide future studies.