To strengthen the security of large-scale space-air-ground integrated networks (SAGINs), this paper investigates covert communication against non-cooperative ground base stations (BSs) that detect satellite transmissions via signal power monitoring. In this scenario, numerous low-earth-orbit (LEO) satellites deployed across multiple orbital layers provide backhaul support for autonomous aerial vehicles (AAVs), thereby serving ground users. To reduce the probability of detection, the LEO network performs resource allocation to conceal transmission activities under co-channel interference. However, such a strategy may overlook fairness in resource optimization, potentially undermining cooperation between LEO satellites and terrestrial networks. To address this issue, we develop a two-stage hierarchical Stackelberg matching game to characterize the interaction between LEO satellites and non-cooperative ground BSs. At the upper stage, the LEO network acts as the leader and maximizes the communication rate through power allocation while satisfying covert constraints. At the lower stage, the non-cooperative ground BSs act as followers and competitively minimize their detection errors in response to the leader’s actions. To solve this problem efficiently, we integrate hierarchical game theory, the asynchronous Stackelberg decision transformer (ASDT), and multi-agent reinforcement learning (MARL) into a unified framework for multi-agent coordination. Numerical results demonstrate the effectiveness of the proposed hierarchical resource optimization strategy and provide useful insights for secure SAGIN deployment.
Min Wu, Ke-Feng Guo, Theodoros A. Tsiftsis et al.· IEEE Transactions on Wireles...· 0 citations
To address the high mobility impacts and the ultra-reliable and low-latency communications (URLLC) requirements in autonomous driving scenarios, rate-splitting multiple access (RSMA) combined with short-packet communication (SPC) emerges as a promising solution. Autonomous vehicles rely on real-time information exchange to ensure safety and coordination, making information freshness essential. By jointly capturing transmission delays and packet errors, age of information (AoI) serves as a comprehensive metric for freshness. In this paper, we investigate short-packet rate splitting to enhance information freshness measured by the AoI. By splitting the unicast messages into common and private parts, encoding all common parts together with the multicast message into a common stream, and encoding each private part into a private stream, RSMA effectively manages interference and enables achieving lower AoI. By considering critical factors such as transmit power, vehicle velocity, blocklength, and the number of transmit antennas, we derive closed-form expressions for the average AoI (AAoI) of the common stream under partial decoding and the overall AAoI under complete decoding. To enhance the AAoI performance, we propose the multi-start two-step successive convex approximation (SCA) algorithm. This algorithm first optimizes the power allocation and subsequently optimizes the rate splitting under the quality of service (QoS) trade-off constraint. Simulation results demonstrate that our short-packet rate-splitting scheme significantly improves the AAoI performance while ensuring system fairness and enabling ultra-low AAoI through the common stream, meeting the requirements of autonomous driving applications. Moreover, the trade-off between the common and overall performance is revealed, indicating that the overall performance can be further enhanced while maintaining the advantages of the common stream.
Zi-Ru Zheng, Yingyang Chen, Xinyue Pei et al.· IEEE Transactions on Wireles...· 0 citations
This paper investigates vehicle-to-vehicle (V2V) integrated sensing and communication (ISAC) networks under ultra-reliable low-latency communication (URLLC) constraints in the presence of a vehicular eavesdropper (VE). To address the lack of instantaneous eavesdropper channel state information (CSI) in high-mobility scenarios, a vehicular jammer (VJ) is employed to perform radar-based sensing and extended Kalman filter (EKF)-based tracking of the VE’s kinematic state. The estimated state information and its posterior uncertainty are shared with the legitimate transmitter and are used to construct uncertainty-aware spatial covariance matrices for the VE-related channels. Based on these covariance matrices, the VJ transmits artificial noise (AN) in the null space of the legitimate receiver, thereby degrading the VE’s reception while avoiding interference to the intended link. In this context, a finite-blocklength (FBL) secrecy-rate framework is developed together with a two-time-scale optimization strategy, where the sensing resources are optimized at the slot level to enhance EKF tracking accuracy, while the transmit covariance and AN covariance matrices are optimized at the frame level to maximize the average FBL secrecy rate. The resulting non-convex problem is handled through semidefinite relaxation (SDR), alternating optimization (AO), and successive convex approximation (SCA). Simulation results show that the proposed framework improves secrecy robustness against mobility and sensing-induced spatial uncertainty.
E. T. Michailidis, Theodoros A. Tsiftsis, N. Miridakis· Italian National Conference...· 0 citations
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