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
—Unmanned aerial vehicles (UAVs) have been extensively deployed in wireless communication scenario. However, UAV communication faces the challenges of information leakage and energy limitation. Therefore, this paper studies energy-efficient covert communication in adversarial UAV-enabled wireless systems, where a UAV covertly delivers information to a legitimate ground receiver under the detection of a malicious detector with noise uncertainty. Our objective is to maximize covert energy efficiency, defined as the achievable covert throughput per unit of energy consumption, via the joint design of transmit power and flying location. To this end, we derive the detector’s minimum detection error probability to establish a covertness constraint. Based on this model, we formulate a three-dimensional joint optimization problem for transmit power and two-dimensional location, capturing the fundamental tradeoff among covertness, communication reliability, and energy efficiency. Through sys-tem geometric exploration, metric monotonicity analysis, and theoretical derivation, the original three-dimensional problem is reduced to a one-dimensional search over the flying angle, which enables efficient computation of the optimal UAV configuration via vectorized computation. Numerical results verify the theoretical derivations and illustrate the superiority of the joint design as well as the impact of system parameters on energy efficiency performance.
Yang-Fan Xu, Bin Yang, Yulong Shen et al.· IEEE Transactions on Dependa...· 0 citations
This paper proposes a real-time and security-oriented restructuring of SHCUA-based UAV control, which transforms its outputs into contract-bound UAV skill invocations with explicit timing, state, authority, fallback, and evidence semantics.
Di Lu, Bo Zhang, Xiyuan Li et al.· arXiv.org· 0 citations
The massive influx of uplink task offloading in Multi-access Edge Computing (MEC) systems poses a significant challenge to the capacity of wireless networks. This challenge highlights a fundamental trade-off between Orthogonal Multiple Access (OMA), which provides interference-free but spectrally inefficient communication, and Non-Orthogonal Multiple Access (NOMA), which enhances capacity at the cost of significant inter-user interference. To navigate this trade-off, we introduce a novel Hybrid NOMA (H-NOMA) framework that offers differentiated communication services. The framework allows users to choose between premium OMA channels for latency-sensitive tasks and shared NOMA channels for others, creating an economy where performance can be traded for cost. Within this framework, we formulate the resource allocation problem with the objective of maximizing the total system utility, defined as the sum of all individual user utilities, under budget, computation, and communication constraints. To solve this NP-hard problem, we devise a novel multi-stage game-theoretic algorithm, the Matching-Coalition Game with Coordinate Descent (MCGCD). Our approach synergistically combines matching theory for a fast and initial channel assignment, a cooperative coalition game to refine allocations by explicitly managing NOMA externalities, and a coordinate-descent-based algorithm for optimal power control. Extensive simulations demonstrate that our proposed algorithm significantly outperforms benchmark methods in improving system utility, reducing average task completion latency, and increasing the number of admitted tasks.
Haolin Liu, Hao Yin, Haibo Zhou et al.· IEEE Transactions on Mobile...· 0 citations
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