发表机构
Daegu Gyeongbuk Institute of Science and Technology (DGIST); Institute of Innovation for Future Army, Republic of Korea Army; Interdisciplinary Studies of Artificial Intelligence, DGIST; Autonomous Driving Intelligence Research Section, Electronics and Telecommunications Research Institute (ETRI); Department of Electrical Engineering and Computer Science, DGIST（大邱庆尚北道科学技术院; 韩国陆军未来军队创新研究所; 大邱庆尚北道科学技术院人工智能跨学科研究; 电子通信研究院自动驾驶智能研究部; 大邱庆尚北道科学技术院电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对V2X-ISAC系统，提出QoS约束的资源模式选择框架，联合优化通信可靠性与感知性能，采用带单次交换的贪婪算法，在满足PRR约束下接近最优感知性能。
AI 中文摘要
集成感知与通信（ISAC）已成为车联网（V2X）系统的一种有前景的方法，它能够在共享无线电资源上实现通信与感知，而无需额外安装专用传感器。然而，由于信道条件和资源竞争的变化，候选资源可能经历不同的通信质量，在设计感知资源模式时应考虑这一点。在本信中，我们提出了一种服务质量（QoS）约束的资源模式选择框架，该框架联合考虑通信可靠性和感知性能。感知目标基于Fisher信息矩阵制定，用于联合距离和速度估计，同时将最小数据包接收率（PRR）作为通信QoS约束。为避免穷举搜索的复杂性，我们开发了一种带单次交换优化的贪婪选择算法。仿真结果表明，所提方法在满足PRR要求的同时，相比传统资源模式提升了感知性能，并且性能接近穷举搜索最优和仅感知最优。
英文摘要
Integrated sensing and communication (ISAC) has emerged as a promising approach for vehicle-to-everything (V2X) systems by enabling communication and sensing over shared radio resources without additional installation of dedicated sensors. However, candidate resources may experience different communication qualities due to varying channel conditions and resource contention, which should be considered when designing sensing resource patterns. In this letter, we propose a quality-of-service (QoS)-constrained resource pattern selection framework that jointly considers communication reliability and sensing performance. The sensing objective is formulated based on the Fisher information matrix for joint range and velocity estimation, while a minimum packet reception ratio (PRR) is imposed as the communication QoS constraint. To avoid the complexity of exhaustive search, a greedy selection algorithm with one-swap refinement is developed. Simulation results show that the proposed method improves sensing performance over conventional resource patterns while satisfying the PRR requirement and achieves performance close to the exhaustive-search optimum and sensing-only optimum.
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