The future mobile communication systems will support intelligent applications such as Internet of Vehicles (IoV) and Extended Reality (XR). Integrated Sensing and Communication (ISAC) is regarded as one of the key technologies satisfying the high data rate communication and highly accurate sensing for these intelligent applications in future mobile communication systems. With the explosive growth of wireless devices and services, the shortage of spectrum resources leads to the fragmentation of available frequency bands for ISAC systems, which degrades sensing performance. Facing the above challenges, this paper proposes a Carrier Aggregation (CA)-based ISAC signal aggregating high and low-frequency bands to improve the sensing performance, where the CA-based ISAC signal can use four different aggregated pilot structures for sensing. Then, an ISAC signal processing algorithm with Compressed Sensing (CS) is proposed and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is used to solve the reconfiguration convex optimization problem. Finally, the Cram'er-Rao Lower Bounds (CRLBs) are derived for the CA-based ISAC signal. Simulation results show that CA efficiently improves the accuracy of range and velocity estimation.

翻译：未来移动通信系统将支持车联网（IoV）和扩展现实（XR）等智能化应用。集成感知与通信（ISAC）被视为满足未来移动通信系统这些智能化应用高数据速率通信与高精度感知需求的关键技术之一。随着无线设备与服务的爆炸式增长，频谱资源短缺导致ISAC系统可用频段碎片化，从而降低感知性能。针对上述挑战，本文提出一种基于载波聚合（CA）的ISAC信号，通过聚合高-低频段提升感知性能，其中基于CA的ISAC信号可采用四种不同的聚合导频结构进行感知。随后，提出一种基于压缩感知（CS）的ISAC信号处理算法，并采用快速迭代收缩阈值算法（FISTA）求解重构凸优化问题。最后推导了基于CA的ISAC信号的克拉美-罗下界（CRLB）。仿真结果表明，CA有效提升了距离与速度估计的精度。