AI 中文总结
该研究针对无人机网络ISAC的孔径与自干扰问题，提出含CPA的共享孔径架构，经CRB分析与交替优化算法验证，可提升感知性能与通信和速率，优于基准方案。
AI 中文摘要
集成感知与通信（ISAC）可在无人机（UAV）网络中同时实现通信与环境感知，但其性能受限于物理天线孔径及全双工（FD）感知中的残余自干扰（SI）。为解决这些问题，本文提出一种共享孔径ISAC架构：在均匀线性阵列（ULA）网格中嵌入稀疏互质阵列（CPA）以用于FD感知，剩余天线位置则支持时分双工（TDD）通信。本文通过阶数克拉美罗界（CRB）分析表征感知性能，结果显示，在单目标及非退化多目标场景下，CPA相较分区ULA基准可实现更强的渐近感知增益；同时揭示了相同物理孔径下的空-时间采样权衡关系。基于所提架构，本文构建了非凸联合资源分配问题，该问题需在感知精度、基站（BS）发射功率、通信服务质量（QoS）及残余SI约束下，通过联合设计感知发射协方差、下行预编码器及上行接收波束成形器，最大化加权下行-上行和速率。本文提出一种基于交替优化的算法，仿真结果表明，该算法相较所考虑的基准方案可实现稳定的性能增益，且验证了CPA虚拟孔径与感知协方差优化的互补优势。
英文摘要
Integrated sensing and communication (ISAC) enables simultaneous communication and environmental sensing in unmanned aerial vehicle (UAV) networks, but its performance is constrained by the physical antenna aperture and residual self-interference (SI) in full-duplex (FD) sensing. To address these issues, we propose a shared-aperture ISAC architecture in which a sparse co-prime array (CPA) is embedded in a uniform linear array (ULA) grid for FD sensing, while the remaining antenna positions support time-division duplexing (TDD) communication. We characterize the sensing performance through an order-wise Cramer-Rao bound (CRB) analysis, showing that the CPA achieves a stronger asymptotic sensing gain than the partitioned ULA benchmark in both single-target and nondegenerate multi-target scenarios. We further reveal a space-time sampling tradeoff under the same physical aperture. Based on the proposed architecture, we formulate a non-convex joint resource allocation problem that maximizes the weighted downlink-uplink sum rate by jointly designing the sensing transmit covariance, downlink precoder, and uplink receive beamformers under sensing accuracy, BS transmit-power, communication QoS, and residual SI constraints. An alternating-optimization-based algorithm is developed. Simulations demonstrate consistent performance gains over the considered baselines and confirm the complementary benefits of the CPA virtual aperture and sensing covariance optimization.
