用于连续孔径集成感知与通信系统的符号级预编码
Symbol-Level Precoding for Continuous-Aperture ISAC Systems
- University of Electronic Science and Technology of China（电子科技大学）
- Tianfu Jiangxi Laboratory（天府江西实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究基于连续孔径阵列的下行链路ISAC系统，采用符号级预编码和接收极化合并，通过优化发射电流解决无限维非凸问题，开发算法联合优化相关参数，相比传统基线实现更高感知效用和通信可靠性。
AI中文摘要：
连续孔径阵列（CAPA）为集成感知与通信（ISAC）提供了丰富的电磁自由度，但优化连续电流分布会导致具有挑战性的无限维问题。本文研究了一种具有符号级预编码和接收极化合并的基于CAPA的下行链路ISAC系统。通过优化发射电流，在满足通信用户的相长干扰约束的同时，最大化加权目标照明功率。为解决由此产生的无限维非凸问题，确定最优电流分布位于由通信和传感电磁响应所跨越的有限维子空间中。这一结果产生了基于有限维系数的精确、保结构的重新表述。然后开发了一种惩罚投影梯度算法，以联合优化电流系数和极化合并器。仿真结果表明，与传统的傅里叶基CAPA和空间离散阵列基线相比，所提出的框架实现了更高的感知效用和更好的通信可靠性。
英文摘要：
Continuous-aperture arrays (CAPAs) offer rich electromagnetic degrees of freedom for integrated sensing and communication (ISAC), but optimizing continuous current distributions leads to challenging infinite-dimensional problems. This paper investigates a CAPA-enabled downlink ISAC system with symbol-level precoding and receive polarization combining. The transmit current is optimized to maximize weighted target-illumination power while enforcing constructive-interference constraints for communication users. To address the resulting infinite-dimensional nonconvex problem, we establish that the optimal current distribution lies in a finite-dimensional subspace spanned by the communication and sensing electromagnetic responses. This result yields an exact, structure-preserving reformulation in terms of finite-dimensional coefficients. A penalty projected-gradient algorithm is then developed to jointly optimize the current coefficients and polarization combiners. Simulation results demonstrate that the proposed framework achieves higher sensing utility and improved communication reliability than conventional Fourier-basis CAPA and spatially discrete array baselines.
