AI 中文总结
本文针对EG-XL-IRS辅助的多用户ISAC，提出秩感知单元分组策略，平衡增益-秩权衡，联合优化发射协方差与反射相位，可大幅降低发射功率。
AI 中文摘要
本文研究由单元分组超大规模智能反射面（EG-XL-IRS）辅助的高能效多用户集成感知与通信（ISAC）技术。分组模式利用慢变统计信道状态信息（S-CSI）设计，使与IRS相关的信道获取和在线被动波束成形均在分组域而非单元域运行。研究揭示了单元分组引发的基本增益-秩权衡：相位一致的分组可相干增强选定的确定性传播分量，而过度聚焦于公共确定性模式会降低多用户信道的有效空间秩，对于扩展目标而言，还会降低目标散射体响应的多样性。基于此，本文提出一种任务自适应秩感知分组策略，在保留任务相关空间维度的同时，平衡弱用户增强与目标散射体照明需求。对于每个候选分组模式，在通信和感知服务质量约束下联合优化发射协方差与分组级反射相位，随后进行物理相位恢复与可行性验证。数值结果表明，在相同分组维度和在线优化预算下，所提设计相比代表性分组基准方案可大幅降低所需发射功率。
英文摘要
We investigate power-efficient multiuser integrated sensing and communication (ISAC) assisted by an element-grouping extremely large-scale intelligent reflecting surface (EG-XL-IRS). The grouping pattern is designed using slowly varying statistical channel state information (S-CSI), so that both IRS-related channel acquisition and online passive beamforming operate in the group domain rather than the element domain. We reveal a fundamental gain-rank tradeoff induced by element grouping: phase-consistent grouping can coherently enhance selected deterministic propagation components, while excessive concentration on a common deterministic mode can reduce the effective spatial rank of the multiuser channel and, for extended targets, the diversity of desired-scatterer responses. Motivated by this observation, we develop a task-adaptive rank-aware grouping strategy that balances weak-user enhancement and target-scatterer illumination while preserving task-relevant spatial dimensions. For each candidate grouping pattern, the transmit covariances and group-wise reflection phases are jointly optimized under communication and sensing quality-of-service constraints, followed by physical phase recovery and feasibility verification. Numerical results show that the proposed design substantially reduces the required transmit power compared with representative grouping benchmarks under the same grouping dimension and online optimization budget.
