发表机构
RWTH Aachen University; Indian Institute of Technology Jodhpur（亚琛工业大学; 焦特普尔印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多单站感知中忽略功能分离约束的问题，提出SARAMS算法，联合优化功率、带宽和观测时间，最小化最坏情况SPEB，仿真显示较等功率分配降低65.7%且接近最优。
AI 中文摘要
协作式多单站感知通过在集成感知与通信（ISAC）网络中融合基站（BS）在中心单元（CU）处的观测，能够实现亚米级被动定位。然而，现有研究将融合数据视为在CU处理想可用，忽略了每基站3GPP功能分离如何共同约束前传比特率、计算负载以及CU处相干或非相干融合的可行性。我们提出分离感知的多单站感知联合资源分配（SARAMS）算法，该算法通过在前传、计算和功率约束下联合优化每基站的功率、带宽和观测时间，最小化最坏情况下的多目标平方位置误差界（SPEB）。一个依赖于分离的系数将可行的融合类型嵌入费舍尔信息矩阵（FIM），将SPEB最小化转化为混合整数非线性规划（MINLP），通过用于功率分配的半定规划和在优势剪枝配置集上的块坐标下降（BCD）求解。仿真结果表明，与等功率分配相比，SARAMS将90百分位最坏情况SPEB降低了65.7%，同时相对于穷举搜索实现了8.7%的最优性差距。
英文摘要
Cooperative multi-monostatic sensing enables sub-meter passive localization in integrated sensing and communication~(ISAC) networks by fusing base station~(BS) observations at a central unit~(CU). Existing studies, however, treat the fused data as ideally available at the CU, overlooking how the per-BS 3GPP functional split jointly constrains fronthaul bitrate, computational load, and whether coherent or non-coherent fusion is feasible at the CU. We propose the \textit{Split-Aware Joint Resource Allocation for Multi-Monostatic Sensing}~(SARAMS) algorithm, which minimizes the worst-case multi-target squared position error bound~(SPEB) by jointly optimizing per-BS power, bandwidth, and observation time under fronthaul, computational, and power constraints. A split-dependent coefficient embeds the feasible fusion type into the Fisher information matrix~(FIM), casting SPEB minimization as a mixed-integer nonlinear program (MINLP) solved via a semidefinite program for power allocation and block coordinate descent (BCD) over a dominance-pruned configuration set. Simulation results demonstrate that SARAMS reduces the 90th-percentile worst-case SPEB by $65.7\%$ over equal power allocation while attaining an $8.7\%$ optimality gap relative to exhaustive search.
Comments7 pages, 6 figures. Accepted for Publication in the IEEE GLOBECOM 2026 Conference
