SARAMS: Split-Aware Joint Resource Allocation for Multi-Monostatic Sensing
Abstract
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.
Authors 3
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Affiliation as printed
Chair of Mobile Communications and Computing (MCC) , RWTH Aachen University , Aachen , Germany
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Indian Institute of Technology Jodhpur
Affiliation as printed
Department of Electrical Engineering , Indian Institute of Technology Jodhpur , Rajasthan , India
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Affiliation as printed
Chair of Mobile Communications and Computing (MCC) , RWTH Aachen University , Aachen , Germany