Cooperative OFDM-ISAC Networks: Performance Analysis and Resource Allocation

TL;DR

This paper analyzes the performance and resource allocation strategies for cooperative OFDM-based ISAC networks, comparing signal-level and parameter-level fusion architectures with derived bounds and optimization methods.

Contribution

It introduces a comprehensive performance analysis framework for cooperative OFDM-ISAC networks, deriving bounds and proposing resource allocation algorithms considering practical constraints.

Findings

SLF CRB derived for joint target estimation.

PLF achieves asymptotic SLF CRB under certain conditions.

Numerical results show improved localization and velocity estimation.

Abstract

Cooperative integrated sensing and communication (ISAC) based on orthogonal frequency-division multiplexing (OFDM) enables network-wide sensing by exploiting the spatial diversity of multi-base-station (BS). This paper studies performance analysis and time-frequency resource allocation for a multi-BS cooperative OFDM-ISAC network with fine-grained resource-element (RE)-level orthogonal coordination. Two fusion architectures are considered: signal-level fusion (SLF), which forwards raw echoes to a fusion center, and parameter-level fusion (PLF), which reports only local delay/Doppler estimates and their uncertainty information. For SLF, we derive the Cram\'er--Rao bound (CRB) for joint target position and velocity estimation. For PLF, we develop a two-stage CRB-like metric by combining local delay/Doppler uncertainty characterization with first-order geometric error propagation, and show that only an oracle ML-based PLF benchmark can asymptotically attain the SLF CRB under restrictive conditions. Based on these results, we formulate a joint RE-selection and power-allocation problem under network-wide RE exclusivity, per-BS power budgets, a communication sum-rate constraint, and a sidelobe-amplitude constraint on the delay-Doppler ambiguity function. An efficient solution is developed via Schur-complement reformulations and penalty-based alternating optimization. Numerical results validate the analysis, demonstrate effective ambiguity-sidelobe suppression and consistent localization/velocity gains over representative baselines, while revealing geometry-dependent SLF-PLF performance gaps.