Array Rotation in Near-Field ISAC: Communication, Detection, and Estimation Under Low-Complexity Transmit Architectures

Abstract

This paper studies near-field integrated sensing and communication (ISAC) with a rotatable base-station array under an exact spherical-wave propagation model. For a general transmit covariance, we derive a Cram\'er--Rao bound (CRB) decomposition that separates target-side geometry from transmit-covariance-dependent terms. We then specialize the framework to two representative low-complexity architectures: a single-stream superposition architecture, and a dual-waveform split architecture design. We characterize the communication-, detection-, and estimation-optimal rotations for each architecture using the exact finite-array model. In addition, we show how a second-order approximation yields compact approximate expressions for the optimal rotation angles. We also study uncertainty in the target state used for beamforming design through a correlated Gaussian perturbation model, and derive approximations of the optimal robust rotation angles for target sensing and detection. The analysis shows that the two architectures behave differently under rotation: the single-stream design can align communication and detection tasks, whereas the dual-waveform design exhibits a direct communication--detection tradeoff and can incur a larger communication loss when sensing is prioritized. Numerical results show clear gains over a fixed array orientation, especially for range estimation, and close agreement between the approximate and exact rotation choices. Detection is less affected by target-state errors than estimation in the considered cases.