Multi-cell Coordinated Joint Sensing and Communications
Nithin Babu, Christos Masouros
TL;DR
The paper addresses joint sensing and communication in a dense multi-cell MISO network by formulating a block-level precoder design that minimizes the target-angle CRB (via the Fisher information $FIM$) while maximizing the minimum user SINR. It develops SDR-based alternating optimization strategies for two operation modes, Coordinated Beamforming (CBF) and Coordinated Multi-point (CoMP), to handle the nonconvexities and incorporate inter-cell leakage constraints. By deriving $FIM$ expressions for both modes and solving the resulting optimization problems, the approach demonstrates that including inter-cell reflections and neighbor-cell signals yields substantial gains in sensing accuracy and communication fairness compared to baselines that ignore such signals. The findings highlight the importance of IC links in ISAC precoding for dense networks and show that CoMP with larger antenna arrays provides the strongest sensing benefits, albeit with higher coordination overhead.
Abstract
This paper proposes block-level precoder (BLP) designs for a multi-input single-output (MISO) system that performs joint sensing and communication across multiple cells and users. The Cramer-Rao-Bound for estimating a target's azimuth angle is determined for coordinated beamforming (CBF) and coordinated multi-point (CoMP) scenarios while considering inter-cell communication and sensing links. The formulated optimization problems to minimize the CRB and maximize the minimum-signal-to-interference-plus-noise-ratio (SINR) are non-convex and are represented in the semidefinite relaxed (SDR) form to solve using an alternate optimization algorithm. The proposed solutions show improved performance compared to the baseline scenario that neglects the signal component from neighboring cells.