  This paper proposes a framework for designing robust precoders for a
multi-input single-output (MISO) system that performs integrated sensing and
communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound
(CRB) to measure the sensing performance and derive its expressions for two
multi-cell scenarios, namely coordinated beamforming (CBF) and coordinated
multi-point (CoMP). In the CBF scheme, a BS shares channel state information
(CSI) and estimates target parameters using monostatic sensing. In contrast, a
BS in the CoMP scheme shares the CSI and data, allowing bistatic sensing
through inter-cell reflection. We consider both block-level (BL) and
symbol-level (SL) precoding schemes for both the multi-cell scenarios that are
robust to channel state estimation errors. The formulated optimization problems
to minimize the CRB in estimating the parameters of a target and maximize the
minimum communication signal-to-interference-plus-noise-ratio (SINR) while
satisfying a given total transmit power budget are non-convex. We tackle the
non-convexity using a combination of semidefinite relaxation (SDR) and
alternating optimization (AO) techniques. Simulations suggest that neglecting
the inter-cell reflection and communication links degrades the performance of
an ISAC system. The CoMP scenario employing SL precoding performs the best,
whereas the BL precoding applied in the CBF scenario produces relatively high
estimation error for a given minimum SINR value.
