  Integrated sensing and communication (ISAC) systems may face a heavy
computation burden since the sensory data needs to be further processed. This
paper studies a novel system that integrates sensing, communication, and
computation, aiming to provide services for different objectives efficiently.
This system consists of a multi-antenna multi-functional base station (BS), an
edge server, a target, and multiple singleantenna communication users. The BS
needs to allocate the available resources to efficiently provide sensing,
communication, and computation services. Due to the heavy service burden and
limited power budget, the BS can partially offload the tasks to the nearby edge
server instead of computing them locally. We consider the estimation of the
target response matrix, a general problem in radar sensing, and utilize
Cramer-Rao bound (CRB) as the corresponding performance metric. To tackle the
non-convex optimization problem, we propose both semidefinite relaxation
(SDR)-based alternating optimization and SDR-based successive convex
approximation (SCA) algorithms to minimize the CRB of radar sensing while
meeting the requirement of communication users and the need for task computing.
Furthermore, we demonstrate that the optimal rankone solutions of both the
alternating and SCA algorithms can be directly obtained via the solver or
further constructed even when dealing with multiple functionalities. Simulation
results show that the proposed algorithms can provide higher target estimation
performance than state-of-the-art benchmarks while satisfying the communication
and computation constraints.
