Abstract
In this paper, we introduce a novel resource allocation approach for
integrated sensing-communication (ISAC) using the
Kullbackâ\euro“Leibler divergence (KLD) metric. Specifically, we
consider a base-station with limited power and antenna resources serving
a number of communication users and detecting multiple targets
simultaneously. First, we analyze the KLD for two possible antenna
deployments, which are the separated and shared deployments, then use
the results to optimize the resources of the base-station through
minimising the average KLD for the network while satisfying a minimum
predefined KLD requirement for each user equipment (UE) and target. To
this end, the optimisation is formulated and presented as a mixed
integer nonlinear programming (MINLP) problem and then solved using two
approaches. In the first approach, we employ a genetic algorithm, which
offers remarkable performance but demands substantial computational
resources; and in the second approach, we propose a rounding-based
interior-point method (RIPM) that provides a more
computationally-efficient alternative solution at a negligible
performance loss. The results demonstrate that the KLD metric can be an
effective means for optimising ISAC networks, and that both optimisation
solutions presented offer superior performance compared to uniform power
and antenna allocation.Â