Optimal Radio Resource Management for ISAC Under Imperfect Information: A Resource Economy-Driven Perspective

TL;DR

This paper develops an optimal radio resource management framework for downlink ISAC systems that jointly optimizes multiple parameters to minimize resource consumption under imperfect information, ensuring reliable operation.

Contribution

It introduces a novel tailor-made solution for a complex nonconvex MINLP in ISAC RRM, reformulating it as a solvable MISDP for global optimality.

Findings

Achieves up to 88% performance improvement over baseline schemes.

Reveals key interdependencies among RRM components.

Provides a structured approach to handle imperfect information in ISAC.

Abstract

This work investigates the radio resource management (RRM) design for downlink integrated sensing and communications (ISAC) systems, jointly optimizing timeslot allocation, beam adaptation, functionality selection, and user-target pairing, with the goal of economizing resource consumption under imperfect information. Timeslot allocation assigns a number of discrete channel uses to targets and users, while beam adaptation selects transmit and receive beams with suitable directions, power levels, and beamwidths. Functionality selection determines whether each timeslot is used for sensing, communication, or their simultaneous operation, while user-target pairing specifies which users and targets are jointly served within the same timeslot. To ensure reliable operation, information imperfections arising from motion, quantization, feedback delays, and hardware limitations are considered. Resource economization is achieved by minimizing energy and time consumption through a multi-objective function, with strict prioritization of time savings. The resulting RRM problem is formulated as a semi-infinite, nonconvex mixed-integer nonlinear program (MINLP). Given the lack of generic methods for solving such problems, we propose a tailor-made approach that exploits the underlying structure of the problem to uncover hidden convexities. This enables an exact reformulation as a mixed-integer semidefinite program (MISDP), which can be solved to global optimality. Simulations reveal important interdependencies among the considered RRM components and show that the proposed approach achieves substantial performance improvements over baseline schemes, with gains up to 88%.