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

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%.

Figures (10)

• Figure 1: System model comprising a BS, users, and targets.
• Figure 2: Diagram illustrating the proposed approach.
• Figure 3: Impact of user-target alignment on beam adaptation (Scenario I). The system can dynamically adapt beam direction, beamwidth, and transmit power in response to variations in user-target alignment, thereby ensuring that both communication and sensing requirements are satisfied. In addition, mutual coupling increases power/energy consumption by inducing energy leakage among adjacent antennas, which distorts the beampattern and reduces the effective power concentrated in the main lobe.
• Figure 4: Impact of SNR/SINR requirements and functionality selection on energy consumption (Scenario II). The energy consumption is affected by and requirements, with higher demands leading to greater infeasible regions. While static functionality selection (\ref{['fig:results-scenario-2a']} to \ref{['fig:results-scenario-2d']}) is more prone to infeasibility, flexible functionality selection (\ref{['fig:results-scenario-2e']}) significantly increases the feasible solution space and improves adaptability via intelligent switching between joint and separate user/target service.
• Figure 5: Impact of imperfect AOD and RC on energy consumption (Scenario III). Energy expenditure is impacted by and uncertainty. Their combined effect reveals operational regions characterized by constant total energy consumption despite imperfect information. This stability is attributed to the discrete power levels, which often provides a surplus margin in satisfying and requirements, thereby providing an inherent degree of robustness.

Theorems & Definitions (25)

• Example
• Remark 1
• Remark 2
• Remark 3
• Remark 4
• Lemma 1
• proof
• Proposition 1
• proof
• Proposition 2