Simulation-Based Power Allocation for Integrated Sensing and Communication Systems Balancing Communication Capacity and Target Detection.

Abstract

Integrated Sensing and Communication (ISAC) systems require efficient power allocation to balance communication quality and sensing performance while operating under limited spectrum and power resources. Conventional equal power allocation methods often fail to achieve an effective trade-off between communication channel capacity and target detection probability (PDP) because they do not adequately account for the different objectives of communication and sensing. This study proposes a simulation-based power allocation strategy for an Orthogonal Frequency Division Multiplexing (OFDM)-based ISAC system using a normalized multi-objective optimization framework. An improved Butterfly Optimization Algorithm (BOA), incorporating Dynamic Switching Probability and Dynamic Variance Gaussian Mutation strategies, is employed to enhance global search capability, convergence speed, and solution diversity. Numerical simulations were performed under different communication weighting factors, antenna configurations, subcarrier numbers, and signal-to-noise ratios to evaluate the proposed approach. The optimized strategy achieved improved convergence behavior while providing a balanced compromise between communication channel capacity and PDP across multiple simulation scenarios. These findings demonstrate that the proposed optimization framework can effectively support simulation-based resource allocation for OFDM-based ISAC systems while providing a reproducible methodology for evaluating communication-sensing trade-offs under different operating conditions.